diff --git a/Contents.m b/Contents.m
index f22501f..6ddcfde 100644
--- a/Contents.m
+++ b/Contents.m
@@ -1,5 +1,5 @@
% megconnectome
-% Version 1.0 www.humanconnectome.org 24-Feb-2014
+% Version 2.0 www.humanconnectome.org 10-Jul-2014
% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
%
diff --git a/analysis_functions/hcp_ICA_RMEG_classification.m b/analysis_functions/hcp_ICA_RMEG_classification.m
index d606ea1..13ce5ea 100644
--- a/analysis_functions/hcp_ICA_RMEG_classification.m
+++ b/analysis_functions/hcp_ICA_RMEG_classification.m
@@ -54,11 +54,9 @@
cfgin.saveres = ft_getopt(options, 'saveres');
cfgin.saveformat = ft_getopt(options, 'saveformat');
-% if isempty(cfgin.dataset) , cfgin.dataset = '0'; end
if isempty(cfgin.ref_channels) , cfgin.ref_channels = {'E31', 'E32'}; end
if isempty(cfgin.bpfreq) , cfgin.bpfreq = iteration(1,1).comp(1,1).bandpass ; end
if isempty(cfgin.bsfreq) , cfgin.bsfreq = iteration(1,1).comp(1,1).bandstop ; end
-% if isempty(cfgin.grad) , sens=ft_read_sens(cfgin.dataset) ; cfgin.grad = sens ; end
if isempty(cfgin.showbestit), cfgin.showbestit = 'no'; end
if isempty(cfgin.plottype) , cfgin.plottype = 'summary'; end
if isempty(cfgin.store_bestcomp) , cfgin.store_bestcomp = 'yes'; end
@@ -70,11 +68,6 @@
if isfield(cfgin,'ref_channels')
- % if ischar(ref_data)
- % options=ft_setopt(options,'channels',cfgin.ref_channels)
- % ref_data = hcp_ICA_preprocessing(ref_data, options);
- % end
-
nref= size(ref_data.trial{1},1);
ntrl= size(ref_data.trial,2)
@@ -100,7 +93,7 @@
end
imgname=[subject '_icaclass_refch'];
hcp_write_figure(imgname, h);
- clear h; % FIXME should this be clear or close?
+ clear h;
% ----- Time Course of Power of Electric Channels -----------------------
@@ -133,7 +126,6 @@
selec = sqrt(freq_ref_data.powspctrm);
end
-%% Performing IC CLASSIFICATION
n_iter=size(iteration,2);
for j=1:n_iter
@@ -162,14 +154,6 @@
%----> IC POWER TIME COURSE <----
%--------------------------------
- % cfg = [];
- %
- % cfg.bpfilter = 'yes'; %'no' or 'yes' bandpass filter (default = 'no')
- % cfg.bpfreq = cfgin.bpfreq;
- % cfg.hilbert='abs';
- %
- % pow_data = ft_preprocessing(cfg,comp_iter);
- %
pow_data=comp_iter.pow_data;
temp_struc(j).pow_data=pow_data;
pIC = cell2mat(pow_data.trial).^2;
@@ -187,7 +171,6 @@
IC=cell2mat(comp_iter.trial);
if isfield(cfgin,'ref_channels')
- % elec=ref_data.trial{1};
if nref > 0
% ----- Correlation between the ICs and electric channels
@@ -218,23 +201,6 @@
% ----- Fit with 1/f spectrum, flat spectrum "quantification", IC time kurtosis
-
- %!FROM GIORGOS:
- % Replaced below the fittype function with hcp_fitspectrum, so the compiled
- % version can run on WashU, according to instructions from the help
- % of the function i.e.:
- % ------------------------------------------
- % Example:
- % x = (1:100)';
- % y = 2./x + 3 + 0.1*rand(size(x));
- % [fitx, goodness] = hcp_fitspectrum(x, y)
- %
- % This replaces the following use of the fit() function from
- % the Mathworks curve fitting toolbox
- % g = fittype('abs(a)*1/x + abs(b)')
- % [fitx, goodness] = fit(x, y, g)
- %--------------------------------------------
- %g = fittype('abs(a)*1/x + b'); % COMMENTED OUT BY GIORGOS
frq = [cfgin.bpfreq(1,1) 13];
frq2 = [2 78];
vec = find(F > frq(1) & F < frq(2));
@@ -242,7 +208,6 @@
for ix = 1:Nc
sspettro = mspettro(ix,:);
sspettro_fit=sspettro./sspettro(vec(1,1));
- %[fitx,goodness] = fit(F(vec)',sspettro_fit(vec)',g); % COMMENTED OUT BY GIORGOS
[fitx,goodness] = hcp_fitspectrum(F(vec)',sspettro_fit(vec)');
if(fitx.a>0)
G(ix) = goodness.rsquare;
@@ -261,8 +226,7 @@
thres_d = 0.5; % PSD 1/f
thres_e = 2.02; % Spectrum Flat
thres_f = 15; % IC Time Kurtosis
- % thres_g = 0.1;
- % thres_h = 15;
+
%----------------------------------------------------------
% classification: 0=artifact 1=brain signal
diff --git a/analysis_functions/hcp_ICA_freq.m b/analysis_functions/hcp_ICA_freq.m
index 9005f40..3189d65 100644
--- a/analysis_functions/hcp_ICA_freq.m
+++ b/analysis_functions/hcp_ICA_freq.m
@@ -28,32 +28,47 @@
% saveres: 'yes' or 'no' save the resutls in a file (default = 'no')
% saveformat: image file format (default 'fig')
% grad: fieldtrip gradiometer structure
+%
+% See also HCP_ICA_PLOT HCP_ICA_PLOTCLASSIFICATION HCP_ICA_RMEG_CLASSIFICATION
+
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
switch nargin
case 3
% reconstruct the component time courses from the data and the unmixing
% matrix
- % comp.time = datain.time;
cfg = [];
cfg.unmixing = comp.unmixing;
cfg.topolabel = comp.topolabel;
cfg.order = comp.order;
comp = ft_componentanalysis(cfg, datain);
comp.order = cfg.order;
- % comp.trial{1} = comp.unmixing*datain.trial{1};
case 2
% check whether IC time courses are in the first input
if ~isfield(comp,'trial') , error('IC time course not defined, data matrix required'); end
case 1
error('too few input arguments');
- % FIXME in principle this should work because then all options should
- % be set to default within the function
if ~isfield(comp,'trial') , error('IC time course not defined, data matrix required'); end
otherwise
error('too many input arguments');
end
-cfgin.doplot = ft_getopt(options, 'doplot', 'no'); % FIXME I think the plotting should not be done within this function
+cfgin.doplot = ft_getopt(options, 'doplot', 'no');
cfgin.grad = ft_getopt(options, 'grad');
if ~isempty(cfgin.grad),
@@ -76,10 +91,7 @@
freq_comp = ft_freqanalysis(cfg,comp_seg);
comp.freq_comp=freq_comp;
-%--------------------------------
%----> IC POWER TIME COURSE <----
-%--------------------------------
-
cfg = [];
cfg.bpfilter = 'no'; %'no' or 'yes' bandpass filter (default = 'no')
cfg.bpfreq = [1 150];
@@ -94,5 +106,5 @@
comp.pow_data = pow_data;
if strcmp(cfgin.doplot,'yes')
- hcp_ICA_plot(comp,options);
+ hcp_ICA_plot(comp,options); % plot results
end
diff --git a/analysis_functions/hcp_ICA_plot.m b/analysis_functions/hcp_ICA_plot.m
index 1b924ae..6cd2758 100644
--- a/analysis_functions/hcp_ICA_plot.m
+++ b/analysis_functions/hcp_ICA_plot.m
@@ -7,7 +7,7 @@ function hcp_ICA_plot(comp_freq, options)
% two different layouts (see cfg.plottype)
%
% Use as
-% hcp_ICA_freq(comp_freq, options, datain)
+% hcp_ICA_plot(comp_freq, options, datain)
% where the input comp_freq structure should be obtained from FT_COMPONENTANALYSIS and
% the datain structure is a fieldtrip data structure contining the channel time courses
% used as imput of the FT_COMPONENTANALYSIS .
@@ -20,13 +20,32 @@ function hcp_ICA_plot(comp_freq, options)
% saveres : 'yes' or 'no' save the resutls in a file (default = 'no')
% saveformat : image file format (default 'fig')
% grad : fieldtrip gradiometer structure
+%
+% See also HCP_ICA_FREQ HCP_ICA_PLOTCLASSIFICATION HCP_ICA_RMEG_CLASSIFICATION
+
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
Nc = size(comp_freq.topo,2);
cfgin.plottype = ft_getopt(options, 'plottype', 'components'); %summary or components
-cfgin.saveres = ft_getopt(options, 'saveres');
+cfgin.saveres = ft_getopt(options, 'saveres');
cfgin.saveformat = ft_getopt(options, 'saveformat', 'fig');
-cfgin.fileout = ft_getopt(options, 'fileout');
+cfgin.fileout = ft_getopt(options, 'fileout');
cfgin.parameter = ft_getopt(options, 'parameter', 'topo');
cfgin.component = ft_getopt(options, 'component', 1:Nc);
cfgin.comment = ft_getopt(options, 'comment', 'no');
@@ -125,7 +144,6 @@ function hcp_ICA_plot(comp_freq, options)
if(~isempty(cfgin.posi))
f = figure;
set(f, 'visible', fvis,'on','Position', cfgin.posi);
- % set(f, 'Position', cfgin.posi)
else
f = figure;
set(f, 'visible', fvis);
diff --git a/analysis_functions/hcp_ICA_plotclassification.m b/analysis_functions/hcp_ICA_plotclassification.m
index 047448e..0062a25 100644
--- a/analysis_functions/hcp_ICA_plotclassification.m
+++ b/analysis_functions/hcp_ICA_plotclassification.m
@@ -1,5 +1,19 @@
function hcp_ICA_plotclassification(comp,options_ICA,datain)
+% HCP_ICA_PLOTCLASSIFICATION allows the plots of Independent
+% Components automatically classified by HCP_ICA_RMEG_CLASSIFICATION function. This
+% function is able to classify Independent Components into Brain Activity
+% and ECG/EOG related artifacts provided that ECG and EOR reference
+% channels are used.
+%
+% Use as
+% hcp_ICA_plotclassification(comp,options_ICA,datain)
+%
+% where the input comp structure should be obtained from
+% HCP_ICA_RMEG_CLASSIFICATION and the datain structure is a fieldtrip data
+% structure contining the channel time courses used as imput of the
+% FT_COMPONENTANALYSIS .
+%
% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
%
% This file is part of megconnectome.
@@ -21,7 +35,7 @@ function hcp_ICA_plotclassification(comp,options_ICA,datain)
bandpass = ft_getopt(options_ICA, 'bandpass');
bandstop = ft_getopt(options_ICA, 'bandstop');
grad = ft_getopt(options_ICA, 'grad');
-modality = ft_getopt(options_ICA, 'modality', 'MEG'); % GIORGOS
+modality = ft_getopt(options_ICA, 'modality', 'MEG');
if strcmp(modality,'MEG')
layout='4D248.mat';
@@ -37,7 +51,7 @@ function hcp_ICA_plotclassification(comp,options_ICA,datain)
if(~isfield(comp,'pow_data'))
- doplot='no'; % Summury Results cn be plotted also at this level without calling hcp_plotICA
+ doplot='no';
options = {'doplot',doplot, 'grad', grad,'plottype','components'};
[comp_freq]=hcp_ICA_freq(comp, options, datain);
@@ -46,7 +60,7 @@ function hcp_ICA_plotclassification(comp,options_ICA,datain)
end
n_IC=size(comp.unmixing,1);
-frange=bandpass; %to be decided if in compo or other
+frange=bandpass;
cfgin =[];
cfgin.grad=grad;
@@ -68,7 +82,7 @@ function hcp_ICA_plotclassification(comp,options_ICA,datain)
leg={'OneOverF (>0.5) ' ; 'Specflat (<2.0) ' ; 'Kurtosis (>15) ' ; 'elecSig (>0.1) '; 'elecPow (>0.25) '; 'elecSpe (>0.95) '};
thrs=[0.5;2.0;15;0.1;0.25;0.95];
class=comp.class;
-% size_s=get(0,'ScreenSize');
+
for ix=1:n_IC
str(1,:)={num2str(abs(comp.class.spectrum_one_over_f(1,ix)),'%5.3f')};
str(2,:)={num2str(abs(comp.class.spectrum_flat(1,ix)),'%5.1f')};
@@ -86,28 +100,16 @@ function hcp_ICA_plotclassification(comp,options_ICA,datain)
str_ic = 'ARTIFACT';
if(find(class.brain_ic==ix)), str_ic='BRAIN'; end
-%%% In the cluster version all this settings are not required
-% X = 29.7; %# A3 paper size
-% Y = 14.35; %# A3 paper size
-% xMargin = 1; %# left/right margins from page borders
-% yMargin = 1; %# bottom/top margins from page borders
-% xSize = X - 2*xMargin; %# figure size on paper (widht & hieght)
-% ySize = Y - 2*yMargin; %# figure size on paper (widht & hieght)
+
figure('Menubar','none');
%
set(gca, 'XTickLabel',[], 'YTickLabel',[], ...
'Units','normalized', 'Position',[0 0 1 1])
-%
-% %# figure size on screen (50% scaled, but same aspect ratio)
-% set(gcf, 'Units','centimeters', 'Position',[5 5 xSize ySize])
%# figure size printed on paper
set(gcf, 'visible', 'off')
set(gcf, 'PaperUnits','inches')
-% set(gcf, 'PaperSize',[X Y])
-% set(gcf, 'PaperPosition',[xMargin yMargin xSize ySize])
-% set(gcf, 'PaperOrientation','portrait')
-%%%
+
set(gcf, 'paperposition', [1 1 20 12]);
subplot(2,3,1)
@@ -115,7 +117,6 @@ function hcp_ICA_plotclassification(comp,options_ICA,datain)
cfgin.layout=layout;
cfgin.colorbar='yes';
ft_topoplotIC(cfgin, comp);
-% xlabel(['IC ' num2str(ix)],'color','k');
title('IC sensor map','FontSize',12);
subplot(2,3,4)
@@ -130,8 +131,6 @@ function hcp_ICA_plotclassification(comp,options_ICA,datain)
str_icnum = {['ic ' num2str(ix) ' = ' str_ic]};
-% text(1.2,0.5,leg,'Units','normalized','FontSize',16)
-% text(2,0.5,str,'Units','normalized','FontSize',16,'color',strcolor)
text(1.2 ,0.65,leg{1},'Units','normalized','FontSize',22,'color',strcolor(1))
text(2,0.65, str{1},'Units','normalized','FontSize',22,'color',strcolor(1))
text(1.2,0.59,leg{2},'Units','normalized','FontSize',22,'color',strcolor(2))
@@ -145,10 +144,7 @@ function hcp_ICA_plotclassification(comp,options_ICA,datain)
text(1.2,0.335,leg{6},'Units','normalized','FontSize',22,'color',strcolor(6))
text(2,0.335,str{6},'Units','normalized','FontSize',22,'color',strcolor(6))
-% text(1.5,0.2,str_icnum,'Units','normalized','FontSize',16)
-% text(1.5,0.1,str_ic,'Units','normalized','FontSize',16,'color','r')
text(1.2,0.8,str_icnum,'Units','normalized','FontSize',22,'color','b')
-% text(1.8,0.8,str_ic,'Units','normalized','FontSize',16,'color','b')
subplot(2,3,[2 3])
timeic=cell2mat(comp.time);
@@ -173,7 +169,6 @@ function hcp_ICA_plotclassification(comp,options_ICA,datain)
disp('SAVING');
hcp_write_figure(imgname, gcf, 'resolution', 300);
disp('SAVED');
-% clear h;
close
end
diff --git a/analysis_functions/hcp_ICA_qualitycheck_pipeline.m b/analysis_functions/hcp_ICA_qualitycheck_pipeline.m
index 9fd4a02..dfc342f 100644
--- a/analysis_functions/hcp_ICA_qualitycheck_pipeline.m
+++ b/analysis_functions/hcp_ICA_qualitycheck_pipeline.m
@@ -60,11 +60,9 @@
ch_method = ft_getopt(options_ICA, 'bad_ch_method', 'std_thr'); % 'std_thr' or 'max_weight' (default='std_thr')
selmode = ft_getopt(options_ICA, 'mode', 'auto'); % skipped interval selection 'auto' or 'user' (default='auto')
saveit = ft_getopt(options_ICA, 'iter_results'); % save the results of each iteration
-modality = ft_getopt(options_ICA, 'modality','MEG'); %Francesco
+modality = ft_getopt(options_ICA, 'modality','MEG');
resamplefs = ft_getopt(options_ICA, 'resamplefs');
-% Add some options to the options_ICA cell-array that will be passed to
-% lower level functions. Should these be configurable?
options_ICA = ft_setopt(options_ICA, 'save_comp', 'yes');
options_ICA = ft_setopt(options_ICA, 'ica_iterations', 1);
@@ -74,28 +72,24 @@
end
%------------------------
-% GIORGOS
if strcmp(modality,'MEG')
layout='4D248.mat';
threshold_std=10;
elseif strcmp(modality,'EEG')
layout='EEG1010.lay';
- threshold_std=15; % The original threshold for MEG was 10 but many channels were identified as bad. Increased the threshold to be adapted to the EEG signal characteristics
+ threshold_std=15;
end
%-----------------------------
-% These are hard coded thresholds. Should we consider to make them
-% configurable?
num_sig = 12; % number of standard deviation out of the mean from the artifact detection
sic_thr_par = 5; % factor of multiplication of the threshold for the determination of the intervals to be skipped
-% size_s = get(0,'ScreenSize');
size_s = [1,1 1200 700];
flag = 1; % flag that checks convergence
it = 0; % iteration counter
-bch2 = []; % some variable
-segcount = 0; % some variable
+bch2 = [];
+segcount = 0;
while flag
clear datain
disp('doing ICA preprocessing')
@@ -106,8 +100,6 @@
comp = iteration.comp;
% normalize the time courses to unit standard deviation (and zero mean)
- % -> this shouldn't matter because the component time courses are by
- % definition zero mean
disp('STARTING ft_channelnormalise');
tmp = ft_channelnormalise([], comp);
disp('DONE ft_channelnormalise');
@@ -127,8 +119,6 @@
disp('DONE ft_ICA_plot');
end
- % HACK JM work it back into the 1 trial case in order for the rest of the
- % code to work: I suggest however to clean this up at some point
disp('STAGE POST A');
fs = 0;
nsmp = 0;
@@ -138,8 +128,6 @@
end
fs = nsmp./fs;
- % timelim = [comp.time{1}(1) comp.time{end}(end)];
- % timeaxis = timelim(1):1/fs:timelim(2);
if(isempty(resamplefs))
nSamples = dataraw.sampleinfo(1,2);
else
@@ -166,9 +154,7 @@
% Smoothing of the IC Power Time Course (used in the peak analysis)
sIC = zeros(size(pIC));
for i=1:size(IC,1)
- % this relies on the curve fitting toolbox
- % sIC(i,:)=(smooth(pIC(i,:),101))';
- sIC(i,:)=ft_preproc_smooth(pIC(i,:), 101);
+ sIC(i,:)=ft_preproc_smooth(pIC(i,:), 101);
end
@@ -176,7 +162,6 @@
disp('STAGE POST E');
disp('unworking channel identification...');
- % declare some variables
ch_ic_list = [];
bad_channels = [];
bad_channels2 = [];
@@ -193,17 +178,15 @@
if (strcmp(ch_method,'max_weight'))
- if vt(1)/vt(2) > 10 % One channel is discarded if the IC weight is 10 times grater than the others
+ if vt(1)/vt(2) > 10
flag_bad=1;
mth='val';
end
else
- % [histo_ch bins_ch]=hist(abs(A(:,i)),400);
mean_weight=prctile(vt,50,1);
std_weight=std(vt(2:end));
- if vt(1)> mean_weight+threshold_std*std_weight % One channels is discarded if the IC weight is 10 times std out of the mean
+ if vt(1)> mean_weight+threshold_std*std_weight
h_fig=figure;
- % set(h_fig, 'Position', [(size_s(1,3)-(border_x+size_fx)) border_x size_fx size_fy])
set(h_fig, 'visible', 'off','paperposition', [1 1 10 7]);
[histo_ch,bin_ch]=hist(vt,400);
hist(vt,400);
@@ -217,8 +200,7 @@
end
if(flag_bad==1)
posi=[border_x border_x size_fx size_fy];
- % options={'plottype','components','component',i,'position',posi};
- % hcp_ICA_plot(comp_freq,options) % summary plots of the IC
+
if(strcmp(mth,'std'))
n_chan=size(find(vt>mean_weight+10*std_weight),1);
chan_lab(1,1:n_chan) = iteration(1,1).comp.topolabel(order(1:n_chan));
@@ -305,7 +287,7 @@
end
- %%%%%%%%%%%%% Graphically or automatically select the time intervals to be excluded %%%%%%%%%
+ %%%%%%%%%%%%% Manually or Automatically select the time intervals to be excluded %%%%%%%%%
int_vect=[];
while (ic_ind)
disp('performing bad segments selection ');
@@ -336,7 +318,6 @@
for i=1:20
infseg=max_ind2-i*500; if(infseg<1), infseg=1; end
supseg=max_ind2+i*500; if(supseg>size(IC2,2)), supseg=size(IC2,2); end
- % [junk over_thr]=find(sIC2(infseg+1:supseg)>sic_thr_par*ref_sIC);
[junk over_thr]=find(sIC2(infseg:supseg)>sic_thr_par*ref_sIC);
if(~isempty(over_thr))
@@ -376,7 +357,6 @@
cfg.layout=layout;
h1_fig=figure;
- % set(h1_fig, 'Position', [75 75 size_s(1,3)-150 size_s(1,4)-150])
set(h1_fig, 'visible', 'off','paperposition', [1 1 10 7]);
subplot(2,3,3) ; plot(points_all,IC(ic_ind,:))
@@ -389,7 +369,6 @@
line([mean_std+10*std_std mean_std+10*std_std], [0 max(histo)],'Color','r')
legend('std distribution','mean and peak thr','Location','NorthOutside')
line([mean_std mean_std], [0 max(histo)],'Color','r')
- % subplot(2,3,[1 2]) ; plot(IC2,'k')
subplot(2,3,6) ; plot(sIC2,'k')
line([1 size(IC2,2)], [sic_thr_par*ref_sIC sic_thr_par*ref_sIC],'Color','r')
axis([1 length(sIC2) 0 max(sIC2)])
@@ -401,7 +380,6 @@
title('Select time interval to be excluded...');
- % time_points=[1:cut_interval(1) cut_interval(2):length(IC)];
if(strcmp(selmode,'user'))
[X,Y] = ginput(2);
@@ -512,8 +490,7 @@
end
end
- %modified in order to properly manage bad segments in samples. Should
- %be done better
+
options_ICA = ft_setopt(options_ICA, 'skipped_intervals', skint*dataraw.fsample);
flag_int=1;
end
@@ -522,25 +499,25 @@
flag_bch=0;
if(~isempty(bad_channels))
flag_bch=1;
- pv_bad_ch=ft_getopt(options_ICA, 'knownbadchannels'); % GIORGOS
+ pv_bad_ch=ft_getopt(options_ICA, 'knownbadchannels');
bad_channels=horzcat(pv_bad_ch,bad_channels);
- options_ICA = ft_setopt(options_ICA, 'knownbadchannels', bad_channels); % GIORGOS
+ options_ICA = ft_setopt(options_ICA, 'knownbadchannels', bad_channels);
bch2=horzcat(bch2,bad_channels2);
end
flag=(flag_bch+flag_int)-(flag_bch*flag_int);
pause(3);
it=it+1;
- iter_res(1,it).bch=ft_getopt(options_ICA, 'knownbadchannels'); % GIORGOS
+ iter_res(1,it).bch=ft_getopt(options_ICA, 'knownbadchannels');
iter_res(1,it).bch2=bch2;
iter_res(1,it).skint=ft_getopt(options_ICA, 'skipped_intervals');
end
disp(' Finishing ICAqc ');
-bch= ft_getopt(options_ICA, 'knownbadchannels'); % GIORGOS
+bch= ft_getopt(options_ICA, 'knownbadchannels');
bad_segments=round(skint*dataraw.fsample);
bad_channels=unique(bch2);
if isempty(bad_channels)
- bad_channels=[]; % This is because in Matlab 2013a unique of an empty matrix return a 0x1 empty matrix
+ bad_channels=[];
end
imgname=[resultprefix '_icaqc_results' ];
options={'plottype','components','saveres','yes','grad', dataraw.grad,'modality',modality,'saveformat','png','fileout',imgname,'visible','off'};
diff --git a/analysis_functions/hcp_ICA_unmix.m b/analysis_functions/hcp_ICA_unmix.m
index 6334100..8f7116e 100644
--- a/analysis_functions/hcp_ICA_unmix.m
+++ b/analysis_functions/hcp_ICA_unmix.m
@@ -69,39 +69,39 @@
cfg.demean = 'no';
for j = 1:ica_iterations
-
pack;
randn('state',j);
cfg.fastica.initGuess = randn(numel(data_meg.label));
comp = hcp_componentanalysis(cfg, data_meg);
- A = comp.topo;
- W = comp.unmixing;
-
- [chan, Nc] = size(A);
-
- %%% Ordering ICs according to the Power
- if Nc > 0
- power = mean(A.^2);
- [~, order] = sort(power, 'descend');
-
- A = A(:,order);
- W = W(order,:);
- end
-
- % copy the stuff back into the output structure
- comp.order = order;
- comp.topo = A;
- comp.unmixing = W;
- if strcmp(saveIC,'yes')
- for iic=1:size(comp.sampleinfo,1), comp.trial{iic} = comp.trial{iic}(order,:); end
- else
+% A = comp.topo;
+% W = comp.unmixing;
+%
+% [chan, Nc] = size(A);
+ [chan, Nc] = size(comp.topo);
+ comp.order = [1:Nc];
+%
+% %%% Ordering ICs according to the Power
+% if Nc > 0
+% power = mean(A.^2);
+% [~, order] = sort(power, 'descend');
+%
+% A = A(:,order);
+% W = W(order,:);
+% end
+%
+% comp.order = order;
+% comp.topo = A;
+% comp.unmixing = W;
+% if strcmp(saveIC,'yes')
+% for iic=1:size(comp.sampleinfo,1), comp.trial{iic} = comp.trial{iic}(order,:); end
+% else
+ if strcmp(saveIC,'no')
comp = rmfield(comp, 'trial');
comp = rmfield(comp, 'time');
end
- % put in the output structure
iteration(j).comp = comp; % structure containing all the iterations
-
- clear comp A W IC power order
+ clear comp
+% clear comp A W IC power
end
diff --git a/analysis_functions/hcp_check_pipelineoutput.m b/analysis_functions/hcp_check_pipelineoutput.m
index 93272fa..c11cab9 100644
--- a/analysis_functions/hcp_check_pipelineoutput.m
+++ b/analysis_functions/hcp_check_pipelineoutput.m
@@ -294,24 +294,24 @@ function hcp_check_pipelineoutput(pipeline, varargin)
end
case 'anatomy'
- assert_file('%s_anatomy_anatomical.nii', subject);
- assert_file('%s_anatomy_fiducials.txt', subject);
- assert_file('%s_anatomy_landmarks.txt', subject);
- assert_file('%s_anatomy_transform.txt', subject);
- assert_file('%s_anatomy_sourcemodel2d.mat', subject);
- assert_file('%s_anatomy_sourcemodel3d4mm.mat', subject);
- assert_file('%s_anatomy_sourcemodel3d6mm.mat', subject);
- assert_file('%s_anatomy_sourcemodel3d8mm.mat', subject);
- assert_file('%s_anatomy_headshape.mat', subject);
- assert_file('%s_anatomy_headshapemri.mat', subject);
- assert_file('%s_anatomy_headmodel.mat', subject);
- assert_file('%s_anatomy_headmodel.png', subject);
- assert_file('%s_anatomy_sourcemodel_2d.png', subject);
- assert_file('%s_anatomy_sourcemodel_3d.png', subject);
- assert_file('%s_anatomy_slice1.png', subject);
- assert_file('%s_anatomy_slice2.png', subject);
- assert_file('%s_anatomy_slice3.png', subject);
- assert_file('%s_anatomy_headshape.png', subject);
+ assert_file('%s_MEG_anatomy_anatomical.nii', subject);
+ assert_file('%s_MEG_anatomy_fiducials.txt', subject);
+ assert_file('%s_MEG_anatomy_landmarks.txt', subject);
+ assert_file('%s_MEG_anatomy_transform.txt', subject);
+ assert_file('%s_MEG_anatomy_sourcemodel_2d.mat', subject);
+ assert_file('%s_MEG_anatomy_sourcemodel_3d4mm.mat', subject);
+ assert_file('%s_MEG_anatomy_sourcemodel_3d6mm.mat', subject);
+ assert_file('%s_MEG_anatomy_sourcemodel_3d8mm.mat', subject);
+ assert_file('%s_MEG_anatomy_headshape.mat', subject);
+ assert_file('%s_MEG_anatomy_headshapemri.mat', subject);
+ assert_file('%s_MEG_anatomy_headmodel.mat', subject);
+ assert_file('%s_MEG_anatomy_headmodel.png', subject);
+ assert_file('%s_MEG_anatomy_sourcemodel_2d.png', subject);
+ assert_file('%s_MEG_anatomy_sourcemodel_3d4mm.png', subject);
+ assert_file('%s_MEG_anatomy_slice1.png', subject);
+ assert_file('%s_MEG_anatomy_slice2.png', subject);
+ assert_file('%s_MEG_anatomy_slice3.png', subject);
+ assert_file('%s_MEG_anatomy_headshape.png', subject);
case 'datacheck'
assert_file('%s_%s_datacheck_info.txt', experiment, scan);
@@ -329,7 +329,7 @@ function hcp_check_pipelineoutput(pipeline, varargin)
assert_file('%s_%s_icamne_%s_1.png', experiment, scan, sourcemodel_type);% there can be more figures, but at least one is expected
case 'icaimagcoh'
- assert_file('%s_%s_icaimagcoh_%s_freq%s.mat', experiment, scan, sourcemodel_type, freq);
+ assert_file('%s_%s_icaimagcoh_%sHz.mat', experiment, scan, freq);
case 'icapowenv'
assert_file('%s_%s_blp_%s_%s.mat', experiment, scan, sourcemodel_type, band);
diff --git a/analysis_functions/hcp_connectivity_mim.m b/analysis_functions/hcp_connectivity_mim.m
new file mode 100644
index 0000000..bcce8f4
--- /dev/null
+++ b/analysis_functions/hcp_connectivity_mim.m
@@ -0,0 +1,66 @@
+function [m] = hcp_connectivity_mim(input, varargin)
+
+% HCP_CONNECTIVITY_MIM computes the multivariate interaction measure from a data-matrix
+% containing the cross-spectral density. It implements the method described
+% in: Ewald et al., Estimating true brain connectivity from EEG/MEG data invariant to linear and
+% static trasformations in sensor space. Neuroimage, 2012; 476:488.
+%
+% Use as
+% [m] = hcp_connectivity_mim(input, varargin)
+%
+% The input data input should be organized as:
+%
+% Parcel*Ori x Parcel*Ori x Frequency
+%
+% The output m contains the interaction measure
+%
+% See also FT_CONNECTIVITYANALYSIS
+
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+
+% FIXME: interpretation
+
+indices = ft_getopt(varargin, 'indices');
+
+if isempty(indices) && isequal(size(input), [2 2])
+ % simply assume two channels
+ indx1 = 1;
+ indx2 = 2;
+else
+ % it should be a vector like [1 1 1 2 2 2]
+ indx1 = indices==1;
+ indx2 = indices==2;
+end
+
+cs_aa_re = real(input(indx1,indx1));
+cs_bb_re = real(input(indx2,indx2));
+cs_ab_im = imag(input(indx1,indx2));
+%
+inv_cs_bb_re = pinv(cs_bb_re);
+inv_cs_aa_re = pinv(cs_aa_re);
+transp_cs_ab_im = transpose(cs_ab_im);
+m = trace(inv_cs_aa_re*cs_ab_im*inv_cs_bb_re*transp_cs_ab_im); % try to speed up by dividing calculation in steps
+
+% taking the mldivide and mrdivide operators doesn't change the results, but speeds up by a factor of 4 over 1000 iterations (on LM Notebook)
+% m = trace(cs_aa_re\cs_ab_im*inv_cs_bb_re*transp_cs_ab_im);
+
+% % % block_a=cs_aa_re\cs_ab_im;
+% % % block_b=cs_bb_re\transpose(cs_ab_im);
+% % % m = trace(block_a*block_b);
+
+% m = trace(cs_aa_re\cs_ab_im*(cs_bb_re\transpose(cs_ab_im)));
diff --git a/analysis_functions/hcp_cs2csvv.m b/analysis_functions/hcp_cs2csvv.m
new file mode 100644
index 0000000..2b79a43
--- /dev/null
+++ b/analysis_functions/hcp_cs2csvv.m
@@ -0,0 +1,26 @@
+
+function csvv = hcp_cs2csvv(CSf,W,ndim)
+
+if not(ndim==3)
+ error('ndim must be = 3');
+end
+
+[U,S,V] = svd(CSf);
+
+[d1,d2] = size(W);
+nvox = d1/ndim;
+ord_ind = reshape((repmat([1:nvox]',1,ndim) + repmat([0 nvox 2*nvox],nvox,1))',1,[]);
+W = W(ord_ind,:);
+
+sv=sparse(diag(S));
+iL = 1:d1;
+jL = reshape(padarray(1:nvox,2,'replicate','post'),1,[]);
+
+csvv = sparse(d1,d1);
+for i=1:d2
+ L = sparse(iL,jL,W*U(:,i),d1,nvox);
+ R = sparse(jL,iL,(V(:,i)')*(W'),nvox,d1);
+ csvv = csvv + sv(i).*(L*R);
+end
+
+return
\ No newline at end of file
diff --git a/analysis_functions/hcp_eravg_contrasts.m b/analysis_functions/hcp_eravg_contrasts.m
index d150d67..a401bcd 100644
--- a/analysis_functions/hcp_eravg_contrasts.m
+++ b/analysis_functions/hcp_eravg_contrasts.m
@@ -1,9 +1,45 @@
function [outStatus] = hcp_eravg_contrasts(inCfg)
-
-% This function loads time frequency data and computes its trial average as well
-% as the average of its planar gradient
-% outdatafile is the file where the averaged data will be saved
-% outinfofile is the ZIP file where any plotted figures will be saved
+%% This function computes the average of ERFs for different conditions and contrasts for a given experiment.
+% It loads clean data and computes its trial average as well
+% as the average of its planar gradient.
+% It performes this average over the trials of BOTH scans for a given
+% experiment.
+%
+%
+% INPUT:
+%-------------------------------------------------------------------
+% inCfg : This is a structure containing required parameters for the
+% analysis
+% Fields:
+% .subjectid: Subject ID
+% .experimentid: Experiment ID
+% .multiscanid: This an ID the describes both scans for a
+% given Task. i.e. for scans 10-Motort and
+% 11-Motort, the average of the concatanated
+% datasets is represented by the multiscanid
+% 'Motort'.
+% .contrastlist: This is a cell containing all different
+% conditions and constrasts for a specific task data set for
+% which the eravg is to be computed. It is produced by hte
+% contrasts_$MULTISCANID.m functions.
+%
+% OUTPUT:
+%---------------------------------------------------------------------
+% outStatus: A dummy output flag
+%
+%
+%
+%
+% NAMING OF FILES WERE RESULTS ARE SAVED
+%------------------------------------------------------------------------------
+% The average ERF is saved in a typical fieltrip timelock data structure names 'data'.
+% This structure is saved in a file named according to the convention
+% [experimentid,'_',scanmnem,'_',contrast_mnemonic,'_[MODE-',avgmode,']'];
+% contrast_mnemonic is a string ID contained within each contrast in the
+% input inCfg.contrastlist variable. avgmode is 'meg' for magnetic field
+% representation or 'planar' for planar gradient representation.
+%-----------------------------------------------------------------------------
+
% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
%
diff --git a/analysis_functions/hcp_extract_allfromrun.m b/analysis_functions/hcp_extract_allfromrun.m
index 06c7cb9..c2a3456 100644
--- a/analysis_functions/hcp_extract_allfromrun.m
+++ b/analysis_functions/hcp_extract_allfromrun.m
@@ -1,4 +1,50 @@
function [cleanTrl] = hcp_extract_allfromrun(inCfg)
+%% This function performs the core processing of the tmegpreproc and rmegpreproc pipelines.
+% It extracts trial data for a give data group and fuses them with the
+% results from hcp_baddata.m pipeline so that bad channels and trials that
+% coincide with noisy periods are removed. In the case that a trial spans
+% a long block of data, in order to avoid removing it entirely, the bad
+% segments in the trials are replaced with nan. (This is the case for the Story/Math data groups BSENT and BUN).
+% Then the IC components identified as related to heart or eye activity by hcp_icaclass.m pipeline are removed.
+% Then the data is resampled to one fourth of the original sampling frequency in order to reduce the size of the
+% dataset.
+%
+%
+% INPUT:
+%-------------------------------------------------------------------
+% inCfg : This is a structure containing required parameters for the
+% analysis
+% Fields:
+% .datafile: This is the raw data filename for a given scan.
+% .trl: This is the trials definition matrix. It has 3 columns and number of rows equal to number of trials. The
+% first column is the start sample, the second is the end sample and time offset of the start of the trial
+% relative to the 0 reference point. This information is created by the trial definition functions.
+% .badchanfile: This is the file containing information about bad channels. This information is created by
+% hcp_baddata.m pipeline.
+% .badsegmfile: This is the file containing information about bad segments. This information is created by
+% hcp_baddata.m pipeline.
+% .icainfofile: This is the file containing information about artifactual Independent Components in the data. This
+% information is created hcp_icaclass.m pipeline
+% .badsegmode: This variable defines if trials containing bad segments will be removed in full or the bad segments will be replaced by NANs.
+% 'remfull' for remove full trial or 'repnan'
+% for replacing with NANs. This field is set in the alltrialdefparams_*.m scripts.
+%
+% .montage: This is the montage containing the emg channels. This
+% variable is constructed by the hcp_exgmontage.m function.
+% In .labelnew subfield , the expected emg channels names are expected to be
+% {'EMG_LH','EMG_LF','EMG_RH','EMG_RF'}
+% .lineFreq: Numerical array that contain the frequencies
+% of line current to be filtered out i.e. [60 120].
+% .outputfile: The filename of the data file where the cleaned data will be saved.
+%
+%
+% OUTPUT
+%-----------------------------------------------------------
+% cleanTrl : % This is a numerical matrix similar to the input inCfg.trl trials definition matrix described above.
+% It has 3 columns and number of rows equal to number of CLEAN trials that remained after the cleaning performed.
+% The first column is the start sample, the second is the end sample and time offset of the start of the trial
+% relative to the 0 reference point.
+%-------------------------------------------------------------
% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
%
@@ -40,6 +86,11 @@
cfgDefTr.demean = 'no';
dataRaw = ft_preprocessing(cfgDefTr);
+% remove the Supine balancing coefficients, because it's likely incorrect.
+% the call to ft_datatype_sens is needed because ft_apply_montage strips
+% the chanunit and chantype for some reason
+dataRaw.grad = ft_datatype_sens(ft_apply_montage(dataRaw.grad, dataRaw.grad.balance.Supine, 'inverse', 'yes', 'keepunused', 'yes'));
+
origFs = dataRaw.fsample;
% =====================
% -- Define new sampling frequency to 500 Hz
diff --git a/analysis_functions/hcp_ica_blp.m b/analysis_functions/hcp_ica_blp.m
new file mode 100644
index 0000000..6d9ce8c
--- /dev/null
+++ b/analysis_functions/hcp_ica_blp.m
@@ -0,0 +1,174 @@
+function [ source ] = hcp_ica_blp(source, comp, options_blp)
+
+% HCP_ICA_BLP allows the calculation of Band Limited Power.
+
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+
+
+
+subject = ft_getopt(options_blp, 'dataprefix');
+band_prefix = ft_getopt(options_blp, 'band_prefix');
+
+blp_band = ft_getopt(options_blp, 'blp_band', [6 15]);
+if strcmp(band_prefix,'beta')
+ N_hp=8; N_lp=10 ;
+elseif strcmp(band_prefix,'alpha')
+ N_hp=7; N_lp=8 ;
+elseif strcmp(band_prefix,'theta')
+ N_hp=7; N_lp=7 ;
+elseif strcmp(band_prefix,'delta')
+ N_hp=6; N_lp=6 ;
+elseif strcmp(band_prefix,'lowgamma')
+ N_hp=10; N_lp=14 ;
+elseif strcmp(band_prefix,'highgamma')
+ N_hp=12; N_lp=14 ;
+end
+
+step = ft_getopt(options_blp, 'blp_step', 20);
+window = ft_getopt(options_blp, 'blp_window', 400);
+band_str=[num2str(blp_band(1,1)) '-' num2str(blp_band(1,2))];
+
+disp(['blp band -> ' band_str ' Hz'])
+disp(['power calculation step -> ' num2str(step) ' ms'])
+disp(['power calculation window -> ' num2str(window) ' ms'])
+% brainic_indx=comp.class.brain_ic;
+if(~isfield(comp.class,'brain_ic_vs')) comp.class.brain_ic_vs=comp.class.brain_ic; end
+
+brainic_indx=comp.class.brain_ic_vs;
+nsource = numel(source.inside);
+
+
+% Reading from file and filtering
+
+if(strcmp(band_prefix,'delta'))
+ cfgin = [];
+ cfgin.lpfilter = 'yes';
+ cfgin.lpfreq = blp_band(1,2);
+ cfgin.lpfiltord=N_lp;
+ comp_blp = ft_preprocessing(cfgin,comp);
+elseif(strcmp(band_prefix,'highgamma'))
+ cfgin = [];
+ cfgin.hpfilter = 'yes';
+ cfgin.hpfreq = blp_band(1,1);
+ cfgin.hpfiltord=N_hp;
+ comp_blp = ft_preprocessing(cfgin,comp);
+elseif(strcmp(band_prefix,'whole'))
+ comp_blp=comp;
+else
+ cfgin = [];
+ cfgin.hpfilter = 'yes';
+ cfgin.hpfiltord=N_hp;
+ cfgin.hpfreq = blp_band(1,1);
+ junk = ft_preprocessing(cfgin,comp);
+
+ cfgin = [];
+ cfgin.lpfilter = 'yes';
+ cfgin.lpfiltord=N_lp;
+ cfgin.lpfreq = blp_band(1,2);
+ comp_blp = ft_preprocessing(cfgin,junk);
+end
+clear junk
+
+% % % % % compappo=comp;
+% % % % % compappo.trial=comp_blp.trial;
+% % % % % options = {'doplot', 'no', 'grad', comp.grad, 'plottype', 'components'}; % perform frequency analysis
+% % % % % disp('STARTING ft_ICA_freq');
+% % % % % comp_freq = hcp_ICA_freq(compappo, options);
+% % % % % disp('DONE ft_ICA_freq');
+% % % % %
+% % % % % options = {'doplot', 'no', 'grad', comp.grad, 'plottype', 'components'}; % perform frequency analysis
+% % % % % disp('STARTING ft_ICA_freq');
+% % % % % comp_freq2 = hcp_ICA_freq(comp, options);
+% % % % % disp('DONE ft_ICA_freq');
+% % % % % %
+% % % % %
+% % % % %
+% % % % % imgname=[subject '_blp_iccheck_' band_prefix];
+% % % % % options={'plottype','components','component',9,'saveres','no','grad', comp.grad,'modality','MEG','saveformat','png','fileout',imgname,'visible','on'};
+% % % % % hcp_ICA_plot(comp_freq2,options) % summary plots of the IC
+% % % % %
+% % % % % mspec = sqrt(comp_freq.freq_comp.powspctrm(9,:));
+% % % % % F = comp_freq.freq_comp.freq;
+% % % % %
+% % % % % subplot(2,2,[3 4])
+% % % % % hold on
+% % % % % plot(F,mspec,'r')
+
+junk=cell2mat(comp_blp.trial);
+
+IC=junk(brainic_indx,:);
+pIC=size(IC,2);
+source_sig=zeros(3,pIC);
+sigt=zeros(1,pIC);
+
+
+% difines step and window in points;
+step_pnt=round(comp.fsample*step/1000);
+window_pnt=round(comp.fsample*window/1000);
+
+nwin=fix((size(sigt,2)-window_pnt)/step_pnt);
+
+for k=1:nwin
+ time_power(k)=(1/comp.fsample)*mean((k-1)*step_pnt+1:(k-1)*step_pnt+window_pnt); % in seconds
+end
+
+power=zeros(nsource,nwin-1);
+ft_progress('init', 'text', 'Please wait...');
+str=['evaluating power for ' subject ' band ' band_prefix];
+disp(str)
+
+% create a sparse matrix that is essentially an averaging operator
+% and prune the IC time course matrix
+ix = zeros(window_pnt*(nwin-1),1);
+iy = zeros(window_pnt*(nwin-1),1);
+for k = 1:(nwin-1)
+ indx = (k-1)*window_pnt+(1:window_pnt);
+ ix(indx) = (k-1)*step_pnt+(1:window_pnt);
+ iy(indx) = k;
+end
+iz = ones(numel(iy),1)./window_pnt;
+P = sparse(ix,iy,iz);
+IC = IC(:,1:size(P,1)); % the last bunch of samples are not used anyway
+source_sig= source_sig(:,1:size(P,1));
+
+for is=1:nsource
+ ft_progress(is/nsource, 'evaluating power voxel %d from %d\n', is, nsource);
+
+ source_sig(:,:)=source.avg.mom{source.inside(is)'}(:,brainic_indx)*(IC*1E15);
+ sigt=sum(source_sig.^2,1);
+
+ %for k=1:(nwin-1)
+ % power(is,k)=mean(sigt((k-1)*step_pnt+1:(k-1)*step_pnt+window_pnt));
+ %end
+ power(is,:) = sigt*P;
+end
+
+if isfield(source,'avg'); source=rmfield(source,'avg'); end
+if isfield(source,'time'); source=rmfield(source,'time'); end
+if isfield(source,'val'); source=rmfield(source,'val'); end
+if isfield(source,'noise'); source=rmfield(source,'noise'); end
+source.power=power;
+source.blp_band=blp_band;
+source.step=step;
+source.window=window;
+source.step_pnt=step_pnt;
+source.window_pnt=window_pnt;
+source.time=time_power;
+source.time_power=time_power;
+end
+
diff --git a/analysis_functions/hcp_ica_plotreport.m b/analysis_functions/hcp_ica_plotreport.m
index 0955b28..8b9f44d 100644
--- a/analysis_functions/hcp_ica_plotreport.m
+++ b/analysis_functions/hcp_ica_plotreport.m
@@ -1,5 +1,19 @@
function hcp_ica_plotreport(comp,options_ICA,datain)
+% HCP_ICA_PLOTREPORT allows the plots of Independent
+% Components automatically classified by HCP_ICA_RMEG_CLASSIFICATION function. This
+% function is able to classify Independent Components into Brain Activity
+% and ECG/EOG related artifacts provided that ECG and EOR reference
+% channels are used.
+%
+% Use as
+% hcp_ica_plotreport(comp,options_ICA,datain)
+%
+% where the input comp structure should be obtained from
+% HCP_ICA_RMEG_CLASSIFICATION and the datain structure is a fieldtrip data
+% structure contining the channel time courses used as imput of the
+% FT_COMPONENTANALYSIS .
+%
% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
%
% This file is part of megconnectome.
@@ -22,7 +36,6 @@ function hcp_ica_plotreport(comp,options_ICA,datain)
bandpass = ft_getopt(options_ICA, 'bandpass');
bandstop = ft_getopt(options_ICA, 'bandstop');
tsize = ft_getopt(options_ICA, 'textsize',22)
-% grad = ft_getopt(options_ICA, 'grad');
modality = ft_getopt(options_ICA, 'modality', 'MEG'); % GIORGOS
grad=ft_getopt(options_ICA, 'grad');
@@ -42,7 +55,7 @@ function hcp_ica_plotreport(comp,options_ICA,datain)
if(~isfield(comp,'pow_data'))
- doplot='no'; % Summury Results cn be plotted also at this level without calling hcp_plotICA
+ doplot='no';
options = {'doplot',doplot, 'grad', grad,'plottype','components'};
[comp_freq]=hcp_ICA_freq(comp, options, datain);
@@ -51,7 +64,7 @@ function hcp_ica_plotreport(comp,options_ICA,datain)
end
n_IC=size(comp.unmixing,1);
-frange=bandpass; %to be decided if in compo or other
+frange=bandpass;
cfgin =[];
cfgin.grad=grad;
@@ -73,7 +86,7 @@ function hcp_ica_plotreport(comp,options_ICA,datain)
leg={'OneOverF (>0.5) ' ; 'Specflat (<2.0) ' ; 'Kurtosis (>15) ' ; 'elecSig (>0.1) '; 'elecPow (>0.25) '; 'elecSpe (>0.95) '};
thrs=[0.5;2.0;15;0.1;0.25;0.95];
class=comp.class;
-% size_s=get(0,'ScreenSize');
+
for ix=1:n_IC
str(1,:)={num2str(abs(comp.class.spectrum_one_over_f(1,ix)),'%5.3f')};
str(2,:)={num2str(abs(comp.class.spectrum_flat(1,ix)),'%5.1f')};
@@ -101,28 +114,16 @@ function hcp_ica_plotreport(comp,options_ICA,datain)
elseif(isempty(find(class.brain_ic==ix)) && ~isempty(find(class.brain_ic_vs==ix)))
str_ic='BRAIN CORRECTED';
end
- %%% In the cluster version all this settings are not required
- % X = 29.7; %# A3 paper size
- % Y = 14.35; %# A3 paper size
- % xMargin = 1; %# left/right margins from page borders
- % yMargin = 1; %# bottom/top margins from page borders
- % xSize = X - 2*xMargin; %# figure size on paper (widht & hieght)
- % ySize = Y - 2*yMargin; %# figure size on paper (widht & hieght)
+
+
figure('Menubar','none');
%
set(gca, 'XTickLabel',[], 'YTickLabel',[], ...
'Units','normalized', 'Position',[0 0 1 1])
- %
- % %# figure size on screen (50% scaled, but same aspect ratio)
- % set(gcf, 'Units','centimeters', 'Position',[5 5 xSize ySize])
-
- %# figure size printed on paper
+
set(gcf, 'visible', 'off')
set(gcf, 'PaperUnits','inches')
- % set(gcf, 'PaperSize',[X Y])
- % set(gcf, 'PaperPosition',[xMargin yMargin xSize ySize])
- % set(gcf, 'PaperOrientation','portrait')
- %%%
+
set(gcf, 'paperposition', [1 1 20 12]);
subplot(2,3,1)
@@ -130,7 +131,6 @@ function hcp_ica_plotreport(comp,options_ICA,datain)
cfgin.layout=layout;
cfgin.colorbar='yes';
ft_topoplotIC(cfgin, comp);
- % xlabel(['IC ' num2str(ix)],'color','k');
title('IC sensor map','FontSize',12);
subplot(2,3,4)
@@ -145,8 +145,7 @@ function hcp_ica_plotreport(comp,options_ICA,datain)
str_icnum = {['ic ' num2str(ix) ' = ' str_ic]};
- % text(1.2,0.5,leg,'Units','normalized','FontSize',15)
- % text(2,0.5,str,'Units','normalized','FontSize',16,'color',strcolor)
+
text(1.2 ,0.65,leg{1},'Units','normalized','FontSize',tsize,'color',strcolor(1))
text(2,0.65, str{1},'Units','normalized','FontSize',tsize,'color',strcolor(1))
text(1.2,0.59,leg{2},'Units','normalized','FontSize',tsize,'color',strcolor(2))
@@ -160,10 +159,7 @@ function hcp_ica_plotreport(comp,options_ICA,datain)
text(1.2,0.335,leg{6},'Units','normalized','FontSize',tsize,'color',strcolor(6))
text(2,0.335,str{6},'Units','normalized','FontSize',tsize,'color',strcolor(6))
- % text(1.5,0.2,str_icnum,'Units','normalized','FontSize',16)
- % text(1.5,0.1,str_ic,'Units','normalized','FontSize',16,'color','r')
text(1.2,0.8,str_icnum,'Units','normalized','FontSize',tsize,'color','b')
- % text(1.8,0.8,str_ic,'Units','normalized','FontSize',16,'color','b')
subplot(2,3,[2 3])
timeic=cell2mat(comp.time);
@@ -187,7 +183,6 @@ function hcp_ica_plotreport(comp,options_ICA,datain)
imgname=[resultprefix '_icaclass_vs_' num2str(ix) '.png'];
hcp_write_figure(imgname, gcf, 'resolution', 300)
- % clear h;
close
end
diff --git a/analysis_functions/hcp_icaemcw_estimate.m b/analysis_functions/hcp_icaemcw_estimate.m
new file mode 100644
index 0000000..b7890ed
--- /dev/null
+++ b/analysis_functions/hcp_icaemcw_estimate.m
@@ -0,0 +1,510 @@
+function [connect_mcw] = hcp_icaemcw_estimate(source, sourcemodel, options)
+
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+
+subject= ft_getopt(options, 'subjectid');
+outputfile = ft_getopt(options, 'outputfile');
+label=ft_getopt(options, 'label');
+step = ft_getopt(options, 'mcw_step', 200);
+window = ft_getopt(options, 'mcw_window', 10000);
+dofig = ft_getopt(options, 'dofig', 'yes');
+ac=ft_getopt(options, 'ac', 0.1);
+thresh_tn=ft_getopt(options, 'thresh', 0.6);
+extnode=ft_getopt(options, 'extnode');
+% outputfile=[outputfile '_test']
+
+disp('reading mri template')
+mri=ft_read_mri('mni_icbm152_t1_tal_nlin_sym_09a.nii');
+mri=ft_convert_units(mri, 'mm');
+
+sourcemodel=ft_convert_units(sourcemodel, 'mm');
+sourcemodel.inside=source.inside;
+sourcemodel.outside=source.outside;
+
+source=ft_convert_units(source, 'mm');
+% oldpos=source.pos;
+
+source.pos=sourcemodel.pos;
+source.coordsys = 'spm';
+
+X = 45; %
+Y = 20; %
+xMargin = 1; %
+yMargin = 1; %
+xSize = X - 2*xMargin; %
+ySize = Y - 2*yMargin; %
+
+%________________ MCW sampling details _______
+Fs = 50; % hard coded... to be retrievied from the blp structure
+step2=round(Fs*step/1000);
+window2=round(Fs*window/1000); % in points;
+%______________________________________________
+
+
+% % % % % % label1=label(1,:)';
+% % % % % % label2=label(2,:)';
+% % % % % % label3=label(3,:)';
+% % % % % % label4=label(4,:)';
+% % % % % % % %____________ EXT NODE
+% % % % % % label5=label(5,:)';
+
+
+%_____________ SETTINGS (THESE WILL BE MANAGED FROM SOMEWHERE ELSE) _______
+
+% % % % % % % % % % % % xdim = source.dim(1);
+% % % % % % % % % % % % ydim = source.dim(2);
+% % % % % % % % % % % % zdim = source.dim(3);
+
+
+%__________________________________________________________________________
+%_______ all power is mean removed and divided for std once and for all
+%_______ here, not needed later
+disp('removing mean from power')
+source.power=source.power.*1e15;
+source.power = (source.power-...
+ repmat(squeeze(mean(source.power,2)),1,size(source.power,2)))./...
+ (repmat(std(source.power,1,2),1,size(source.power,2)));
+
+%________________________________________________________________________
+position = source.pos(source.inside,:);
+if ~isempty(extnode)
+% label(5,:)=[-35, -88, 4];
+label(5,:)=extnode;
+end
+for inode=1:5
+ ref_p=label(inode,:)';
+
+ disp('finding seed voxels')
+ abs2=sqrt((position(:,1)-ref_p(1)*ones(length(position(:,1)),1)).^2+...
+ (position(:,2)-ref_p(2)*ones(length(position(:,2)),1)).^2+...
+ (position(:,3)-ref_p(3)*ones(length(position(:,3)),1)).^2);
+
+ [x, iref(inode)]=min(abs2);
+
+ ref(inode,:) = source.power(iref(inode),:);
+ seed_pos(inode,:)=position(iref(inode),:);
+end
+
+ntp=size(ref,2);
+nwin=fix((ntp-window2)/step2);
+
+
+disp('estimating seed correlations')
+for k=1:nwin
+ vect1=[(k-1)*step2+1:(k-1)*step2+window2];
+ corr_wins(k,:)=[(k-1)*step2+1 (k-1)*step2+window2];
+ time_corr(k)=(1/Fs)*mean(vect1); % in seconds
+
+ tmp=find(vect1 >= 1 & vect1 <= ntp);
+
+ r=corrcoef(ref(1,vect1(tmp)),ref(2,vect1(tmp))); c_p1(1,k)=r(1,2);
+ r=corrcoef(ref(1,vect1(tmp)),ref(3,vect1(tmp))); c_p1(2,k)=r(1,2);
+ r=corrcoef(ref(1,vect1(tmp)),ref(4,vect1(tmp))); c_p1(3,k)=r(1,2);
+ r=corrcoef(ref(1,vect1(tmp)),ref(5,vect1(tmp))); c_p1(4,k)=r(1,2);
+
+ r=corrcoef(ref(2,vect1(tmp)),ref(1,vect1(tmp))); c_p2(1,k)=r(1,2);
+ r=corrcoef(ref(2,vect1(tmp)),ref(3,vect1(tmp))); c_p2(2,k)=r(1,2);
+ r=corrcoef(ref(2,vect1(tmp)),ref(4,vect1(tmp))); c_p2(3,k)=r(1,2);
+ r=corrcoef(ref(2,vect1(tmp)),ref(5,vect1(tmp))); c_p2(4,k)=r(1,2);
+
+ r=corrcoef(ref(3,vect1(tmp)),ref(1,vect1(tmp))); c_p3(1,k)=r(1,2);
+ r=corrcoef(ref(3,vect1(tmp)),ref(2,vect1(tmp))); c_p3(2,k)=r(1,2);
+ r=corrcoef(ref(3,vect1(tmp)),ref(4,vect1(tmp))); c_p3(3,k)=r(1,2);
+ r=corrcoef(ref(3,vect1(tmp)),ref(5,vect1(tmp))); c_p3(4,k)=r(1,2);
+
+ r=corrcoef(ref(4,vect1(tmp)),ref(1,vect1(tmp))); c_p4(1,k)=r(1,2);
+ r=corrcoef(ref(4,vect1(tmp)),ref(2,vect1(tmp))); c_p4(2,k)=r(1,2);
+ r=corrcoef(ref(4,vect1(tmp)),ref(3,vect1(tmp))); c_p4(3,k)=r(1,2);
+ r=corrcoef(ref(4,vect1(tmp)),ref(5,vect1(tmp))); c_p4(4,k)=r(1,2);
+end
+c_sn(1,:)=min(abs(c_p1(1:3,:)));
+c_sn(2,:)=min(abs(c_p2(1:3,:)));
+c_sn(3,:)=min(abs(c_p3(1:3,:)));
+c_sn(4,:)=min(abs(c_p4(1:3,:)));
+c_out=[c_p1(4,:) ; c_p2(4,:) ; c_p3(4,:) ; c_p4(4,:)];
+
+disp('finding mcw')
+iii=[];
+
+junk=[];
+
+for inode=1:1
+ t_n=max(abs(c_sn(inode,:)));
+ while(t_n>thresh_tn)
+ t_e=t_n-0.9;
+ while(t_e<(thresh_tn-ac))
+ junk=[find(abs(c_sn(inode,:))>t_n & abs(c_out(inode,:))5))==0
+% % % % % tmpindx=iii(1); iw(1)=iii(1);
+% % % % % if(size(iii,2)>1)
+% % % % % for ip=2:size(iii,2)
+% % % % % if(iii(ip)>(tmpindx+5))
+% % % % % cindx=cindx+1;
+% % % % % iw(cindx)=iii(ip);
+% % % % % tmpindx=iii(ip);
+% % % % % end
+% % % % % end
+% % % % % end
+% % % % % % else
+% % % % % % iw=iii(1);
+% % % % % % end
+% % % % % end
+
+
+ if isempty(iii)==0
+ if isempty(find(diff(iii)>5))==0
+ iw=[iii(1) iii(find(diff(iii)>5)+ones(1,length(find(diff(iii)>5))))];
+
+ else
+ iw=iii(1);
+ end
+ end
+
+ time_corr_win=(1/Fs)*window2/2;
+
+if(~isempty(iw))
+ % if(strcmp(dofig,'yes'))
+ h=figure; set(gcf, 'Units','centimeters', 'Position',[1 1 xSize ySize],'Color',[0.8 0.8 0.8])
+ set(h, 'paperposition', [1 1 24 6]);
+ plot(time_corr,c_p1(1,:),'b'); hold on;
+ plot(time_corr,c_p1(2,:),'r')
+ plot(time_corr,c_p1(3,:),'g')
+ plot(time_corr,c_p1(4,:),'k')
+ % plot(time_corr,c_mean,'k:')
+ axis([0 time_corr(1,end) 0 1])
+% plot(time_corr,repmat(0.6,1,size(time_corr,2)),'-')
+ line([time_corr(1) time_corr(end)], [0.6 0.6],'color','k')
+ line([time_corr(1) time_corr(end)], [0.5 0.5],'color','k')
+ for il=1:size(iii,2)
+ line([time_corr(iii(il)) time_corr(iii(il))], [0 c_p1(4,iii(il))],'color','y')
+ end
+% for il=1:size(iw,2)
+% max_corr= max([c_p1(1,iw(il)) c_p1(2,iw(il)),c_p1(3,iw(il))])
+% line([time_corr(iw(il)) time_corr(iw(il))], [0 1],'color','k')
+% end
+ % grid on
+ xlabel('time (s)');
+ ylabel('corr over windows');
+ % legend('rpips','lfef','rfef','out','mean')
+ legend('rpips','lfef','rfef','out','Location','NorthEastOutside')
+ plot(time_corr(iw),c_p1(1,iw),'*');
+ plot(time_corr(iw),c_p1(2,iw),'r*')
+ plot(time_corr(iw),c_p1(3,iw),'g*')
+ plot(time_corr(iw),c_p1(4,iw),'m*')
+ plot(time_corr(iw),c_p1(1,iw),'o');
+ plot(time_corr(iw),c_p1(2,iw),'ro')
+ plot(time_corr(iw),c_p1(3,iw),'go')
+ plot(time_corr(iw),c_p1(4,iw),'mo')
+ % if(~isempty(tmpindx))
+ % plot(time_corr(tmpindx),c_p1(tmpindx),'ro')
+ % end
+ set(gca,'Color',[0.5 0.5 0.5]);
+ imgname = [outputfile '_seed_correlations.png'];
+ hcp_write_figure(imgname, h);
+ close(h)
+ % end
+end
+
+if isempty(iii)==0
+ xp=time_corr(iw);
+ clear extr extr2
+
+ %____ extremes in seconds _________________________________________
+ extr=[xp'-window/2000*ones(size(xp))' xp'+window/2000*ones(size(xp))'];
+ %____ extremes in points _____________________
+ extr2=round(Fs*extr);
+ if extr2(1)==0
+ extr2(1)=1;
+ end
+
+ ref1 = ref(1,:);
+ %_________________________________________________________________
+
+ %_____________________ CONNECTIVITY MAP COMPUTATION ______________
+
+ dm1_pre = zeros(size(extr2,1),size(source.power,1));
+ z_fisher_dm1 = zeros(size(extr2,1),size(source.power,1));
+
+ disp('Connectivity map Computation')
+ for w=1:size(extr2,1)
+ disp(num2str(w))
+
+
+ dm1_pre(w,:) = corr(ref1(1,extr2(w,1):extr2(w,2))',source.power(:,extr2(w,1):extr2(w,2))');
+
+
+ z_fisher_dm1(w,:)=0.5.*log((1+dm1_pre(w,:))./(1-dm1_pre(w,:)));
+
+% if(strcmp(dofig,'yes'))
+%
+% h=figure; set(gcf, 'Units','centimeters', 'Position',[5 5 xSize ySize],'Color',[0.8 0.8 0.8])
+% plot(dm1_pre(w,:),'m') ; hold on
+% plot(iref,dm1_pre(w,iref),'k*')
+% plot(iref2,dm1_pre(w,iref2),'*')
+% plot(iref3,dm1_pre(w,iref3),'r*')
+% plot(iref4,dm1_pre(w,iref4),'g*')
+% plot(iref5,dm1_pre(w,iref5),'y*')
+% xlabel('voxel');
+% ylabel('correlation with seed');
+% legend('seed', 'ref1','ref2','ref3','ref4','out')
+% set(gca,'Color',[0.5 0.5 0.5]);
+% line([1 size(dm1_pre,2)], [0.6 0.6])
+% imgname = [outputfile '_voxel_correlations_' num2str(w) '.png'];
+% hcp_write_figure(imgname, h);
+% close(h)
+% end
+ end
+
+% for izf=1:size(z_fisher_dm1,1)
+% [junk seed_indx]=max(z_fisher_dm1(izf,:));
+% z_fisher_dm1(izf,seed_indx)=0;
+% z_fisher_dm1(izf,seed_indx)=max(z_fisher_dm1(izf,:));
+% end
+
+ connect_mcw=source;
+ connect_mcw=rmfield(connect_mcw,'power')
+ connect_mcw=rmfield(connect_mcw,'time_power')
+ if(isfield(connect_mcw,'avg')) connect_mcw=rmfield(connect_mcw,'avg'); end
+
+ connect_mcw.time=[1:size(dm1_pre,1)];
+ connect_mcw.mcw=dm1_pre;
+ connect_mcw.mcwextr=extr2;
+
+ connect_mcw.seeds_indx=iref;
+
+ disp('Evaluating Z-Score MAP')
+
+% mu_base=mean(mean(z_fisher_dm1,2),1);
+% mu_s=mean(z_fisher_dm1,1);
+% sigma_s=std(z_fisher_dm1,1,1);
+% z_s=(mu_s-mu_base).*sqrt(size(z_fisher_dm1,1))./sigma_s;
+%
+ mu_base=mean(mean(dm1_pre,2),1);
+ mu_s=mean(dm1_pre,1);
+ sigma_s=std(dm1_pre,1,1);
+ z_s=(mu_s-mu_base).*sqrt(size(dm1_pre,1))./sigma_s;
+
+ for iz=1:5
+ [junk seed_indx(iz)]=max(z_s);
+ z_s(seed_indx(iz))=0;
+ end
+ z_s(seed_indx)=max(z_s);
+ connect_mcw.z_s=z_s;
+
+
+
+ if(strcmp(dofig,'yes'))
+ h=figure; set(gcf, 'Units','centimeters', 'Position',[5 5 xSize ySize],'Color',[0.8 0.8 0.8])
+
+ plot(z_s,'m') ; hold on
+ plot(iref(1,1),z_s(1,iref(1,1)),'k*')
+ plot(iref(1,2),z_s(1,iref(1,2)),'*')
+ plot(iref(1,3),z_s(1,iref(1,3)),'r*')
+ plot(iref(1,4),z_s(1,iref(1,4)),'g*')
+ plot(iref(1,5),z_s(1,iref(1,5)),'y*')
+ xlabel('voxel');
+ ylabel('z-score');
+ legend('seed', 'ref1','ref2','ref3','ref4','out')
+ set(gca,'Color',[0.5 0.5 0.5]);
+ % line([1 size(dm1_pre,2)], [0.6 0.6])
+ imgname = [outputfile '_voxel_z_score.png'];
+ hcp_write_figure(imgname, h);
+ close(h)
+ end
+
+ if(strcmp(dofig,'yes'))
+
+ intcfg=[];
+ intcfg.parameter = 'avg.pow';
+ tmp = connect_mcw;
+ % tmp.pos=oldpos;
+ tmp.avg.pow=zeros(size(source.pos,1),1);
+ tmp.avg.pow(source.inside) = z_s;
+ s_interp = ft_sourceinterpolate(intcfg, tmp, mri);
+ [junk thr_ind junk2 thr_ind_dep]= hcp_mcw_fdr(z_s,0.05,size(connect_mcw.mcw,1)); % [z_thr thr3 z_thr_dep thr3_dep]
+
+
+% indxmask=find(s_interp.avg.pow>thr_ind_dep);
+ indxmask=find(s_interp.avg.pow>0);
+
+ s_interp.avg.mask=zeros(size(s_interp.avg.pow));
+ s_interp.avg.mask(indxmask)=1;
+ % s_interp.avg.mask=zeros(size(s_interp.avg.pow));
+ % maxabs=max(max(max(s_interp.avg.pow)));
+ % indxmask=find(s_interp.avg.pow>1.5);
+ % s_interp.avg.mask(indxmask)=1;
+ %
+ intcfg=[];
+ intcfg.parameter = 'avg.pow';
+ % intcfg.downsample = 1;
+
+ tmp = connect_mcw;
+
+ for k = 1:numel(connect_mcw.time)
+ tmp.avg.pow=zeros(size(source.pos,1),1);
+ tmp.avg.pow(connect_mcw.inside) = connect_mcw.mcw(k,:);
+ s_interp2(k) = ft_sourceinterpolate(intcfg, tmp, mri);
+ s_interp2(k).avg.mask=zeros(size(s_interp2(k).avg.pow));
+ maxabs=max(max(max(s_interp2(k).avg.pow)));
+ indxmask=find(s_interp2(k).avg.pow>0.4*maxabs);
+ s_interp2(k).avg.mask(indxmask)=1;
+ end
+
+
+ tmp = connect_mcw;
+ tmp.time=1;
+
+ tmp.avg.pow=zeros(size(tmp.pos,1),1);
+ tmp.avg.pow(:,:)=0;
+
+ tmp.avg.pow(tmp.inside(iref(1,1))) = -10;
+ tmp.avg.pow(tmp.inside(iref(1,2))) = 10;
+ tmp.avg.pow(tmp.inside(iref(1,3))) = 10;
+ tmp.avg.pow(tmp.inside(iref(1,4))) = 10;
+ tmp.avg.pow(tmp.inside(iref(1,5))) = 10;
+
+ s_interp_seed = ft_sourceinterpolate(intcfg, tmp, mri);
+ s_interp_seed.avg.mask=zeros(size(s_interp_seed.avg.pow));
+ maxabs=max(max(max(s_interp_seed.avg.pow)));
+ indxmask=find(abs(s_interp_seed.avg.pow)>0.4*maxabs);
+ s_interp_seed.avg.mask(indxmask)=1;
+
+ % netstring=net_seed{seed_indx};
+ % tmpindx=strfind(netstring, '_')
+ % netstring(tmpindx)='-';
+ % imgname = ['mcw_netseed_' net_seed{seed_indx}];
+
+
+
+ cfg = [];
+ cfg.method = 'slice';
+ cfg.funcolormap='jet';
+ cfg.nslices=36;
+ % cfg.method = 'surface';
+ % cfg.anaparameter = 'anatomy';
+ % cfg.coordsys = 'mni';
+ % cfg.anaparameter = 'mri';
+ cfg.funparameter = 'avg.pow';
+ % cfg.method = 'ortho';
+ cfg.projmethod = 'nearest';
+ cfg.interactive = 'no';
+% % % % % cfg.funcolorlim=[-10 10];
+ cfg.maskparameter='avg.mask';
+% % % % % ft_sourceplot(cfg, s_interp_seed);
+% % % % %
+% % % % % ax1 = gca;
+
+ cfg.funcolorlim=[0 10];
+ % % % % % cfg.maskparameter='avg.mask';
+
+ imgname = [outputfile '_z_score'];
+ ft_sourceplot(cfg, s_interp);
+% hcp_write_figure(imgname, gcf, 'format', 'fig')
+ disp('step1')
+% % % % % ax2 = gca;
+ h3=gcf;
+% % % % % h3 = figure; %create new figure
+% % % % % set(gca, 'XTickLabel',[], 'YTickLabel',[], ...
+% % % % % 'Units','normalized', 'Position',[0 0 1 1])
+% % % % %
+% % % % % %# figure size on screen (50% scaled, but same aspect ratio)
+% % % % % set(gcf, 'Units','centimeters', 'Position',[5 5 xSize ySize])
+% % % % %
+% % % % % %# figure size printed on paper
+% % % % % set(gcf, 'visible', 'on')
+% % % % % set(gcf, 'PaperUnits','centimeters')
+% % % % % set(gcf, 'PaperSize',[X Y])
+% % % % % set(gcf, 'PaperPosition',[xMargin yMargin xSize ySize])
+% % % % % set(gcf, 'PaperOrientation','portrait')
+
+ disp('step2')
+
+% % % % % s1 = subplot(1,2,1); %create and get handle to the subplot axes
+% % % % % fig1 = get(ax1,'children'); %get handle to all the children in the figure
+% % % % % copyobj(fig1,s1); %copy children to new parent axes i.e. the subplot axes
+% % % % % caxis([-10,10])
+% % % % % colorbar
+
+ disp('step3')
+
+% % % % % s2 = subplot(1,2,2);
+% % % % % fig2 = get(ax2,'children');
+% % % % % copyobj(fig2,s2);
+% % % % % colorbar
+% % % % % caxis(cfg.funcolorlim)
+
+ disp('step4')
+
+ imgname = [outputfile '_z_score.png'];
+
+ hcp_write_figure(imgname, h3);
+ hcp_write_figure(imgname, h3, 'format' ,'fig');
+
+
+ disp('step5')
+
+ close(h3)
+
+ disp('step6')
+
+% close all
+
+ for i = 1:size(s_interp2, 2)
+ imgname = [outputfile '_' num2str(i) '.png'];
+ % maxVal=max(s_interp2(i).avg.pow(:));
+ % minVal=min(s_interp2(i).avg.pow(:));
+ % maxAbs=max([maxVal;minVal])
+ % limSign=unique(sign([minVal maxVal]));
+ % if (length(limSign)==1)&limSign>0,
+ % funLim=maxAbs*[0 1];
+ % elseif (length(limSign)==1)&limSign<0,
+ % funLim=maxAbs*[-1 0];
+ % else
+ % funLim=maxAbs*[-1 1];
+ % end
+ funLim=[0 1];
+ cfg.funcolorlim=funLim;
+
+ ft_sourceplot(cfg, s_interp2(i));
+ h1=gcf;
+% set(h1, 'paperposition', [1 1 10 7]);
+ hcp_write_figure(imgname, h1);
+ close(h1)
+ end
+ end
+
+
+else
+ connect_mcw=[];
+end %_over k ref points
+close all
+clear time_corr c_p1 c_p2 c_p3 c_p4
+end
+
+
+
diff --git a/analysis_functions/hcp_icaemcw_estimate_2d.m b/analysis_functions/hcp_icaemcw_estimate_2d.m
new file mode 100644
index 0000000..dcb8e2b
--- /dev/null
+++ b/analysis_functions/hcp_icaemcw_estimate_2d.m
@@ -0,0 +1,418 @@
+function [connect_mcw] = hcp_icaemcw_estimate_2d(source, sourcemodel, options)
+
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+
+subject= ft_getopt(options, 'subjectid');
+outputfile = ft_getopt(options, 'outputfile');
+net_seeds=ft_getopt(options, 'net_seeds');
+step = ft_getopt(options, 'mcw_step', 200);
+window = ft_getopt(options, 'mcw_window', 10000);
+dofig = ft_getopt(options, 'dofig', 'yes');
+ac=ft_getopt(options, 'ac', 0.1);
+thresh_tn=ft_getopt(options, 'thresh', 0.6);
+extnode=ft_getopt(options, 'extnode');
+% outputfile=[outputfile '_test']
+
+mni_pos=net_seeds.pos;
+
+X = 45; %
+Y = 20; %
+xMargin = 1; %
+yMargin = 1; %
+xSize = X - 2*xMargin; %
+ySize = Y - 2*yMargin; %
+
+%________________ MCW sampling details _______
+Fs = 50; % hard coded... to be retrievied from the blp structure
+step2=round(Fs*step/1000);
+window2=round(Fs*window/1000); % in points;
+%______________________________________________
+
+%__________________________________________________________________________
+%_______ all power is mean removed and divided for std once and for all
+%_______ here, not needed later
+disp('removing mean from power')
+% source.power=source.power.*1e15;
+source.power = (source.power-...
+ repmat(squeeze(mean(source.power,2)),1,size(source.power,2)))./...
+ (repmat(std(source.power,1,2),1,size(source.power,2)));
+
+%________________________________________________________________________
+position = sourcemodel.pnt(source.inside,:);
+if ~isempty(extnode)
+ mni_pos(5,:)=extnode;
+end
+
+inode=5
+ref_p=mni_pos(inode,:)';
+disp('finding seed out')
+abs2=sqrt((position(:,1)-ref_p(1)*ones(length(position(:,1)),1)).^2+...
+ (position(:,2)-ref_p(2)*ones(length(position(:,2)),1)).^2+...
+ (position(:,3)-ref_p(3)*ones(length(position(:,3)),1)).^2);
+
+[x, iref(inode)]=min(abs2);
+
+iref(1:4)=net_seeds.cortex_index
+ref=source.power(iref,:);
+seed_pos=position(iref,:);
+
+ntp=size(ref,2);
+nwin=fix((ntp-window2)/step2);
+
+
+disp('estimating seed correlations')
+for k=1:nwin
+ vect1=[(k-1)*step2+1:(k-1)*step2+window2];
+ corr_wins(k,:)=[(k-1)*step2+1 (k-1)*step2+window2];
+ time_corr(k)=(1/Fs)*mean(vect1); % in seconds
+
+ tmp=find(vect1 >= 1 & vect1 <= ntp);
+
+ r=corrcoef(ref(1,vect1(tmp)),ref(2,vect1(tmp))); c_p1(1,k)=r(1,2);
+ r=corrcoef(ref(1,vect1(tmp)),ref(3,vect1(tmp))); c_p1(2,k)=r(1,2);
+ r=corrcoef(ref(1,vect1(tmp)),ref(4,vect1(tmp))); c_p1(3,k)=r(1,2);
+ r=corrcoef(ref(1,vect1(tmp)),ref(5,vect1(tmp))); c_p1(4,k)=r(1,2);
+
+ r=corrcoef(ref(2,vect1(tmp)),ref(1,vect1(tmp))); c_p2(1,k)=r(1,2);
+ r=corrcoef(ref(2,vect1(tmp)),ref(3,vect1(tmp))); c_p2(2,k)=r(1,2);
+ r=corrcoef(ref(2,vect1(tmp)),ref(4,vect1(tmp))); c_p2(3,k)=r(1,2);
+ r=corrcoef(ref(2,vect1(tmp)),ref(5,vect1(tmp))); c_p2(4,k)=r(1,2);
+
+ r=corrcoef(ref(3,vect1(tmp)),ref(1,vect1(tmp))); c_p3(1,k)=r(1,2);
+ r=corrcoef(ref(3,vect1(tmp)),ref(2,vect1(tmp))); c_p3(2,k)=r(1,2);
+ r=corrcoef(ref(3,vect1(tmp)),ref(4,vect1(tmp))); c_p3(3,k)=r(1,2);
+ r=corrcoef(ref(3,vect1(tmp)),ref(5,vect1(tmp))); c_p3(4,k)=r(1,2);
+
+ r=corrcoef(ref(4,vect1(tmp)),ref(1,vect1(tmp))); c_p4(1,k)=r(1,2);
+ r=corrcoef(ref(4,vect1(tmp)),ref(2,vect1(tmp))); c_p4(2,k)=r(1,2);
+ r=corrcoef(ref(4,vect1(tmp)),ref(3,vect1(tmp))); c_p4(3,k)=r(1,2);
+ r=corrcoef(ref(4,vect1(tmp)),ref(5,vect1(tmp))); c_p4(4,k)=r(1,2);
+end
+c_sn(1,:)=min(abs(c_p1(1:3,:)));
+c_sn(2,:)=min(abs(c_p2(1:3,:)));
+c_sn(3,:)=min(abs(c_p3(1:3,:)));
+c_sn(4,:)=min(abs(c_p4(1:3,:)));
+c_out=[c_p1(4,:) ; c_p2(4,:) ; c_p3(4,:) ; c_p4(4,:)];
+
+disp('finding mcw')
+iii=[];
+
+junk=[];
+
+for inode=1:1
+ t_n=max(abs(c_sn(inode,:)));
+ while(t_n>thresh_tn)
+ t_e=t_n-0.9;
+ while(t_e<(thresh_tn-ac))
+ junk=[find(abs(c_sn(inode,:))>t_n & abs(c_out(inode,:))5))==0
+ tmpindx=iii(1); iw(1)=iii(1);
+ if(size(iii,2)>1)
+ for ip=2:size(iii,2)
+ if(iii(ip)>(tmpindx+9))
+ cindx=cindx+1;
+ iw(cindx)=iii(ip);
+ tmpindx=iii(ip);
+ end
+ end
+ end
+ % else
+ % iw=iii(1);
+ % end
+end
+
+
+time_corr_win=(1/Fs)*window2/2;
+
+if(~isempty(iw))
+ % if(strcmp(dofig,'yes'))
+ h=figure; set(gcf, 'Units','centimeters', 'Position',[1 1 xSize ySize],'Color',[0.8 0.8 0.8])
+ set(h, 'paperposition', [1 1 24 6]);
+ plot(time_corr,c_p1(1,:),'b'); hold on;
+ plot(time_corr,c_p1(2,:),'r')
+ plot(time_corr,c_p1(3,:),'g')
+ plot(time_corr,c_p1(4,:),'k')
+ % plot(time_corr,c_mean,'k:')
+ axis([0 time_corr(1,end) 0 1])
+ % plot(time_corr,repmat(0.6,1,size(time_corr,2)),'-')
+ line([time_corr(1) time_corr(end)], [0.6 0.6],'color','k')
+ line([time_corr(1) time_corr(end)], [0.5 0.5],'color','k')
+ for il=1:size(iii,2)
+ line([time_corr(iii(il)) time_corr(iii(il))], [0 c_p1(4,iii(il))],'color','y')
+ end
+ % for il=1:size(iw,2)
+ % max_corr= max([c_p1(1,iw(il)) c_p1(2,iw(il)),c_p1(3,iw(il))])
+ % line([time_corr(iw(il)) time_corr(iw(il))], [0 1],'color','k')
+ % end
+ % grid on
+ xlabel('time (s)');
+ ylabel('corr over windows');
+ % legend('rpips','lfef','rfef','out','mean')
+ legend(net_seeds.seeds_string{2:5},'Location','NorthEastOutside')
+
+ % legend('rpips','lfef','rfef','out','Location','NorthEastOutside')
+ plot(time_corr(iw),c_p1(1,iw),'*');
+ plot(time_corr(iw),c_p1(2,iw),'r*')
+ plot(time_corr(iw),c_p1(3,iw),'g*')
+ plot(time_corr(iw),c_p1(4,iw),'m*')
+ plot(time_corr(iw),c_p1(1,iw),'o');
+ plot(time_corr(iw),c_p1(2,iw),'ro')
+ plot(time_corr(iw),c_p1(3,iw),'go')
+ plot(time_corr(iw),c_p1(4,iw),'mo')
+ % if(~isempty(tmpindx))
+ % plot(time_corr(tmpindx),c_p1(tmpindx),'ro')
+ % end
+ set(gca,'Color',[0.5 0.5 0.5]);
+ imgname = [outputfile '_seed_correlations.png'];
+ hcp_write_figure(imgname, h);
+ close(h)
+ % end
+end
+
+if isempty(iii)==0
+ xp=time_corr(iw);
+ clear extr extr2
+
+ %____ extremes in seconds _________________________________________
+ extr=[xp'-window/2000*ones(size(xp))' xp'+window/2000*ones(size(xp))'];
+ %____ extremes in points _____________________
+ extr2=round(Fs*extr);
+ if extr2(1)==0
+ extr2(1)=1;
+ end
+
+ ref1 = ref(1,:);
+ %_________________________________________________________________
+
+ %_____________________ CONNECTIVITY MAP COMPUTATION ______________
+
+ dm1_pre = zeros(size(extr2,1),size(source.power,1));
+ z_fisher_dm1 = zeros(size(extr2,1),size(source.power,1));
+
+ disp('Connectivity map Computation')
+ for w=1:size(extr2,1)
+ disp(num2str(w))
+
+
+ dm1_pre(w,:) = corr(ref1(1,extr2(w,1):extr2(w,2))',source.power(:,extr2(w,1):extr2(w,2))');
+
+
+ z_fisher_dm1(w,:)=0.5.*log((1+dm1_pre(w,:))./(1-dm1_pre(w,:)));
+
+ end
+
+ % for izf=1:size(z_fisher_dm1,1)
+ % [junk seed_indx]=max(z_fisher_dm1(izf,:));
+ % z_fisher_dm1(izf,seed_indx)=0;
+ % z_fisher_dm1(izf,seed_indx)=max(z_fisher_dm1(izf,:));
+ % end
+
+ connect_mcw=source;
+ connect_mcw=rmfield(connect_mcw,'power')
+ connect_mcw=rmfield(connect_mcw,'time_power')
+ if(isfield(connect_mcw,'avg')) connect_mcw=rmfield(connect_mcw,'avg'); end
+
+ connect_mcw.time=[1:size(dm1_pre,1)];
+ connect_mcw.mcw=dm1_pre;
+ connect_mcw.mcwextr=extr2;
+
+ connect_mcw.seeds_indx=iref;
+
+ disp('Evaluating Z-Score MAP')
+
+ % mu_base=mean(mean(z_fisher_dm1,2),1);
+ % mu_s=mean(z_fisher_dm1,1);
+ % sigma_s=std(z_fisher_dm1,1,1);
+ % z_s=(mu_s-mu_base).*sqrt(size(z_fisher_dm1,1))./sigma_s;
+ %
+ mu_base=mean(mean(dm1_pre,2),1);
+ mu_s=mean(dm1_pre,1);
+ sigma_s=std(dm1_pre,1,1);
+ z_s=(mu_s-mu_base).*sqrt(size(dm1_pre,1))./sigma_s;
+
+ for iz=1:5
+ [junk seed_indx(iz)]=max(z_s);
+ z_s(seed_indx(iz))=0;
+ end
+ z_s(seed_indx)=max(z_s);
+ connect_mcw.z_s=z_s;
+
+
+
+ if(strcmp(dofig,'yes'))
+ h=figure; set(gcf, 'Units','centimeters', 'Position',[5 5 xSize ySize],'Color',[0.8 0.8 0.8])
+
+ plot(z_s,'m') ; hold on
+ plot(iref(1,1),z_s(1,iref(1,1)),'k*')
+ plot(iref(1,2),z_s(1,iref(1,2)),'*')
+ plot(iref(1,3),z_s(1,iref(1,3)),'r*')
+ plot(iref(1,4),z_s(1,iref(1,4)),'g*')
+ plot(iref(1,5),z_s(1,iref(1,5)),'y*')
+ xlabel('voxel');
+ ylabel('z-score');
+ legend('seed', 'ref1','ref2','ref3','ref4','out')
+ set(gca,'Color',[0.5 0.5 0.5]);
+ % line([1 size(dm1_pre,2)], [0.6 0.6])
+ imgname = [outputfile '_voxel_z_score.png'];
+ hcp_write_figure(imgname, h);
+ close(h)
+ end
+
+ if(strcmp(dofig,'yes'))
+ % tmp = source;
+ % tmp.avg.pow=zeros(size(source.pos,1),1);
+ % tmp.avg.pow(source.inside)=z_s;
+ % tmp.pos=sourcemodel.pnt; tmp.tri=sourcemodel.tri;
+
+ head=ft_read_headshape([subject '.L.midthickness.8k_fs_LR.surf.gii']);
+
+ tmp2=source;
+ tmp2.tri=head.tri;
+ tmp2.pos=head.pnt;
+ tmp2.avg.pow=z_s(1:size(tmp2.pos,1))';
+
+ head2=ft_read_headshape([subject '.R.midthickness.8k_fs_LR.surf.gii']);
+
+ tmp3=source;
+ tmp3.tri=head2.tri;
+ tmp3.pos=head2.pnt;
+ tmp3.avg.pow=z_s((size(tmp3.pos,1))+1:end)';
+
+ figure
+ colorbar_extr=[0 8]
+ h1=gcf;
+ set(gca, 'XTickLabel',[], 'YTickLabel',[], ...
+ 'Units','normalized', 'Position',[0 0 1 1])
+
+ %# figure size on screen (50% scaled, but same aspect ratio)
+ set(gcf, 'Units','centimeters', 'Position',[2 2 xSize+5 ySize+5])
+
+ %# figure size printed on paper
+ set(gcf, 'visible', 'on')
+ set(gcf, 'PaperUnits','centimeters')
+ set(gcf, 'PaperSize',[X Y])
+ set(gcf, 'PaperPosition',[xMargin yMargin xSize ySize])
+ set(gcf, 'PaperOrientation','portrait')
+
+ % subplot(2,4,[2 3])
+ % ft_plot_mesh(tmp,'vertexcolor',tmp.avg.pow)
+ % caxis(colorbar_extr)
+
+ % view(-90,90)
+ % subplot(2,4,[6 7])
+ % ft_plot_mesh(tmp,'vertexcolor',tmp.avg.pow)
+ % caxis(colorbar_extr)
+ % view(90,0)
+ subplot(2,4,2)
+ ft_plot_mesh(tmp2,'vertexcolor',tmp2.avg.pow)
+ caxis(colorbar_extr)
+ view(0,90)
+
+
+ subplot(2,4,6)
+ ft_plot_mesh(tmp2,'vertexcolor',tmp2.avg.pow)
+ caxis(colorbar_extr)
+ view(0,0)
+
+ subplot(2,4,3)
+ ft_plot_mesh(tmp3,'vertexcolor',tmp3.avg.pow)
+ caxis(colorbar_extr)
+ view(0,90)
+
+
+ subplot(2,4,7)
+ ft_plot_mesh(tmp3,'vertexcolor',tmp3.avg.pow)
+ caxis(colorbar_extr)
+ view(0,0)
+
+ subplot(2,4,1)
+ ft_plot_mesh(tmp2,'vertexcolor',tmp2.avg.pow)
+ caxis(colorbar_extr)
+ view(90,0)
+
+ subplot(2,4,5)
+ ft_plot_mesh(tmp2,'vertexcolor',tmp2.avg.pow)
+ caxis(colorbar_extr)
+ view(-90,0)
+ subplot(2,4,4)
+ ft_plot_mesh(tmp3,'vertexcolor',tmp3.avg.pow)
+ caxis(colorbar_extr)
+ view(-90,0)
+ subplot(2,4,8)
+ ft_plot_mesh(tmp3,'vertexcolor',tmp3.avg.pow)
+ caxis(colorbar_extr)
+ view(90,0)
+
+ imgname = [outputfile '_z_score_view.png'];
+ hcp_write_figure(imgname, h1)
+ % % % % % imgname = [outputfile '_voxel_z_score_view.fig'];
+ % % % % % hcp_write_figure(imgname, h1)
+ close(h1)
+
+
+ % % % % % imgname = [outputfile '_z_score.fig'];
+ % % % % %
+ % % % % % ft_plot_mesh(tmp,'vertexcolor',tmp.avg.pow)
+ % % % % % caxis(colorbar_extr)
+ % % % % % view(-90,90)
+ % % % % % hcp_write_figure(imgname, gcf)
+ % % % % %
+ % % % % %
+ tmp = connect_mcw;
+ tmp.time=1;
+
+ tmp.avg.pow=zeros(size(tmp.pos,1),1);
+ tmp.avg.pow(:,:)=0;
+
+ tmp.avg.pow(tmp.inside(iref(1,1))) = 25;
+ tmp.avg.pow(tmp.inside(iref(1,2))) = 25;
+ tmp.avg.pow(tmp.inside(iref(1,3))) = 25;
+ tmp.avg.pow(tmp.inside(iref(1,4))) = 25;
+ tmp.avg.pow(tmp.inside(iref(1,5))) = -25;
+
+ ft_plot_mesh(tmp,'vertexcolor',tmp.avg.pow)
+ colormap('cool')
+
+ view(-90,90)
+ imgname = [outputfile '_seeds.png'];
+ hcp_write_figure(imgname, gcf)
+ imgname = [outputfile '_seeds.fig'];
+ hcp_write_figure(imgname, gcf)
+ close(gcf)
+ end
+
+
+else
+ connect_mcw=[];
+end %_over k ref points
+close all
+clear time_corr c_p1 c_p2 c_p3 c_p4
+end
+
+
+
diff --git a/analysis_functions/hcp_mcw_netdef.m b/analysis_functions/hcp_mcw_netdef.m
new file mode 100644
index 0000000..c72a14a
--- /dev/null
+++ b/analysis_functions/hcp_mcw_netdef.m
@@ -0,0 +1,475 @@
+function [net_seeds] = hcp_mcw_netdef(net,options)
+
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+
+% network= ft_getopt(options, 'network', 'DMN');
+if isempty(net) net='DMN'; end
+custom_seeds = ft_getopt(options, 'custom_seeds');
+
+net_seeds.pos=zeros(5,3);
+%____________ EXT NODE
+net_seeds.pos(5,:)=[9 42 53];
+
+load('hcp_cortex_seeds')
+
+
+if isempty(custom_seeds)
+if(strcmp(net,'DMN'))
+%'DMN'
+label{1}='preCunPC' ; hemi{1}='L';
+label{2}='mPFC2'; hemi{2}='L';
+label{3}='AG'; hemi{3}='R';
+label{4}='AG'; hemi{4}='L';
+net_name={'a3_Default_mode'};
+par_name={'L_G_pariet_inf-Angular'};
+
+elseif(strcmp(net,'DAN'))
+%'DMN'
+label{1}='pIPS-SPL' ; hemi{1}='L';
+label{2}='pIPS-SPL'; hemi{2}='R';
+label{3}='FEF'; hemi{3}='L';
+label{4}='FEF'; hemi{4}='R';
+net_name={'a15_Dorsal_attention'};
+
+
+elseif(strcmp(net,'MN'))
+%'DMN'
+label{1}='CS' ; hemi{1}='L';
+label{2}='CS'; hemi{2}='R';
+label{3}='S2'; hemi{3}='L';
+label{4}='vCS'; hemi{4}='R'; % this is not exactly the same as the previous implementation
+% label{4}='vPoCe'; hemi{4}='R'; % this is not exactly the same as the previous implementation
+% label{4}='mI2'; hemi{4}='R'; % this is not exactly the same as the previous implementation
+net_name={'a4_"Hand"_somatosensory-motor' 'a16_"Mouth"_somatosensory-motor'};
+
+elseif(strcmp(net,'VIS'))
+%'DMN'
+label{1}='CS' ; hemi{1}='L';
+label{2}='CS'; hemi{2}='R';
+label{3}='S2'; hemi{3}='L';
+label{4}='vCS'; hemi{4}='R'; % this is not exactly the same as the previous implementation
+% label{4}='vPoCe'; hemi{4}='R'; % this is not exactly the same as the previous implementation
+% label{4}='mI2'; hemi{4}='R'; % this is not exactly the same as the previous implementation
+net_name={'a5_Visual'};
+
+elseif(strcmp(net,'AUD'))
+%'DMN'
+label{1}='CS' ; hemi{1}='L';
+label{2}='CS'; hemi{2}='R';
+label{3}='S2'; hemi{3}='L';
+label{4}='vCS'; hemi{4}='R'; % this is not exactly the same as the previous implementation
+% label{4}='vPoCe'; hemi{4}='R'; % this is not exactly the same as the previous implementation
+% label{4}='mI2'; hemi{4}='R'; % this is not exactly the same as the previous implementation
+net_name={'a24_Auditory'};
+end
+
+%
+% %___ DMN
+%
+% if(strcmp(net_seeds,'dmn_pcc'))
+% %%_________ DMN NET
+% %__ pcc
+% % label(1,:)=[-3 -54 31];
+% % DMN preCunPC L
+% label(1,:)=[-7.78 -49.59 30.99];
+% label_name{1}= 'lPCPC';
+%
+% %__ lfp
+% %label(2,:)=[-2 50 2];
+% %DMN mPFC2 L
+% label(2,:)=[-1.27 46.05 23.16];
+% label_name{2}='LmPFC'; %This is a little bit hihger than the one we used before
+% %DMN mPFC1 R
+% % label(2,:)=[3.71 49.05 11.26]; % This is actually RmPFC. Perhaps we can use
+% % label_name(2,:)='RmPFC';
+%
+% %__ rag
+% label(3,:)=[51 -64 32];
+% % DMN AG R
+% label(3,:)=[47.97 -64.56 42.25];
+% label_name{3}= 'rAG';
+%
+% %__ lag
+% % label(4,:)=[-43 -76 35];
+% % DMN AG L
+% label(4,:)=[-44.42 -62.52 35.72];
+% label_name{4}= 'lAG';
+%
+%
+% %___ DAN
+%
+% elseif(strcmp(net_seeds,'dan_lpips'))
+% %_________ DAN NET
+% %__ lpips
+% label(1,:)=[-25 -67 48];
+% %DAN pIPS-SPL L
+% label(1,:)=[-15.73 -64.01 53.13];
+% label_name{1}= 'lpIPS';
+%
+% %__ rpips
+% label(2,:)=[23 -69 49];
+% %DAN pIPS-SPL R
+% label(2,:)=[21.85 -65.83 46.07];
+% label_name{2}= 'rpIPS';
+%
+% %__ lfef
+% label(3,:)=[-26 -12 53];
+% %DAN FEF L
+% label(3,:)=[-18.52 -4.02 58.16];
+% label_name{3}= 'lFEF';
+%
+% %__ rfef
+% label(4,:)=[30 -13 53];
+% %DAN FEF R
+% label(4,:)=[25.44 -3.59 55.05];
+% label_name{4}= 'rFEF';
+%
+% %___ DMN
+% elseif(strcmp(net_seeds,'dmn_lmpfc'))
+% %%_________ DMN NET
+% %__ lag
+% label(2,:)=[-43 -76 35];
+% %__ pcc
+% label(4,:)=[-3 -54 31];
+% %__ rag
+% label(3,:)=[51 -64 32];
+% %__ lfp
+% label(1,:)=[-2 50 2];
+%
+%
+%
+% %___ VIS
+%
+% elseif(strcmp(net_seeds,'vis_lv1'))
+% % % %__________ VIS RSN NODES
+% %
+% % % % % _______ LV1
+% % label(1,:)=[-2.4 -99.2 -3.5];
+% % VPN V1v L
+% label(1,:)=[-8.73 -89.12 -4.73];
+% label_name{1}= 'lV1V';
+% % VPN V1d-V2d L -2.10 -91.93 11.14
+%
+% % % % %__________ RV1 ______________
+% % label(2,:)=[10.3 -91 3.5];
+% % VPN V1 R
+% label(2,:)=[14.68 -86.50 10.45];
+% label_name{2}= 'rV1';
+% % VPN V1d R 6.39 -77.08 13.25
+%
+% % % % %_________RV7 _____________
+% % label(3,:)=[30.4 -78.7 22.7];
+% % VPN POSd R
+% label(3,:)=[19.32 -75.62 28.73]; % This is not exactly RV7
+% label_name{3}= 'rPOSd(v7)';
+% % VPN V3-V3A R 12.10 -85.04 35.78
+% % VPN V4v R 23.72 -74.13 -9.49
+%
+% % % % % _______ LV7 dorsal
+% % label(4,:)=[-22.4 -77.9 22.5];
+% % VPN V7 L
+% label(4,:)=[-23.05 -71.18 8.28];
+% label_name{4}= 'lV7)';
+%
+%
+% % % VPN V3-V3A L -13.40 -93.97 22.07
+% % % VPN V3A L -14.16 -93.76 34.75
+% % % VPN POSd L -24.24 -55.58 -1.06
+% % % VPN VP L -11.81 -74.43 -4.46
+% % % VPN V7-POSd L -6.67 -86.22 40.49
+% % % VPN V3-V3A R 21.18 -92.55 21.22
+% % % VPN V1v-RV2v R 10.21 -77.33 -3.72
+% % % VPN VP R 17.58 -63.60 -4.68
+% % % VPN POSv R 20.81 -48.82 -3.50
+%
+%
+% elseif(strcmp(net_seeds,'vis_rv1'))
+%
+% %___ VIS RSN NODES
+% %
+% % % % % _______ LV1
+% label(2,:)=[-2.4 -99.2 -3.5];
+% % % % %__________ RV1 ______________
+% label(1,:)=[10.3 -91 3.5];
+% % % % %_________RV7 _____________
+% label(3,:)=[30.4 -78.7 22.7];
+% % % % % _______ LV7 dorsal
+% label(4,:)=[-22.4 -77.9 22.5];
+%
+%
+%
+% elseif(strcmp(net_seeds,'vis_rv7'))
+%
+% %___ VIS RSN NODES
+% %
+% % % % % _______ LV1
+% label(3,:)=[-2.4 -99.2 -3.5];
+% % % % %__________ RV1 ______________
+% label(2,:)=[10.3 -91 3.5];
+% % % % %_________RV7 _____________
+% label(1,:)=[30.4 -78.7 22.7];
+% % % % % _______ LV7 dorsal
+% label(4,:)=[-22.4 -77.9 22.5];
+%
+%
+%
+% elseif(strcmp(net_seeds,'vis_lv7'))
+%
+% %___ VIS RSN NODES
+% %
+% % % % % _______ LV1
+% label(4,:)=[-2.4 -99.2 -3.5];
+% % % % %__________ RV1 ______________
+% label(2,:)=[10.3 -91 3.5];
+% % % % %_________RV7 _____________
+% label(3,:)=[30.4 -78.7 22.7];
+% % % % % _______ LV7 dorsal
+% label(1,:)=[-22.4 -77.9 22.5];
+%
+%
+%
+% %___ VAN
+%
+% elseif(strcmp(net_seeds,'van_rstg'))
+% % % %__________ VAN RSN NODES
+% %
+% % % %__________ RSTG ______________
+% label(1,:)=[57.8 -48.2 10.4];
+% % % %_________RMFG _____________
+% label(2,:)=[41.8 17.2 31.3];
+% % % % _______ RPCS
+% label(3,:)=[41.1 1.8 50.2];
+% % _______ RVFG
+% label(4,:)=[39.9 20.8 -3.8];
+%
+%
+%
+% elseif(strcmp(net_seeds,'van_rmfg'))
+% % % %__________ VAN RSN NODES
+% %
+% % % %__________ RSTG ______________
+% label(3,:)=[57.8 -48.2 10.4];
+% % % %_________RMFG _____________
+% label(1,:)=[41.8 17.2 31.3];
+% % % % _______ RPCS
+% label(2,:)=[41.1 1.8 50.2];
+% % _______ RVFG
+% label(4,:)=[39.9 20.8 -3.8];
+%
+%
+%
+% elseif(strcmp(net_seeds,'van_rpcs'))
+% % % %__________ VAN RSN NODES
+% %
+% % % %__________ RSTG ______________
+% label(4,:)=[57.8 -48.2 10.4];
+% % % %_________RMFG _____________
+% label(2,:)=[41.8 17.2 31.3];
+% % % % _______ RPCS
+% label(1,:)=[41.1 1.8 50.2];
+% % _______ RVFG
+% label(3,:)=[39.9 20.8 -3.8];
+%
+%
+%
+% elseif(strcmp(net_seeds,'van_rvfg'))
+% % % %__________ VAN RSN NODES
+% %
+% % % %__________ RSTG ______________
+% label(4,:)=[57.8 -48.2 10.4];
+% % % %_________RMFG _____________
+% label(2,:)=[41.8 17.2 31.3];
+% % % % _______ RPCS
+% label(3,:)=[41.1 1.8 50.2];
+% % _______ RVFG
+% label(1,:)=[39.9 20.8 -3.8];
+%
+%
+%
+% %___ DMN
+%
+% elseif(strcmp(net_seeds,'dmn_lag'))
+% %%_________ DMN NET
+% %__ lag
+% label(1,:)=[-43 -76 35];
+% %__ pcc
+% label(4,:)=[-3 -54 31];
+% %__ rag
+% label(3,:)=[51 -64 32];
+% %__ lfp
+% label(2,:)=[-2 50 2];
+%
+%
+%
+% elseif(strcmp(net_seeds,'dmn_rag'))
+% %%_________ DMN NET
+% %__ lag
+% label(4,:)=[-43 -76 35];
+% %__ pcc
+% label(3,:)=[-3 -54 31];
+% %__ rag
+% label(1,:)=[51 -64 32];
+% %__ lfp
+% label(2,:)=[-2 50 2];
+%
+%
+%
+% elseif(strcmp(net_seeds,'dmn_lfp'))
+% %%_________ DMN NET
+% %__ lag
+% label(4,:)=[-43 -76 35];
+% %__ pcc
+% label(3,:)=[-3 -54 31];
+% %__ rag
+% label(2,:)=[51 -64 32];
+% %__ lfp
+% label(1,:)=[-2 50 2];
+%
+%
+%
+% %___ MOT
+%
+% elseif(strcmp(net_seeds,'mot_ls2'))
+% %__________ MOT RSN NODES
+% %______________ LS2
+% label(1,:)=[-39.4 -26.7 18.2];
+% % % _______ RS2
+% label(2,:)=[36.4 -22.7 20.6];
+% % % %__________ lcs_stef ______________
+% label(3,:)=[-32 -25 55];
+% % %__________ rcs_stef ______________
+% label(4,:)=[35 -26 55];
+%
+%
+%
+% elseif(strcmp(net_seeds,'mot_rs2'))
+% %__________ MOT RSN NODES
+% %______________ LS2
+% label(2,:)=[-39.4 -26.7 18.2];
+% % % _______ RS2
+% label(1,:)=[36.4 -22.7 20.6];
+% % % %__________ lcs_stef ______________
+% label(3,:)=[-32 -25 55];
+% % %__________ rcs_stef ______________
+% label(4,:)=[35 -26 55];
+%
+%
+%
+% elseif(strcmp(net_seeds,'mot_lcs'))
+% %__________ MOT RSN NODES
+% %______________ LS2
+% label(2,:)=[-39.4 -26.7 18.2];
+% % % _______ RS2
+% label(3,:)=[36.4 -22.7 20.6];
+% % % %__________ lcs_stef ______________
+% label(1,:)=[-32 -25 55];
+% % %__________ rcs_stef ______________
+% label(4,:)=[35 -26 55];
+%
+%
+%
+% elseif(strcmp(net_seeds,'mot_rcs'))
+% %__________ MOT RSN NODES
+% %______________ LS2
+% label(2,:)=[-39.4 -26.7 18.2];
+% % % _______ RS2
+% label(3,:)=[36.4 -22.7 20.6];
+% % % %__________ lcs_stef ______________
+% label(4,:)=[-32 -25 55];
+% % %__________ rcs_stef ______________
+% label(1,:)=[35 -26 55];
+%
+%
+%
+% %___ DAN
+%
+% elseif(strcmp(net_seeds,'dan_lpips'))
+% %_________ DAN NET
+% %__ lpips
+% label(1,:)=[-25 -67 48];
+% %__ rpips
+% label(2,:)=[23 -69 49];
+% %__ lfef
+% label(3,:)=[-26 -12 53];
+% %__ rfef
+% label(4,:)=[30 -13 53];
+%
+%
+%
+% elseif(strcmp(net_seeds,'dan_rpips'))
+% %_________ DAN NET
+% %__ lpips
+% label(2,:)=[-25 -67 48];
+% %__ rpips
+% label(1,:)=[23 -69 49];
+% %__ lfef
+% label(3,:)=[-26 -12 53];
+% %__ rfef
+% label(4,:)=[30 -13 53];
+%
+%
+%
+% elseif(strcmp(net_seeds,'dan_lfef'))
+% %_________ DAN NET
+% %__ lpips
+% label(2,:)=[-25 -67 48];
+% %__ rpips
+% label(3,:)=[23 -69 49];
+% %__ lfef
+% label(1,:)=[-26 -12 53];
+% %__ rfef
+% label(4,:)=[30 -13 53];
+%
+%
+%
+% elseif(strcmp(net_seeds,'dan_rfef'))
+% %_________ DAN NET
+% %__ lpips
+% label(2,:)=[-25 -67 48];
+% %__ rpips
+% label(3,:)=[23 -69 49];
+% %__ lfef
+% label(4,:)=[-26 -12 53];
+% %__ rfef
+% label(1,:)=[30 -13 53];
+%
+%
+end
+for i=1:4
+ indx_hemi = find(strcmp(hcp_seeds.hemisphere,hemi{i}));
+ indx_lab=find(strcmp(hcp_seeds.label,label{i}));
+
+ [junk temp_indx]=ismember(indx_lab,indx_hemi);
+ [junk junk2 temp_indx]=find(temp_indx);
+ indx_seed=indx_hemi(temp_indx);
+
+ net_seeds.pos(i,:)=hcp_seeds.pos(indx_seed,:);
+ net_seeds.seeds_string{i}=[hemi{i} '-' label{i}];
+ net_seeds.cortex_pos(i,:)=hcp_seeds.pos_cortex(indx_seed,:);
+ net_seeds.cortex_index(i,:)=hcp_seeds.cortex_indx_both(indx_seed,1);
+ net_seeds.cortex_index_hemi(i,:)=hcp_seeds.cortex_indx_lf(indx_seed,1);
+end
+net_seeds.seeds_string{5}=['out' '-' 'seed'];
+
+net_seeds.hemi=hemi;
+net_seeds.label=label;
+net_seeds.net_name=net_name;
+% net_seeds.par_name=par_name;
+end
+
+
diff --git a/analysis_functions/hcp_mergestruct.m b/analysis_functions/hcp_mergestruct.m
new file mode 100644
index 0000000..786caed
--- /dev/null
+++ b/analysis_functions/hcp_mergestruct.m
@@ -0,0 +1,22 @@
+function s = hcp_mergestruct(varargin)
+
+% HCP_MERGESTRUCT combines multiple structures that can have different fields
+% into a single structure array.
+%
+% Use as
+% s = hcp_mergestruct(a, b, ...)
+%
+% The resulting struct "s" contains all fields of all input structures combined.
+
+% Copyright (C) 2004, Robert Oostenveld
+%
+% $ Log$
+
+s = struct;
+for i=1:length(varargin)
+ fn = fieldnames(varargin{i});
+ for j=1:length(fn)
+ s = setfield(s, fn{j}, getfield(varargin{i}, fn{j}));
+ end
+end
+
diff --git a/analysis_functions/hcp_read_atlas.m b/analysis_functions/hcp_read_atlas.m
new file mode 100755
index 0000000..c7d1f9f
--- /dev/null
+++ b/analysis_functions/hcp_read_atlas.m
@@ -0,0 +1,110 @@
+function atlas = hcp_read_atlas(inputfile)
+
+% HCP_READ_ATLAS converts a set of gii label-files containing a parcellation into a
+% FieldTrip compatible parcellation structure that can be saved as a mat file. The
+% parcellations corresponding to the left and right hemisphere are concatenated, LEFT
+% HEMISPHERE FIRST, and the indices for the labels for the right hemisphere parcels
+% are adjusted accordingly. The parcel labels are moreover prefixed with L_ and R_.
+%
+% The input should be a cell-array with the file names for the left and right
+% hemisphere. For example
+%
+% labels = hcp_read_atlas({'parcellations_VGD11b.L.8k_fs_LR.label.gii', 'parcellations_VGD11b.R.8k_fs_LR.label.gii'})
+%
+% parcellation1: [15684x1 double]
+% parcellation1label: {109x1 cell}
+% parcellation2: [15684x1 double]
+% parcellation2label: {86x1 cell}
+% parcellation3: [15684x1 double]
+% parcellation3label: {4x1 cell}
+% hemisphere: [15684x1 double]
+% hemispherelabel: {2x1 cell}
+%
+% This can be combined with
+%
+% geometry = ft_read_headshape({'Conte69.L.midthickness.8k_fs_LR.surf.gii', 'Conte69.R.midthickness.8k_fs_LR.surf.gii'})
+%
+% pnt: [15684x3 double]
+% tri: [31360x3 double]
+% hemisphere: [15684x1 double]
+% hemispherelabel: {2x1 cell}
+%
+% and subsequently combined into a parcellation as
+%
+% atlas = hcp_mergestruct(labels, geometry)
+%
+% parcellation1: [15684x1 double]
+% parcellation1label: {109x1 cell}
+% parcellation2: [15684x1 double]
+% parcellation2label: {86x1 cell}
+% parcellation3: [15684x1 double]
+% parcellation3label: {4x1 cell}
+% coordsys: 'unknown'
+% hemispherelabel: {2x1 cell}
+% hemisphere: [15684x1 double]
+% pnt: [15684x3 double]
+% tri: [31360x3 double]
+%
+% See also FT_READ_ATLAS, FT_READ_HEADSHAPE, FT_DATATYPE_PARCELLATION
+
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+
+atlasleft = ft_read_atlas(inputfile{1});
+atlasright = ft_read_atlas(inputfile{2});
+
+fnames = fieldnames(atlasleft);
+sel = false(size(fnames));
+for k = 1:numel(fnames)
+ sel(k) = isfield(atlasleft, [fnames{k} 'label']);
+end
+fnames = fnames(sel);
+
+% update the right hemisphere indices
+for k = 1:numel(fnames)
+ atlasright.(fnames{k}) = atlasright.(fnames{k})+max(atlasleft.(fnames{k})(:));
+end
+
+% prefix the labels
+for k = 1:numel(fnames)
+ pname = [fnames{k},'label'];
+ if all(strncmp(atlasleft.(pname), 'L_', 2))
+ % don't prefix
+ else
+ for m = 1:numel(atlasleft.(pname))
+ atlasleft.(pname){m} = ['L_',atlasleft.(pname){m}];
+ end
+ end
+ if all(strncmp(atlasright.(pname), 'R_', 2))
+ % don't prefix
+ else
+ for m = 1:numel(atlasright.(pname))
+ atlasright.(pname){m} = ['R_',atlasright.(pname){m}];
+ end
+ end
+end
+
+% concatenate the parcellations
+atlas = atlasleft;
+for k = 1:numel(fnames)
+ atlas.(fnames{k}) = cat(1,atlasleft.(fnames{k}),atlasright.(fnames{k}));
+ atlas.([fnames{k},'label']) = cat(1,atlasleft.([fnames{k},'label']),atlasright.([fnames{k},'label']));
+end
+
+% add the indices and labels for the hemispheres, just like ft_read_headshape
+atlas.hemispherelabel = inputfile(:);
+atlas.hemisphere = cat(1, 1*ones(length(atlasleft.(fnames{1})),1), 2*ones(length(atlasleft.(fnames{1})),1));
diff --git a/analysis_functions/hcp_tfavg_contrasts.m b/analysis_functions/hcp_tfavg_contrasts.m
index fa82fad..67c7ef5 100644
--- a/analysis_functions/hcp_tfavg_contrasts.m
+++ b/analysis_functions/hcp_tfavg_contrasts.m
@@ -1,10 +1,51 @@
-function [outStatus] = hcp_tfavg_contrastsonthefly(inCfg)
-
-% This function loads time frequency data and computes its trial average as well
-% as the average of its planar gradient
+function [outStatus] = hcp_tfavg_contrasts(inCfg)
+%% This function computes the average of Time-Frequency power spectrum for different conditions and contrasts for a given experiment.
+% It loads clean data, computes its time-frequency representation and computes the trial average as well
+% as the average of the planar gradient representation.
+% It performes this average over the trials of BOTH scans for a given
+% experiment.
+%
+%
+% INPUT:
+%-------------------------------------------------------------------
+% inCfg : This is a structure containing required parameters for the
+% analysis
+% Fields:
+% .subjectid: Subject ID
+% .experimentid: Experiment ID
+% .multiscanid: This an ID the describes both scans for a
+% given Task. i.e. for scans 10-Motort and
+% 11-Motort, the average of the concatanated
+% datasets is represented by the multiscanid
+% 'Motort'.
+% .bandinfo: This variable contains the definition of
+% frequency bands. It is ONLY USED FOR PLOTTING .
+% i.e.
+% {'A', [7 14]
+% 'B', [15 25]}
+%
+% .contrastlist: This is a cell containing all different
+% conditions and constrasts for a specific task data set for
+% which the eravg is to be computed. It is produced by hte
+% contrasts_$MULTISCANID.m functions.
+%
+% OUTPUT:
+%---------------------------------------------------------------------
+% outStatus: A dummy output flag
+%
%
-% outdatafile is the file where the averaged data will be saved
-% outinfofile is the ZIP file where any plotted figures will be saved
+%
+%
+% NAMING OF FILES WERE RESULTS ARE SAVED
+%------------------------------------------------------------------------------
+% The average TR is saved in a typical fieltrip timelock data structure names 'data'.
+% This structure is saved in a file named according to the convention
+% [experimentid,'_',scanmnem,'_',contrast_mnemonic,'_[MODE-',avgmode,']'];
+% contrast_mnemonic is a string ID contained within each contrast in the
+% input inCfg.contrastlist variable. avgmode is 'meg' for magnetic field
+% representation or 'planar' for planar gradient representation.
+%-----------------------------------------------------------------------------
+
% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
%
diff --git a/attic/hcp_icaimagcoh_slow.m b/attic/hcp_icaimagcoh_slow.m
new file mode 100644
index 0000000..9aca38f
--- /dev/null
+++ b/attic/hcp_icaimagcoh_slow.m
@@ -0,0 +1,441 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% setup the execution environment
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+opengl software;
+
+% ensure that the time and date of execution are not stored in the provenance information
+global ft_default
+ft_default.trackcallinfo = 'no';
+
+% allow the user to specify the path where additional data is present, e.g. the channel layout or anatomy files
+if exist('path', 'var')
+ addpath(path)
+end
+
+if ~exist('filename', 'var')
+ error('filename should be specified')
+end
+
+% the filename is assumed to be something like
+% 'rawdatadir/Phase1MEG/Subjects/CP10018/Experiments/CP10018_MEG/Scans/1-Rnoise_MNN_V1/Resources/4D/c,rfDC'
+tok = tokenize(filename, '/');
+
+if ~exist('subjectid', 'var')
+ subjectid = tok{end-7};
+end
+
+if ~exist('experimentid', 'var')
+ experimentid = tok{end-5};
+end
+
+if ~exist('scanid', 'var')
+ scanid = tok{end-3};
+end
+
+if ~exist('pipelinedatadir', 'var')
+ pipelinedatadir = hcp_pathdef;
+end
+
+if ~exist('freque', 'var')
+ error('frequency of interest should be specified')
+end
+
+resultprefix = sprintf('%s_%s', experimentid, scanid);
+
+% print the matlab and megconnectome version to screen for provenance
+ver('megconnectome')
+
+% print the value of all local variables to screen for provenance
+w = whos;
+w = {w.name};
+w = setdiff(w, {'w', 'ans'});
+for i=1:length(w)
+ fprintf(hcp_printstruct(w{i}, eval(w{i})));
+end
+
+% change to the location of the processed data (input and output)
+cd(pipelinedatadir)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% execute the pipeline
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+smodel_type = {'2D';'3D'}; dimindx=1; % 1 for 2D cortical sheet and 2 for 3D gird
+griddim = {'4mm';'6mm';'8mm'}; gridindx=1; % $ 1,2,3 for 3D 4mm,6mm and 8mm grid
+
+if(strcmp(smodel_type{dimindx},'2D'))
+ sourcemodel_type=smodel_type{dimindx};
+elseif(strcmp(smodel_type{dimindx},'3D'))
+
+
+ sourcemodel_type=[smodel_type{dimindx} griddim{gridindx}];
+
+end
+
+%------------------------
+% declare the output file
+outputfile = [resultprefix,'_icaimagcoh_' sourcemodel_type];
+
+
+%------------------------------------------------------------
+% ensure that the output from the previous pipelines is present
+% hcp_check_pipelineoutput('baddata', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
+% hcp_check_pipelineoutput('icaclass', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
+% hcp_check_pipelineoutput('icamne', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid, 'sourcemodel', sourcemodel_type);
+
+
+inputfile4 = fullfile([resultprefix,'_icaclass_vs.mat']);
+hcp_read_matlab(inputfile4);
+
+inputfile5 = fullfile([resultprefix,'_icamne_' sourcemodel_type]);
+hcp_read_matlab(inputfile5,'source');
+
+if(~isfield(comp_class,'trial'))
+
+ cfg = [];
+ cfg.dataset = filename;
+ dataraw=ft_preprocessing(cfg);
+
+ badsegments = hcp_read_ascii([resultprefix '_baddata_badsegments.txt']);
+ % badsegments = badsegments.badsegment.all;
+
+ disp('bad segments concatenation')
+ % collect the results in a structure
+ maxsmp2 = max(badsegments.badsegment.ica(:));
+ maxsmp3 = max(badsegments.badsegment.manual(:));
+ maxsmp = max([maxsmp2, maxsmp3]);
+ badsample = zeros(1,maxsmp);
+ if ~isempty(badsegments.badsegment.ica)
+ for j=1:size(badsegments.badsegment.ica,1)
+ badsample(badsegments.badsegment.ica(j,1):badsegments.badsegment.ica(j,2)) = 1;
+ end
+ end
+ if ~isempty(badsegments.badsegment.manual)
+ for j=1:size(badsegments.badsegment.manual,1)
+ badsample(badsegments.badsegment.manual(j,1):badsegments.badsegment.manual(j,2)) = 1;
+ end
+ end
+
+ if ~isempty(badsample)
+ flank = diff([0 badsample 0]);
+ badsegment_ica=[];
+ badsegment_ica(:,1) = find(flank== 1);
+ badsegment_ica(:,2) = find(flank==-1) - 1;
+
+ badsegments.badsegment.ica = badsegment_ica;
+ end
+
+ badsegments = badsegments.badsegment.ica;
+
+ badchannels = hcp_read_ascii([resultprefix '_baddata_badchannels.txt']);
+ badchannels = badchannels.badchannel.all;
+
+
+ sel_channels={'MEG'};
+ grad=dataraw.grad;
+
+
+ %------------------------------------------
+
+ if ~(isempty([badchannels{:}])),
+ for ich=1:size(badchannels,2)
+ sel_channels(1,ich+1)={['-' badchannels{1,ich}]};
+ end
+ end
+ bandpass = [1.3 150]; % band pass frequency
+ if strcmp(subjectid,'CP10128') || strcmp(subjectid,'CP10129')
+ bandstop = [49 51 ; 99 101]; % band stop frequency
+ else
+ bandstop = [59 61 ; 119 121]; % band stop frequency
+ end
+
+
+ options = {'dataprefix', resultprefix, 'channels', sel_channels, 'skipped_intervals', badsegments, 'bandpass', bandpass, 'bandstop', bandstop};
+
+
+ data = hcp_ICA_preprocessing(dataraw, options);
+ cfg = [];
+ cfg.unmixing = comp_class.unmixing;
+ cfg.topolabel = comp_class.topolabel;
+ comp = ft_componentanalysis(cfg, data);
+ comp.class = comp_class.class;
+ comp.topo = comp_class.topo;
+ clear data
+else
+ comp=comp_class;
+end
+
+mixing = comp_class.topo;
+if(max(size(source.val))>2)
+ for i = 1:size(mixing, 2)
+ mixing(:, i) = mixing(:, i)/source.val(i);
+ for jic=1:size(comp.trial,2)
+ comp.trial{jic}(i,:)=comp.trial{jic}(i,:)*source.val(i);
+ end
+ end
+end
+comp.topo=mixing;
+
+
+
+% get only the brain ICs
+comp_bic=comp;
+if(~isfield(comp_bic.class,'brain_ic_vs')) comp_bic.class.brain_ic_vs=comp_bic.class.brain_ic; end
+
+comp_bic=rmfield(comp_bic,'trial');
+for j=1:numel(comp.trial)
+ comp_bic.trial{j}(:,:)=comp.trial{j}(comp.class.brain_ic_vs,:).*1e15;
+end
+comp_bic.topo=comp.topo(:,comp.class.brain_ic_vs);
+comp_bic.unmixing=comp.unmixing(comp.class.brain_ic_vs,:);
+comp_bic.label=comp.label(comp.class.brain_ic_vs,:);
+
+% channel-data is not needed any more
+clear data
+
+% adjust the time axis to avoid memory problems during resampling:
+% exact time information is discarded anyway
+old_time=comp_bic.time;
+for k = 1:numel(comp_bic.time)
+ comp_bic.time{k} = comp_bic.time{k} - comp_bic.time{k}(1);
+end
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% get the topographies of the components at the level of the
+% sources from the mne pipeline
+
+source_bic=source;
+source_bic.time=1:comp_bic.class.brain_ic_vs_number;
+source_bic.avg.pow=zeros(size(source.pos,1),comp_bic.class.brain_ic_vs_number);
+source_bic.avg=rmfield(source_bic.avg,'mom');
+
+for ii=1:numel(source.inside)
+ source_bic.avg.mom{source.inside(ii)}=source.avg.mom{source.inside(ii)}(:,comp_bic.class.brain_ic_vs);
+ source_bic.avg.pow(source.inside(ii),:)=source.avg.pow(source.inside(ii),comp_bic.class.brain_ic_vs);
+end
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% working piece of code to test ft function behaviour on components
+cfg = [];
+cfg.length = 1;
+cfg.overlap = 0.5;
+cfg.trials = 'all';
+[comp1] = ft_redefinetrial(cfg, comp_bic);
+
+cfg = [];
+cfg.output = 'fourier';
+cfg.method = 'mtmfft';
+cfg.taper = 'hanning';
+[comp2] = ft_freqanalysis(cfg, comp1);
+
+comp2=ft_selectdata(comp2,'foilim',freque);
+% comp2=ft_selectdata(comp2,'rpt',[1:10]);
+
+[ntrials nic nfreq]=size(comp2.fourierspctrm);
+
+cfg = [];
+cfg.method = 'csd';
+cfg.complex = 'complex';
+[comp3] = ft_connectivityanalysis(cfg, comp2);
+%comp_csd dimord=[chan x chan x freq]
+
+nsource = numel(source_bic.inside);
+% nfreq = length(comp3.freq);
+%nfreq = 20; % FIXME why is this hardcoded?
+estimate = nsource * nsource * nfreq * 8; % 2 because complex, 8 because double precision
+fprintf('estimated memory requirement = %f GB\n', estimate/(1024^3));
+
+nsource = numel(source_bic.inside);
+[ndim,nbic] = size(source_bic.avg.mom{source_bic.inside(1)});
+
+% freque=10;
+% disp(freque)
+
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% working piece of code to test ft function behaviour on components
+cfg = [];
+cfg.length = 1;
+cfg.overlap = 0.5;
+cfg.trials = 'all';
+[comp1] = ft_redefinetrial(cfg, comp_bic);
+
+cfg = [];
+cfg.output = 'fourier';
+cfg.method = 'mtmfft';
+cfg.taper = 'hanning';
+[comp2] = ft_freqanalysis(cfg, comp1);
+
+comp2=ft_selectdata(comp2,'foilim',[1:30]);
+% comp2=ft_selectdata(comp2,'rpt',[1:10]);
+
+[ntrials nic nfreq]=size(comp2.fourierspctrm);
+
+cfg = [];
+cfg.method = 'csd';
+cfg.complex = 'complex';
+[comp3] = ft_connectivityanalysis(cfg, comp2);
+%comp_csd dimord=[chan x chan x freq]
+
+nsource = numel(source_bic.inside);
+% nfreq = length(comp3.freq);
+%nfreq = 20; % FIXME why is this hardcoded?
+estimate = nsource * nsource * nfreq * 8; % 2 because complex, 8 because double precision
+fprintf('estimated memory requirement = %f GB\n', estimate/(1024^3));
+
+nsource = numel(source_bic.inside);
+
+
+for f=freque
+ % allocate memory for single frequency connectomes
+ mimf = zeros(nsource, nsource,'single');
+ % powenvcorrf = zeros(nsource, nsource);
+
+ % show the progress
+ % concatenate all fourier coefficients into a single matrix
+ allfourier = cat(1,source_bic.avg.mom{source_bic.inside(:)'})*comp2.fourierspctrm(:,:,f).';
+
+ % concatenate all powerenvelope values (demeaned) into a single matrix
+ % allpowenv = abs(allfourier).^2;
+ % allpowenv = allpowenv - mean(allpowenv,2)*ones(1,size(allpowenv,2));
+
+ % create a cell-array containing the within source csd and one
+ % cell-array with the within source power envelope covariance
+ % this needs to be computed only once for all sources
+ autocsd = cell(1,nsource);
+
+ % autopowcov = cell(1,nsource);
+ gim = zeros(1,nsource,'single');
+ n = zeros(1,nsource);
+ pow = zeros(1,nsource);
+ % powpow = zeros(1,nsource);
+ indx = 0;
+ for i=1:nsource
+ n(i) = size(source_bic.avg.mom{source_bic.inside(i)},1);
+ indx = indx(end)+(1:n(i));
+ autocsd{i} = allfourier(indx,:)*allfourier(indx,:)';
+ end
+
+ % for j=1:nsource
+ % [eigvec{j} eigval{j}]=eig(autocsd{j});
+ % [junk indxeig{j}]=sort(real(diag(eigval{j})),'descend');
+ % autocsd_new{j}=eigvec{j}(indxeig{j}(1:2),indxeig{j}(1:2))*eigval{j}(indxeig{j}(1:2),indxeig{j}(1:2))*inv(eigvec{j}(indxeig{j}(1:2),indxeig{j}(1:2)));
+ % n(j)=2;
+ % n2(j)=3;
+ % end
+% clear autocsd;
+% autocsd=autocsd_new;
+% clear eigvec eigval indxeig
+
+ for j=1:numel(indx)
+ autocsd{i}(j,j)=real(autocsd{i}(j,j));
+ end
+ for i=1:nsource
+ pow(i) = trace((autocsd{i}));
+
+ % autopowcov{i} = allpowenv(indx,:)*allpowenv(indx,:)';
+ % powpow(i) = trace(autopowcov{i});
+ gim(i) = cast(hcp_connectivity_mim([autocsd{i} autocsd{i};autocsd{i}' autocsd{i}], 'indices', [ones(1,n(i)) ones(1,n(i))*2]),'single');
+ end
+
+ indx = 0;
+ for i=1:nsource
+ % disp(i);
+ % treat source i as the reference source and compute the
+ % metrics of interest against all other sources. compute the
+ % cross-terms only once
+ indx = indx(end)+(1:n(i));
+ crosscsd = allfourier*allfourier(indx,:)';
+
+ % crosspowcov = allpowenv*allpowenv(indx,:)';
+
+ indx2 = cumsum(n(1:i));
+ for j=(i+1):nsource
+ % now compute the metric per voxel pair
+ indx2 = indx2(end)+(1:n(j));
+
+ % [eigvec eigval]=eig(crosscsd(indx2,:));
+ % [junk indxeig]=sort(real(diag(eigval)),'descend');
+ % crosscsd_new=eigvec(indxeig(1:2),indxeig(1:2))*eigval(indxeig(1:2),indxeig(1:2))*inv(eigvec(indxeig(1:2),indxeig(1:2)));
+ %
+ %
+ % combine the auto and cross-voxel csd matrices into a
+ % single matrix, for the mim computation
+ % avecsd = [autocsd{i} crosscsd';crosscsd autocsd{j}];
+ avecsd = [autocsd{i} crosscsd(indx2,:)';crosscsd(indx2,:) autocsd{j}];
+ mim = hcp_connectivity_mim(avecsd, 'indices', [ones(1,n(i)) ones(1,n(j))*2]);
+
+ % power envelope correlation
+ % powenvcorr = trace(crosspowcov(indx2,:))./sqrt(powpow(i)*powpow(j)); %FIXME the trace-operator does not work with non-square matrices
+
+ % fill the connectivity matrix
+ mimf(i,j)=cast(mim,'single');
+ mimf(j,i)=cast(mim,'single');
+
+ % powenvcorrf(i,j) = powenvcorr;
+ % powenvcorrf(j,i) = powenvcorr;
+
+ end % nsource
+ end % nsource
+ mimf = mimf + cast(diag(gim),'single');
+
+ source2=source_bic;
+ source2=rmfield(source2,'avg');
+ source2=rmfield(source2,'time');
+ % source2 = [];
+ % source2.pos = source.pos(source.inside,:);
+ % source2.inside = 1:nsource;
+ source2.freq = comp2.freq(f);
+ source2.dimord = 'pos_pos_freq';
+ source2.mimspctrm = mimf;
+ % source2.powenvcorrspctrm = powenvcorrf;
+
+ imagcoh=[];
+ imagcoh.freq = comp2.freq(f);
+ imagcoh.dimord = 'pos_pos_freq';
+ imagcoh.mimspctrm = mimf;
+%
+% save([outputfile,'_freq',num2str(f)], 'imagcoh','-v7.3');
+%
+% imagcoh.autocsd = autocsd;
+% save([outputfile,'_full_freq',num2str(f)], 'imagcoh','-v7.3');
+
+ % imagcoh.powenvcorrspctrm = powenvcorrf;
+
+
+ hcp_write_matlab([outputfile,'_freq',num2str(f)], 'imagcoh');
+
+
+% clear source2 imagcoh mimf allfourier allpowenv autocsd gim n pow
+% clear crosscsd crosspowcov avecsd mim
+ hcp_check_pipelineoutput('icaimagcoh', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid, 'sourcemodel', sourcemodel_type, 'freq', num2str(f));
+
+
+ clear source2 imagcoh mimf allfourier allpowenv autocsd gim n pow
+ clear crosscsd crosspowcov avecsd mim
+ % hcp_check_pipelineoutput('icaimagcoh', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid, 'sourcemodel', sourcemodel_type, 'freq', num2str(f));
+
+end % nfreq
+
diff --git a/bin/megconnectome b/bin/megconnectome
index 7bf69cd..084bf87 100755
Binary files a/bin/megconnectome and b/bin/megconnectome differ
diff --git a/build/compile_megconnectome.m b/build/compile_megconnectome.m
index b9ca66e..5f726d0 100644
--- a/build/compile_megconnectome.m
+++ b/build/compile_megconnectome.m
@@ -30,7 +30,10 @@ function compile_megconnectome(fieldtriproot, hcproot)
% along with megconnectome. If not, see .
% clear all variables, globals, functions and MEX links
-clear all
+% clear all; % don't do this because it clears the input arguments
+clear global;
+clear fun;
+clear mex;
fname = 'megconnectome';
diff --git a/pipeline_scripts/hcp_anatomy.m b/pipeline_scripts/hcp_anatomy.m
index 2f40d77..d53607d 100644
--- a/pipeline_scripts/hcp_anatomy.m
+++ b/pipeline_scripts/hcp_anatomy.m
@@ -47,79 +47,55 @@
end
if ~exist('structuralpreprocdir', 'var')
- % we cannot use the strucural preprocessing results, revert to old-style
- % pipeline
+ % we cannot use the strucural preprocessing results, revert to old-style pipeline
fprintf('not using the high quality structural preprocessing results\n');
else
fprintf('using the structural preprocessing results from %s\n', structuralpreprocdir);
- hrmrifile = fullfile(structuralpreprocdir, 'T1w', 'T1w_acpc_dc_restore.nii');
- inputsurffile = fullfile(structuralpreprocdir, 'T1w', 'fsaverage_LR32k', [subjectid,'.L.midthickness.32k_fs_LR.surf.gii']);
- inputsphere = fullfile(structuralpreprocdir, 'MNINonLinear', 'fsaverage_LR32k', [subjectid,'.L.sphere.32k_fs_LR.surf.gii']);
- outputsphere = fullfile(outputdir, 'Sphere.8k.L.surf.gii');
+ hrmrifile = fullfile(structuralpreprocdir, 'T1w', 'T1w_acpc_dc_restore.nii.gz');
+ inputsurffile = fullfile(structuralpreprocdir, 'T1w', 'fsaverage_LR32k', [subjectid,'.L.midthickness.32k_fs_LR.surf.gii']);
+ inputsphere = fullfile(structuralpreprocdir, 'MNINonLinear', 'fsaverage_LR32k', [subjectid,'.L.sphere.32k_fs_LR.surf.gii']);
+ outputsphere = fullfile(outputdir, 'Sphere.8k.L.surf.gii');
outputsurffile = fullfile(outputdir, [subjectid,'.L.midthickness.8k_fs_LR.surf.gii']);
end
% the following flags pertain to the three main parts of the pipeline
-if ~exist('dopipeinteractive', 'var')
- % for the interactive coregistration
- dopipeinteractive = 0;
-end
-if ~exist('dopipeautomatic', 'var')
- % for all other computations
- dopipeautomatic = 0;
-end
-if ~exist('doqualitycheck', 'var')
- % for the qualitycheck
- doqualitycheck = 0;
-end
+if ~exist('dopipeinteractive', 'var'), dopipeinteractive = 0; end
+if ~exist('dopipeautomatic', 'var'), dopipeautomatic = 0; end
+if ~exist('doqualitycheck', 'var'), doqualitycheck = 0; end
if dopipeinteractive,
- % we need a pointer to a file in the dicom-series that contain the
- % T1-weighted anatomical image
- if ~exist('dofiducials', 'var')
- dofiducials = 1;
- end
- if ~exist('dolandmarks', 'var')
- dolandmarks = 1;
- end
+ % set flags to facilitate debugging or running parts of the pipeline
+ if ~exist('dofiducials', 'var'), dofiducials = 1; end
+ if ~exist('dolandmarks', 'var'), dolandmarks = 1; end
+ if ~exist('docoregistration', 'var'), docoregistration = 1; end
+ if docoregistration && ~exist('docoreg_spm', 'var'), docoreg_spm = 1; end
+ if docoregistration && ~exist('docoreg_bti', 'var'), docoreg_bti = 1; end
+
+ % specify some default parameters
+ if docoreg_bti && ~exist('docoreg_bti_icp', 'var'), docoreg_bti_icp = 1; end
+ if docoreg_bti && ~exist('docoreg_bti_interactive', 'var'), docoreg_bti_interactive = 1; end
+
+ % perform some checks on conditional presence of some variables
if (dofiducials || dolandmarks) && ~exist('dicomfile', 'var')
+ % we need a pointer to a file in the dicom-series that contains the T1-weighted anatomical image
error('for the interactive part of the anatomy pipeline, a pointer to a file from the dicom series is needed');
end
- if ~exist('docoregistration', 'var')
- docoregistration = 1;
- end
end
if dopipeautomatic,
% set flags to facilitate debugging or running parts of the pipeline
- if ~exist('doheadmodel', 'var')
- doheadmodel = 1;
- end
- if ~exist('dosourcemodel3d', 'var')
- dosourcemodel3d = 1;
- end
- if ~exist('dosourcemodel2d', 'var')
- dosourcemodel2d = 1;
- end
- if ~exist('dofreesurfer', 'var')
- dofreesurfer = 0;
- end
- if ~exist('domnesuite', 'var')
- domnesuite = 0;
- end
+ if ~exist('doheadmodel', 'var'), doheadmodel = 1; end
+ if ~exist('dosourcemodel3d', 'var'), dosourcemodel3d = 1; end
+ if ~exist('dosourcemodel2d', 'var'), dosourcemodel2d = 1; end
+ if ~exist('dofreesurfer', 'var'), dofreesurfer = 0; end % this functionality can probably be removed
+ if ~exist('domnesuite', 'var'), domnesuite = 0; end % this functionality can probably be removed
- if dosourcemodel3d && ~exist('gridresolution', 'var')
- % specify a default grid resolution
- gridresolution = [4 6 8];
- end
-
- if doheadmodel && ~exist('headmodelthr', 'var')
- headmodelthr = 0.5;
- end
- if doheadmodel && ~exist('headmodelsmooth', 'var')
- headmodelsmooth = 5;
- end
+ % specify some default parameters
+ if dosourcemodel3d && ~exist('gridresolution', 'var'), gridresolution = [4 6 8]; end
+ if doheadmodel && ~exist('headmodelthr', 'var'), headmodelthr = 0.5; end
+ if doheadmodel && ~exist('headmodelsmooth', 'var'), headmodelsmooth = 5; end
+ % perform some checks on conditional presence of some variables
if dosourcemodel2d && ~exist('structuralpreprocdir', 'var') && ~exist('mnepath', 'var')
error('when computing the cortical sheet based source model the path to MNE-suite software needs to be specified as ''mnepath''');
end
@@ -130,8 +106,8 @@
error('when computing the cortical sheet based source model a pointer to the directory where the labeling of the surfaces is stored needs to be provided as ''surflabeldir''');
end
- % we need a pointer to an hs-file
if docoregistration && ~exist('hsfile', 'var')
+ % we need a pointer to an hs-file
error('for the automatic coregistration a pointer to an hsfile is needed');
end
end
@@ -211,6 +187,9 @@
else
error('for the automatic part of the anatomy pipeline a nifti file containing the anatomy is needed: run the interactive part of the pipeline first and/or ensure that the nifti file created in this step is in the output directory');
end
+ mriorig = mri;
+ % mriorig.transform = eye(4); % FIXME does this need to be done?
+ % what when voxels are not isotropic
if exist(textfile_fiducials, 'file')
hcp_read_ascii(textfile_fiducials);
@@ -223,24 +202,29 @@
error('for the automatic coregistration a textfile with landmark locations needs to exist');
end
- % check which version of the coregistration needs to be run: if the
- % option-list contains a key: filename_hr, a high-resolution, ACPC-space
- % aligned volume exists, use this one for coregistration
-
- mriorig = mri;
- % mriorig.transform = eye(4); % FIXME does this need to be done?
- % what when voxels are not isotropic
-
- % do the coregistration to MNI space
- fprintf('\n');
- fprintf('Coregistering the anatomy to the axes of the MNI coordinate system\n');
- fprintf('\n');
-
- cfg = [];
- cfg.landmark = landmarks;
- mri = ft_volumerealign(cfg, mriorig);
+ if exist(textfile_transform, 'file')
+ hcp_read_ascii(textfile_transform);
+ end
- if exist('hrmrifile', 'var') && exist(hrmrifile, 'file')
+ if docoreg_spm
+ if exist('transform', 'var')
+ % if the transform already exists
+ % scrub the fields that have 'spm' in them
+ fn = fieldnames(transform);
+ removefields = ~cellfun('isempty', strfind(fn, 'spm'));
+ transform = rmfield(transform, fn(removefields));
+ end
+
+ % do the coregistration to MNI space
+ fprintf('\n');
+ fprintf('Coregistering the anatomy to the axes of the MNI coordinate system\n');
+ fprintf('\n');
+
+ cfg = [];
+ cfg.landmark = landmarks;
+ mri = ft_volumerealign(cfg, mriorig);
+
+ if exist('hrmrifile', 'var') && exist(hrmrifile, 'file')
% coregister the low-resolution MRI to the high resolution in
% ACPC-space; landmarks are not needed.
fprintf('\n');
@@ -251,168 +235,238 @@
targetmri = ft_read_mri(hrmrifile);
targetmri.coordsys = 'spm';
- cfg = [];
- cfg.method = 'spm';
+ cfg = [];
+ cfg.method = 'spm';
cfg.spm.regtype = 'rigid';
- %cfg.method = 'fsl';
- %cfg.fsl.reslice = 'no';
- %cfg.fsl.searchrange = [-90 90];
- mri = ft_volumerealign(cfg, mri, targetmri);
+ mri = ft_volumerealign(cfg, mri, targetmri);
+
+ % here make a control figure to check the coregistration
+ % reconstruct the scalp surface from the mris, and overlay them
+ tmpcfg = [];
+ tmpcfg.output = 'scalp';
+ seg = ft_volumesegment(tmpcfg, mri);
+ seg_target = ft_volumesegment(tmpcfg, targetmri);
+
+ tmpcfg = [];
+ tmpcfg = [];
+ tmpcfg.tissue = 'scalp';
+ tmpcfg.method = 'projectmesh';
+ tmpcfg.numvertices = 20000;
+ scalp = ft_prepare_mesh(tmpcfg, seg);
+ scalp_target = ft_prepare_mesh(tmpcfg, seg_target);
+
+ figure;
+ subplot('position',[0.01 0.51 0.48 0.48]);hold on;
+ ft_plot_mesh(scalp, 'edgecolor','none','facecolor','r','facealpha',0.3);
+ ft_plot_mesh(scalp_target,'edgecolor','none','facecolor','b','facealpha',0.3); view(180,-90);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ subplot('position',[0.51 0.51 0.48 0.48]);hold on;
+ ft_plot_mesh(scalp, 'edgecolor','none','facecolor','r','facealpha',0.3);
+ ft_plot_mesh(scalp_target,'edgecolor','none','facecolor','b','facealpha',0.3); view(0,90);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ subplot('position',[0.01 0.01 0.48 0.48]);hold on;
+ ft_plot_mesh(scalp, 'edgecolor','none','facecolor','r','facealpha',0.3);
+ ft_plot_mesh(scalp_target,'edgecolor','none','facecolor','b','facealpha',0.3); view(90,0);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ subplot('position',[0.51 0.01 0.48 0.48]);hold on;
+ ft_plot_mesh(scalp, 'edgecolor','none','facecolor','r','facealpha',0.3);
+ ft_plot_mesh(scalp_target,'edgecolor','none','facecolor','b','facealpha',0.3); view(0,0);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ axis on;
+ grid on;
+ set(gcf,'color','w')
+ hcp_write_figure([outputprefix,'_coregistration_lowres2hires.png'], gcf, 'resolution', 300); close all;
transform.vox2spm_interactive = mri.transformorig;
transform.vox2spm_registered = mri.transform;
+ end
+
+ % set the transformation matrix
+ transform.vox2spm = mri.transform;
+ transform.spm2vox = inv(transform.vox2spm);
end
- % set the transformation matrix
- transform.vox2spm = mri.transform;
-
- fprintf('\n');
- fprintf('-------------------------------------------------------------------------\n');
- fprintf('\n');
- fprintf('Coregistering the anatomy to the axes of the MEG coordinate system\n');
- fprintf('\n');
-
- % do an initial coregistration to BTI space
- cfg = [];
- cfg.fiducial = fiducials;
- mri = ft_volumerealign(cfg, mriorig);
-
- transform.vox2bti_interactive = mri.transform;
-
- % refine the coregistration by doing a icp-based coregistration using
- % the hs_file and the scalp surface reconstructed from the 1mm anatomy
- fprintf('\n');
- fprintf('Refining the coregistration using the headshape file\n');
- fprintf('\n');
-
- cfg = [];
- cfg.method = 'headshape';
- cfg.headshape = ft_read_headshape(hsfile);
- % weight the points below the xy-plane and on the forehead more
- cfg.weights = ones(size(cfg.headshape.pnt,1),1);
- cfg.weights(cfg.headshape.pnt(:,3)<0) = 1.5;
- cfg.weights(cfg.headshape.pnt(:,3)>0.08 & cfg.headshape.pnt(:,3)<0.1) = 1.5;
- cfg.weights(cfg.headshape.pnt(:,1)>0.05) = 2;
- cfg.weights(cfg.headshape.pnt(:,1)<-0.05) = 2;
- cfg.scalpsmooth = 'no';
- cfg.scalpthreshold = 0.08;
- mri = ft_volumerealign(cfg, mri);
-
- headshape = struct(mri.cfg.headshape); % convert back from config object
- headshape.coordsys = 'bti';
- headshapemri = struct(mri.cfg.headshapemri);
- headshapemri.coordsys = 'bti';
-
- mrifid.pnt = ft_warp_apply(mri.transform, [fiducials.nas;fiducials.lpa;fiducials.rpa]);
- mrifid.label = {'NZinteractive';'Linteractive';'Rinteractive'};
- headshapemri.fid = mrifid;
-
- % write the headshapes
- hcp_write_matlab([outputprefix,'_headshape'], 'headshape');
- hcp_write_matlab([outputprefix,'_headshapemri'], 'headshapemri');
-
- % set the transformation matrix
- transform.vox2bti_registered = mri.transform;
- transform.vox2bti = mri.transform;
-
- % create some additional transformation matrices
- transform.bti2vox = inv(transform.vox2bti);
- transform.spm2vox = inv(transform.vox2spm);
+ if docoreg_bti
+ if exist('transform', 'var'),
+ % scrub the fields that have 'bti' in them
+ fn = fieldnames(transform);
+ removefields = ~cellfun('isempty', strfind(fn, 'bti'));
+ transform = rmfield(transform, fn(removefields));
+ end
+
+ fprintf('\n');
+ fprintf('-------------------------------------------------------------------------\n');
+ fprintf('\n');
+ fprintf('Coregistering the anatomy to the axes of the MEG coordinate system\n');
+ fprintf('\n');
+
+ % do an initial coregistration to BTI space
+ cfg = [];
+ cfg.fiducial = fiducials;
+ mri = ft_volumerealign(cfg, mriorig);
+
+ transform.vox2bti_interactive = mri.transform;
+
+ % refine the coregistration by doing a icp-based coregistration using
+ % the hs_file and the scalp surface reconstructed from the 1mm anatomy
+ fprintf('\n');
+ fprintf('Refining the coregistration using the headshape file\n');
+ fprintf('\n');
+
+ cfg = [];
+ cfg.method = 'headshape';
+ cfg.headshape.headshape = ft_read_headshape(hsfile);
+ cfg.headshape.icp = docoreg_bti_icp;
+ cfg.headshape.interactive = docoreg_bti_interactive;
+ % weight the points below the xy-plane and on the forehead more
+ cfg.weights = ones(size(cfg.headshape.headshape.pnt,1),1);
+ %cfg.weights(cfg.headshape.headshape.pnt(:,3)<0) = 1.5;
+ %cfg.weights(cfg.headshape.headshape.pnt(:,3)>0.08 & cfg.headshape.headshape.pnt(:,3)<0.1) = 1.5;
+ %cfg.weights(cfg.headshape.headshape.pnt(:,1)>0.05) = 2;
+ %cfg.weights(cfg.headshape.headshape.pnt(:,1)<-0.05) = 2;
+ cfg.headshape.scalpsmooth = 1;%'no';
+ cfg.headshape.scalpthreshold = 0.08;
+ mri = ft_volumerealign(cfg, mri);
+
+ if isfield(mri.cfg.headshape, 'headshape')
+ headshape = struct(mri.cfg.headshape.headshape); % convert back from config object
+ headshapemri = struct(mri.cfg.headshape.headshapemri);
+ else
+ % backward compatibility
+ headshape = struct(mri.cfg.headshape); % convert back from config object
+ headshapemri = struct(mri.cfg.headshapemri);
+ end
+ headshape.coordsys = 'bti';
+ headshapemri.coordsys = 'bti';
+
+ mrifid.pnt = ft_warp_apply(mri.transform, [fiducials.nas;fiducials.lpa;fiducials.rpa]);
+ mrifid.label = {'NZinteractive';'Linteractive';'Rinteractive'};
+ headshapemri.fid = mrifid;
+
+ % write the headshapes
+ hcp_write_matlab([outputprefix,'_headshape'], 'headshape');
+ hcp_write_matlab([outputprefix,'_headshapemri'], 'headshapemri');
+
+ % quality check for the coregistration between headshape and mri
+ headshape = hcp_ensure_units(headshape, 'mm');
+ headshapemri = hcp_ensure_units(headshapemri, 'mm');
+
+ figure;
+ subplot('position',[0.01 0.51 0.48 0.48]);hold on;
+ ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.7 0.7 0.7],'fidcolor','y','facealpha',0.3);
+ ft_plot_mesh(headshapemri,'edgecolor','none','vertexcolor',headshapemri.distance); view(180,-90);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ subplot('position',[0.51 0.51 0.48 0.48]);hold on;
+ ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.7 0.7 0.7],'fidcolor','y','facealpha',0.3);
+ ft_plot_mesh(headshapemri,'edgecolor','none','vertexcolor',headshapemri.distance); view(0,90);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ subplot('position',[0.01 0.01 0.48 0.48]);hold on;
+ ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.7 0.7 0.7],'fidcolor','y','facealpha',0.3);
+ ft_plot_mesh(headshapemri,'edgecolor','none','vertexcolor',headshapemri.distance); view(90,0);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ subplot('position',[0.51 0.01 0.48 0.48]);hold on;
+ ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.7 0.7 0.7],'fidcolor','y','facealpha',0.3);
+ ft_plot_mesh(headshapemri,'edgecolor','none','vertexcolor',headshapemri.distance); view(0,0); colorbar('east');
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ axis on;
+ grid on;
+ set(gcf,'color','w')
+ hcp_write_figure([outputprefix,'_headshape_distance.png'], gcf, 'resolution', 300); close all;
+
+ v = headshapemri.pnt;
+ f = headshapemri.tri;
+ [f,v]=reducepatch(f,v, 0.2);
+ headshapemri.pnt = v;
+ headshapemri.tri = f;
+
+ figure;
+ subplot('position',[0.01 0.51 0.48 0.48]);hold on;
+ ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.5 0.6 0.8],'fidcolor','y','facealpha',0.3);
+ ft_plot_headshape(headshape,'vertexsize',3); view(180,-90);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ subplot('position',[0.51 0.51 0.48 0.48]);hold on;
+ ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.5 0.6 0.8],'fidcolor','y','facealpha',0.3);
+ ft_plot_headshape(headshape,'vertexsize',3); view(0,90);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ subplot('position',[0.01 0.01 0.48 0.48]);hold on;
+ ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.5 0.6 0.8],'fidcolor','y','facealpha',0.3);
+ ft_plot_headshape(headshape,'vertexsize',3); view(90,0);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ subplot('position',[0.51 0.01 0.48 0.48]);hold on;
+ ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.5 0.6 0.8],'fidcolor','y','facealpha',0.3);
+ ft_plot_headshape(headshape,'vertexsize',3); view(0,0);
+ plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
+ axis on;
+ grid on;
+ set(gcf,'color','w')
+ hcp_write_figure([outputprefix,'_headshape.png'], gcf, 'resolution', 300); close all;
+
+ % create figures at the landmarks' locations, for QC
+ crosshair=@(pos)plot([-100 100],pos(2),'y',pos(1)*[1 1],[-100 100],'y');
+ cfg = [];
+ cfg.locationcoordinates = 'voxel';
+ cfg.location = landmarks.ac;
+ cfg.interactive = 'no';
+ figure;ft_sourceplot(cfg, mri);
+ hcp_write_figure([outputprefix,'_landmarks_ac.png'], gcf, 'resolution', 500); close;
+ cfg.location = landmarks.pc;
+ figure;ft_sourceplot(cfg, mri);
+ hcp_write_figure([outputprefix,'_landmarks_pc.png'], gcf, 'resolution', 500); close;
+ cfg.location = landmarks.xzpoint;
+ figure;ft_sourceplot(cfg, mri);
+ hcp_write_figure([outputprefix,'_landmarks_xzpoint.png'], gcf, 'resolution', 500); close;
+ cfg.location = landmarks.rpoint;
+ figure;ft_sourceplot(cfg, mri);
+ hcp_write_figure([outputprefix,'_landmarks_rpoint.png'], gcf, 'resolution', 500); close;
+
+ % create figures at the fiducials' location, for QC
+ cfg = [];
+ cfg.locationcoordinates = 'voxel';
+ cfg.location = fiducials.lpa;
+ cfg.interactive = 'no';
+ figure;ft_sourceplot(cfg, mri);
+ hcp_write_figure([outputprefix,'_fiducials_lpa.png'], gcf, 'resolution', 500); close;
+ cfg.location = fiducials.rpa;
+ figure;ft_sourceplot(cfg, mri);
+ hcp_write_figure([outputprefix,'_fiducials_rpa.png'], gcf, 'resolution', 500); close;
+ cfg.location = fiducials.nas;
+ figure;ft_sourceplot(cfg, mri);
+ hcp_write_figure([outputprefix,'_fiducials_nas.png'], gcf, 'resolution', 500); close;
+ cfg.location = fiducials.zpoint;
+ figure;ft_sourceplot(cfg, mri);
+ hcp_write_figure([outputprefix,'_fiducials_zpoint.png'], gcf, 'resolution', 500); close;
+
+ transform.vox2bti = mri.transform;
+ transform.bti2vox = inv(transform.vox2bti);
+ end
+ % add additional transformations
transform.spm2bti = transform.vox2bti/transform.vox2spm;
transform.bti2spm = transform.vox2spm/transform.vox2bti;
+ [p,f,e] = fileparts(nifti_anatomical);
+ transform.vox_filename = [f,e];
- % write the transform structure
- hcp_write_ascii(textfile_transform, 'transform', transform, '%% FIXME put some comment here');
-
- % quality check for the coregistration between headshape and mri
- headshape = hcp_ensure_units(headshape, 'mm');
- headshapemri = hcp_ensure_units(headshapemri, 'mm');
-
- figure;
- subplot('position',[0.01 0.51 0.48 0.48]);hold on;
- ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.7 0.7 0.7],'fidcolor','y','facealpha',0.3);
- ft_plot_mesh(headshapemri,'edgecolor','none','vertexcolor',headshapemri.distance); view(180,-90);
- plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
- subplot('position',[0.51 0.51 0.48 0.48]);hold on;
- ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.7 0.7 0.7],'fidcolor','y','facealpha',0.3);
- ft_plot_mesh(headshapemri,'edgecolor','none','vertexcolor',headshapemri.distance); view(0,90);
- plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
- subplot('position',[0.01 0.01 0.48 0.48]);hold on;
- ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.7 0.7 0.7],'fidcolor','y','facealpha',0.3);
- ft_plot_mesh(headshapemri,'edgecolor','none','vertexcolor',headshapemri.distance); view(90,0);
- plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
- subplot('position',[0.51 0.01 0.48 0.48]);hold on;
- ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.7 0.7 0.7],'fidcolor','y','facealpha',0.3);
- ft_plot_mesh(headshapemri,'edgecolor','none','vertexcolor',headshapemri.distance); view(0,0); colorbar('east');
- plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
- axis on;
- grid on;
- set(gcf,'color','w')
- hcp_write_figure([outputprefix,'_headshape_distance.png'], gcf, 'resolution', 500); close all;
-
- v = headshapemri.pnt;
- f = headshapemri.tri;
- [f,v]=reducepatch(f,v, 0.2);
- headshapemri.pnt = v;
- headshapemri.tri = f;
-
- figure;
- subplot('position',[0.01 0.51 0.48 0.48]);hold on;
- ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.5 0.6 0.8],'fidcolor','y','facealpha',0.3);
- ft_plot_headshape(headshape,'vertexsize',3); view(180,-90);
- plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
- subplot('position',[0.51 0.51 0.48 0.48]);hold on;
- ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.5 0.6 0.8],'fidcolor','y','facealpha',0.3);
- ft_plot_headshape(headshape,'vertexsize',3); view(0,90);
- plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
- subplot('position',[0.01 0.01 0.48 0.48]);hold on;
- ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.5 0.6 0.8],'fidcolor','y','facealpha',0.3);
- ft_plot_headshape(headshape,'vertexsize',3); view(90,0);
- plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
- subplot('position',[0.51 0.01 0.48 0.48]);hold on;
- ft_plot_mesh(headshapemri,'edgecolor','none','facecolor',[0.5 0.6 0.8],'fidcolor','y','facealpha',0.3);
- ft_plot_headshape(headshape,'vertexsize',3); view(0,0);
- plot3([-130 130],[0 0],[0 0],'k');plot3([0 0],[-120 120],[0 0],'k');plot3([0 0],[0 0],[-100 150],'k');
- axis on;
- grid on;
- set(gcf,'color','w')
- hcp_write_figure([outputprefix,'_headshape.png'], gcf, 'resolution', 500); close all;
+ if exist('hrmrifile', 'var') && exist(hrmrifile, 'file')
+ targetmri = ft_read_mri(hrmrifile);
+ targetmri.coordsys = 'spm';
+
+ % create some additional transformation matrices
+ transform.vox07mm2spm = targetmri.transform;
+ transform.vox07mm2bti = transform.spm2bti*transform.vox07mm2spm;
+
+ transform.bti2vox07mm = inv(transform.vox07mm2bti);
+ transform.spm2vox07mm = inv(transform.vox07mm2spm);
+
+ [p,f,e] = fileparts(hrmrifile);
+ transform.vox07mm_filename = [f,e];
+ end
- % create figures at the landmarks' locations, for QC
- crosshair=@(pos)plot([-100 100],pos(2),'y',pos(1)*[1 1],[-100 100],'y');
- cfg = [];
- cfg.locationcoordinates = 'voxel';
- cfg.location = landmarks.ac;
- cfg.interactive = 'no';
- figure;ft_sourceplot(cfg, mri);
- hcp_write_figure([outputprefix,'_landmarks_ac.png'], gcf, 'resolution', 500); close;
- cfg.location = landmarks.pc;
- figure;ft_sourceplot(cfg, mri);
- hcp_write_figure([outputprefix,'_landmarks_pc.png'], gcf, 'resolution', 500); close;
- cfg.location = landmarks.xzpoint;
- figure;ft_sourceplot(cfg, mri);
- hcp_write_figure([outputprefix,'_landmarks_xzpoint.png'], gcf, 'resolution', 500); close;
- cfg.location = landmarks.rpoint;
- figure;ft_sourceplot(cfg, mri);
- hcp_write_figure([outputprefix,'_landmarks_rpoint.png'], gcf, 'resolution', 500); close;
+ try, transform = rmfield(transform, 'vox2spm_interactive'); end
+ try, transform = rmfield(transform, 'vox2bti_interactive'); end
+ try, transform = rmfield(transform, 'vox2spm_registered'); end
- % create figures at the fiducials' location, for QC
- cfg = [];
- cfg.locationcoordinates = 'voxel';
- cfg.location = fiducials.lpa;
- cfg.interactive = 'no';
- figure;ft_sourceplot(cfg, mri);
- hcp_write_figure([outputprefix,'_fiducials_lpa.png'], gcf, 'resolution', 500); close;
- cfg.location = fiducials.rpa;
- figure;ft_sourceplot(cfg, mri);
- hcp_write_figure([outputprefix,'_fiducials_rpa.png'], gcf, 'resolution', 500); close;
- cfg.location = fiducials.nas;
- figure;ft_sourceplot(cfg, mri);
- hcp_write_figure([outputprefix,'_fiducials_nas.png'], gcf, 'resolution', 500); close;
- cfg.location = fiducials.zpoint;
- figure;ft_sourceplot(cfg, mri);
- hcp_write_figure([outputprefix,'_fiducials_zpoint.png'], gcf, 'resolution', 500); close;
+ % write the transform structure
+ hcp_write_ascii(textfile_transform, 'transform', transform, '%% FIXME put some comment here');
fprintf('\n');
fprintf('-------------------------------------------------------------------------\n');
@@ -444,8 +498,14 @@
error('when the automatic part of the anatomy pipeline is run without coregistration, a textfile with coregistration information needs to exist');
end
- mri4D = mri; mri4D.transform = transform.vox2bti; mri4D.coordsys = 'bti';
- mriRAS = mri; mriRAS.transform = transform.vox2spm; mriRAS.coordsys = 'spm';
+ if isfield(transform, 'vox2bti'),
+ % old style transform structure
+ mri4D = mri; mri4D.transform = transform.vox2bti; mri4D.coordsys = 'bti';
+ mriRAS = mri; mriRAS.transform = transform.vox2spm; mriRAS.coordsys = 'spm';
+ else
+ mri4D = mri; mri4D.transform = transform.vox2bti; mri4D.coordsys = 'bti';
+ mriRAS = mri; mriRAS.transform = transform.vox2spm; mriRAS.coordsys = 'spm';
+ end
%---------------------------------------------
% execute the non-interactive part of the pipeline
@@ -776,137 +836,54 @@
outputsurffile = surffile;
end
- fprintf('\n');
- fprintf('-------------------------------------------------------------------------\n');
- fprintf('Adding parcellation information to the cortical sheet source model\n');
- fprintf('\n');
-
- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
- % get the three Freesurfer parcellations and add these to the sourcemodel
- if ft_filetype(outputsurffile, 'caret_surf')
- [p,f,e] = fileparts(outputsurffile);
- tmpfilename = [f,e];
- tmpfilename = strrep(tmpfilename, rootstr, 'aparc');
- tmpfilename = strrep(tmpfilename, '.surf.', '.label.');
- tmpfile1 = fullfile(surflabeldir, tmpfilename);
- tmpfile1b = outputsurffile;
- tmpfilename = strrep(tmpfilename, rootstr, 'aparc');
- tmpfilename = strrep(tmpfilename, '.surf.', '.label.');
- tmpfilename = strrep(tmpfilename, '.L.', '.R.');
- tmpfile2 = fullfile(surflabeldir, tmpfilename);
- tmpfile2b = strrep(outputsurffile, '.L.', '.R.');
- else
- % this assumes the freesurfer directory to be visible
- tmpfile1 = fullfile(surflabeldir,'lh.aparc.annot');
- tmpfile1b = outputsurffile;
- tmpfile2 = fullfile(surflabeldir,'rh.aparc.annot');
- tmpfile2b = strrep(outputsurffile, 'lh.', 'rh.');
- end
- atlasleft = ft_read_atlas({tmpfile1 tmpfile1b}, 'format', 'freesurfer_aparc');
- atlasright = ft_read_atlas({tmpfile2 tmpfile2b}, 'format', 'freesurfer_aparc');
-
- % combine left and right atlases
- nlabel = numel(atlasleft.aparclabel);
- for k = 1:nlabel
- aparclabel{k} = ['L_',atlasleft.aparclabel{k}];
- aparclabel{k+nlabel} = ['R_',atlasright.aparclabel{k}];
- end
-
- % update the indexing
- tmp = atlasright.aparc;
- tmp(tmp>0) = tmp(tmp>0)+nlabel;
- aparc = cat(1, atlasleft.aparc, tmp);
-
- if isfield(sourcemodel2d, 'orig')
- sourcemodel2d.aparc = aparc(sourcemodel2d.orig.inuse>0);
- else
- sourcemodel2d.aparc = aparc;
- end
- sourcemodel2d.aparclabel = aparclabel;
-
- if ft_filetype(outputsurffile, 'caret_surf')
- [p,f,e] = fileparts(outputsurffile);
- tmpfilename = [f,e];
- tmpfilename = strrep(tmpfilename, rootstr, 'aparc.a2009s');
- tmpfilename = strrep(tmpfilename, '.surf.', '.label.');
- tmpfile1 = fullfile(surflabeldir, tmpfilename);
- tmpfile1b = outputsurffile;
- tmpfilename = strrep(tmpfilename, rootstr, 'aparc.a2009s');
- tmpfilename = strrep(tmpfilename, '.surf.', '.label.');
- tmpfilename = strrep(tmpfilename, '.L.', '.R.');
- tmpfile2 = fullfile(surflabeldir, tmpfilename);
- tmpfile2b = strrep(outputsurffile, '.L.', '.R.');
- else
- tmpfile1 = fullfile(surflabeldir,'lh.aparc.a2009s.annot');
- tmpfile1b = outputsurffile;
- tmpfile2 = fullfile(surflabeldir,'rh.aparc.a2009s.annot');
- tmpfile2b = strrep(outputsurffile, 'lh.', 'rh.');
- end
- atlasleft = ft_read_atlas({tmpfile1 tmpfile1b}, 'format', 'freesurfer_a2009s');
- atlasright = ft_read_atlas({tmpfile2 tmpfile2b}, 'format', 'freesurfer_a2009s');
-
- % combine left and right atlases
- nlabel = numel(atlasleft.a2009slabel);
- for k = 1:nlabel
- a2009slabel{k} = ['L_',atlasleft.a2009slabel{k}];
- a2009slabel{k+nlabel} = ['R_',atlasright.a2009slabel{k}];
- end
-
- % update the indexing
- tmp = atlasright.a2009s;
- tmp(tmp>0) = tmp(tmp>0)+nlabel;
- a2009s = cat(1, atlasleft.a2009s, tmp);
-
- if isfield(sourcemodel2d, 'orig')
- sourcemodel2d.a2009s = a2009s(sourcemodel2d.orig.inuse>0);
- else
- sourcemodel2d.a2009s = a2009s;
- end
- sourcemodel2d.a2009slabel = a2009slabel;
+ % write the sourcemodel
+ hcp_write_matlab([outputprefix,'_sourcemodel_2d'], 'sourcemodel2d');
- if ft_filetype(outputsurffile, 'caret_surf')
- [p,f,e] = fileparts(outputsurffile);
- tmpfilename = [f,e];
- tmpfilename = strrep(tmpfilename, rootstr, 'BA');
- tmpfilename = strrep(tmpfilename, '.surf.', '.label.');
- tmpfile1 = fullfile(surflabeldir, tmpfilename);
- tmpfile1b = outputsurffile;
- tmpfilename = strrep(tmpfilename, rootstr, 'BA');
- tmpfilename = strrep(tmpfilename, '.surf.', '.label.');
- tmpfilename = strrep(tmpfilename, '.L.', '.R.');
- tmpfile2 = fullfile(surflabeldir, tmpfilename);
- tmpfile2b = strrep(outputsurffile, '.L.', '.R.');
+ % qualitycheck figures, for this we need the mri
+ if exist('hrmrifile', 'var')
+ mri = ft_read_mri(hrmrifile);
+ mri.coordsys = 'bti';
+ mri.transform = transform.vox07mm2bti;
else
- tmpfile1 = fullfile(surflabeldir,'lh.BA.annot');
- tmpfile1b = outputsurffile;
- tmpfile2 = fullfile(surflabeldir,'rh.BA.annot');
- tmpfile2b = strrep(outputsurffile, 'lh.', 'rh.');
- end
- atlasleft = ft_read_atlas({tmpfile1 tmpfile1b}, 'format', 'freesurfer_ba');
- atlasright = ft_read_atlas({tmpfile2 tmpfile2b}, 'format', 'freesurfer_ba');
-
- % combine left and right atlases
- nlabel = numel(atlasleft.BAlabel);
- for k = 1:nlabel
- BAlabel{k} = ['L_',atlasleft.BAlabel{k}];
- BAlabel{k+nlabel} = ['R_',atlasright.BAlabel{k}];
+ mri = ft_read_mri(nifti_anatomical);
+ mri.coordsys = 'bti';
+ mri.transform = transform.vox2bti;
end
- % update the indexing
- tmp = atlasright.BA;
- tmp(tmp>0) = tmp(tmp>0)+nlabel;
- BA = cat(1, atlasleft.BA, tmp);
-
- if isfield(sourcemodel2d, 'orig')
- sourcemodel2d.BA = BA(sourcemodel2d.orig.inuse>0);
- else
- sourcemodel2d.BA = BA;
- end
- sourcemodel2d.BAlabel = BAlabel;
+ % also read in the headmodel: this will provide a crash if it does
+ % not exist.
+ hcp_read_matlab([outputprefix,'_headmodel']);
+ % and the sourcemodel2d should be in mm
+ sourcemodel2d = ft_convert_units(sourcemodel2d, 'mm');
+ headmodel = ft_convert_units(headmodel, 'mm');
- % write the sourcemodel
- hcp_write_matlab([outputprefix,'_sourcemodel2d'], 'sourcemodel2d');
+ figure;
+ options = {'transform',mri.transform,'intersectmesh',{sourcemodel2d headmodel.bnd}};
+ subplot(2,2,1); hold on; ft_plot_slice(mri.anatomy, 'location', [0 0 60], 'orientation', [0 0 1], options{:}); view(0,90);
+ subplot(2,2,2); hold on; ft_plot_slice(mri.anatomy, 'location', [0 0 20], 'orientation', [0 0 1], options{:}); view(0,90);
+ subplot(2,2,3); hold on; ft_plot_slice(mri.anatomy, 'location', [0 20 0], 'orientation', [1 0 0], options{:}); view(90,0);
+ subplot(2,2,4); hold on; ft_plot_slice(mri.anatomy, 'location', [0 20 0], 'orientation', [0 1 0], options{:}); view(0,0);
+ set(gcf, 'Renderer', 'zbuffer')
+ hcp_write_figure([outputprefix,'_sourcemodel_2d.png']);
+
+ options = {'transform', mri.transform,'nslice',16,'intersectmesh',{sourcemodel2d headmodel.bnd},...
+ 'intersectlinewidth',1,'slicesize',[300 300]};
+
+ figure;
+ ft_plot_montage(mri.anatomy, 'location', [0 0 0], 'orientation', [0 0 1], 'slicerange', [-20 120], options{:});
+ set(gcf, 'Renderer', 'zbuffer');
+ hcp_write_figure([outputprefix,'_slice1.png'], 'resolution', 300);
+
+ figure;
+ ft_plot_montage(mri.anatomy, 'location', [0 0 0], 'orientation', [0 1 0], 'slicerange', [-60 60], options{:});
+ set(gcf, 'Renderer', 'zbuffer');
+ hcp_write_figure([outputprefix,'_slice2.png'], 'resolution', 300);
+
+ figure;
+ ft_plot_montage(mri.anatomy, 'location', [0 0 0], 'orientation', [1 0 0], 'slicerange', [-70 110], options{:});
+ set(gcf, 'Renderer', 'zbuffer');
+ hcp_write_figure([outputprefix,'_slice3.png'], 'resolution', 300);
fprintf('\n');
fprintf('-------------------------------------------------------------------------\n');
diff --git a/pipeline_scripts/hcp_baddata.m b/pipeline_scripts/hcp_baddata.m
index fedfb6c..0c95d18 100644
--- a/pipeline_scripts/hcp_baddata.m
+++ b/pipeline_scripts/hcp_baddata.m
@@ -364,7 +364,7 @@
badsegment.all = [];
end
-if(~isempty(badsegment.ica) & ~isempty(badsegment.ica))
+if(~isempty(badsegment.ica) && ~isempty(badsegment.manual))
flagica=zeros(1,badsegment.manual(end,2));
for i=1:size(badsegment.ica,1)
flagica(badsegment.ica(i,1):badsegment.ica(i,2))=1;
@@ -382,6 +382,7 @@
badsegment.ica=badsegmentica;
end
+
% write it to an ascii file, this works just like a normal save command
hcp_write_ascii(sprintf('%s_badsegments.txt', resultprefix), 'badsegment');
% hcp_write_ascii(sprintf('%s_times.txt', resultprefix), 'baddata_time');
diff --git a/pipeline_scripts/hcp_datacheck.m b/pipeline_scripts/hcp_datacheck.m
index 5e64e49..52fb1eb 100644
--- a/pipeline_scripts/hcp_datacheck.m
+++ b/pipeline_scripts/hcp_datacheck.m
@@ -204,11 +204,17 @@
if exist(hsname, 'file')
shape = ft_read_headshape(hsname);
f1 = figure;
- a = [-0.15 0.15 -0.10 0.10 -0.10 0.15];
- subplot(2,2,1); ft_plot_headshape(shape); view([-90 90]); axis(a); % top
- subplot(2,2,2); ft_plot_headshape(shape); view([ 00 00]); axis(a); % right
- subplot(2,2,3); ft_plot_headshape(shape); view([-90 00]); axis(a); % back
- subplot(2,2,4); hold on; ft_plot_headshape(shape, 'fidlabel', false, 'fidcolor', 'none'); view([0 0]); ft_plot_sens(hdr.grad, 'chantype', 'megmag', 'coildiameter', coildiameter); axis(a);
+ a = [-0.15 0.15 -0.10 0.10 -0.10 0.15];
+ subplot(2,3,1); ft_plot_headshape(shape); view([-90 90]); axis(a); % top
+ subplot(2,3,4); hold on; ft_plot_headshape(shape, 'fidlabel', false, 'fidcolor', 'none'); view([-90 90]); ft_plot_sens(hdr.grad, 'chantype', 'megmag', 'coildiameter', coildiameter);
+ subplot(2,3,2); ft_plot_headshape(shape); view([ 00 00]); axis(a); % right
+ subplot(2,3,5); hold on; ft_plot_headshape(shape, 'fidlabel', false, 'fidcolor', 'none'); view([0 0]); ft_plot_sens(hdr.grad, 'chantype', 'megmag', 'coildiameter', coildiameter);
+ subplot(2,3,3); ft_plot_headshape(shape); view([-90 00]); axis(a); % back
+ subplot(2,3,6); hold on; ft_plot_headshape(shape, 'fidlabel', false, 'fidcolor', 'none'); view([90 0]); ft_plot_sens(hdr.grad, 'chantype', 'megmag', 'coildiameter', coildiameter);
+ % subplot(2,2,1); ft_plot_headshape(shape); view([-90 90]); axis(a); % top
+ % subplot(2,2,2); ft_plot_headshape(shape); view([ 00 00]); axis(a); % right
+ % subplot(2,2,3); ft_plot_headshape(shape); view([-90 00]); axis(a); % back
+ % subplot(2,2,4); hold on; ft_plot_headshape(shape, 'fidlabel', false, 'fidcolor', 'none'); view([0 0]); ft_plot_sens(hdr.grad, 'chantype', 'megmag', 'coildiameter', coildiameter); axis(a);
figurefile = [output, '_headshape'];
hcp_write_figure(figurefile, f1);
close(f1);
@@ -520,7 +526,8 @@
cfg.detrend = 'no';
cfg.bsfilter = 'yes';
cfg.bsfreq = lfreq+[-1 1];
- dataELECnew2 = ft_preprocessing(cfg, dataELECnew);clear dataELECnew;
+ dataELECnew2 = ft_preprocessing(cfg, dataELECnew);
+ clear dataELECnew
cfg = [];
cfg.detrend = 'no';
diff --git a/pipeline_scripts/hcp_icablpcorr.m b/pipeline_scripts/hcp_icablpcorr.m
new file mode 100644
index 0000000..c374f74
--- /dev/null
+++ b/pipeline_scripts/hcp_icablpcorr.m
@@ -0,0 +1,184 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% setup the execution environment
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+opengl software;
+
+% ensure that the time and date of execution are not stored in the provenance information
+global ft_default
+ft_default.trackcallinfo = 'no';
+
+% allow the user to specify the path where additional data is present, e.g. the channel layout or anatomy files
+if exist('path', 'var')
+ addpath(path)
+end
+
+if ~exist('filename', 'var')
+ error('filename should be specified')
+end
+
+% the filename is assumed to be something like
+% 'rawdatadir/Phase1MEG/Subjects/CP10018/Experiments/CP10018_MEG/Scans/1-Rnoise_MNN_V1/Resources/4D/c,rfDC'
+tok = tokenize(filename, '/');
+
+if ~exist('subjectid', 'var')
+ subjectid = tok{end-7};
+end
+
+if ~exist('experimentid', 'var')
+ experimentid = tok{end-5};
+end
+
+% if ~exist('scanid', 'var')
+% scanid = tok{end-3};
+% end
+
+scanid={'3-Restin' ; '4-Restin' ; '5-Restin'};
+
+if ~exist('pipelinedatadir', 'var')
+ pipelinedatadir = hcp_pathdef;
+end
+
+% print the matlab and megconnectome version to screen for provenance
+ver('megconnectome')
+
+% % print the value of all local variables to screen for provenance
+% w = whos;
+% w = {w.name};
+% w = setdiff(w, {'w', 'ans'});
+% for i=1:length(w)
+% fprintf(hcp_printstruct(w{i}, eval(w{i})));
+% end
+
+% change to the location of the processed data (input and output)
+cd(pipelinedatadir)
+
+% hcp_check_pipelineoutput('anatomy', 'subject', subjectid);
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% execute the pipeline
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+smodel_type = {'2D';'3D'}; dimindx=1; % 1 for 2D cortical sheet and 2 for 3D gird
+griddim = {'4mm';'6mm';'8mm'}; gridindx=1; % $ 1,2,3 for 3D 4mm,6mm and 8mm grid
+
+if(strcmp(smodel_type{dimindx},'2D'))
+ hcp_read_matlab([subjectid '_MEG_anatomy_sourcemodel_2d']);
+ sourcemodelsubj = sourcemodel2d;
+ sourcemodel_type=smodel_type{dimindx};
+ head=ft_read_headshape([subjectid '.L.midthickness.8k_fs_LR.surf.gii']);
+ head2=ft_read_headshape([subjectid '.R.midthickness.8k_fs_LR.surf.gii']);
+ sourcemodel=head;
+ sourcemodel.pnt=[head.pnt ; head2.pnt];
+ sourcemodel.tri=[head.tri ; head2.tri];
+elseif(strcmp(smodel_type{dimindx},'3D'))
+ % junk=hcp_read_matlab([subjectid '_anatomy_sourcemodel3D' griddim{gridindx}]);
+ % gridname = 'sourcemodel3d';
+ % sourcemodelsubj=junk.(gridname);
+ % sourcemodelsubj=ft_convert_units(sourcemodelsubj,'mm');
+
+ hcp_read_matlab(['standard_sourcemodel3d' griddim{gridindx}]);
+ sourcemodel_type=[smodel_type{dimindx} griddim{gridindx}];
+end
+
+bands=[1 2 3 4 5 6 7];
+blp_bands = [ 1.3 4 ; 3 8 ; 6 15 ; 12.5 28.5 ; 30 75 ; 70 150 ; 1 150];
+band_prefix={
+ 'delta'
+ 'theta'
+ 'alpha'
+ 'beta'
+ 'lowgamma'
+ 'highgamma'
+ 'whole'
+ };
+
+window_corr=25; %window for stationary corr in sec
+Fs=50;
+window_corr2=window_corr*Fs;
+source_blp=[];
+for ib=bands
+ nwin_tot=0;
+ for i_scan=1:3
+ clear source_blp
+
+ resultprefix = sprintf('%s_%s', experimentid, scanid{i_scan});
+
+ outputfile=[experimentid '_blpcorr_' band_prefix{ib}];
+
+ % hcp_check_pipelineoutput('icapowenv', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid,'band',band_prefix{ib});
+ outstr=[resultprefix '_blp_' sourcemodel_type '_' band_prefix{ib}];
+ disp(['loading blp file ' outstr])
+ hcp_read_matlab([resultprefix '_blp_' sourcemodel_type '_' band_prefix{ib}])
+
+ if (i_scan==1)
+ connect_stat=zeros(size(source_blp.power,1));
+ end
+ ntp=size(source_blp.power,2);
+ nwin=floor(ntp/(Fs*window_corr))
+ for i=1:nwin
+ vect=[((i-1)*window_corr2)+1:window_corr2*i];
+ connect_stat=corr(source_blp.power(:,vect)')+connect_stat;
+ end
+ nwin_tot=nwin_tot+nwin;
+ end
+
+ connect_stat=connect_stat/nwin_tot;
+ connect_stat = cast(connect_stat,'single');
+
+ atlas_rsn_l=ft_read_atlas('RSN-networks.L.8k_fs_LR.label.gii');
+ atlas_rsn_r=ft_read_atlas('RSN-networks.R.8k_fs_LR.label.gii');
+ indxvoxels=[];
+ jn=0;
+ for in=3:numel(atlas_rsn_r.parcellation4label)
+ jn=jn+1;
+ networkl{jn}=atlas_rsn_r.parcellation4label{in};
+ nindxl=find(strcmp(atlas_rsn_l.parcellation4label,atlas_rsn_r.parcellation4label{in}));
+ if ~isempty(nindxl)
+ indxvoxels=[indxvoxels ; find(atlas_rsn_l.parcellation4==nindxl)];
+ end
+ indxvoxels=[indxvoxels ; (find(atlas_rsn_r.parcellation4==in)+numel(atlas_rsn_l.parcellation4))];
+ if (jn==1)
+ net_max(jn,:)=[1 numel(indxvoxels)];
+ else
+ net_max(jn,:)=[(net_max(jn-1,2)+1) numel(indxvoxels)];
+ end
+ end
+
+ connect.networkl=networkl;
+ connect.connect_stat=connect_stat;
+ connect.net_extr=net_max;
+ connect.indxvoxels=indxvoxels;
+ hcp_write_matlab(outputfile)
+
+ dofig='yes';
+ if strcmp(dofig,'yes')
+ figure
+ imagesc(connect_stat(indxvoxels,indxvoxels));
+ caxis([0.5 1])
+ colorbar
+ imgname = [outputfile '.png'];
+ hcp_write_figure(imgname, gcf)
+
+ close all
+ end
+ clear source_blp connect
+end
\ No newline at end of file
diff --git a/pipeline_scripts/hcp_icablpenv.m b/pipeline_scripts/hcp_icablpenv.m
new file mode 100644
index 0000000..9b6adc9
--- /dev/null
+++ b/pipeline_scripts/hcp_icablpenv.m
@@ -0,0 +1,167 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% setup the execution environment
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+opengl software;
+
+% ensure that the time and date of execution are not stored in the provenance information
+global ft_default
+ft_default.trackcallinfo = 'no';
+
+% allow the user to specify the path where additional data is present, e.g. the channel layout or anatomy files
+if exist('path', 'var')
+ addpath(path)
+end
+
+if ~exist('filename', 'var')
+ error('filename should be specified')
+end
+
+% the filename is assumed to be something like
+% 'rawdatadir/Phase1MEG/Subjects/CP10018/Experiments/CP10018_MEG/Scans/1-Rnoise_MNN_V1/Resources/4D/c,rfDC'
+tok = tokenize(filename, '/');
+
+if ~exist('subjectid', 'var')
+ subjectid = tok{end-7};
+end
+
+if ~exist('experimentid', 'var')
+ experimentid = tok{end-5};
+end
+
+if ~exist('scanid', 'var')
+ scanid = tok{end-3};
+end
+
+if ~exist('pipelinedatadir', 'var')
+ pipelinedatadir = hcp_pathdef;
+end
+
+if ~exist('aband', 'var')
+ aband=[];
+end
+
+
+resultprefix = sprintf('%s_%s', experimentid, scanid);
+
+% change to the location of the processed data (input and output)
+cd(pipelinedatadir)
+
+% ensure that the output from the previous pipelines is present
+% hcp_check_pipelineoutput('baddata', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
+% hcp_check_pipelineoutput('icaclass', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
+% hcp_check_pipelineoutput('icamne', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
+
+% print the matlab and megconnectome version to screen for provenance
+ver('megconnectome')
+
+% print the value of all local variables to screen for provenance
+w = whos;
+w = {w.name};
+w = setdiff(w, {'w', 'ans'});
+for i=1:length(w)
+ fprintf(hcp_printstruct(w{i}, eval(w{i})));
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% execute the pipeline
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%------------------------
+% declare the input files
+inputfile1 = fullfile([resultprefix,'_baddata_badsegments.txt']);
+inputfile2 = fullfile([resultprefix,'_baddata_badchannels.txt']);
+inputfile4 = fullfile([resultprefix,'_icaclass_vs.mat']);
+inputfile5 = fullfile([resultprefix,'_icamne.mat']);
+
+%------------------------
+% declare the output file
+outputfile = [resultprefix,'_icablpenv'];
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% get the channel level representation of the data,
+% using the information from the qualitycheck pipeline
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% get the component level data (and information with respect
+% to which components are the actual brain components) from
+% the classification pipeline
+% FIXME it seems that the output fo the classification pipeline contains
+% the options: I suggest to create a text file just like the one specifying
+% the badchannels/segments
+
+hcp_read_matlab(inputfile4);
+hcp_read_matlab(inputfile5, 'source');
+
+comp=comp_class;
+mixing = comp_class.topo;
+if(max(size(source.val))>2)
+for i = 1:size(mixing, 2)
+ mixing(:, i) = mixing(:, i)/source.val(i);
+ for jic=1:size(comp.trial,2)
+ comp.trial{jic}(i,:)=comp.trial{jic}(i,:)*source.val(i);
+ end
+end
+end
+comp.topo=mixing;
+
+% adjust the time axis to avoid memory problems during resampling:
+% exact time information is discarded anyway
+
+% old_time=comp.time;
+% for k = 1:numel(comp.time)
+% comp.time{k} = comp.time{k} - comp.time{k}(1);
+% end
+
+if isempty(aband)
+ aband=[1 2 3 4 5 6 7];
+end
+
+blp_bands = [ 1.3 4 ; 3 8 ; 6 15 ; 12.5 28.5 ; 30 75 ; 70 150 ; 1 150];
+band_prefix={
+ 'delta'
+ 'theta'
+ 'alpha'
+ 'beta'
+ 'lowgamma'
+ 'highgamma'
+ 'whole'
+ };
+
+blp_step=20; % in ms
+blp_window=400; % in ms
+
+for ib=aband
+
+ options_blp = {'dataprefix', resultprefix, 'band_prefix', band_prefix{ib}, ...
+ 'blp_band', blp_bands(ib,:), 'blp_step', blp_step, 'blp_window', blp_window};
+
+ source_blp = hcp_ica_blp(source,comp,options_blp);
+ disp('saving icapowenv results')
+
+ hcp_write_matlab([outputfile '_' band_prefix{ib}],'source_blp');
+% save([outputfile '_' band_prefix{ib}],'source_blp','-v7.3');
+
+ % hcp_check_pipelineoutput('icapowenv', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid, 'sourcemodel', sourcemodel_type, 'band', band_prefix{ib});
+
+ clear source_blp;
+end
\ No newline at end of file
diff --git a/pipeline_scripts/hcp_icaclass.m b/pipeline_scripts/hcp_icaclass.m
index 4c14a5f..e54882a 100644
--- a/pipeline_scripts/hcp_icaclass.m
+++ b/pipeline_scripts/hcp_icaclass.m
@@ -223,7 +223,7 @@
error(['NO ECG VEOG ot HEOG found in the dataset . Check data and/or montage for ',experimentid,'_',scanid]);
return;
end
- clear tmpIndx1 tmpIndx2;
+ clear tmpIndx1 tmpIndx2
options = ft_setopt(options, 'ref_channels', icaref);
options = ft_setopt(options, 'subject', resultprefix);
@@ -232,7 +232,7 @@
ref_data = hcp_ICA_preprocessing(dataELECnew, options_ref);
end
%========================================================
-clear icaref options_ref;
+clear icaref options_ref
comp_class = hcp_ICA_RMEG_classification(ref_data,options,iteration,datain);
@@ -251,11 +251,11 @@
vs.physio=[];
hcp_write_ascii(sprintf('%s_icaclass.txt', resultprefix), 'vs');
-clear iteration datain comp_class options;
-clear resultprefix dataraw badsegments badchannels sel_channels grad bandpass bandstop ica_iter;
-clear data_meg montage dataELEC dataELECnew ref_data;
-clear oldMotorExperimentIds isMotorTask isScanWithOldMotor;
-clear cfg;
+clear iteration datain comp_class options
+clear resultprefix dataraw badsegments badchannels sel_channels grad bandpass bandstop ica_iter
+clear data_meg montage dataELEC dataELECnew ref_data
+clear oldMotorExperimentIds isMotorTask isScanWithOldMotor
+clear cfg
% ensure that the expected output files were created
hcp_check_pipelineoutput('icaclass', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
diff --git a/pipeline_scripts/hcp_icaimagcoh.m b/pipeline_scripts/hcp_icaimagcoh.m
new file mode 100644
index 0000000..b66632d
--- /dev/null
+++ b/pipeline_scripts/hcp_icaimagcoh.m
@@ -0,0 +1,362 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% setup the execution environment
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+opengl software;
+
+% ensure that the time and date of execution are not stored in the provenance information
+global ft_default
+ft_default.trackcallinfo = 'no';
+
+% allow the user to specify the path where additional data is present, e.g. the channel layout or anatomy files
+if exist('path', 'var')
+ addpath(path)
+end
+
+if ~exist('filename', 'var')
+ error('filename should be specified')
+end
+
+if ~exist('f', 'var')
+ error('frequency should be specified as "f"')
+end
+freque = f;
+
+% the filename is assumed to be something like
+% 'rawdatadir/Phase1MEG/Subjects/CP10018/Experiments/CP10018_MEG/Scans/1-Rnoise_MNN_V1/Resources/4D/c,rfDC'
+tok = tokenize(filename, '/');
+
+if ~exist('subjectid', 'var')
+ subjectid = tok{end-7};
+end
+
+if ~exist('experimentid', 'var')
+ experimentid = tok{end-5};
+end
+
+if ~exist('scanid', 'var')
+ scanid = tok{end-3};
+end
+
+if ~exist('pipelinedatadir', 'var')
+ pipelinedatadir = hcp_pathdef;
+end
+
+% print the matlab and megconnectome version to screen for provenance
+ver('megconnectome')
+
+% print the value of all local variables to screen for provenance
+w = whos;
+w = {w.name};
+w = setdiff(w, {'w', 'ans'});
+for i=1:length(w)
+ fprintf(hcp_printstruct(w{i}, eval(w{i})));
+end
+
+% change to the location of the processed data (input and output)
+cd(pipelinedatadir)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% execute the pipeline
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+smodel_type = {'2D';'3D'}; dimindx=1; % 1 for 2D cortical sheet and 2 for 3D gird
+griddim = {'4mm';'6mm';'8mm'}; gridindx=1; % $ 1,2,3 for 3D 4mm,6mm and 8mm grid
+
+if(strcmp(smodel_type{dimindx},'2D'))
+ sourcemodel_type=smodel_type{dimindx};
+elseif(strcmp(smodel_type{dimindx},'3D'))
+ sourcemodel_type=[smodel_type{dimindx} griddim{gridindx}];
+end
+
+%------------------------
+% declare the output file
+resultprefix = sprintf('%s_%s', experimentid, scanid);
+outputfile = [resultprefix,'_icaimagcoh'];
+
+% ------------------------------------------------------------
+% ensure that the output from the previous pipelines is present
+% hcp_check_pipelineoutput('baddata', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
+% hcp_check_pipelineoutput('icaclass', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
+% hcp_check_pipelineoutput('icamne', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid, 'sourcemodel', sourcemodel_type);
+
+inputfile4 = fullfile([resultprefix '_icaclass_vs.mat']);
+hcp_read_matlab(inputfile4);
+
+inputfile5 = fullfile([resultprefix '_icamne.mat']);
+hcp_read_matlab(inputfile5,'source');
+
+if(~isfield(comp_class,'trial'))
+
+ cfg = [];
+ cfg.dataset = filename;
+ dataraw=ft_preprocessing(cfg);
+
+ badsegments = hcp_read_ascii([resultprefix '_baddata_badsegments.txt']);
+
+ disp('bad segments concatenation')
+ % collect the results in a structure
+ maxsmp2 = max(badsegments.badsegment.ica(:));
+ maxsmp3 = max(badsegments.badsegment.manual(:));
+ maxsmp = max([maxsmp2, maxsmp3]);
+ badsample = zeros(1,maxsmp);
+ if ~isempty(badsegments.badsegment.ica)
+ for j=1:size(badsegments.badsegment.ica,1)
+ badsample(badsegments.badsegment.ica(j,1):badsegments.badsegment.ica(j,2)) = 1;
+ end
+ end
+ if ~isempty(badsegments.badsegment.manual)
+ for j=1:size(badsegments.badsegment.manual,1)
+ badsample(badsegments.badsegment.manual(j,1):badsegments.badsegment.manual(j,2)) = 1;
+ end
+ end
+
+ if ~isempty(badsample)
+ flank = diff([0 badsample 0]);
+ badsegment_ica=[];
+ badsegment_ica(:,1) = find(flank== 1);
+ badsegment_ica(:,2) = find(flank==-1) - 1;
+
+ badsegments.badsegment.ica = badsegment_ica;
+ end
+
+ badsegments = badsegments.badsegment.ica;
+
+ badchannels = hcp_read_ascii([resultprefix '_baddata_badchannels.txt']);
+ badchannels = badchannels.badchannel.all;
+
+ sel_channels={'MEG'};
+ grad=dataraw.grad;
+
+
+ %------------------------------------------
+
+ if ~(isempty([badchannels{:}])),
+ for ich=1:size(badchannels,2)
+ sel_channels(1,ich+1)={['-' badchannels{1,ich}]};
+ end
+ end
+
+ bandpass = [1.3 150]; % band pass frequency
+ if strcmp(subjectid,'CP10128') || strcmp(subjectid,'CP10129')
+ bandstop = [49 51 ; 99 101]; % band stop frequency, these were recorded in Glasgow
+ else
+ bandstop = [59 61 ; 119 121]; % band stop frequency, all others were recorded at SLU
+ end
+
+ options = {'dataprefix', resultprefix, 'channels', sel_channels, 'skipped_intervals', badsegments, 'bandpass', bandpass, 'bandstop', bandstop};
+
+ data = hcp_ICA_preprocessing(dataraw, options);
+
+ cfg = [];
+ cfg.unmixing = comp_class.unmixing;
+ cfg.topolabel = comp_class.topolabel;
+ comp = ft_componentanalysis(cfg, data);
+ comp.class = comp_class.class;
+ comp.topo = comp_class.topo;
+ clear data
+else
+ comp = comp_class;
+end
+
+mixing = comp_class.topo;
+if(max(size(source.val))>2)
+ for i = 1:size(mixing, 2)
+ mixing(:, i) = mixing(:, i)/source.val(i);
+ for jic=1:size(comp.trial,2)
+ comp.trial{jic}(i,:)=comp.trial{jic}(i,:)*source.val(i);
+ end
+ end
+end
+comp.topo = mixing;
+
+% get only the brain ICs
+comp_bic=comp;
+
+comp_bic=rmfield(comp_bic,'trial');
+for j=1:numel(comp.trial)
+ comp_bic.trial{j}(:,:)=comp.trial{j}(comp.class.brain_ic_vs,:);
+end
+comp_bic.topo=comp.topo(:,comp.class.brain_ic_vs);
+comp_bic.unmixing=comp.unmixing(comp.class.brain_ic_vs,:);
+comp_bic.label=comp.label(comp.class.brain_ic_vs,:);
+
+
+% adjust the time axis to avoid memory problems during resampling:
+% exact time information is discarded anyway
+old_time=comp_bic.time;
+for k = 1:numel(comp_bic.time)
+ comp_bic.time{k} = comp_bic.time{k} - comp_bic.time{k}(1);
+end
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% get the topographies of the components at the level of the
+% sources from the mne pipeline
+
+source_bic=source;
+source_bic.time=1:comp_bic.class.brain_ic_vs_number;
+source_bic.avg.pow=zeros(size(source.pos,1),comp_bic.class.brain_ic_vs_number);
+source_bic.avg=rmfield(source_bic.avg,'mom');
+
+for ii=1:numel(source.inside)
+ source_bic.avg.mom{source.inside(ii)}=source.avg.mom{source.inside(ii)}(:,comp_bic.class.brain_ic_vs);
+ source_bic.avg.pow(source.inside(ii),:)=source.avg.pow(source.inside(ii),comp_bic.class.brain_ic_vs);
+end
+
+
+
+%%hack to remove balancing
+% gradBalanced = comp_bic.grad;
+% gradBalanced = ft_apply_montage(gradBalanced, gradBalanced.balance.comp, 'keepunused', 'yes', 'inverse', 'yes');
+% gradBalanced = ft_apply_montage(gradBalanced, gradBalanced.balance.Supine, 'keepunused', 'yes', 'inverse', 'yes');
+%
+% comp_bic.grad=gradBalanced;
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% working piece of code to test ft function behaviour on components
+cfg = [];
+cfg.length = 1;
+cfg.overlap = 0.5;
+cfg.trials = 'all';
+[comp1] = ft_redefinetrial(cfg, comp_bic);
+
+cfg = [];
+cfg.output = 'fourier';
+cfg.method = 'mtmfft';
+cfg.taper = 'hanning';
+[comp2] = ft_freqanalysis(cfg, comp1);
+comp2=ft_selectdata(comp2,'foilim',[1:80]);
+
+[ntrials nic nfreq]=size(comp2.fourierspctrm);
+
+cfg = [];
+cfg.method = 'csd';
+cfg.complex = 'complex';
+[comp3] = ft_connectivityanalysis(cfg, comp2);
+%comp_csd dimord=[chan x chan x freq]
+
+nsource = numel(source_bic.inside);
+nfreq = length(freque);
+
+
+nsource = numel(source_bic.inside);
+[ndim,nbic] = size(source_bic.avg.mom{source_bic.inside(1)});
+
+
+rpi=20;
+
+allit = ceil(nsource/rpi);
+count = 0;
+% tStart = tic;
+% dispstat('','init'); % One time only initialization
+% dispstat(sprintf('MIM - Beginning ...'),'keepthis','timestamp');
+
+for f=freque
+ % allocate memory for single frequency connectomes
+ mimf = zeros(nsource, nsource,'single');
+
+
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ WlfAic = cat(1,source_bic.avg.mom{source_bic.inside(:)'});
+ WlfAic = reshape( permute( reshape(WlfAic,ndim,nsource,[]),[2 1 3]),nsource*ndim,[]) ;
+ ndum = size(WlfAic,1);
+
+ CS = comp3.crsspctrm(:,:,f);
+
+ cs_bb = hcp_cs2csvv(CS,WlfAic,ndim);
+ pro_prcsbb = zeros(ndim,ndum);
+ for iv=1:nsource
+ pro_prcsbb(:,(iv-1)*ndim + [1:ndim]) = pinv(real( full(cs_bb((iv-1)*ndim + [1:ndim],(iv-1)*ndim + [1:ndim]))));
+ end
+ pro_prcsbb = repmat(pro_prcsbb,1,rpi);
+
+ iBD = reshape(padarray(reshape(1:ndum*rpi,ndim,[]),ndim,'circular'),[],1);
+ jBD = reshape(padarray(1:ndum*rpi,1,'replicate'),[],1);
+ prcsbb = sparse(iBD,jBD,reshape(pro_prcsbb,[],1),ndum*rpi,ndum*rpi);
+
+ iv_set = reshape( padarray([1:nsource]',(rpi*ceil(nsource/rpi))-nsource,'post'),rpi,[])' ;
+ for iset = 1:size(iv_set,1)
+
+ iv = nonzeros(iv_set(iset,:))';
+ jv = iv(1):nsource;
+
+ niv = length(iv);
+ njv = length(jv);
+
+ pro_prcsaa = zeros(ndim,ndim,niv);
+ for i = 1:niv
+ pro_prcsaa(:,:,i) = prcsbb((iv(i)-1)*ndim + [1:ndim],(iv(i)-1)*ndim + [1:ndim]);
+ end
+ pro_prcsaa = repmat(pro_prcsaa,[1 njv 1]);
+ prcsaa = sparse(iBD(1:ndim*ndim*njv*niv),jBD(1:ndim*ndim*njv*niv),reshape(pro_prcsaa,[],1),ndim*njv*niv,ndim*njv*niv);
+
+ indA = reshape(repmat(iv',1,ndim) + repmat([0 nsource 2*nsource],niv,1),1,[]);
+ indB = reshape(repmat(jv',1,ndim) + repmat([0 nsource 2*nsource],njv,1),1,[]);
+
+ CSso_iv = WlfAic(indA,:)* CS * (WlfAic(indB,:)');
+
+ nvox_iv = size(CSso_iv,1)/ndim;
+ nvox_jv = size(CSso_iv,2)/ndim;
+
+ CSso_iv = reshape(CSso_iv,niv,ndim,ndim*njv);
+ CSso_iv = permute(CSso_iv,[1 3 2]);
+ CSso_iv = reshape(CSso_iv,niv,njv,ndim,ndim);
+ CSso_iv = permute(CSso_iv,[1 2 4 3]);
+
+ cs_ab = sparse(iBD(1:ndim*ndim*njv*niv),jBD(1:ndim*ndim*njv*niv),reshape(permute(CSso_iv,[3 4 2 1]),[],1),ndim*njv*niv,ndim*njv*niv);
+
+ sub_ind = zeros(niv,njv*3);
+ for i=1:niv
+ sub_ind(i,:) = (i-1)*nsource*ndim + reshape( ( repmat((jv-1)'*ndim,1,ndim) + repmat([1:ndim],njv,1) )',1,[]);
+ end
+ sub_ind = reshape(sub_ind',1,[]);
+
+ pro_MIM = full( diag(prcsaa * imag(cs_ab) * prcsbb(sub_ind,sub_ind) * imag(cs_ab)')) ;
+
+ mimf(iv,jv) = cast( squeeze(permute(sum( reshape(pro_MIM,ndim,[],niv),1),[3 2 1])),'single');
+
+ count = count+1;
+ % dispstat(sprintf('Progress
+ % %d%%',round(100*count/allit)),'timestamp');
+
+ end
+ mimf = ( triu(mimf) + triu(mimf,1)' );
+ mimf = mimf - diag(0.5*diag(mimf));
+
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+ imagcoh=[];
+ imagcoh.freq = comp2.freq(f);
+ imagcoh.dimord = 'pos_pos';
+ imagcoh.mimspctrm = mimf;
+ imagcoh.pos = source.pos(source.inside,:);
+
+ hcp_write_matlab([outputfile,'_',num2str(round(comp3.freq(f))),'Hz'], 'imagcoh');
+ hcp_check_pipelineoutput('icaimagcoh', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid, 'freq', num2str(round(comp3.freq(f))));
+
+end % nfreq
+
+% dispstat('Finished.','keepprev');
+% tElapsed = toc(tStart);
+% disp(['Elapsed time = ',num2str(floor(tElapsed/60)),' min and ',num2str(round(tElapsed - floor(tElapsed/60)*60)),' sec.']);
diff --git a/pipeline_scripts/hcp_icaimagcoh_qc.m b/pipeline_scripts/hcp_icaimagcoh_qc.m
new file mode 100644
index 0000000..c0e2276
--- /dev/null
+++ b/pipeline_scripts/hcp_icaimagcoh_qc.m
@@ -0,0 +1,104 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% setup the execution environment
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+opengl software;
+
+% allow the user to specify the path where additional data is present, e.g. the channel layout or anatomy files
+if exist('path', 'var')
+ addpath(path)
+end
+
+if ~exist('filename', 'var')
+ error('filename should be specified')
+end
+
+% the filename is assumed to be something like
+% 'rawdatadir/Phase1MEG/Subjects/CP10018/Experiments/CP10018_MEG/Scans/1-Rnoise_MNN_V1/Resources/4D/c,rfDC'
+tok = tokenize(filename, '/');
+
+if ~exist('subjectid', 'var')
+ subjectid = tok{end-7};
+end
+
+if ~exist('experimentid', 'var')
+ experimentid = tok{end-5};
+end
+
+if ~exist('scanid', 'var')
+ scanid = tok{end-3};
+end
+
+if ~exist('pipelinedatadir', 'var')
+ pipelinedatadir = hcp_pathdef;
+end
+
+% print the matlab and megconnectome version to screen for provenance
+ver('megconnectome')
+
+% print the value of all local variables to screen for provenance
+w = whos;
+w = {w.name};
+w = setdiff(w, {'w', 'ans'});
+for i=1:length(w)
+ fprintf(hcp_printstruct(w{i}, eval(w{i})));
+end
+
+% change to the location of the processed data (input and output)
+cd(pipelinedatadir)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% execute the pipeline
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+smodel_type = {'2D';'3D'}; dimindx=1; % 1 for 2D cortical sheet and 2 for 3D gird
+griddim = {'4mm';'6mm';'8mm'}; gridindx=1; % $ 1,2,3 for 3D 4mm,6mm and 8mm grid
+
+if(strcmp(smodel_type{dimindx},'2D'))
+ sourcemodel_type=smodel_type{dimindx};
+elseif(strcmp(smodel_type{dimindx},'3D'))
+ gridname = 'sourcemodel3d';
+ sourcemodel_type=[smodel_type{dimindx} griddim{gridindx}];
+end
+
+%------------------------
+% declare the output file
+resultprefix = sprintf('%s_%s', experimentid, scanid);
+outputfile = [resultprefix,'_icaimagcoh_connect_' sourcemodel_type];
+
+freque = [20];
+
+for f=freque
+
+ hcp_read_matlab([resultprefix,'_icaimagcoh_' sourcemodel_type,'_freq',num2str(f)]);
+ imagesc(imagcoh.mimspctrm)
+ caxis([0 0.1])
+ h1 = gcf;
+ % set(h1, 'paperposition', [1 1 10 7]);
+ % hcp_write_figure(imgname, h0, 'format', 'png')
+ % hcp_write_figure(imgname, h1)
+ hcp_write_figure([outputfile '_freq',num2str(f)], h1, 'format', 'png')
+ % hcp_write_figure(outputfile, h1, 'format', 'fig')
+ close(h1)
+
+ clear imagcoh
+end % nfreq
+
diff --git a/pipeline_scripts/hcp_icamne.m b/pipeline_scripts/hcp_icamne.m
new file mode 100644
index 0000000..06108d8
--- /dev/null
+++ b/pipeline_scripts/hcp_icamne.m
@@ -0,0 +1,276 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% setup the execution environment
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+opengl software;
+
+% ensure that the time and date of execution are not stored in the provenance information
+global ft_default
+ft_default.trackcallinfo = 'no';
+
+% allow the user to specify the path where additional data is present, e.g. the channel layout or anatomy files
+if exist('path', 'var')
+ addpath(path)
+end
+
+if ~exist('filename', 'var')
+ error('filename should be specified')
+end
+
+% the filename is assumed to be something like
+% 'rawdatadir/Phase1MEG/Subjects/CP10018/Experiments/CP10018_MEG/Scans/1-Rnoise_MNN_V1/Resources/4D/c,rfDC'
+tok = tokenize(filename, '/');
+
+if ~exist('subjectid', 'var')
+ subjectid = tok{end-7};
+end
+
+if ~exist('experimentid', 'var')
+ experimentid = tok{end-5};
+end
+
+if ~exist('scanid', 'var')
+ scanid = tok{end-3};
+end
+
+if ~exist('pipelinedatadir', 'var')
+ pipelinedatadir = hcp_pathdef;
+end
+
+resultprefix = sprintf('%s_%s', experimentid, scanid);
+
+% change to the location of the processed data (input and output)
+cd(pipelinedatadir)
+
+% hcp_check_pipelineoutput('baddata', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
+% hcp_check_pipelineoutput('icaclass', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
+% hcp_check_pipelineoutput('anatomy', 'subject', subjectid);
+
+% print the matlab and megconnectome version to screen for provenance
+ver('megconnectome')
+
+% print the value of all local variables to screen for provenance
+w = whos;
+w = {w.name};
+w = setdiff(w, {'w', 'ans'});
+for i=1:length(w)
+ fprintf(hcp_printstruct(w{i}, eval(w{i})));
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% execute the pipeline
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+hcp_read_matlab([resultprefix '_icaclass_vs.mat'])
+% comp_class.grad=rmfield(comp_class.grad,'balance');
+
+% read the source and volume conduction model from current dir with
+% outputs of previous pipelines
+
+hcp_read_matlab([subjectid '_MEG_anatomy_sourcemodel_2d']);
+sourcemodel2d=ft_convert_units(sourcemodel2d, 'cm');
+sourcemodel2d.inside = 1:size(sourcemodel2d.pos,1);
+sourcemodel2d.outside = [];
+sourcemodelsubj = sourcemodel2d;
+
+hcp_read_matlab(sprintf('%s.mat', [subjectid '_MEG_anatomy_headmodel']));
+headmodel = ft_convert_units(headmodel, 'cm');
+
+mri = ft_read_mri([subjectid '_MEG_anatomy_anatomical.nii']);
+mri=ft_convert_units(mri, 'cm');
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% ensure the correct geometrical units
+% sourcemodel = ft_convert_units(sourcemodelsubj, 'mm');
+grad = ft_read_sens(filename);
+gradBalanced = grad;
+gradBalanced = ft_apply_montage(gradBalanced, gradBalanced.balance.Supine, 'keepunused', 'yes', 'inverse', 'yes');
+grad=gradBalanced;
+grad = ft_convert_units(grad, 'cm');
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% deal with the component data in order for ft_sourceanalysis to be able to
+% swallow it
+mixing = comp_class.topo;
+channels = comp_class.topolabel;
+% normalisation of the topographies
+for i = 1:size(mixing, 2)
+ val(i) = 0.01*max(abs(mixing(:, i)));
+ mixing(:, i) = mixing(:, i)/val(i);
+end
+
+
+% create a 'timelock' structure
+tlck = [];
+tlck.label = channels;
+tlck.cov = eye(numel(tlck.label)); % perhaps this one should be scaled
+tlck.time=1;
+tlck.grad = grad;
+tlck.dimord = 'chan_time';
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% prepare the leadfields: this needs to be done only once:
+% if I understand the method well, each component will be reconstructed by
+% using an MNE with different regularisation
+
+% keep parcel info if present
+if isfield(sourcemodelsubj, 'label')
+ % parcellation.label = sourcemodelsubj.label;
+ % parcellation.labelindx = sourcemodelsubj.labelindx;
+ % parcellation.annotation = sourcemodelsubj.annotation;
+ % parcellation.ctable = sourcemodelsubj.ctable;
+ parcellation.label = sourcemodelsubj.aparclabel;
+ parcellation.labelindx = sourcemodelsubj.aparc;
+ % parcellation.annotation = sourcemodelsubj.annotation;
+ % parcellation.ctable = sourcemodelsubj.ctable;
+end
+
+
+cfg = [];
+cfg.vol = headmodel;
+cfg.grid = sourcemodelsubj;
+cfg.grad = grad;
+cfg.channel = channels;
+cfg.normalize = 'yes';
+cfg.reducerank = 2;
+gridLF = ft_prepare_leadfield(cfg);
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% do the source reconstruction. In this case all components are
+% reconstructed using the same amount of regularisation
+
+noise_level=8;
+
+cfg = [];
+cfg.method = 'mne';
+cfg.grid = gridLF;
+cfg.vol = headmodel;
+cfg.channel = channels;
+cfg.mne.prewhiten = 'yes';
+cfg.mne.noisecov=eye(numel(channels))*noise_level;
+
+nic=size(mixing,2);
+for i=1:nic
+ tlck.avg = mixing(:,i);
+
+ mgfp(i)=sqrt(mean((mixing(:,i)-mean(mixing(:,i))).^2));
+ cfg.mne.snr=mgfp(i)/noise_level;
+
+ noisevec(i)=cfg.mne.snr;
+
+ tmp = ft_sourceanalysis(cfg, tlck);
+ if i==1
+ source=tmp;
+ else
+ % concatenate the mom here
+ for k = 1:numel(tmp.inside)
+ source.avg.mom{source.inside(k)} = cat(2,source.avg.mom{source.inside(k)}, tmp.avg.mom{source.inside(k)});
+ end
+ source.avg.pow = horzcat(source.avg.pow,tmp.avg.pow);
+ end
+end
+source.val=val;
+source.time=1:size(mixing,2);
+source.noise=noisevec;
+
+
+if(isfield(sourcemodelsubj,'tri')) source.tri=sourcemodelsubj.tri; end
+
+if exist('parcellation', 'var')
+ % store them back in the source structure
+ source.label = parcellation.label;
+ source.labelindx = parcellation.labelindx;
+ % source.annotation = parcellation.annotation;
+end
+
+
+cfgtopo =[];
+cfgtopo.grad=grad;
+cfgtopo.zlim='maxabs';
+cfgtopo.component=[];
+cfgtopo.parameter = 'topo';
+cfgtopo.comment = 'no';
+cfgtopo.colorbar = 'yes';
+cfgtopo.layout='4D248.mat';
+tmpclass=comp_class;
+tmpclass.trial{1}(1,1)=0;
+tmpclass.time{1}(1,1)=0;
+tmpclass.topo=mixing;
+
+X = 29.7; %# A3 paper size
+Y = 14.35; %# A3 paper size
+xMargin = 1; %# left/right margins from page borders
+yMargin = 1; %# bottom/top margins from page borders
+xSize = X - 2*xMargin; %# figure size on paper (widht & hieght)
+ySize = Y - 2*yMargin; %# figure size on paper (widht & hieght)
+% figure('Menubar','none');
+
+
+cfg = [];
+cfg.funparameter = 'avg.pow';
+cfg.method = 'surface';
+cfg.funcolormap='jet';
+cfg.interactive = 'no';
+
+tmp = source;
+for k = 1:numel(source.time)
+ tmp.avg.pow = source.avg.pow(:, k);
+ tmp.time=source.time(k);
+ % tmp.avg.mask=zeros(size(tmp.avg.pow));
+ maxabs=max(max(max(tmp.avg.pow)));
+ % indxmask=find(tmp.avg.pow>0.4*maxabs);
+ % tmp.avg.mask(indxmask)=1;
+ imgname = [resultprefix '_icamne_' num2str(k) '.png'];
+
+ ft_plot_mesh(tmp,'vertexcolor',tmp.avg.pow)
+ view(-90,90)
+
+ h1=gcf;
+ set(gca, 'XTickLabel',[], 'YTickLabel',[], ...
+ 'Units','normalized', 'Position',[0 0 1 1])
+
+ %# figure size on screen (50% scaled, but same aspect ratio)
+ set(gcf, 'Units','centimeters', 'Position',[5 5 xSize ySize])
+
+ %# figure size printed on paper
+ set(gcf, 'visible', 'on')
+ set(gcf, 'PaperUnits','centimeters')
+ set(gcf, 'PaperSize',[X Y])
+ set(gcf, 'PaperPosition',[xMargin yMargin xSize ySize])
+ set(gcf, 'PaperOrientation','portrait')
+
+ fax(1)=gca;
+ set(fax(1),'position',[0.25 0.1 0.95 0.8]);
+ fax(2)= axes('position',[0 0.1 0.5 0.8]);
+
+ cfgtopo.component=k;
+ ft_topoplotIC(cfgtopo, tmpclass);
+
+ hcp_write_figure(imgname, h1)
+ close(h1)
+end
+hcp_write_matlab([resultprefix,'_icamne'],'source');
+
+
+
+% ensure that the expected output files were created
+% hcp_check_pipelineoutput('icamne', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid);
\ No newline at end of file
diff --git a/pipeline_scripts/hcp_parcellate.m b/pipeline_scripts/hcp_parcellate.m
new file mode 100644
index 0000000..f926a01
--- /dev/null
+++ b/pipeline_scripts/hcp_parcellate.m
@@ -0,0 +1,135 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Copyright (C) 2011-2014 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
+%
+% This file is part of megconnectome.
+%
+% megconnectome is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% megconnectome is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with megconnectome. If not, see .
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% setup the execution environment
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+opengl software;
+
+% ensure that the time and date of execution are not stored in the provenance information
+global ft_default
+ft_default.trackcallinfo = 'no';
+
+% allow the user to specify the path where additional data is present, e.g. the channel layout or anatomy files
+if exist('path', 'var')
+ addpath(path)
+end
+
+if ~exist('subjectid', 'var')
+ subjectid = tok{end-7};
+end
+
+if ~exist('pipelinedatadir', 'var')
+ pipelinedatadir = hcp_pathdef;
+end
+
+% print the matlab and megconnectome version to screen for provenance
+ver('megconnectome')
+
+% print the value of all local variables to screen for provenance
+w = whos;
+w = {w.name};
+w = setdiff(w, {'w', 'ans'});
+for i=1:length(w)
+ fprintf(hcp_printstruct(w{i}, eval(w{i})));
+end
+
+% change to the location of the processed data (input and output)
+cd(pipelinedatadir)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% execute the pipeline
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+% ensure that the anatomy data exists
+hcp_check_pipelineoutput('anatomy', 'subject', subjectid);
+
+% first make a selection of the files that contain dense connectomes: this
+% will be more straightforward once the names of the files have become
+% standardized.
+fnames = {'177746_MEG_3-Restin_icaimagcoh_2D_freq1.mat'};
+
+% load the cortical sheet description for this subject
+anatomydatadir = strrep(pipelinedatadir, 'analysis', 'anatomy');
+fname_corticalsheet = fullfile(anatomydatadir, [subjectid, '_MEG_anatomy_sourcemodel_2d']);
+hcp_read_matlab(fname_corticalsheet);
+
+% specify the parcellations and load them
+parcellations = {'expFM200AtlasFile_8k.mat', ...
+ 'expFM400AtlasFile_8k.mat', ...
+ 'expFM800AtlasFile_8k.mat', ...
+ 'parcellations_VGD11b_8k.mat', ...
+ 'RSN-networks_8k.mat'}';
+
+parceldir = '/home/language/jansch/matlab/megconnectome/template/parcellations';
+atlas = cell(numel(parcellations),1);
+for k = 1:numel(parcellations)
+ parcellations{k} = fullfile(parceldir, parcellations{k});
+ tmp = load(parcellations{k});
+ atlas{k} = tmp.atlas;
+end
+
+% do the parcellation and save the results
+cfg = [];
+cfg.method = 'mean';
+cfg.parcellation = 'parcellation';
+for k = 1:numel(fnames)
+
+ % read in the dense connectome and get it represented with a uniform
+ % variable name
+ tmp = hcp_read_matlab(fnames{k});
+ fn = fieldnames(tmp);
+ if numel(fn)>1
+ error('more than one variable per file is not supported')
+ else
+ data_dense = tmp.(fn{1});
+ clear tmp;
+ end
+
+
+ for m = 1:numel(atlas)
+
+ % select the potential parcellations (there can be more than one
+ % parcellation per atlas
+ fn = fieldnames(atlas{m});
+ sel = ~cellfun('isempty', strfind(fn, 'label'));
+
+ labfn = fn(sel);
+ fn = fn(~sel);
+ sel = false(numel(fn),1);
+ for p = 1:numel(labfn)
+ sel = sel | strcmp(fn, labfn{p}(1:end-5));
+ end
+ parcelparam = fn(sel);
+
+ for p = 1:numel(parcelparam)
+ sourcemodel2d.parcellation = atlas{m}.(parcelparam{p});
+ sourcemodel2d.parcellationlabel = atlas{m}.([parcelparam{p},'label']);
+ data = ft_sourceparcellate(cfg, data_dense, sourcemodel2d);
+
+ [pn,f,e] = fileparts(fnames{k});
+ [pn,f2,e] = fileparts(parcellations{m});
+ outputfilename = fullfile(pipelinedatadir, [f,'_',f2,'_',parcelparam{p}]);
+ hcp_write_matlab(outputfilename, 'data');
+ clear data;
+ end % for number of parcellations in a given atlas
+ end % for atlas
+end % for file
+
+%hcp_check_pipelineoutput('tmegpreproc', 'subject', subjectid, 'experiment', experimentid, 'scan', scanid,'datagroup',trlInfo.lockNames);
diff --git a/template/Sphere.8k.L.surf.gii b/template/Sphere.8k.L.surf.gii
new file mode 100644
index 0000000..90539d5
--- /dev/null
+++ b/template/Sphere.8k.L.surf.gii
@@ -0,0 +1,107 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 1 0 0 0
+ 0 1 0 0
+ 0 0 1 0
+ 0 0 0 1
+
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+ 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diff --git a/template/Sphere.8k.R.surf.gii b/template/Sphere.8k.R.surf.gii
new file mode 100644
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eJw03HW0VeXa8GEQwQ12g3Qq3d0gnXYr0t0d0t1ISJd0d5ciHSqI2N2NIor1vfeZ13fGuMfvGnvDWnM+z/OPh7lWqlSpUt3wf3OvCT+jN/Iz/KzexM/yc3ozP8fN9BZuxs/rrfw8N9fbuDm30Nu5BbfUO7glt9I7uRW31ru4NbfRu7kNt9WM3JbbaSZux+31Hm7PHTQzd+COmoU7cifNyp24s2bjztxFs3MX7qo5uCt305zcjbtrLu7OPTQ39+Cemod7ci/Ny724t+bj3lxDr+M4h3Hm7jPhPvos9+G++hz35X7ajPtxf32e+/MAbc4DeKC24IE8SFvyIH5BW/ELPFhb82Aeom14CA/VtjyUh2k7HsbDtT0P5xHagUfwSO3II3mUduJRPFo782geo114DI/VrjyWx2k3HsfjtTuP5wnagyfwRO3JE3mS9uJJPFl782SuqTU4zlucrfwmPEX78hSeqv14Kk/T/jyNX9QB/CJP14E8nWfoIJ7BM/UFnsmzdDDP4pd0CL/Es3Uoz+Y5Oozn8FwdznN5no7geTxfR/J8XqCjeAEv1NG8kBfpGF7Ei3UsL+YlOo6X8FIdz0v5ZZ3AL/MyncjLeLlO4uW8QifzCr5fa3KcqzhDBUx4pU7llbxKp/EqXq0v8mpeo9N5Da/VGbyW1+lMXsfrdRav5w36Em/gjTqbN/ImncObeLPO5c28RefxFt6q83krb9MFvI2360Lezjt0Ee/gnbqYd/IuXcK7eLcu5d28R1/mPbxXl/Fe3qfLeR/v1xW8n2vp/RznJ85KQRM+oKv4AB/U1XyQD+kaPsSv6Fp+hV/VdfwqH9b1fJhf0w38Gh/RjXyEj+omPsrHdDMf4+O6hY/zCd3KJ/ikbuOTfEq38yk+rTv4NJ/RnXyGz+ouPsuv625+nd/QPfwGv6l7+U0+p/v4HJ/X/Xyea2stjnMSZ6KQCb+lB/ktvqCH+AK/ra/w23xRX+WL/I4e5nf4XX2N3+X39Ai/x+/rUX6fP9Bj/AF/qMf5Q/5IT/BH/LGe5I/5Ez3Fn/Cnepo/5c/0DH/Gn+tZ/py/0Nf5C/5S3+Av+St9k7/ir/Ucf83f6Hn+hutobY7zEHtf2IS/1Qv8LX+nb/N3/L1e5O/5B32Hf+Af9V3+kX/S9/gn/lnf55/5F/2Af+FL+iFf4l/1I/6Vf9OP+Te+rJ/wZf5dP+Xf+Yp+xlf4D/2c/+A/9Qv+k6/ql3yV/9Kv+C/+W7/mv/kf/Yb/4bpahwvb4yIm/K9+x//yf/o9/8epUif9gWPCqfVHjglfoz9xTDiN/swx4Wv1F44Jp9VLHBNOp79yTPg6/Y1jwil6mWPC6fV3jgln0CscE75e/+CY8A36J8eEb9SrHBO+Sf/imPDN+jfHhG/RfzgmXM/P6nIRe1nUhG9NnfQ/jgnfprGXt5nw7Zqab+c79Bq+g+/UNHwn36XX8l18t6bluzmjpuOMnEmv40x8j6bwPZxZ03NmzqIZOAtn1es5K2fTGzgbZ9cbOTvn0Js4B+fUmzkn59JbOBfXt8exn/XtY+xZsVTJhHPbx9s4N+fR2zkP59U7OC/n0zs5H9+rd/G9fJ/ezfdxfs3I+bmAZuICXFDv4YJcSDNzIS6sWbgwF9GsXISLajYuysU0Oxfj4pqDi3MJzckluKTm4pLcIFXS2LcG9iv2pniqZMKl7FceLsWlNS+X5jKaj8twWb2Xy3I5vY/LcXnNz+W5ghbgClxRC3JFrqSFuBJX1sJcmatoEa7CVbUoV+VqWoyrcXUtztW5hpbgGlxTS3JNbpgqaexPQ/sSe1AiVTLh++1Lab6fa2kZrsW1tSzX5jpajutwXS3PdbmeVuB6XF8rcn1uoJW4ATfUytyQG2kVbsSNtSo35iZajZtwU63OTfkBrcEP8INakx/kRqmSxj40sv6x1iVTJRN+yPrX4of4Ya3ND/MjWocf4Ue1Lj/Kj2k9fowf1/r8OD+hDfgJflIb8pP8lDbip/hpbcxP8zPahJ/hZ7UpP8vP6QP8HDfTB7kZN06VNNa7sXWONS2VKpnw89b5YX6em+sj3Jxb6KPcglvqY9ySW+nj3Ipb6xPcmtvok9yG2+pT3Jbb6dPcjtvrM9yeO+iz3IE76nPckTtpM+7ETVIljXVtYj1j7UqnSibc2Xo2587cRVtwF+6qLbkrd9NW3I27a2vuzj20DffgntqWe3Ivbce9uLe2597cRztwH+6rHbkv99NO3I+bpkoa69fUusUalUmVTLi/devC/XmAduUBPFC78UAepN15EL+gPfgFHqw9eTAP0V48hIdqbx7Kw7QPD+Ph2peH8wjtxyP4gVRJY50esD6xFmVTJRMeaX0G8EgepQN5FI/WQTyax+gLPIbH6mAey+N0CI/j8TqUx/MEHcYTeKIO54k8SUfwJH4wVdJYjwetQ9xzuVTJhCdbh1E8mafoaJ7CU3UMT+VpOpan8Ys6jl/k6Tqep/MMncAzeKZO5Jk8SyfxLH4oVdK474fcb9xb+VTJhF9yv1P4JZ6tU3k2z9FpPIfn6os8l+fpdJ7H83UGz+cFOpMX8EKdxQv54VRJ4/4edl9xDxVSJRNe5L5m8yJerHN4MS/RubyEl+o8Xsov63x+mZfpAl7Gy3UhL+dHUiWN+3jE9ce1VkyVTHiF61/MK3ilLuGVvEqX8iperS/zal6jy3gNr9XlvJYfTZU0rvdR1xnXVClVMuF1rnMlr+P1uorX8wZdzRt4o67hjbxJ1/ImfixV0riux1xPvHflVMmEN7ue9byZt+gG3sJbdSNv5W26ibfx46mSxvs/7n3jPaqkSia83ftu4e28Q7fyDt6p23gnP5EqabzPE14/XqtqqmTCu7z+Dt7Fu3Un7+YnUyWN13vS68TfqZYqmfAer7Ob9/BTqZLG33vKn4/fVU+VTPhpjd8/7efRlFTJ//amTp5XuoEvpU66jy/xr7qff+Xf9AD/xpf1IF/m3/UQ/85X9BW+wn/oq/wH/6mH+U++qq/xVf5Lj/Bf/Lce5b/5Hz3G//C/epz/5f/0BP/Hqa5JepJjwqn1FMeEr9HTHBNOo2c4JnytnuWYcFp9nWPC6fQNjglfp29yTDhFz3FMOL2e55hwBn2LY8LX6wWOCb/tZ2lSJY5zGGcunpW7kW+4JumvHBO+UX/jmPBNepljwjfr7xwTvkWvcEz4Vv2DY8K36Z8cE75dr3JM+A79i2PCd+rfHBO+S//hmPDd+i/HhDPqfxwTzqRx5jKZ8D2amu/hzHoNZ+YsmoazcFa9lrNyNk3L2Ti7puPsnEOv4xycU1M4J+fS9JyLc2sGzs159HrOwxedxTh3F525OFvxPOZNnNd5u5Hzcj69ifPxvXoz38v36S18H+fXWzk/F9DbuAAX1Nu5IBfSO7gQF9Y7uTAX0bu4CBfVu7koF9OMXIyLayYuziX0Hi7BJTUzl+RSmoVLcWnNyqW5jGbjMlxWs3NZLqc5uByX15xcnitoLq7AFTU3V+RKmocr8Tupk8b5Cse5ijMUz/bezJWvSZqPK3MVvZercFW9j6tyNc3P1bi6FuDqXEMLcg2uqYW4Jt+vhfl+rqVFuBbX1qJcm+toMa7DdbU41+V6WoLrcX0tyfW5gZbiBtxQS3NDbqRluBE31rLcmJtoOW7CTbU8N+UHtAI/wA9qRX6QH9JK/BC/mzppnKNwnJ84K/E8+C388DVJq/DD/IhW5Uf4Ua3Gj/JjWp0f48e1Bj/OT2hNfoKf1Pv5SX5Ka/FT/LTW5qf5Ga3Dz/CzWpef5ee0Hj/HzbQ+N+PntQE/z821ITfnFtqIW3BLbcwtuZU24VbcWptya26jD3AbbqsPcltupw9xO34vddI4L+E4J3Em4rMCt3L7a5I+wu25gz7KHbijPsYduZM+zp24sz7BnbmLPslduKs+xV25mz7N3bi7PsPduYc+yz24pz7HPbmXNuNe3Fuf597cR5tzH+6rLbgv99OW3I/7ayvuzwO0NQ/ggdqGB/IgbcuD+AVtxy/w+6mTxrkIx3mIvY/Pi9zGg52HDjyYh2hHHsJDtRMP5WHamYfxcO3Cw3mEduURPFK78Ugepd15FI/WHjyax2hPHsNjtReP5XHam8fxeO3D43mC9uUJPFH78USepP15Ek/WATyZp+hAnsJTdRBP5Wn6Ak/jD1Injf0Px77HHsdng27nF+37EH6Rp+tQns4zdBjP4Jk6nGfyLB3Bs/glHckv8WwdxbN5jo7mOTxXx/BcnqdjeR7P13E8nxfoeF7AC3UCL+RFOpEX8WKdxIt5iU7mJbxUp/BSflmn8su8TKfxMv4wddLY53Dsb+xlfN7rDl5uf6fzcl6hM3gFr9SZvJJX6Sxexav1JV7Na3Q2r+G1OofX8jqdy+t4vc7j9bxB5/MG3qgLeCNv0oW8iTfrIt7MW3Qxb+GtuoS38jZdytt4u77M23mHLuMd/FHqpLGf4djH2LP4rN6dvNM+ruCdvEtX8i7erat4N+/R1byH9+oa3sv7dC3v4/26jvfzAV3PB/igbuCDfEg38iF+RTfxK/yqbuZX+bBu4cP8mm7l1/iIbuMjfFS381E+pjv4GH+cOmnsWzj2K/YmPk95Fx+3X7v4OJ/Q3XyCT+oePsmndC+f4tO6j0/zGd3PZ/isHuCz/Loe5Nf5DT3Eb/Cb+gq/yef0VT7H5/Uwn+e39DV+iy/oEb7Ab+tRfpsv6jG+yJ+kThr7E459iT2Iz7beze/YlxP8Dr+rJ/ldfk9P8Xv8vp7m9/kDPcMf8Id6lj/kj/R1/og/1jf4Y/5E3+RP+FM9x5/yZ3qeP+PP9S3+nL/QC/wFf6lv85f8lV7kr/jT1EljH8Kx/rHW8XnijPy19X+Xv+Zv9D3+hr/V9/lb/k4/4O/4e/2Qv+cf9CP+gX/Uj/lH/kk/4Z/4Z/2Uf+Zf9DP+hS/p53yJf9Uv+Ff+Tb/k3/iyfsWX+bPUSWO9w7HOsabxWe1M/Lt1/oZ/5yv6LV/hP/Q7/oP/1O/5T76qP/BV/kt/5L/4b/2J/+Z/9Gf+h//VX/hf/k8v8X8c/1hxyZr+7x8u0iROrb9xTPgavcwx4c9TJ411Dcd6xtrF593v4TRpkl7hmPC1+gfHhNPqnxwTTqdXOSZ8nf7FMeEU/Ztjwun1H44JZ9B/OSZ8vf7HMeEbNNbuBhO+UVPzjXyTXsM38Repk8b6hWPdYo3iuwEy883W7Vq+mW/RtHwL36rp+Fa+Ta/j2/h2TeHb+Q5Nz3fwnZqB7+S79Hq+i+/WG/huzqg3ckbOpDdxJv4yddJYp3CsT6xFfF9CFr7H+tzC93BmvZUzcxa9jbNwVr2ds3I2vYOzcXa9k7NzDr2Lc3BOvZtzci7NyLk4t2bi3PxV6qSxHuFYh7jn+K6IrJzHOmTmPJxXs3BezqdZOR/fq9n4Xr5Ps/N9nF9zcH4uoDm5ABfUXFyQC2luLsRfp04a9x2O+417i+/DyMaF3W9eLsxFNB8X4aJ6LxflYnofF+Pimp+LcwktwCW4pBbkklxKC3Ep/iZ10ri/cNxX3EN8t0d2Lu2+inBpLqNFuQyX1WJclstpcS7H5bUEl+cKWpIrcEUtxRX529RJ4z7Ccf1xrTlSJROu5PrLcCWurGW5MlfRclyFq2p5rsrVtAJX4+pakavzd6mTxvWG4zrjmnKmSiZcw3VW5hpcU6twTb5fq/L9XEurcS2urdW5Nn+fOmlcVziuJ947V6pkwnVcT02uw3X1fq7L9bQW1+P6Wpvr8w+pk8b7h+N94z1yp0om3MD71uUG3FDrcUNupPW5Ef+YOmm8TzheP14rvm8mDzf2+g25MTfRRtyEf0qdNF4vHK8Tfye+qyYvN/U6Tbgp/5w6afy9cPz5+F18n00+/iV10vh9OH4evc7zSw+kSZ5XigmPTpP0QR7NY/QhHsNj9WEey+P0ER7H4/VRHs8T9DGewBP1cZ7Ik/QJnsST9UmezFP0KZ7CU/VpnsrT9Bmexi/qs/wiT9fneDrP0GY8g2fq8zyTZ2lznsUvaQt+iWdrS57Nc7QVz+G52prn8jxtw/N4vrbl+bxA2/ECXqjteSEv0g68iDtqPGYTjnMYZy6eldvHi9MkHcOLeYmO5SW8VMfxUn5Zx/PLvEwn8DJerhN5Oa/QSbyCV+pkXsmrdAqv4tU6lVfzGp3Ga3itvshreZ1O53W8Xmfwet6gM3kDb9RZvJE36Uu8iTfrbN7MW3QOb+GtOpe38jadx9t4u87n7bxDF/AO3qkLeSfv0kW8iztpR47zFmcrnsfcz7vTJF3Cu3mPLuU9vFdf5r28T5fxPt6vy3k/H9AVfIAP6ko+yId0FR/iV3Q1v8Kv6hp+lQ/rWj7Mr+k6fo2P6Ho+wkd1Ax/lY7qRj/Fx3cTH+YRu5hN8UrfwST6lW/kUn9ZtfJrP6HY+w2d1B5/l13Unv85v6C5+gztrJ45zFWconu09wG+mSbqH3+RzupfP8Xndx+f5Ld3Pb/EFPcAX+G09yG/zRT3EF/kdfYXf4Xf1VX6X39PD/B6/r6/x+/yBHuEP+EM9yh/yR3qMP+KP9Th/zJ/oCf6EP9WT/Cl/pqf4M/5cT/Pn/IWe4S/4Sz3LX/JX+jp/xV/rG/w1d9HOHOcnzko8D36Qv0mT9Bx/w9/qef6Wv9O3+Dv+Xi/w9/yDvs0/8I96kX/kn/Qd/ol/1nf5Z/5F3+Nf+JK+z5f4V/2Af+Xf9EP+jS/rR3yZf9eP+Xe+op/wFf5DP+U/+E/9jP/kq/o5X+W/9Av+i//WL/lv/ke/4n/4X/2a/+Wu2oXjnMSZiM8KHOL/0iT9lv/jVNcm/Y5jwqn1e44JX6M/cEw4jf7IMeFr9SeOCafVnzkmnE5/4ZjwdXqJY8Ip+ivHhNPrbxwTzqCXOSZ8vf7OMeEb9ArHhG/UPzgmfJP+yTHhm/Uqx4Rv0b84Jnyr/s0x4dv0H44J367/cky4m5915TgPsffxeZFX+I5rk8be32HCd2pqvpPv0mv4Lr5b0/DdnFGv5YycSdNyJr5H0/E9nFmv48ycRVM4C2fV9JyVs2kGzsbZ9XrOzjn0Bs7BOfVGzsm59CbOxbn1Zs7NefQWzsN59VbOy/n0Ns7H9+rtfC93dyZi/7vb+9jj+GzQq3yffb+T7+P8ehfn5wJ6NxfggpqRC3IhzcSFuLDew4W5iGbmIlxUs3BRLqZZuRgX12xcnEtodi7BJTUHl+RSmpNLcWnNxaW5jObmMlxW83BZLqd5uRyX13xcnivovVyBe6RJGvscjv2NvYzPex3mivY3P1fkSlqAK3FlLciVuYoW4ipcVQtzVa6mRbgaV9eiXJ1raDGuwTW1ONfk+7UE38+1tCTX4tpaimtzHS3NdbiuluG6XE/Lcj2ur+W4PjfQ8tyAG2oFbsg90ySN/QzHPsaexWf1XuNG9rESN+LGWpkbcxOtwk24qVblpvyAVuMH+EGtzg/yQ1qDH+KHtSY/zI/o/fwIP6q1+FF+TGvzY/y41uHH+Qmty0/wk1qPn+SntD4/xU9rA36an9GG/Az3SpM09i0c+xV7E5+nPMLP2q/G/Cw/p034OW6mTbkZP68P8PPcXB/k5txCH+IW3FIf5pbcSh/hVtxaH+XW3EYf4zbcVh/nttxOn+B23F6f5PbcQZ/iDtxRn+aO3Emf4U7cO03S2J9w7EvsQXy29Sh3ti/PcWfuos24C3fV57krd9Pm3I27awvuzj20JffgntqKe3Ivbc29uLe24d7cR9tyH+6r7bgv99P23I/7awfuzwO0Iw/ggdqJB3KfNEljH8Kx/rHW8XniYzzI+nfhQfyCduUXeLB248E8RLvzEB6qPXgoD9OePIyHay8eziO0N4/gkdqHR/Io7cujeLT249E8RvvzGB6rA3gsj9OBPI77pkka6x2OdY41jc9qH+fx1vkFHs8TdDBP4Ik6hCfyJB3Kk3iyDuPJPEWH8xSeqiN4Kk/TkTyNX9RR/CJP19E8nWfoGJ7BM3Usz+RZOo5ncb80SWNdw7GesXbxefcT/JL1nMAv8WydyLN5jk7iOTxXJ/NcnqdTeB7P16k8nxfoNF7AC/VFXsiLdDov4sU6gxfzEp3JS3ipzuKl3D9N0li/cKxbrFF8N8BJftm6zeaXeZnO4WW8XOfycl6h83gFr9T5vJJX6QJexat1Ia/mNbqI1/BaXcxreZ0u4XW8Xpfyeh6QJmmsUzjWJ9Yivi/hFG+wPst4A2/U5byRN+kK3sSbdSVv5i26irfwVl3NW3mbruFtvF3X8nbeoet4B+/U9byTB6ZJGusRjnWIe47vijjNu6zDRt7Fu3UT7+Y9upn38F7dwnt5n27lfbxft/F+PqDb+QAf1B18kA/pTj7Eg9IkjfsOx/3GvcX3YZzhV9zvbn6FX9U9/Cof1r18mF/TffwaH9H9fISP6gE+ysf0IB/j43qIj/MLaZLG/YXjvuIe4rs9zvIJ9/Uqn+CTephP8il9jU/xaT3Cp/mMHuUzfFaP8Vl+XY/z6zw4TdK4j3Bcf1xrfB/J6/yG6z/Jb/Cbeorf5HN6ms/xeT3D5/ktPctv8QV9nS/wkDRJ43r/59SuK7X5P7/tOt/kt/minuOL/I6e53f4XX2L3+X39AK/x0PTJI3rCsf1/O+9UycTft/1XOT3+QN9hz/gD/Vd/pA/0vf4Ix6WJmm8fzjeN94jvhvmHH/sfT/gj/kT/ZA/4U/1I/6Uh6dJGu8TjteP14rvmznPn3n9T/gz/lw/5c95RJqk8XrheJ34O/FdNW/xF17nc/6CR6ZJGn8vHH8+fhffZ3OBR6VJGr8Px8+j8X9JxzMjX16b/Pt9TDhL2qRfcUw4q37NMeFs+g3HhLPrtxwTzqHfcUw4p37PMeFc+gPHhHPrjxwTzqM/cUw4r/7MMeF8+gvHhO/VSxwTvk9/5Zhwfv2NY8IF9DLHhAvq7xwTLqRXOCZcWP/gmHAR/ZNjwkX1KseEi+lfHBMurn9zTLiE/sMx4ZL6L8eES+l/HBMuranSJo4Jp9Z4FD8c5zDOXPx7fycu4xxm5TJcVrNxWS6n2bkcl9ccXJ4raE6uwBU1F1fkSpqbK3FlzcOVuYrm5SpcVfNxVa6m93I1rq73cXWuofm5BtfUAlyT79eCfD/X0kJci2trYa7NdbQI1+G6WpTrcj0txvW4vhbn+txAS3ADbqgluSE30lLciBtraW7M12hqjvMWZyueD+jMTdImLctNuKmW46b8gJbnB/hBrcAP8kNakR/ih7USP8yPaGV+hB/VKvwoP6ZV+TF+XKvx4/yEVucn+EmtwU/yU1qTn+Kn9X5+mp/RWvwMP6u1+Vl+Tuvwc9xM63Izfl7r8fPcXOtzc26hDbgFt9SG3JJbaSNuxa21MbfmNHoNx7mKMxTPE3ThNmmTNuU23FYf4LbcTh/kdtxeH+L23EEf5g7cUR/hjtxJH+VO3Fkf487cRR/nLtxVn+Cu3E2f5G7cXZ/i7txDn+Ye3FOf4Z7cS5/lXtxbn+Pe3EebcR/uq89zX+6nzbkf99cW3J8HaEsewAO1FQ/kQdqaB/G1mobj/MRZ6eoZhPALaZO25Rd4sLbjwTxE2/MQHqodeCgP0448jIdrJx7OI7Qzj+CR2oVH8ijtyqN4tHbj0TxGu/MYHqs9eCyP0548jsdrLx7PE7Q3T+CJ2ocn8iTty5N4svbjyTxF+/MUnqoDeCpP04E8jV/UQfwip9VrOc5JnIlunlkJT0+bdDBP5xk6hGfwTB3KM3mWDuNZ/JIO55d4to7g2TxHR/IcnqujeC7P09E8j+frGJ7PC3QsL+CFOo4X8iIdz4t4sU7gxbxEJ/ISXqqTeCm/rJP5ZV6mU3gZL9epvJxX6DRewSv1RV7J6TQtx3mIve/uWZbwqrRJZ/AqXq0zeTWv0Vm8htfqS7yW1+lsXsfrdQ6v5w06lzfwRp3HG3mTzudNvFkX8Gbeogt5C2/VRbyVt+li3sbbdQlv5x26lHfwTn2Zd/IuXca7eLcu5928R1fwHt6rK3kvX6fpOPY99riHZ1zC++z7at7H+3UN7+cDupYP8EFdxwf5kK7nQ/yKbuBX+FXdyK/yYd3Eh/k13cyv8RHdwkf4qG7lo3xMt/ExPq7b+Tif0B18gk/qTj7Jp3QXn+LTuptP8xndw2f4rO7ls5yi13Hsb+xlT8++hF+3v/v5dX5DD/Ab/KYe5Df5nB7ic3xeX+Hz/Ja+ym/xBT3MF/htfY3f5ot6hC/yO3qU3+F39Ri/y+/pcX6P39cT/D5/oCf5A/5QT/GH/JGe5o/4Yz3DH/MnepY/4fSawrGPsWe9PBMT/tQ+vsGf8mf6Jn/Gn+s5/py/0PP8BX+pb/GX/JVe4K/4a32bv+Zv9CJ/w9/qO/wtf6fv8nf8vb7H3/MP+j7/wD/qB/wj/6Qf8k/8s37EP/Mv+jH/wpf0E77EGTQ9x37F3vT2rEz4V/v1Gf/Kv+nn/Btf1i/4Mv+uX/LvfEW/4iv8h37Nf/Cf+g3/yVf1W77Kf+l3/Bf/rd/z3/yP/sD/8L/6I//L/+lP/B+nSpf0Z44Jp9ZfOCZ8jV7imPD1fpaBY19iD/p4hiacJl3S3zgmfK1e5phwWv2dY8Lp9ArHhK/TPzgmnKJ/ckw4vV7lmHAG/Ytjwtfr3xwTvkH/4Zjwjfovx4Rv0v84Jnyzxh7cbMK3aGq+hW/Va/hWvsHexD7cYA9irft6tiZ8m/W/lm/j2zUt3853aDq+g+/U6/hOvktT+C6+W9Pz3ZxRM3BGzqTXcya+R2/geziz3siZOYvexFk4q97MWTmb3sLZOLveytn5xrRJY73Dsc6xpv08cxPOYZ1v5xycU+/gnJxL7+RcnFvv4tycR+/mPJxXM3JezqeZOB/fq/fwvXyfZub7OL9m4fxcQLNyAS6o2bggF9LsXIhvSps01jUc6xlr19+zOOHC1jMnF+YimouLcFHNzUW5mObhYlxc83JxLqH5uASX1Hu5JJfS+7gUl9b8XJrLaAEuw2W1IJflclqIy/HNaZPG+oVj3WKNBnhGJ1zeuhXh8lxBi3IFrqjFuCJX0uJciStrCa7MVbQkV+GqWoqrcjUtzdW4upbh6lxDy3INrqnluCbfkjZprFM41ifWYqBnd8L3W58KfD/X0opci2trJa7NdbQy1+G6WoXrcj2tyvW4vlbj+txAq3MDbqg1uCE30prciG9NmzTWIxzrEPc8yDM94cbWoRY35iZam5twU63DTfkBrcsP8INajx/kh7Q+P8QPawN+mB/RhvwIP6qN+FG+LW3SuO9w3G/c2wue9Qk/5n6b8GP8uDblx/kJfYCf4Cf1QX6Sn9KH+Cl+Wh/mp/kZfYSf4Wf1UX6Wb0+bNO4vHPcV9zDYM0Dh59zX4/wcN9MnuBk/r0/y89xcn+Lm3EKf5hbcUp/hltxKn+VWfEfapHEf4bj+uNYhng0Kt3b9zbg1t9HnuQ231ebclttpC27H7bUlt+cO2oo78J1pk8b1huM645qGemYo3NF1tuGO3EnbcifurO24M3fR9tyFu2oH7sp3pU0a1xWO64n3HuZZonA319OJu3F37czduYd24R7cU7tyT747bdJ4/3C8b7zHcM8YhXt53+7ci3trD+7NfbQn9+GMaZPG+4Tj9eO1Rnj2KNzX6/fmvtxP+3A/zpQ2abxeOF4n/s5IzySF+3udftyf70mbNP5eOP58/G6UZ5XCmdMmjd+H4+fR///80oB0yfNKMeFN6ZIO5E28WQfxZt6iL/AW3qqDeStv0yG8jbfrUN7OO3QY7+CdOpx38i4dwbt4t47k3bxHR/Ee3qujeS/v0zG8j/frWN7PB3QcH+CDOp4P8iGdwIf4FZ3Ir/CrOolf5cM6mQ/zazqFX+MjOpWP8FGdxkf5mL7Ix/i4TufjfEJn8AneqPGft+E4h3HmvvIMXfhkuqSb+SSf0i18ik/rVj7NZ3Qbn+Gzup3P8uu6g1/nN3Qnv8Fv6i5+k8/pbj7H53UPn+e3dC+/xRd0H1/gt3U/v80X9QBf5Hf0IL/D7+ohfpff01f4PX5fX+X3+QM9zB/wh/oaf8gf6RH+iD/Wo/wxf6LH+BP+VI/zp/yZnuDPeINu5Dhvcba+9pxm+PN0SU/x5/yFnuYv+Es9w1/yV3qWv+Kv9XX+mr/RN/gb/lbf5G/5Oz3H3/H3ep6/5x/0Lf6Bf9QL/CP/pG/zT/yzXuSf+Rd9h3/hS/ouX+Jf9T3+lX/T9/k3vqwf8GX+XT/k3/mKfsRX+A/9mP/gP/UT/pOv6qd8lf/Sz/gvXq8bOM5VnKFvPPMb/jtd0i/4b/5Hv+R/+F/9iv/l//Rr/o/jyxaj33BMOLV+yzHha/Q7jgmn0e85Jnyt/sAx4bT6I8eE0+lPHBO+Tn/mmHCK/sIx4fR6iWPCGfRXjglfr79xTPgGvcwx4Rv1d44J36RXOCZ8s/7BMeFb9E+OCd+qVzkmfJv+xTHhdX62nuP8xFn51nPi4duvS/oPx4Tv0H85Jnyn/scx4bs0zspdJny3pua7OaNewxk5k6bhTHyPXsv3cGZNy5k5i6bjLJxVr+OsnE1TOBtn1/ScnXNoBs7BOfV6zsm59AbOxbn1Rs7NefQmzsN59WbOy/n0Fs7H9+qtfC/fp7fxfbzWGYrzstZZiTPxnc8QhPM7J3dwfi6gd3IBLqh3cUEupHdzIS6sGbkwF9FMXISL6j1clItpZi7GxTULF+cSmpVLcEnNxiW5lGbnUlxac3BpLqM5uQyX1Vxclstpbi7H5TUPl+cKmpcrcEXNxxW5kt7Llbiy3seVeU26pHEuwnEeYu+/9zmScBXnoQBX4apakKtyNS3E1bi6FubqXEOLcA2uqUW5Jt+vxfh+rqXFuRbX1hJcm+toSa7DdbUU1+V6WprrcX0tw/W5gZblBtxQy3FDbqTluRE31grcmJtoRW7CTbUSN+UHtDI/wKvTJY39D8e+xx7/4DND4Qfte1V+kB/SavwQP6zV+WF+RGvwI/yo1uRH+TG9nx/jx7UWP85PaG1+gp/UOvwkP6V1+Sl+Wuvx0/yM1udn+FltwM/yc9qQn+Nm2oib8fPamJ/n5tqEm3MLbcotuKU+wC15Vbqksc/h2N/Yyx99Dizcyv4+xK24tT7MrbmNPsJtuK0+ym25nT7G7bi9Ps7tuYM+wR24oz7JHbmTPsWduLM+zZ25iz7DXbirPstduZs+x924uzbj7txDn+ce3FObc0/upS24F/fWltybV6ZLGvsZjn2MPfvJZ/jCfexja+7DfbUN9+V+2pb7cX9tx/15gLbnATxQO/BAHqQdeRC/oJ34BR6snXkwD9EuPISHalceysO0Gw/j4dqdh/MI7cEjeKT25JE8SnvxKB6tvXk0r0iXNPYtHPsVe/Ozz1mGx9ivvjyGx2o/HsvjtD+P4/E6gMfzBB3IE3iiDuKJPElf4Ek8WQfzZJ6iQ3gKT9WhPJWn6TCexi/qcH6Rp+sIns4zdCTP4Jk6imfyLB3Ns3h5uqSxP+HYl9iDX3zmNfySfRnLL/FsHcezeY6O5zk8VyfwXJ6nE3kez9dJPJ8X6GRewAt1Ci/kRTqVF/FincaLeYm+yEt4qU7npfyyzuCXeZnO5GW8XGfxcl6Wzs/SJY71j7W+5HPG4RXWfzav4JU6h1fyKp3Lq3i1zuPVvEbn8xpeqwt4La/ThbyO1+siXs8bdDFv4I26hDfyJl3Km3izvsybeYsu4y28VZfzVn45XdJY73Csc6zprz7DHd5mnVfyNt6uq3g779DVvIN36hreybt0Le/i3bqOd/MeXc97eK9u4L28TzfyPt6vm3g/H9DNfIAP6hY+yId0Kx/ipemSxrqGYz1j7X7zOfjwK9ZzO7/Cr+oOfpUP604+zK/pLn6Nj+huPsJHdQ8f5WO6l4/xcd3Hx/mE7ucTfFIP8Ek+pQf5FJ/WQ3yal6RLGusXjnWLNbrsOwPCZ6zbq3yGz+phPsuv62v8Or+hR/gNflOP8pt8To/xOT6vx/k8v6Un+C2+oCf5Ar+tp/htvqin+SIvTpc01ikc6xNr8bvvUQi/Y33O8jv8rr7O7/J7+ga/x+/rm/w+f6Dn+AP+UM/zh/yRvsUf8cd6gT/mT/Rt/oQ/1Yv8KS9KlzTWIxzrEPd8xXdIhD+zDu/yZ/y5vsef8xf6Pn/BX+oH/CV/pR/yV/y1fsRf8zf6MX/D3+on/C1/p5/yd7wwXdK473Dcb9zbH74nI/y9+/2cv+cf9Av+gX/UL/lH/km/4p/4Z/2af+Zf9Bv+hS/pt3yJf9Xv+FdekC5p3F847ivu4U/f+RH+zX39wL/xZf2RL/Pv+hP/zlf0Z77Cf+gv/Af/qZf4T76qv/JVnp8uadxHOK4/rvWq7ykJ/+X6L/Nf/Lf+zn/zP3qF/+F/9Q/+l//TP/k/TpWS9CrHhOelSxrX+z9f67p8h0o4dUrSvzkmfI3+wzHhNPovx4Sv1f84JpxW45rSmvDcdEnjusJxPf97b9/jEk7neq7hdHydpuHrOEWv5RROr2k5Pc9JlzTePxzvG+/xj++MCWfwvtdxBr5eU/h6vkHT8w08O13SeJ9wvH681r++hyZ8o9e/nm/km/QGvolfSpc0Xi8crxN/5z/fYRO+2evcxDfzrHRJ4++F48/H7/73MEjaxDPTJY3fh+Pn0Wv8sVv+73cFTfh5vZWf5+Z6GzfnFno7t+CWege35FZ6J7fi1noXt+Y2eje34baakdtyO83E7bi93sPtuYNm5g7cUbNwR+6kWbkTd9Zs3Jm7aHbuwl01B3flbpqTu3F3zcXduYfm5h7cU/NwT+6lebkX99Z83Jv76L3ch/vqfdyX+2l+7sf9tQD359qaPlXiOIdx5gqZ8ABtzgN4oLbggTxIW/IgfkFb8Qs8WFvzYB6ibXgID9W2PJSHaTsexsO1PQ/nEdqBR/BI7cgjeZR24lE8WjvzaB6jXXgMj9WuPJbHaTcex+O1O4/nCdqDJ/BE7ckTeZL24kk8WXvzZJ6ifXgKT9W+PJWnaT+exi9qf36R62htjvMWZ6uwCU/XgTydZ+ggnsEz9QWeybN0MM/il3QIv8SzdSjP5jk6jOfwXB3Oc3mejuB5PF9H8nxeoKN4AS/U0byQF+kYXsSLdSwv5iU6jpfwUh3PS/llncAv8zKdyMt4uU7i5bxCJ/MKXqlTeCWv0qm8ilfrNF7Na/RFXsN1tQ7HuYozVMSE1+oMXsvrdCav4/U6i9fzBn2JN/BGnc0beZPO4U28WefyZt6i83gLb9X5vJW36QLextt1IW/nHbqId/BOXcw7eZcu4V28W5fybt6jL/Me3qvLeC/v0+W8j/frCt7PB3QlH+CDuooP8iFdzYf4FV3Dr3A9rctxfuKsFDXhV3Udv8qHdT0f5td0A7/GR3QjH+GjuomP8jHdzMf4uG7h43xCt/IJPqnb+CSf0u18ik/rDj7NZ3Qnn+GzuovP8uu6m1/nN3QPv8Fv6l5+k8/pPj7H53U/n+e39AC/xRf0IF/gt/UQv80X9RW+yPW1Hsc5iTNRzITf0cP8Dr+rr/G7/J4e4ff4fT3K7/MHeow/4A/1OH/IH+kJ/og/1pP8MX+ip/gT/lRP86f8mZ7hz/hzPcuf8xf6On/BX+ob/CV/pW/yV/y1nuOv+Rs9z9/wt/oWf8vf6QX+jr/Xt/l7/kEv8g/cQOtzMXtf3IR/1Hf5R/5J3+Of+Gd9n3/mX/QD/oUv6Yd8iX/Vj/hX/k0/5t/4sn7Cl/l3/ZR/5yv6GV/hP/Rz/oP/1C/4T76qX/JV/ku/4r/4b/2a/+Z/9Bv+h//Vb/lf/k+/4/84/kPqO3v/v/+oSp84tf7AMeGGftaAi9vjEiZ8TfqkP3FMOI3+zDHha/UXjgmn1UscE06nv3JM+Dr9jWPCKXqZY8Lp9XeOCWfQKxwTvl7/4JjwDfonx4Rv1KscE75J/+KY8M36N8eEb9F/OCZ8q/7LMeHb9D+OCd+usce3m/Admprv4Eb2Pva5kf2NvSyZkkz4Tvubhu/ku/Ravovv1rR8N2fUdJyRM+l1nInv0RS+hzNres7MWTQDZ+Gsej1n5Wx6A2fj7HojZ+ccehPn4Jx6M+fkXHoL5+Lceivn5jx6G+fhvHo75+V8egfn48YpSWM/G9vH2LNSKcmE77WPd/G9fJ/ezfdxfs3I+bmAZuICXFDv4YJcSDNzIS6sWbgwF9GsXISLajYuysU0Oxfj4pqDi3MJzckluKTm4pJcSnNzKS6tebg0l9G8XIbLaj4uy01Sksa+NbFfsTelU5IJl7Nf93E5Lq/5uTxX0AJcgStqQa7IlbQQV+LKWpgrcxUtwlW4qhblqlxNi3E1rq7FuTrX0BJcg2tqSa7J92spvp9raWmuxbW1DNfmOlqW63DTlKSxP03tS+xBmZRkwnXtS3muy/W0Atfj+lqR63MDrcQNuKFW5obcSKtwI26sVbkxN9Fq3ISbanVuyg9oDX6AH9Sa/CA/pPfzQ/yw1uKH+RGtzY/wo1qHH+UHUpLGPjxg/WOty6YkE37M+tfjx/hxrc+P8xPagJ/gJ7UhP8lPaSN+ip/Wxvw0P6NN+Bl+Vpvys/ycPsDPcTN9kJvx8/oQP8/N9WFuzi30EW7BLfVRbskPpiSN9X7QOsealktJJtzKOj/Orbi1PsGtuY0+yW24rT7FbbmdPs3tuL0+w+25gz7LHbijPscduZM2407cWZ/nztxFm3MX7qotuCt305bcjR9KSRrr+pD1jLUrn5JMuLv1bM3duYe24R7cU9tyT+6l7bgX99b23Jv7aAfuw321I/flftqJ+3F/7cz9eYB24QE8ULvyQB6k3XgQP5ySNNbvYesWa1QhJZnwC9atB7/Ag7UnD+Yh2ouH8FDtzUN5mPbhYTxc+/JwHqH9eASP1P48kkfpAB7Fo3Ugj+YxOojH8CMpSWOdHrE+sRYVU5IJj7U+g3ksj9MhPI7H61AezxN0GE/giTqcJ/IkHcGTeLKO5Mk8RUfxFJ6qo3kqT9MxPI0fTUka6/GodYh7rpSSTPhF6zCOX+TpOp6n8wydwDN4pk7kmTxLJ/Esfkkn80s8W6fwbJ6jU3kOz9VpPJcfS0ka9/2Y+417q5ySTHie+53O83i+zuD5vEBn8gJeqLN4IS/Sl3gRL9bZvJiX6Bxewkt1Li/lx1OSxv097r7iHqqkJBN+2X3N55d5mS7gZbxcF/JyXqGLeAWv1MW8klfpEl7Fq3Upr+YnUpLGfTzh+uNaq6YkE17j+pfxGl6ry3ktr9MVvI7X60pezxt0FW/gjbqaN/KTKUnjep90nXFN1VKSCW9ynWt5E2/WdbyZt+h63sJbdQNv5W26kbfxUylJ47qecj3x3tVTkglvdz2beTvv0C28g3fqVt7Ju3Qb7+KnU5LG+z/tfeM9aqQkE97tfXfwbt6jO3kP79VdvJefSUka7/OM14/XqpmSTHif19/D+3i/7uX9/GxK0ni9Z71O/J37U5IJH/A6+/kAP5eSNP7ec/58/K5WSjLhZhq/b+bn0Qypkjn4f7+7bMK3Zkh6iGPCt+krHBO+XV/lmPAdephjwnfqaxwTvkuPcEz4bj3KMeGMeoxjwpn0OMeE79ETHBPOrCc5JpxFT3FMOKue5phwNj3DMeHsepZjwjn0dY4J59Q3OCacS9/kmHBuPccx4Tx6nmPCefUtjgnn0wscE75X3+aY8H16kWPC+fUdjgkX0Hc5JnyLXp8q8WXn7rf0yYQLOoe3cUEupLdzIS6sd3BhLqJ3chEuqndxUS6md3MxLq4ZuTiX0ExcgkvqPVySS2lmLsWlNQuX5jKalctwWc3GZbmcZudyXF5zcHmuoDm5AlfUXFyRK2lursSVNQ9X5iqal6twVc3HVbma3svVuLrex9W5hubnGlxTC3BNvllv4ThvcbZ+TZ9M+P4MSQvx/VxLC3Mtrq1FuDbX0aJch+tqMa7L9bQ41+P6WoLrcwMtyQ24oZbihtxIS3MjbqxluDE30bLchJtqOW7KD2h5foAf1Ar8ID+kFfkhflgr8cP8iFbmR/hRrcKP8mNalR/jx7UaP85PaHV+gp/UGvwkP6U1+Sm+SW/mOFdxhi6lTyb8dIaktfhpfkZr8zP8rNbhZ/k5rcvPcTOtx834ea3Pz3NzbcDNuYU25BbcUhtxS26ljbkVt9Ym3JrbaFNuw231AW7L7fRBbsft9SFuzx30Ye7AHfUR7sid9FHuxJ31Me7MXfRx7sJd9Qnuyt30Se7G3fUp7s436k0c5yfOyi/pkwn3yJD0Ge7BPfVZ7sm99Dnuxb21GffmPvo89+G+2pz7cj9twf24v7bk/jxAW/EAHqiteSAP0jY8iF/QtvwCD9Z2PJiHaHsewkO1Aw/lYdqRh/Fw7cTDeYR25hE8UrvwSB6lXXkUj9ZuPJrHaHcewzfojRznJM7Ez+mTCY/NkLQnj+Vx2ovH8XjtzeN5gvbhCTxR+/JEnqT9eBJP1v48mafoAJ7CU3UgT+VpOoin8Yv6Ar/I03UwT+cZOoRn8EwdyjN5lg7jWfySDueXeLaO4Nk8R0fyHJ6ro3guz9PRPI/n6xiez9frDRznIfb+p/TJhBdkSDqOF/BCHc8LeZFO4EW8WCfyYl6ik3gJL9XJvJRf1in8Mi/TqbyMl+s0Xs4r9EVewSt1Oq/kVTqDV/FqncmreY3O4jW8Vl/itbxOZ/M6Xq9zeD1v0Lm8gTfqPN7Im3Q+b+IMej3Hvsce/5g+mfBm+76QN/MWXcRbeKsu5q28TZfwNt6uS3k779CXeQfv1GW8k3fpct7Fu3UF7+Y9upL38F5dxXt5n67mfbxf1/B+PqBr+QAf1HV8kA/pej7Er+gGfoVf1Y38Kh/WTXyY02sGjv2NvfwhfTLh1+zvFn6Nj+hWPsJHdRsf5WO6nY/xcd3Bx/mE7uQTfFJ38Uk+pbv5FJ/WPXyaz+hePsNndR+f5dd1P7/Ob+gBfoPf1IP8Jp/TQ3yOz+srfJ7f0lf5Lb6gh/kCp2h6jn2MPfs+fTLht+3jEX6bL+pRvsjv6DF+h9/V4/wuv6cn+D1+X0/y+/yBnuIP+EM9zR/yR3qGP+KP9Sx/zJ/o6/wJf6pv8Kf8mb7Jn/Hneo4/5y/0PH/BX+pb/CV/pRf4K75OUzj2K/bmu/TJhL+2Xxf5a/5G3+Fv+Ft9l7/l7/Q9/o6/1/f5e/5BP+Af+Ef9kH/kn/Qj/ol/1o/5Z/5FP+Ff+JJ+ypf4V/2Mf+Xf9HP+jS/rF3yZf9cv+Xe+ol/xFU6n13HsS+zBt+mTCf9hX77hP/hP/Zb/5Kv6HV/lv/R7/ov/1h/4b/5Hf+R/+F/9if/l//Rn/o/j/3z92R787/+IvT5xar3EMeFr9FeOCafR3zgmfK1e5phwWv2dY8Lp9ArHhNP+/59xrP//1jp9MuHrrk/6J8eEU/Qqx4TT618cE86gf3NM+Hr9h2PCN+i/HBO+Uf/jmPBNGmt9kwnfrKn5Zr5Fr+Fb+FZNw7fybXot38a3a1q+ne/QdHwHX2sPYr2vtdaxpl+nTyZ8p3VO4Tv5Lk3Pd/HdmoHv5ox6PWfkTHoDZ+J79Ea+hzPrTZyZs+jNnIWz6i2clbPprZyNs+ttnJ1z6O2cg3PqHZyT02RIGusajvWMtfsqfTLhXNbzLs7FufVuzs15NCPn4byaifNyPr2H8/G9mpnv5fs0C9/H+TUr5+cCmo0LcEHNzgW5kObgQlxYc3JhviZD0li/cKxbrNGX6ZMJF7FuubkIF9U8XJSLaV4uxsU1HxfnEnovl+CSeh+X5FKan0txaS3ApbmMFuQyXFYLcVkup4W5HKfOkDTWKRzrE2vxRfpkwuWtT1EuzxW0GFfgilqcK3IlLcGVuLKW5MpcRUtxFa6qpbkqV9MyXI2ra1muzjW0HNfg+EfwctYmHOsQ9/x5+mTCNa1DBa7J92tFvp9raSWuxbW1MtfmOlqF63Bdrcp1uZ5W43pcX6tzff5/NN1luFVV27BhFd2CSINdoCIoJSCldHd3d6dIg9jd3YpKdzdKN0gq3SAgFlICfs/9zvP7cR/XeezNXmvOMcY/51pW13Jcnf9LkzTu+z/3G/d2OE0y4RrutwLX4JpakWtyLa3Etbi2VubaXEercB2uq1W5LtfTalyP62t1rs9X0ySN+7vqvuIeDqVJJtzAfdXkBtxQa3FDbqS1uRE31jrcmJtoXW7CTbUeN+VmWp+b8ZU0SeM+rrj+uNaDaZIJN3f9Dbk5t9BG3IJbamNuya20Cbfi1tqUW3MbbcZt+HKapHG9l11nXNOBNMmE27rOFtyW22lLbsfttRW35w7amjtwR23DHfnfNEnjuv51PfHe+9MkE+7ketpxJ+6s7bkzd9EO3IW7akfuypfSJI33v+R94z32pUkm3M37duZu3F27cHfuoV25B19MkzTe56LXj9famyaZcE+v3517ci/twb34Qpqk8XoXvE78zZ40yYR7e51e3JvPp0kaf3fev4/f7U6TTPicxu/P+Xk0vorp+v/Nq//73fcmPFpf4dE8Rl/mMTxWX+KxPE5f5HE8Xl/g8TxBn+cJPFGf44k8SZ/lSTxZn+HJPEVH8hSeqk/zVJ6mI3gaT9fhPJ1n6DCewTN1KM/kWTqEZ/FsHcyzeY4O4jk8VwfyXJ6nA3gez9eneD4v0P68gBfqk7yQF2k/XsSLtS8v5iXah5dw91RJ4/9lGI5zGGfuOxP+QcfwD/yjjuUfeamO46W8TMfzMl6uE3g5r9CJvIJX6iReyat0Mq/i1TqFV/MancpreK1O47W8TqfzOl6vM3g9b9CZvIE36izeyJt0Nm/izTqHN/NPOpd/4i06j7fwVp3PW3mbLuBtvF0X8nbeoYt4B+/UxbyTf9Yl/DN3S5U0zl04zlucrW9N+Bf9kX/hXbqUd/FuXca7eY8u5z28V1fwXt6nK3kf79dVvJ8P6Go+wAd1DR/kQ7qWD/FhXceH+Yiu5yN8VDfwUT6mG/kYH9dNfJxP6GY+wb/qT/wrn9QtfJJP6VY+xad1G5/m33Q7/8ZndAef4d91J//Of+jP/Ad3TZU0zlc4zlWcoVEm/Kfu4j/5L93Nf/Hfuof/5rO6l8/yP7qP/+Fzup/P8Xk9wOf5gh7kC3xRD/FFvqSH+RL/q0f4X76sR/kyX9FjfIWv6nG+yv/pCf6Pr7k56a8cE75WT3JM+Do9xTHhVHqaY8LX628cE75Bz3BMOEV/55jwjfoHx4S7pEoa5yg8yln5xoRT35z0L44Jp9G/OSZ8k57lmHBa/YdjwjfrOY4Jp9PzHBNOrxc4JpxBL3JMOKNe4phwJv2XY8KZ9TLHhLPoFY4JZ9WrHBPOpv9xTPgWjbNyiwnfqtfyrXybXse38e2aim/nO/R6voPv1Bv4Tr5LU/guvltv5Lu5c6qkcV7C3zgXX6dNJnyPc5KG7+F79Sa+l+/TtHwfZ9ebOTvn0HScg+/X9Hw/P6AZ+AF+UDPyg5xTM3FOfkgz80OcS7NwLs6tWTk3P6zZ+GF+RG/hRziP3sp5OK/exnk5n97O+Ti/3sH5uYDeyQX4Ub2LH+WCejcX5E6pksa5CMd5iL3/Km0y4ULOw71ciAvrfVyYH9Ps/BgX0RxchIvq/VyUi+kDXIyL64NcnEtoTi7Bj+tD/Dg/obn4CS6pubkkl9KHuRSX1ke4NJfRPFyGy2peLsvlNB+X4/Kan8tzBS3AFbiiPsoVuZIW5ErcMVXS2P9w7Hvs8ZdpkwlXtu+FuTJX0ce4ClfVIlyVq2lRrsbVtRhX5xpanGtwTS3BNbmWPs61uLY+wbW5jpbkOlxXS3FdrqeluR7X1zJcnxtoWW7ADbUcN+RGWp4bcWOtwI25iVbkJtxUK3FT7pAqaexzOPY39vKLtMmEm9nfKtyMm2tVbs4ttBq34JZanVtyK63Brbi11uTW3EZrcRtuq7W5LbfTOtyO22tdbs8dtB534I5anztyJ23AnbizNuTO3EUbcRfuqo25K3fTJtyNu2tT7s7tUyWN/QzHPsaefZ42mXAP+9ice3BPbcE9uZe25F7cW1txb+6jrbkP99U23Jf7aVvux09qO36S+2t77s9PaQd+igdoRx7AA7UTD+RB2pkH8WDtwoN5iHblITxUu/FQHqbdeRi3S5U09i0c+xV781naZMLD7VdPHs4jtBeP4Ke1Nz/NI7UPj+RntC8/w89qP36Wn9Mn+Tl+Xvvz8/yCPsUv8Is6gF/kl3Qgv8Qv6yB+mV/RwfwKv6pD+FV+TYfya/y6DuPXuW2qpLE/4diX2INP0yYTfsO+jOA3+E19mt/kt3Qkv8Vv6zP8Nr+jz/I7/K4+x+/ye/o8v8fv6wv8Pn+gL/IH/KG+xB/yR/oyf8Qf6yv8MX+ir/In/Km+xp/yZ/o6f8ZtUiWNfQjH+sdaf5I2mfDn1v9N/py/0Lf4C/5S3+Yv+St9h7/ir/Vd/pq/0ff4Gx6l7/Mo/lY/4G/5O/2Qv+Pv9SP+nkfrxzyax+gnPIbH6qc8lsfpZzyOW6dKGusdjnWONf04bTLh8db5Cx7PE/RLnsAT9SueyJP0a57Ek/UbnsxTdBRP4an6LU/lafodT+Pp+j1P5xk6mmfwTB3DM3mWjuVZPFvH8WxulSpprGs41jPW7qO0yYTnWM8JPIfn6kSey/N0Es/j+TqZ5/MCncILeKFO5YW8SKfxIl6s03kxL9EZvIR/0Jn8A/+os/hHXqqzeSm3TJU01i8c6xZr9GHaZMLLrNtcXsbLdR4v5xU6n1fwSl3AK3mVLuRVvFoX8Wpeo4t5Da/VJbyW1+kPvI7X64+8njfoUt7ALVIljXUKx/rEWnyQNpnwRuuznDfyJl3Bm3izruTN/JOu4p94i67mLbxV1/BW3qZreRtv13W8nXfoet7BO3UD7+TmqZLGeoRjHeKe30+bTPhn67CJf+ZfdDP/wrv0J97Fu3UL7+Y9upX38F7dxnt5n27nfbxfd/B+PqA7+QA3S5U07jsc9xv39l7aZMIH3e8vfJAP6S4+xId1Nx/mI7qHj/BR3ctH+Zju42N8XPfzcT6hB/gEN02VNO4vHPcV9/Bu2mTCv7qvQ/wrn9TDfJJP6RE+xaf1KJ/m3/QY/8Zn9Dif4d/1BP/OTVIljfsIx/XHtb6TNpnwH67/JP/Bf+op/pP/0tP8F/+tv/HffFbP8Fn+R3/nf7hxqqRxveG4zrimt9MmEz7nOv/kc3xe/+LzfEH/5gt8Uc/yRb6k//AlbpQqaVxXOK4n3vuttMmE/3U95/lfvqwX+DJf0Yt8ha/qJb7KDVMljfcPx/vGe7yZNpnwf973Mv/H16RLeoVjwtfqVY4JN0iVNN4nHK8fr/VG2mTC16VLGq91nQmn0ms5FddPlTReLxyvE3/zetpkwtd7nVR8PddLlTT+Lhz/Pn73WtpkwnVTJY3fh+Pn0Ws8v3RDuuR5pZjwfemSpvB9nF1v5OycQ1NzDr5f0/D9/IDexA/wg5qWH+ScejPn5Ic0HT/EuTQ95+LcmoFz88OakR/mRzQTP8J5NDPn4byahfNyPs3K+Ti/ZuP8XEBv4QL8qN7Kj3JBvY0LciG9nQtxYb2DC/Njeic/xkX0Li7CRfVuLsrF9B4uxsX1Xi7OH6Qkjf+XYTjOYZy5eFbuFS6RLml2LsGPaw5+nJ/Q+/kJLqkPcEkupQ9yKS6tObk0l9GHuAyX1Vxclstpbi7H5fVhLs8V9BGuwBU1D1fkSpqXK3FlzceVuYrm5ypcVQtwVa6mj3I1rq4FuTrX0EJcg2tqYa7JtfQxrsW1tQjX5jpalOtwXS3GdbmeFud6/H5K0jh34ThvcbbiecyXuX66pI9zfW6gT3ADbqgluSE30lLciBtraW7MTbQMN+GmWpabcjMtx824uZbn5txCK3ALbqkVuSW30krciltrZW7NbbQKt+G2WpXbcjutxu24vVbn9txBa3AH7qg1uSN30lrciTtrbe7MXbQOd+GuWpe7cjetx934vZSkcb7Cca7iDMWzvS9x93RJG3B37qENuQf31Ebck3tpY+7FvbUJ9+Y+2pT7cF9txn25nzbnfvyktuAnub+25P78lLbip3iAtuYBPFDb8EAepG15EA/WdjyYh2h7HsJDtQMP5WHakYfxcO3Ew3mEduYR/LR24ad5pHblkfyMduNn+N2UpHGOwnF+4qzE8+Av8rPpkvbgZ/k57cnP8fPai5/nF7Q3v8Avah9+kV/SvvwSv6z9+GV+RZ/kV/hV7c+v8mv6FL/Gr+sAfp3f0IH8Br+pg/hNfksH81v8tg7ht/kdHcrv8Ls6jN/l93Q4v8fv6wh+nz/Qp/kD/lBH8of8kT7DH/E7KUnjvITjnMSZiM8KvMAfp0v6HH/Mn+jz/Al/qi/wp/yZvsif8ef6En/OX+jL/AV/qa/wl/yVvspf8df6Gn/N3+jr/A2P0jd4FH+rb/K3/J2+xd/x9/o2f8+j9R0ezWP0XR7DY/U9Hsvj9H0ex+P1Ax7PE/RDnsAT9SOeyG+nJI1zEY7zEHsfnxd5nic5D5/wJJ6sn/JknqKf8RSeqp/zVJ6mX/A0nq5f8nSeoV/xDJ6pX/NMnqXf8CyeraN4Ns/Rb3kOz9XveC7P0+95Hs/X0TyfF+gYXsALdSwv5EU6jhfxYh3Pi3mJTuAl/INO5B/4rZSksf/h2PfY4/hs0HP8o32fzD/yUp3CS3mZTuVlvFyn8XJeodN5Ba/UGbySV+lMXsWrdRav5jU6m9fwWp3Da3mdzuV1vF7n8XreoPN5A2/UBbyRN+lC3sSbdRFv5p90Mf/EW3QJb+Gt+gNv5TdTksY+h2N/Yy/j817P8jb7u5S38XZdxtt5hy7nHbxTV/BO/llX8s/8i67iX3iXruZdvFvX8G7eo2t5D+/VdbyX9+l63sf7dQPv5wO6kQ/wQd3EB/mQbuZDfFh/4sN8RLfwET6qW/kov5GSNPYzHPsYexaf1XuGj9nH7XyMj+sOPs4ndCef4F/1Z/6VT+ovfJJP6S4+xad1N5/m33QP/8ZndC+f4d91H//Of+h+/oP/1AP8J/+lB/kv/lsP8d98Vg/zWf5Hj/A/fE6P8jl+PSVp7Fs49iv2Jj5POZLP26/jfJ4v6Am+wBf1V77Il/QkX+J/9RT/y5f1NF/mK/obX+Greoav8n/6O//H16RP+gfHhK/VPzkmfJ3+xTHhVPo3x4Sv17McE75B/+GYcIqe45jwaylJY3/CsS+xB/HZ1qf5xvRJL3BMOLVe5JhwGr3EMeGb9F+OCafVyxwTvlmvcEw4nV7lmHB6/Y9jwhk09iCDCWfUazkjZ9LrOBNn1lScmbPo9ZyFs+oNnJWzaQpn41dTksY+hGP9Y63j88Qj+Bbrn5pv4Vs1Dd/Kt+lNfBvfrmn5dr5Db+Y7+E5Nx3fyXZqe7+K7NQPfzfdoRr6H79VMfC/fp5n5Ps6uWTg759CsnIPv12x8P7+SkjTWOxzrHGsan9Uezg9Y51v5AX5Qb+MHOafezjn5Ib2DH+Jceifn4tx6F+fmh/Vufpgf0Xv4Ec6j93Iezqv3cV7Op9k5H+fXHJyfC+j9XIBfTkka6xqO9Yy1i8+7D+NHreeD/CgX1JxckAvpQ1yIC2suLsyPaW5+jIvow1yEi+ojXJSLaR4uxsU1LxfnEpqPS/Djmp8f5ye0AD/BL6UkjfULx7rFGsV3AwzlktatIJfkUlqIS3FpLcyluYw+xmW4rBbhslxOi3I5Lq/FuDxX0OJcgStqCa7IlfRxrsSV9QmuzC+mJI11Csf6xFrE9yUM4SrWpxRX4apamqtyNS3D1bi6luXqXEPLcQ2uqeW5JtfSClyLa2tFrs11tBLX4bpamevyCylJYz3CsQ5xz/FdEYO5nnWoyvW4vlbj+txAq3MDbqg1uCE30prciBtrLW7MTbQ2N+GmWoebcjOty834+ZSkcd/huN+4t/g+jEHc3P3W5+bcQhtwC26pDbklt9JG3Ipba2NuzW20CbfhttqU23I7bcbt+LmUpHF/4bivuIf4bo+B3N59teD23EFbcgfuqK24I3fS1tyJO2sb7sxdtC134a7ajrvysylJ4z7Ccf1xrfF9JAO4m+vvwN24u3bk7txDO3EP7qmduSf30i7ci3trV+7Nz6QkjesNx3XGNcV3pTzFfVxnd+7DfbUH9+V+2pP78ZPai5/k/tqb+/PIlKRxXeG4nnjv+L6W/vyU6+nLT/EA7ccDeKA+yQN5kPbnQfx0StJ4//9z2uQ94rthnuTB3ncAD+YhOpCH8FAdxEN5RErSeJ9wvH68VnzfTD8e5vWH8DAerkN5+P93StJ4vXC8TvxNfFdNXx7hdYbzCB6W4rVSEse/j9/F99n04aEp/j4lcfw8eq3nl55OnzyvFBP+NH3Skfwpf6bP8Gf8uT7Ln/MX+hx/wV/q8/wlf6Uv8Ff8tb7IX/M3+hJ/w6P0ZR7F3+or/C1/p6/yd/y9vsbf82h9nUfzGH2Dx/BYfZPH8jh9i8fxeH2bx/MEfYcn8ER9lyfyJH2PJ/FkfZ8n8xT9gKfwVP2Qp/I0/Yin8XT9mKfzDP2EZ/DDqZOmuSZxnMM4c/GsXArPTJ/0M57Js/RznsWz9QuezXP0S57Dc/Urnsvz9Guex/P1G57PC3QUL+CF+i0v5EX6HS/ixfo9L+YlOpqX8A86hn/gH3Us/8hLdRwv5WU6npfxcp3Ay3mFTuQVvFIn8UpepZN5Fa/WKbya1+hUXsNrdRqv5XU6ndfxep3B6zl36qRx7sJx3uJsxfOYN/KG9Eln8QbeqLN5I2/SObyJN+tc3sw/6Tz+ibfofN7CW3UBb+VtupC38XZdxNt5hy7mHbxTl/BO/ll/4J/5F/2Rf+FdupR38W5dxrt5jy7nPbxXV/Be3qcreR/v11W8nw/oaj7AB3UNH+RDupYP8WFdx4f5iK7nI5wrddI4X+E4V3GG4tne1Hw0fdKNfJSP6SY+xsd1Mx/nE/oTn+BfdQv/yid1K5/kU7qNT/Fp3c6n+Tfdwb/xGd3JZ/h3/Zl/5z/0F/6D/9Rd/Cf/pbv5L/5b9/DffFb38ln+R/fxP3xO9/M5Pq8H+Dxf0IN8gS/qIb7Il/QwX+J/9Qj/yw+lThrnKBznJ85KPA+ehi+nT3qML/MVPc5X+Kqe4Kv8n/7K//E1GZKe5JjwtXqKY8LX6WmOCafS3zgmfL2e4ZjwDfo7x4RT9A+OCd+of3JMOLX+xTHhNPo3x4Rv0rMcE06r/3BM+GY9xzHhdHqeY8Lp9QLHhDPoRY4JZ9RLHBPOpP9yTDhn6qRxXsJxTuJMxGcFbuLMGZJe4ZhwFr3KMeGs+h/HhLNpnIlsJnyLXsu38K16Hd/Kt2kqvo1v1+v5dr5Db+A7+E5N4Tv5Lr2R7+K7NTXfzfdoGr6H79Wb+F6+T9PyfZxdb+bsnEPTcQ6+X9Pz/fyAZuAH+EHNyA9yTs3EOfnB1H6WOnGch9j7+LxIWn7IecjCD3Euzcq5OLdm49z8sN7CD/Mjeis/wnn0Ns7DefV2zsv59A7Ox/n1Ts7PBfQuLsCP6t38KBfUe7ggF9J7uRAX1vu4MD+m2fkxLqI5uAgX1fu5KBfTB7gYF9cHuTiX0Jxcgh9InTT2Pxz7Hnscnw26mR+377n4cX5Cc/MTXFIf5pJcSh/hUlxa83BpLqN5uQyX1Xxclstpfi7H5bUAl+cK+ihX4IpakCtyJS3ElbiyFubKXEUf4ypcVYtwVa6mRbkaV9diXJ1raHGuwTW1BNfk+1MnjX0Ox/7GXsbnvdJxLfv7BNfi2lqSa3MdLcV1uK6W5rpcT8twPa6vZbk+N9By3IAbanluyI20AjfixlqRG3MTrcRNuKlW5qbcTKtwM26uVbk5t9Bq3IJbanVuya20Brfi1lqTW3OO1EljP8Oxj7Fn8Vm99NzGPtbmNtxW63Bbbqd1uR2313rcnjtofe7AHbUBd+RO2pA7cWdtxJ25izbmLtxVm3BX7qZNuRt312bcnXtoc+7BPbUF9+Re2pJ7cW9txb25j7bmPpw9ddLYt3DsV+xNfJ4yA/e1X225L/fTdtyPn9T2/CT31w7cn5/SjvwUD9BOPIAHamceyIO0Cw/iwdqVB/MQ7cZDeKh256E8THvwMB6uPXk4j9BePIKf1t78NI/UPjyS70udNPYnHPsSexCfbc3Iz9iXfvwMP6tP8rP8nPbn5/h5fYqf5xd0AL/AL+pAfpFf0kH8Er+sg/llfkWH8Cv8qg7lV/k1Hcav8es6nF/nN3QEv8Fv6tP8Jr+lI/ktvjd10tiHcKx/rHV8njgTv239n+W3+R19jt/hd/V5fpff0xf4PX5fX+T3+QN9iT/gD/Vl/pA/0lf4I/5YX+WP+RN9jT/hT/V1/pQ/0zf4M/5c3+TP+Qt9i7/ge1InjfUOxzrHmsZntTPzl9b5Hf6Sv9J3+Sv+Wt/jr/kbfZ+/4VH6AY/ib/VD/pa/04/4O/5eP+bvebR+wqN5jH7KY3isfsZjeZx+zuN4vH7B4/nu1EljXcOxnrF28Xn3LDzBen7FE3iifs0TeZJ+w5N4so7iyTxFv+UpPFW/46k8Tb/naTxdR/N0nqFjeAbP1LE8k2fpOJ7Fs3U8z+a7UieN9QvHusUaxXcDZOU51m0iz+G5Oonn8jydzPN4vk7h+bxAp/ICXqjTeCEv0um8iBfrDF7MS3QmL+EfdBb/wD/qbP6R70ydNNYpHOsTaxHfl5CNl1qfubyUl+k8XsbLdT4v5xW6gFfwSl3IK3mVLuJVvFoX82peo0t4Da/VH3gtr9MfeR3fkTpprEc41iHuOb4r4hZebx2W8XreoMt5A2/UFbyRN+lK3sSbdRVv5p90Nf/EW3QNb+Gtupa38jZdx9v49tRJ477Dcb9xb/F9GLfydve7gbfzDt3IO3inbuKd/LNu5p/5F/2Jf+FduoV38W7dyrt5j27jPXxb6qRxf+G4r7iH+G6P23iv+9rBe3mf7uR9vF9/5v18QH/hA3xQd/FBPqS7+RAf1j18mG9NnfT/7iN1cv1xrfF9JLfzEde/j4/wUd3PR/mYHuBjfFwP8nE+oYf4BP+qh/lXviV10rjecFxnXFN8V8odfNJ1HuWTfEqP8Sk+rcf5NP+mJ/g3PqO/8hnOljppXFc4rifeO76v5U7+3fWc4t/5Dz3Nf/Cf+hv/yX/pGf6Ls6ZOGu8fjveN94jvhrmL//a+f/DffFb/5LP8j/7F/3CW1EnjfcLx+vFa8X0zd/M5r3+Wz/F5/YfPc+bUSeP1wvE68TfxXTX38AWvc54vcKbUSePvwvHv43fxfTb3csbUSeP34fh5NL7vJuaR/3lqSjLhrBmT5uGYcDbNyzHhWzQfx4Rv1fwcE75NC3BM+HZ9lGPCd2hBjgnfqYU4JnyXFuaY8N36GMeE79EiHBO+V4tyTPg+LcYx4exanGPCObQEx4Tv18c5JvyAPsEx4Qe1JMeEc2opjgk/pKU5JpxLy3BMOLeW5Zjww1qOY8KPaHmOCefRChwTzqsVOSacReN/PxuOcxhnbkpKMuF8zmE2zsf59RbOzwX0Vi7Aj+pt/CgX1Nu5IBfSO7gQF9Y7uTA/pnfxY1xE7+YiXFTv4aJcTO/lYlxc7+PiXEKzcwl+XHPw4/yE3s9PcEl9gEtyKX2QS3FpzcmluYw+xGW4rObislxOc3M5Lq8Pc3muoI9wBa6oebgiV9K8XIkzaxaO8xZna3JKMuHKGZPm58pcRQtwFa6qj3JVrqYFuRpX10JcnWtoYa7BNfUxrsm1tAjX4tpalGtzHS3GdbiuFue6XE9LcD2ur49zfW6gT3ADbqgluSE30lLciBtraW7MTbQMN+GmWpabcjMtx824uZbn5txCK3ALbqkVuSW30krcijNpZo5zFWdoUkoy4dYZk1bh1txGq3IbbqvVuC230+rcjttrDW7PHbQmd+COWos7cietzZ24s9bhztxF63IX7qr1uCt30/rcjbtrA+7OPbQh9+Ce2oh7ci9tzL24tzbh3txHm3If7qvNuC/30+bcj5/UFvwk99eW3J+f0lb8FGfUTBznJ87KxJRkwgMyJm3DA3igtuWBPEjb8SAerO15MA/RDjyEh2pHHsrDtBMP4+HamYfzCO3CI/hp7cpP80jtxiP5Ge3Oz/Cz2oOf5ee0Jz/Hz2svfp5f0N78Ar+offhFfkn78kv8svbjl/kVfZJf4Ve1P7/Kr+lT/Bpn0Iwc5yTOxISUZMKvZ0w6kF/nN3QQv8Fv6mB+k9/SIfwWv61D+W1+R4fxO/yuDud3+T0dwe/x+/o0v88f6Ej+gD/UZ/hD/kif5Y/4Y32OP+ZP9Hn+hD/VF/hT/kxf5M/4c32JP+cv9GX+gr/UV/hL/kpf5a/4a32Nv+b0moHjPMTej09JJvxNxqRv8Dc8St/kUfytvsXf8nf6Nn/H3+s7/D2P1nd5NI/R93gMj9X3eSyP0w94HI/XD3k8T9CPeAJP1I95Ik/ST3gST9ZPeTJP0c94Ck/Vz3kqT9MveBpP1y95Os/Qr3gGz9SveSan0/Qc+x57PC4lmfAs+z6KZ/Fs/ZZn8xz9jufwXP2e5/I8Hc3zeL6O4fm8QMfyAl6o43ghL9LxvIgX6wRezEt0Ii/hH3QS/8A/6mT+kZfqFF7Ky3QqL+PlOo2X8wqdzit4pc7glbxKZ/IqvlnTcexv7OXYlGTCq+3vbF7Na3QOr+G1OpfX8jqdx+t4vc7n9bxBF/AG3qgLeSNv0kW8iTfrYt7MP+kS/om36A+8hbfqj7yVt+lS3sbbdRlv5x26nHfwTl3BO/lnXck/8y+6in/htHozxz7Gno1JSSa8yz6u4V28W9fybt6j63gP79X1vJf36Qbex/t1I+/nA7qJD/BB3cwH+ZD+xIf4sG7hw3xEt/IRPqrb+Cgf0+18jI/rDj7OJ3Qnn+Bf9Wf+lU/qL3ySb9K0HPsVezM6JZnwKfu1m0/xad3Dp/k33cu/8Rndx2f4d93Pv/MfeoD/4D/1IP/Jf+kh/ov/1sP8N5/VI3yW/9Gj/A+f02N8js/rcT7PF/QEX+CL+itf5Et6ki9xGr2JY19iD75PSSb8r305zf/yZf2NL/MVPcNX+Kr+zlf5P/2D/+NrMiX9k2PC1+pfHBO+Tv/mmHAqPcsx4ev1H44J36DnOCacouc5JnyjXuCYcGq9yDHhNHqJY8Kp///PONY/1vq7lGTCN2VKepljwmn1CseEb9arHBNOp/9xTDi9xlqnN+EMei1n4Ix6HWfkTJqKM3FmvZ4zcxa9gbNwVk3hrJxNb+RsfIum5lv4Vk3Dt/KN9iDW+0ZrHWv6bUoy4dusc1q+jW/Xm/l2vkPT8R18p6bnO/kuzcB38d2ake/mezQT38P3ama+l+/TLHwfZ9esnJ1zaDbOwffrLXw/P6C38gOckjFprGs41jPWblRKMuEHreft/CDn1Ds4Jz+kd/JDnEvv4lycW+/m3Pyw3sMP8yN6Lz/CefQ+zsN5NTvn5Xyag/Nxfr2f83MBfYAL8A0Zk8b6hWPdYo2+SUkm/Kh1y8mPckF9iAtyIc3Fhbiw5ubC/Jg+zI9xEX2Ei3BRzcNFuZjm5WJcXPNxcS6h+bkEP64F+HG+PmPSWKdwrE+sxdcpyYSfsD4F+QkuqYW4JJfSwlyKS+tjXJrLaBEuw2W1KJflclqMy3F5Lc7luYKW4ApcUR/nipwqY9JYj3CsQ9zzVynJhCtZh5JciStrKa7MVbQ0V+GqWoarcjUty9W4upbj6lxDy3MNrqkVuCbX0opci6/LmDTuOxz3G/f2ZUoy4drutzLX5jpahetwXa3KdbmeVuN6XF+rc31uoDW4ATfUmtyQG2ktbsTXZkwa9xeO+4p7+CIlmXBj91WHG3MTrctNuKnW46bcTOtzM26uDbg5t9CG3IJbaiNuyddkTBr3EY7rj2v9PCWZcCvX34RbcWttyq25jTbjNtxWm3NbbqctuB2315bcnv/LkDSuNxzXGdf0WUoy4Q6uszV34I7ahjtyJ23LnbiztuPO3EXbcxe+miFpXFc4rife+9OUZMJdXU9H7srdtBN34+7ambtzD+3CPfhKhqTx/uF433iPT1KSCff0vt24J/fS7tyLe2sP7s2XMySN9wnH68drfZySTLiP1+/Ffbiv9ua+/G+GpPF64Xid+JuPUpIJ9/M6fbkfX8qQNP4uHP8+fvdhSjLhixmSxu/D8fPoddckz4z8kyb57/cx4TczJT3LMeG39G+OCb+tf3FM+B39k2PC7+ofHBN+T3/nmPD7eoZjwh/obxwT/lBPc0z4Iz3FMeGP9STHhD/RXzkm/Kme4JjwZ3qcY8Kf6zGOCX+hRzkm/KUe4ZjwV3qYY8Jf6yGOCX+jBzkmPEoPcEz4W93PMeHvdB/HhL/XvRwTHq17OCY8RndzTPgN/d+x/D/HOYwzF/+9PzOPdQ7f4rE8Tt/mcTxe3+HxPEHf5Qk8Ud/jiTxJ3+dJPFk/4Mk8RT/kKTxVP+KpPE0/5mk8XT/h6TxDP+UZPFM/45k8Sz/nWTxbv+DZPEe/5Dk8V7/iuTxPv+Z5PF+/4fm8QEfxAl6o3/JCXqTf8SJerN/zYl6io3kJ/6Bj+Ad+Xd/gOG9xtuL5gEz8Y6ak4/hHXqrjeSkv0wm8jJfrRF7OK3QSr+CVOplX8iqdwqt4tU7l1bxGp/EaXqvTeS2v0xm8jtfrTF7PG3QWb+CNOps38iadw5t4s87lzfyTzuOfeIvO5y28VRfwVt6mC3kbb9dFvJ136GLewTt1Ce/kn/UH/plf09c5zlWcoXieICP/kinpUv6Fd+ky3sW7dTnv5j26gvfwXl3Je3mfruJ9vF9X834+oGv4AB/UtXyQD+k6PsSHdT0f5iO6gY/wUd3IR/mYbuJjfFw383E+oT/xCf5Vt/CvfFK38kk+pdv4FJ/W7Xyaf9Md/Buf0Z18hn/Xn/l3flVf4zg/cVbi+YMM/EempLv4D/5Td/Of/Jfu4b/4b93Lf/NZ3cdn+R/dz//wOT3A5/i8HuTzfEEP8QW+qIf5Il/SI3yJ/9Wj/C9f1mN8ma/ocb7CV/UEX+X/9Ff+j6/JnPQkx4Sv1VMcE75OT3NMOJX+xjHh6/UMx4Rv0N85JvyKn73KcU7iTMTzCuk5JXPSPzkmfKP+xTHh1Po3x4TT6FmOCd+k/3BMOK2e45jwzXqeY8Lp9ALHhNPrRY4JZ9BLHBPOqP9yTDiTXuaYcGa9wjHhLHqVY8JZ9T+OCWfTOBPZTPgWvZZv4Vv1Or6Vb9NUfBvfrtfz7XyH3sB38MvOSpyLl52J2Pt4viEd3+k83Mh38l2amu/iuzUN38336E18D9+raflevk9v5vs4u6bj7JxD03MOvl8z8P38gGbkB/hBzcQPck7NzDn5Ic3CD3Euzcq5OLdm49z8sN7CD/Mjeis/wnn0Ns7DefV2zsv59A7Oxy9lShr7H459jz2O5yFu5vz2/S7OzwX0bi7Aj+o9/CgX1Hu5IBfS+7gQF9bsXJgf0xz8GBfR+7kIF9UHuCgX0we5GBfXnFycS+hDXIIf11z8OD+hufkJLqkPc0kupY9wKS6tebg0l9G8XIbLaj4uyy9mShr7HI79jb2M5yfScjn7W4DLcXl9lMtzBS3IFbiiFuKKXEkLcyWurI9xZa6iRbgKV9WiXJWraTGuxtW1OFfnGlqCa3BNfZxrci19gmtxbS3JtbmOluI6XFdLc12up2W4HtfXslyfX8iUNPYzHPsYexbPW9zEDexjeW7ADbUCN+RGWpEbcWOtxI25iVbmJtxUq3BTbqZVuRk312rcnFtodW7BLbUGt+RWWpNbcWutxa25jdbmNtxW63Bbbqd1uR2313rcnjtofe7Az2dKGvsWjv2KvUnjGY1wR/vVkDtyJ23EnbizNubO3EWbcBfuqk25K3fTZtyNu2tz7s49tAX34J7akntyL23Fvbi3tube3EfbcB/uq225L/fTdtyPn9T2/CT31w7cn5/LlDT2Jxz7EnuQ2jM04afsSyd+igdoZx7AA7ULD+RB2pUH8WDtxoN5iHbnITxUe/BQHqY9eRgP1148nEdobx7BT2sffppHal8eyc9oP36Gn9Un+Vl+Tvvzc/xsJj/LlDjWP9b6Rs/WhJ+3/gP4eX5BB/IL/KIO4hf5JR3ML/HLOoRf5ld0KL/Cr+owfpVf0+H8Gr+uI/h1fkOf5jf4TR3Jb/Jb+gy/xW/rs/w2v6PP8Tv8TKaksd7hWOdY0xTP3ITftc4v8Lv8nr7I7/H7+hK/zx/oy/wBf6iv8If8kb7KH/HH+hp/zJ/o6/wJf6pv8Kf8mb7Jn/Hn+hZ/zl/o2/wFf6nv8Jc8MlPSWNdwrGes3Q2exQl/ZT3f46/4a32fv+Zv9AP+hkfphzyKv9WP+Fv+Tj/m7/h7/YS/59H6KY/mMfoZj+Gx+jmP5XH6BY/j8folj+enMyWN9QvHusUaXe8ZnfAE6/Y1T+CJ+g1P5Ek6iifxZP2WJ/MU/Y6n8FT9nqfyNB3N03i6juHpPEPH8gyeqeN4Js/S8TyLR2RKGusUjvWJtUjl2Z3wbOszkWfzHJ3Ec3iuTua5PE+n8Dyer1N5Pi/QabyAF+p0XsiLdAYv4sU6kxfzEp3FS3h4pqSxHuFYh7jn6zzTE/7BOszhH/hHncs/8lKdx0t5mc7nZbxcF/ByXqELeQWv1EW8klfpYl7Fq3UJr+ZhmZLGfYfjfuPervWsT3iN+/2R1/BaXcpreZ0u43W8Xpfzet6gK3gDb9SVvJE36SrexJt1NW/moZmSxv2F477iHq7xDFD4J/e1ln/iLbqOt/BWXc9beZtu4G28XTfydt6hm3gH79TNvJOHZEoa9xGO649rjedvYsI/u/4t/DP/olv5F96l23gX79btvJv36A7ew3t1J+/lwZmSxvWG4zrjmq56Zii8z3X+wvt4v+7i/XxAd/MBPqh7+CAf0r18iAdlShrXFY7rife+4lmi8GHXs58P8xE9wEf4qB7ko3xMD/ExHpgpabx/ON433uOyZ4zCx73vET7OJ/Qon+Bf9Rj/ygMyJY33Ccfrx2v969mj8Emvf4JP8in9lU/xU5mSxuuF43Xiby55Jil82uuc4tPcP1PS+Ltw/Pv43UXPKoWfzJQ0fh+On0fj+aV4ZuRcPAOUOZnw/VmSnueY8AN6gWPCD+pFjgnn1EscE35I/+WYcC69zDHh3HqFY8IP61WOCT+i/3FMOI/GQ1vhmHBevZZjwvn0Oo4J59dUHBMuoNdzTPhRvYFjwgU1hWPChfRGjgkX1tQcE35M03BMuIjexDHhopqWY8LF9GaOCRfXdBwTLqHpOSb8uGbgmPATmpFjwjk07TWJ4xzGmTuTOZlwSefwAS7JpfRBLsWlNSeX5jL6EJfhspqLy3I5zc3luLw+zOW5gj7CFbii5uGKXEnzciWurPm4MlfR/FyFq2oBrsrV9FGuxtW1IFfnGlqIa3BNLcw1uZY+xrW4thbh2lxHi3IdrqvFuC7X0+Jcj+trCa7PDfRxbsAN9QluyNk1B8d5i7P1e+Zkwo2yJC3FjbixlubG3ETLcBNuqmW5KTfTctyMm2t5bs4ttAK34JZakVtyK63Erbi1VubW3EarcBtuq1W5LbfTatyO22t1bs8dtAZ34I5akztyJ63Fnbiz1ubO3EXrcBfuqnW5K3fTetyNu2t97s49tAH34J7akHvyfZqd41zFGfojczLhXlmSNuZe3FubcG/uo025D/fVZtyX+2lz7sdPagt+kvtrS+7PT2krfooHaGsewAO1DQ/kQdqWB/FgbceDeYi25yE8VDvwUB6mHXkYD9dOPJxHaGcewU9rF36aR2pXHsnPaDd+hp/V7vwsP6c9+Dl+Xnvy83yv3sdxfuKs/Jk5mfALWZL25hf4Re3DL/JL2pdf4pe1H7/Mr+iT/Aq/qv35VX5Nn+LX+HUdwK/zGzqQ3+A3dRC/yW/pYH6L39Yh/Da/o0P5HX5Xh/G7/J4O5/f4fR3B7/MH+jR/wB/qSP6QP9Jn+CP+WJ/lj/kTfY4/4U/1ef6U79F7Oc5JnIm/MicT/ixL0hf5M/5cX+LP+Qt9mb/gL/UV/pK/0lf5K/5aX+Ov+Rt9nb/hUfoGj+Jv9U3+lr/Tt/g7/l7f5u95tL7Do3mMvstjeKy+x2N5nL7P43i8fsDjeYJ+yBN4on7EE3mSfsyTeLJ+wpN5in7KU/huvYfjPMTe/505mfDULEk/56k8Tb/gaTxdv+TpPEO/4hk8U7/mmTxLv+FZPFtH8Wyeo9/yHJ6r3/Fcnqff8zyer6N5Pi/QMbyAF+pYXsiLdBwv4sU6nhfzEp3AS/gHncg/8I86iX/kpTqZl/IyncLL+C69m2PfY4/PZk4mvNy+T+PlvEKn8wpeqTN4Ja/SmbyKV+ssXs1rdDav4bU6h9fyOp3L63i9zuP1vEHn8wbeqAt4I2/ShbyJN+si3sw/6WL+ibfoEt7CW/UH3srb9Efextt1KW/nHbqMd/CdehfH/sZe/pM5mfBO+7uCd/LPupJ/5l90Ff/Cu3Q17+LduoZ38x5dy3t4r67jvbxP1/M+3q8beD8f0I18gA/qJj7Ih3QzH+LD+hMf5iO6hY/wUd3KR/mYbuNjfFy383E+oTv4BN+hd3LsY+zZuczJhH+1jz/zr3xSf+GTfEp38Sk+rbv5NP+me/g3PqN7+Qz/rvv4d/5D9/Mf/Kce4D/5Lz3If/Hfeoj/5rN6mM/yP3qE/+FzepTP8Xk9xuf5gh7nC3xRT/BFvl3v4Niv2JvzmZMJX7JfJ/kS/6un+F++rKf5Ml/R3/gKX9UzfJX/09/5P74ma9I/OCZ8rf7JMeHr9C+OCafSvzkmfL2e5ZjwDfoPx4RT9BzHhG/U8xwTTq0XOCacRi9yTPg2P7udY19iDy5kTiZ8U9ak/3JMOK1e5pjwzXqFY8Lp9CrHhNPrfxwTzqCxBxlMOKNeyxk5k17HmTizpuLMnEWv5yycVW/grJxNUzgb36I38i18q6bmW/k2TcO38a32JvbhVnsQa30xczLh261/Wr6d79Cb+Q6+U9PxnXyXpue7+G7NwHfzPZqR7+F7NRPfy/dpZr6Ps2sWzs45NCvn4Ps1G9/PD+gt/AA/qLfyg5xTb+OcfEuWpLHe4VjnWNNLmZMJP2Sd7+CHOJfeybk4t97FuflhvZsf5kf0Hn6E8+i9nIfz6n2cl/Npds7H+TUH5+cCej8X4Ef1AX6UC+qDXJALaU4uxNmyJI11Dcd6xtr9mzmZcGHrmYsL82Oamx/jIvowF+Gi+ggX5WKah4txcc3LxbmE5uMS/Ljm58f5CS3AT3BJfZRLciktyKW4tBbi0pw1S9JYv3CsW6zR5czJhMtYt8e4DJfVIlyWy2lRLsfltRiX5wpanCtwRS3BFbmSPs6VuLI+wZW5ipbkKlxVS3FVrqaluRpnyZI01ikc6xNrcSVzMuHq1qcsV+caWo5rcE0tzzW5llbgWlxbK3JtrqOVuA7X1cpcl+tpFa7H9bUq1+cGWo0bcOYsSWM9wrEOcc9XMycTbmgdanBDbqQ1uRE31lrcmJtobW7CTbUON+VmWpebcXOtx825hdbnFtxSG3BLzpQladx3OO437u2/zMmEW7nfRtyKW2tjbs1ttAm34bbalNtyO23G7bi9Nuf23EFbcAfuqC25I2fMkjTuLxz3FfdwTZZkwp3cV2vuxJ21DXfmLtqWu3BXbcdduZu2527cXTtwd+6hHbkHZ8iSNO4jg+uPa702SzLhnq6/M/fkXtqFe3Fv7cq9uY924z7cV7tzX+6nPbgfp8+SNK43veuMa7ouSzLhJ11nL36S+2tv7s9PaR9+igdoXx7AA7UfD+R0WZLGdaVzPfHeqbIkEx7kevrzIB6sT/FgHqIDeAgP1YE8lG/OkjTe/2bvG+9xfZZkwsO872AexsN1CA/nETqUR3DaLEnjfdJ6/XitG7IkE37a6w/np3mkjuCRfFOWpPF6N3md+JuULMmEn/E6I/kZTpMlafxdGv8+fndjlmTCqTV+n9rP/6/XJM+MfP6/380x4a36GW/lbfopb+Pt+glv5x36Me/gnfoR7+Sf9UP+mX/RD/gX3qXv8y7ere/xbt6j7/Ie3qvv8F7ep2/zPt6vb/F+PqBv8gE+qG/wQT6kr/MhPqyv8WE+oq/yET6qr/BRPqYv8zE+ri/xcT6hL/IJ/lVf4F/5pD7PJ/mUPsen+LQ+y6d5i8ajZeE4h3HmZpvwb7qNf+Mzup3P8O+6g3/nP3Qn/8F/6s/8J/+lv/Bf/Lfu4r/5rO7ms/yP7uF/+Jzu5XN8Xvfxeb6g+/kCX9QDfJEv6UG+xP/qIf6XL+thvsxX9Ahf4at6lK/yf3qM/+NrsiU9zjHha/UEx4Sv0185JpxKT3JM+Ho9xTHhG/Q0x4R/8rMtHOctztYsE07JlvQMx4Rv1N85Jpxa/+CYcBr9k2PCN+lfHBNOq39zTPhmPcsx4XT6D8eE0+s5jgln0PMcE86oFzgmnEkvckw4s17imHAW/Zdjwln1MseEs+kVjgnfolc5Jnyr/scx4ds0ztZtJny7Xsu38x16Hd/Bd2oqvpPv0uv5Lr5bb+C7ebMzF+drs3MVZ2hm1mTC9zhXN/I9fK+m5nv5Pk3D93F2vYmzcw5Nyzn4fr2Z7+cHNB0/wA9qen6Qc2oGzskPaUZ+iHNpJs7FuTUz5+aHNQs/zI9oVn6E82g2zsN59RbOy/n0Vs7H+fU2zs8F9HYuwI/qHfwoF9Q7uSAX0ru4EBfWu7kwb8qaNM7RJucnzsqMrMmEH8uW9F5+jIvofVyEi2p2LsrFNAcX4+J6PxfnEvoAl+DH9UF+nJ/QnPwEl9SHuCSX0lxciktrbi7NZfRhLsNl9REuy+U0D5fj8pqXy3MFzccVuKLm54pcSQtwJa6sj3JlrqIFuQpX1UJclatpYa7GG7MmjfOy0TmJMzE9azLh6tmSFuHqXEOLcg2uqcW4JtfS4lyLa2sJrs119HGuw3X1Ca7L9bQk1+P6WorrcwMtzQ24oZbhhtxIy3IjbqzluDE30fLchJtqBW7KzbQiN+PmWombcwutzC24pVbhltxKq3Irbq3VuDVvyJo0zsUG5yH2flrWZMJtnIca3Ibbak1uy+20Frfj9lqb23MHrcMduKPW5Y7cSetxJ+6s9bkzd9EG3IW7akPuyt20EXfj7tqYu3MPbcI9uKc25Z7cS5txL+6tzbk399EW3If7akvuy/20FffjJ7U1P8nrsyaN/V9v32OPp2ZNJtzfvrfl/vyUtuOneIC25wE8UDvwQB6kHXkQD9ZOPJiHaGcewkO1Cw/lYdqVh/Fw7cbDeYR25xH8tPbgp3mk9uSR/Iz24mf4We3Nz/Jz2oef4+e1Lz/PL2g/foFf1Cf5RV6XNWns8zr7G3s5JWsy4Zfs71P8Er+sA/hlfkUH8iv8qg7iV/k1Hcyv8es6hF/nN3Qov8Fv6jB+k9/S4fwWv60j+G1+R5/md/hdHcnv8nv6DL/H7+uz/D5/oM/xB/yhPs8f8kf6An/EH+uL/DGvzZo09nOtfYw9m5w1mfAn9vFl/oQ/1Vf4U/5MX+XP+HN9jT/nL/R1/oK/1Df4S/5K3+Sv+Gt9i7/mb/Rt/oZH6Ts8ir/Vd/lb/k7f4+/4e32fv+fR+gGP5jH6IY/hsfoRj+Vx+jGP4zVZk8a+rbFfsTeTsiYTHm+/PuXxPEE/4wk8UT/niTxJv+BJPFm/5Mk8Rb/iKTxVv+apPE2/4Wk8XUfxdJ6h3/IMnqnf8Uyepd/zLJ6to3k2z9ExPIfn6liey/N0HM/j1VmTxv6sti+xBxOzJhOeb18m8HxeoBN5AS/USbyQF+lkXsSLdQov5iU6lZfwDzqNf+AfdTr/yEt1Bi/lZTqTl/FyncXLeYXO5hW8UufwSl6lc3kVr9Z5vJpXZfWzrIlj/f9vrbMmE15j/RfwGl6rC3ktr9NFvI7X62Jezxt0CW/gjfoDb+RN+iNv4s26lDfzT7qMf+Itupy38FZdwVt5m67kbbxdV/H2/1fTfYf9WP99HL9USmnJZW8V2XuEQhMpskcSSrSLsvfem2yivfdS2SQjlC0ryt6y3ff7Ph/3H6/j+Thwfb/n+TnP3x+/43cdx4836HLewEtTk8Z5L3XOcaYfpCYLb3TOK3gjb9LfeBNv1pW8mbfoKt7CW3U1b+Vtuoa38Xb9nbfzX7qW/+Iduo538E5dzzt5l/7Bu3i3/sm7eY9u4D28JDVpnOsS5xln935qsvDfznMT/817dTPv5X26hffxP7qV/+F/dRv/y/t1O+/nA/oXH+CDuoMP8iHdyYf4sO7iw3xEd/MRPqp7+CgvTk0a57fYucUZvZeaLHzMue3lY3xc9/FxPqH/8Ak+qf/yST6l+/kUn9YDfJrP6EE+w//pIf6Pz+phPsvn9Aif4/N6lM/zotSkcU6LnE+cxbupycIXnM9xvsAX9QRf5Et6ki/xZT3Fl/mKnuYrnJI56RmOhdPofxwLX6VnORa+Ws9xLHyNnudYeGFq0jiPhc4h7vmd1GThtJmTXuRY+Fq9xLHwdXqZY+F0eoVj4es17vl6C9+gafgGTq9XcXq+Ua/mG/kmvYZv4gWpSeO+F7jfuLe3U5OFb3a/1/LNfItex7fwrZqOb+UMej1n4Nv0Br6NM2p6zsipeiOncia9iTPx/NSkcX/z3Vfcw9zUZOHM7usWzsxZ9FbOwlk1A2flbHobZ+PsmpGzcw5N5RycUzNxTv4lNWncxy+uP651TmqycC7Xn4VzcW7Nyrk5j2bjPJxXs3Nezqc5OB/n15ycn39OTRrX+7PrjGt6KzVZ+HbXmZtv5zs0D9/Bd2pevpMLaD4uwAU1Pxfkn1KTxnX95Hriu2enJgvf5Xru4Lu4kN7JhbiwFuDCXEQLchGel5o0vn+e743vmJWaLFzU9xbiolxMC3MxLq5FuDj/mJo0vudHnx+fNTM1WbiEzy/GJbikFueS/ENq0vi8H3xO/MyM1GThUj6nJJfi71OTxs9979/H301PTRb+TuPvv/Pn0XQpye+MTL45+d/vY+FHMyd9k2Phx3QSx8J1dCLHwnV1AsfCj+t4joXr6TiOhevrWI6FG+gYjoUb6miOhRvpKI6FG+tIjoWb6AiOhZvqcI6Fm+kwjoWb61COhZ/QIRwLt9DBHAs/qYM4Fm6pAzkWfkoHcCzcSvtzLNxa+3Es3Eb7ciz8tPbhWPgZ7c2xcFvtxbFwab0mJXG8h/HOxf/ev5af9R4+xs9yO63D7bi91uX2/Jw+zs/x81qPn+cXtD6/wC9qA36RX9KG/BK/rI34ZX5FG/Mr/Ko24Vf5NW3Kr3EHbcYduKM25478uj7Br/Mb2oLf4E76JHfiztqSO3MXfYq7cFdtxV25m7bmbtxd23B37qFPcw/uqc9wT+6lbbkXl9HSHO9bvFvx+wG/c+/MSdtxb+6j7bkP99XnuC/30+e5H/fXF7g/D9AXeQAP1Jd4IA/Sl3kQD9ZXeDAP0Vd5CA/V13goD9MOPIyHa0ceziP0dR7BI/UNHsmjtBOP4tHamUfzGO3CY3isduWxPE678Tger915PE/QHjyBJ2pPnsiTtBdP4rJahuO9incofp9gDb+ZOWkffpMna1+ezFO0H0/hqdqfp/I0HcDTeLoO5Ok8QwfxDJ6pg3kmz9IhPItn61CezW/pMH6L5+hwnsNzdQTP5bd1JL/N7+gofoff1dH8Lr+nY/g9fl/H8vv8gY7jD/hDHc8f8kc6gT/ij3Uif8yf6CT+hMtpWY73J96V+P2D1fxp5qST+VP+TKfwZ/y5TuXP+Qudxl/wlzqdv+SvdAZ/xV/rTP6av9FZ/A1/q7P5W/5O3+Lv+Hudw9/zDzqXf+Af9W3+kefpOzyPf9J3+Sf+Wd/jn/kXfZ9/4fn6Ac/nBfohL+CF+hEv5EX6MS/ixfoJL+byWo7jPYl3In5fYRUvyZz0M17CS/VzXsrL9Atexsv1S17Ov+pX/Cuv0K95Bf+m3/BvvFK/5ZW8Sr/jVbxav+fVvEZ/4DX8u/7Iv/NancdreZ3+xOt4vf7M6/kP/YX/4D91Pv/JG3QBb+CNupA38iZdxJt4sy7mzVxBy3O8D/Hs4/cbVvKWzEmX8hbeqst4K2/T5byNt+uvvJ3/0hX8F+/Q33gH79SVvJN36Srexbt1Ne/mPbqG9/Df+jv/zXt1Le/lfbqO9/E/up7/4X/1D/6X9+ufvJ8P6AY+wAd1Ix/kQ7qJD/Fh3cyHuaJW4Hju8Yzj9yF+4yOe+1Y+wkd1Gx/lY7qdj/Fx/YuP8wndwSf4pO7kk3xKd/EpPq27+TSf0T18hv/Tv/k/Pqt7+Syf0318js/rP3yeL+i/fIEv6n6+yJf0AF/iy3qQL/MVPcRXOCVL0sMcC9/tzypyPN94lvH7Eys4TZakRzkWvkqPcSx8tR7nWPgaPcGxcFo9ybHwtXqKY+Hr9DTHwun0DMfC1+t/HAvfoGc5Fk6v5zgWvlHPcyx8k17gWPhmvcix8C16iWPhW/Uyx8IZ9ArHwrdpPMvbLFzJn8XzrORZxjOL37f4lTN6jldxRk7VqzmVM+k1nIkza1rOzFn0Ws7CWfU6zsrZNB1n4+x6PWfnHHoD5+Ccmp5zci69kXNxbr2Jc3MevZnzcF69hfNyPr2V83F+zcD5+Xa9jW/nyp5rPLdwPK94NvH7Gcv5Ds8rle/gOzUT38kFNDMX4IKahQvyXZqV7+JCmo0LcWHNzoW5iObgIlxUc3JRLqa5uBgX19xcnEtoHi7BJTUvl+RSmo9LcWnNz6W5jN7OZbhK5qTxfMLxXOIZLPM7NOGynsudXJbLaQEux+W1IJfnCnoXV+CKWogr8t1amO/mSlqEK3FlLcqVuYoW4yp8jxbne/heLcH3clUtyVW5mpbialxdS3N1vk/L8H18T+ak8RzCcf5x1kv9bk34fudfju/nB7Q8P8APagV+kB/SivwQP6x388NcQytxDa6plbkm19IqXIsf0Xv4Ea6t93JtflSr8qP8mFbjx7iOVuc6XFfv47p8b+akcd7hOOc40yV+5yb8uHN+gB/nevog1+P6+hDX5wb6MDfghlqDG3IjrcmNuLHW4sbcRB/hJtxUa3NTbqaPcjNuro9xc35C6/AT3ELrcguumjlpnGs4zjPObrHfxQk/6Tzr8ZPcUutzS35KG/BT3EobciturY24NbfRxtyGn9Ym/DQ/o035GW6rzbgtP6vN+Vlup09wO26vLbg9V8ucNM4vHOcWZ7TI7+iEn3NuLfk5fl6f4uf5BW3FL/CL2ppf5Je0Db/EL+vT/DK/os/wK/yqtuVX+TV9ll/jDtqOO3BHbc8duXrmpHFO4TifOIuFfncn/LrzeZ5f5zf0BX6DO+mL3Ik760vcmbvoy9yFu+or3JW76avcjbvra9yde2gH7sE9tSP35PsyJ43zCMc5xD0v8Ds94V7O4Q3uxb21E/fmPtqZ+3Bf7cJ9uZ925X7cX7txfx6g3XkAD9QePJAHaU8exPdnThr3HY77jXub73d9woPdb28ezEO0Dw/hodqXh/Iw7cfDeLj25+E8QgfwCB6pA3kkj9JBPIofyJw07i8c9xX38IvfAQqPdl9DeDSP0aE8hsfqMB7L43Q4j+PxOoLH8wQdyRN4oo7iifxg5qRxH+G4/rjWn/1uUHiS6x/Dk/hNHctv8mQdx5N5io7nKTxVJ/BUnqYTeRo/lDlpXG84rjOu6Se/MxSe7jrf5Ok8QyfzDJ6pU3gmz9KpPItn6zSezQ9nThrXFY7rie+e53eJwm+5nhn8Fs/RmTyH5+osnstv62x+m2tkThrfH47vje/40e8Yhd/xvXP4HX5X5/K7/J6+ze9xzcxJ43vC8fnxWT/43aPw+z7/XX6fP9D3+AOulTlpfF44Pid+5nu/kxT+0Od8wB/yI5mTxs+F49/H333nd5TCtTMnjb8Px59H4/eXYuHqKcnCH/mZR/gjP/ux1uKPfe8nWpM/cS+fag3+1Ll9pg/zZ57d5/oQf+69+kIf5Fj4S32AY+Gv9H6Ohb/W+zgW/karcyz8rVbjWPg7rcqx8Pd6L8fCP+g9HAv/qFU4Fp6nlTkW/kkrcSz8s97NsfAvWpFj4flagWPhBVqeY+GFWo5j4UValmPhxVqGY+ElWppj4bfTJ43fXwrHexjvXLWUZOGl3sOPeSkv0094GS/XT3k5/6qf8a+8Qj/nFfybfsG/8Ur9klfyKv2KV/Fq/ZpX8xr9htfw7/ot/85r9Ttey+v0e17H6/UHXs9/6I/8B/+p8/hP3qA/8QbeqD/zRt6kv/Am3qzzeTNv0QW8hbfqQt7K23QRb+Ptupi381+6hP/iuemTxnsXjvct3q2qKcnCO7IkXcY7eKcu5528S3/lXbxbV/Bu3qO/8R7+W1fy37xXV/Fe3qereR//o2v4H/5Xf+d/eb+u5f18QNfxAT6o6/kgH9I/+BAf1j/5MB/RDXyEj+pGPsrHdBMf4+O6mY/zCd3CJ/ikbuWTfEq38Sk+rdv5NJ/Rv/gMz0mfNN6vcLxX8Q7dm5Is/F+WpDv5Pz6ru/gsn9PdfI7P6x4+zxf0b77AF3UvX+RLuo8v8WX9hy/zFf2Xr3BK1qT7ORZOowc4Fr5KD3IsfLUe4lj4Gj3MsXBaPcKx8LV6lGPh6/QYx8Lp9DjHwtfrCY6Fb9CTHAun11McC9+opzkWvknPcCz8Vvqk8R6F4/2Jd+WelGThm7MmPcux8C16jmPhW/U8x8IZ9ALHwrfpRY6FM+oljoVT9TLHwpn0CsfCmTXelcwWzqJpOAtn1as4K2fTqzkbZ9drODvn0LScg3PqtZyTc+l1nItzazrOzXn0es7DefUGzsv5ND3n4/x6I+fn2/Umvp1np08a70s43pN4J6qkJAvf4T25he/gO/VWvpMLaAYuwAX1Ni7Id2lGvosLaSoX4sKaiQtzEc3MRbioZuGiXEyzcjEurtm4OJfQ7FyCS2oOLsmlNCeX4tKai0tzGc3NZbis5uGyXE7zcjkur/m4PFfQ/FyBK+rtXJFnpU8a70U43od49pVTkoXv9j7cyXdzJS3AlbiyFuTKXEXv4ip8jxbie/heLcz3clUtwlW5mhblalxdi3F1vk+L8318v5bg+/kBLckP8INaih/kh7Q0P8QPaxl+mGtoWa7BNbUc1+RaWp5r8SNagR/h2lqRa/PM9Enj+YfjucczrpSSLPyo516JH+XHtDI/xnW0CtfhunoP1+XH9V5+nOtpVa7H9bUa1+cGWp0bcEO9jxtyI72fG3FjfYAbcxN9kJtwU32Im3IzfZibcXOtwc35Ca3JT3ALrcUt+El9hJ/kllqbW/KM9EnjOYfj+f7fs01JFn7K832Mn+JWWodbcWuty625jT7ObfhprcdP8zNan5/httqA2/Kz2pCf5XbaiNtxe23M7fk5bcLP8fPalJ/nF7QZv8AvanN+kV/SJ/glfllb8Mv8ij7Jr/Cr2pJf5enpk8bzDMdzjGdWMSVZ+DXPsRW/xh20NXfgjtqGO/Lr+jS/zm/oM/wGd9K23Ik767PcmbtoO+7CXbU9d+Vu+hx34+76PHfnHvoC9+Ce+iL35F76Evfi3voy9+Y++gr34b76KvflaemTxnMLx/OKZ1MhJVm4n+fVgftxf+3I/XmAvs4DeKC+wQN5kHbiQTxYO/NgHqJdeAgP1a48lIdpNx7Gw7U7D+cR2oNH8EjtySN5lPbiUTxae/NoHqN9eAyP1b48lqemTxrPJxzPJZ5B+ZRk4XGeS38ex+N1AI/nCTqQJ/BEHcQTeZIO5kn8pg7hN3myDuXJPEWH8RSeqsN5Kk/TETyNp+tIns4zdBTP4Jk6mmfyLB3Ds3i2juXZPCV90ngO4Tj/OOtyKcnCbzn/8fwWz9EJPIfn6kSey2/rJH6b39E3+R1+Vyfzu/yeTuH3+H2dyu/zBzqNP+APdTp/yB/pDP6IP9aZ/DF/orP4E/5UZ/OnPDl90jjvcJxznGnZlGThz5zzHP6MP9e5/Dl/oW/zF/ylvsNf8lf6Ln/FX+t7/DV/o+/zN/ytfsDf8nf6IX/H3+tH/D3/oB/zD/yjfsI/8jz9lOfxm+mTxrmG4zzj7MqkJAv/5Dw/55/4Z/2Cf+Zf9Ev+hefrVzyfF+jXvIAX6je8kBfpt7yIF+t3vJiX6Pe8hJfqD7yUl+mPvIyX6zxezpPSJ43zC8e5xRmVTkkW/tW5/cy/8gr9hVfwbzqff+OVuoBX8ipdyKt4tS7i1bxGF/Ma/l2X8O+8VpfyWl6ny3gdr9flvJ4npk8a5xSO84mzKJWSLPyH81nBf/Cf+hv/yRt0JW/gjbqKN/ImXc2beLOu4c28RX/nLbxV1/JW3qbreBtv1/W8nSekTxrnEY5ziHsumZIs/Jdz+JP/4h26gXfwTt3IO3mXbuJdvFs3827eo1t4D/+tW/lv3qvbeC/v0+28j8enTxr3HY77jXsrkZIs/I/73cH/8L+6k//l/bqL9/MB3c0H+KDu4YN8SP/mQ3xY9/JhPqL7+AiPS5807i8c9xX3UDwlWfio+/qXj/Ix3c/H+Lge4ON8Qg/yCT6ph/gkn9LDfIpP6xE+zWPTJ437CMf1x7UWS0kWPuP6j/EZ/k+P8398Vk/wWT6nJ/kcn9dTfJ4v6Gm+wGPSJ43rDcd1xjUVTUkWvug6/+OLfEnP8iW+rOf4Ml/R83yFU7IlvcCx8Oj0SeO6wnE98d1FUpKF02RLeolj4av0MsfCV+sVjoWv0fjuayw8Kn3S+P5wfG98R+GUZOG0vvcqTsvX6tV8LV+n1/B1PDJ90viecHx+fFahlGThdD7/Wk7H1+t1fD2PSJ80Pi8cnxM/c1dKsvANPud6voGHp08aPxeOfx9/VzAlWXhY+qTx9+H482iK//+4+P/ryp8xWTh9tqTfcCx8o37NsfBN+hXHwjfrlxwL36JfcCx8q37OsXAG/Yxj4dv0U46FM+onHAun6sccC2fSjzgWzqwfciycRT/gWDirvs+xcDZ9j2Ph7Poux8I59B2OhXPq2xwL59K5HAvn1jkcC+fRtzgWzquzORbOp7M4Fs6vMzkWvl1ncCx8h07nWPiJlKTxu3TheA/jncuXMVn4Tu/hjXwnF9CbuAAX1Ju5IN+lt/BdXEhv5UJcWDNwYS6it3ERLqoZuSgX01QuxsU1ExfnEpqZS3BJzcIluZRm5VJcWrNxaS6j2bkMl9UcXJbLaU4ux+U1F5fnCpqbK3BFzcMV+W7Ny3dzJc3Hlbiy5ufKXEVv5yp8j97B93DzlKTx3oXjfYt3K2/GZOF7syUtwPdyVS3IVbma3sXVuLoW4up8nxbm+/h+LcL38wNalB/gB7UYP8gPaXF+iB/WEvww19CSXINraimuybW0NNfiR7QMP8K1tSzX5ke1HD/Kj2l5fozraAWuw3W1Itflx/VufpzraSWux/W1MtfnBlqFG3BDvYcbcrOUpPF+heO9incoT8Zk4UbZklblRtxYq3FjbqLVuQk31fu4KTfT+7kZN9cHuDk/oQ/yE9xCH+IW/KQ+zE9yS63BLfkprclPcSutxa24tT7CrbmN1uY2/LQ+yk/zM/oYP8NttQ635We1Lj/L7fRxbsfttR635+e0Pj/Hz2sDfp5f0Ib8AjdNSRrvUTjen3hXcmdMFn4xW9LG/CK/pE34JX5Zm/LL/Io241f4VW3Or/Jr+gS/xh20BXfgjvokd+TXtSW/zm/oU/wGd9JW3Ik7a2vuzF20DXfhrvo0d+Vu+gx34+7alrtzD32We3BPbcc9uZe2517cW5/j3txHn+c+3Fdf4L7cJCVp05TE8Z7EO5ErY7Jwv2xJX+J+3F9f5v48QF/hATxQX+WBPEhf40E8WDvwYB6iHXkID9XXeSgP0zd4GA/XTjycR2hnHsEjtQuP5FHalUfxaO3Go3mMducxPFZ78Fgepz15HI/XXjyeJ2hvnsATtQ9P5Enalydx45Sk8V6E432IZ58zY7Lwm96H/vwmT9YBPJmn6ECewlN1EE/laTqYp/F0HcLTeYYO5Rk8U4fxTJ6lw3kWz9YRPJvf0pH8Fs/RUTyH5+ponstv6xh+m9/RsfwOv6vj+F1+T8fze/y+TuD3+QOdyB/whzqJP+RGKUnj+YfjucczzpExWfgjz30yf8Qf6xT+mD/RqfwJf6rT+FP+TKfzZ/y5zuDP+QudyV/wlzqLv+SvdDZ/xV/rW/w1f6Nz+Bv+Vufyt/ydvs3f8ff6Dn/PP+i7/AP/qO/xjzxP3+d5/JN+wD/xz/oh/8wNU5LGcw7H841nmT1jsvAvnu/H/AvP1094Pi/QT3kBL9TPeCEv0s95ES/WL3gxL9EveQkv1a94KS/Tr3kZL9dveDn/qt/yr7xCv+MV/Jt+z7/xSv2BV/Iq/ZFX8Wqdx6t5jf7Ea/h3/Zl/5wYpSeN5huM5xjPLljFZeK3nOJ/X8jpdwOt4vS7k9fyHLuI/+E9dzH/yBl3CG3ijLuWNvEmX8SberMt5M2/RX3kLb9UVvJW36W+8jbfrSt7Of+kq/ot36GrewTt1De/kXfo77+L6KUnjuYXjecWzyZoxWXi357WOd/MeXc97+G/9g//mvfon7+V9uoH38T+6kf/hf3UT/8v7dTPv5wO6hQ/wQd3KB/mQbuNDfFi382E+on/xET6qO/goH9OdfIyP6y4+zvVSksbzCcdziWeQJWOy8AnPZQ+f4JP6N5/kU7qXT/Fp3cen+Yz+w2f4P/2X/+Ozup/P8jk9wOf4vB7k83xBD/EFvqiH+SJf0iN8iS/rUb7MV/QYX+GU7EmPcyz8eErSeA7hOP8468wZk4XTZE96kmPhq/QUx8JX62mOha/RMxwLp9X/OBa+Vs9yLHydnuNYOJ2e51j4er3AsfANepFj4fR6iWPhG/Uyx8I36RWOhW/WOOubLVw3JWmcdzjOOc40U8Zk4Vuc81V8C9+qV/OtnEGv4Qx8m6bl2zijXssZOVWv41TOpOk4E2fW6zkzZ9EbOAtn1fSclbPpjZyNs+tNnJ1z6M2cg+ukJI1zDcd5xtmlZkwWzuk8b+WcnEszcC7Orbdxbs6jGTkP59VUzsv5NBPn4/yamfPz7ZqFb+c7NCvfwXdqNr6TC2h2LsAFNQcX5MdSksb5hePc4owyZkwWvsu55eK7uJDm5kJcWPNwYS6iebkIF9V8XJSLaX4uxsX1di7OJfQOLsEl9U4uyaW0AJfi0lqQS/OjKUnjnMJxPnEWt2VMFi7jfApxGS6rhbksl9MiXI7La1EuzxW0GFfgilqcK/LdWoLv5kpakitxZS3FlbmKluYqXDslaZxHOM4h7jlDxmThe5xDWb6H79VyfC9X1fJclatpBa7G1bUiV+f79G6+j+/XSnw/P6CV+QF+UKvwg/xIStK473Dcb9zbrRmThR9yv/fyQ/ywVuWHuYZW4xpcU6tzTa6l93EtfkTv50e4tj7AtflRfZAf5VopSeP+wnFfcQ+3ZEwWfsx9PcyPcR2twXW4rtbkuvy41uLHuZ4+wvW4vtbm+txAH+UGXDMladxHOK4/rvXmjMnCDV1/HW7IjbQuN+LG+jg35iZaj5twU63PTbmZNuBmXCMlaVxvOK4zrummjMnCzV1nI27OT2hjfoJbaBNuwU9qU36SW2ozbskPpySN6wrH9cR335gxWfgp1/MEP8WttAW34tb6JLfmNtqS2/BDKUnj+8PxvfEd6TMmCz/te1vx0/yMtuZnuK224bb8YErS+J5wfH581g0Zk4Wf9fnP8LPcTttyO34gJWl8Xjg+J37m+ozJwu19Tjtuz/enJI2fC8e/j79LlzFZ+L6UpPH34fjz6HX/u2v/d4dvS0npnz1ZeIAe4Vh4oB7lWHiQHuNYeLAe51h4iJ7gWHionuRYeJie4lh4uJ7mWHiEnuFYeKT+x7HwKD3LsfBoPcex8Bg9z7HwWL3AsfA4vcix8Hi9xLHwBL3MsfBEvcKx8CRNyZg4Fn5T03AsPFmv4lh4il7NsfBUvYZj4WmalmPh6Xotx8Iz9DqOhY+nSRrvY7i/d66fhWfqQJ7Js3QQz+LZOphn81s6hN/iOTqU5/BcHcZz+W0dzm/zOzqC3+F3dSS/y+/pKH6P39fR/D5/oGP4A/5Qx/KH/JGO44/4Yx3PH/MnOoE/4U91In/Kn+kk/ow/1zf5c/5CJ/MX/KVO4S/5K53KX/HXOo2/5m90On/D3+oM/paPpUka71043rd4t/pa+Dudxd/x9zqbv+cf9C3+gX/UOfwjz9O5PI9/0rf5J/5Z3+Gf+Rd9l3/h+foez+cF+j4v4IX6AS/kRfohL+LF+hEv5iX6MS/hpfoJL+Vl+ikv4+X6GS/nX/Vz/pVX6Be8gn/TL/k3Xqlf8UpepV/zKl6t3/BqXqPf8ho+miZpvF/heK/iHepj4d/1e/6d1+oPvJbX6Y+8jtfrPF7Pf+hP/Af/qT/zn7xBf+ENvFHn80bepAt4E2/WhbyZt+gi3sJbdTFv5W26hLfxdl3K2/kvXcZ/8Q5dzjt4p/7KO3mXruBdvFt/4928R1fyHv5bV/HfvFdX817ep2t4Hx9JkzTeo3C8P/Gu9LbwP7qW/+F/dR3/y/t1Pe/nA/oHH+CD+icf5EO6gQ/xYd3Ih/mIbuIjfFQ381E+plv4GB/XrXycT+g2PsEndTuf5FP6F5/i07qDT/MZ3cln+D/dxf/xWd3NZ/mc7uFzfF7/5vN8QffyBb6o+/giH06TNN6XcLwn8U70svAl/Zcv8WXdz5f5ih7gK5ySI+lBjoXT6CGOha/SwxwLX61HOBa+Ro9yLJxWj3EsfK0e51j4Oj3BsXA6Pcmx8PV6imPhG/Q0x8Lp9QzHwjfqfxwL36RnORa+Wc9xLHyLnudY+Fa9wLFwBr3IsfChNEnjvQj38ux7Wvi2HEkvcyycUa9wLJyq8exTLZxJ03AmzqxXcWbOoldzFs6q13BWzqZpORtn12s5O+fQ6zgH59R0nJNz6fWci3PrDZyb82h6zsN59UbOy/n0Js7H+fVmzs+36y18O9+ht/IdfKdm4Dv5YJqk8fzDPT3nHtmThQt47hm5ABfUVC7Id2kmvosLaWYuxIU1CxfmIpqVi3BRzcZFuZhm52JcXHNwcS6hObkEl9RcXJJLaW4uxaU1D5fmMpqXy3BZzcdluZzm53JcXm/n8lxB7+AKXFHv5Ip8IE3SeM7heL7xLLtnTxa+2/MtyHdzJb2LK3FlLcSVuYoW5ip8jxbhe/heLcr3clUtxlW5mhbnalxdS3B1vk9L8n18v5bi+/kBLc0P8INahh/kh7QsP8QPazl+mGtoea7BNbUC1+RaWpFr8f40SeN5huM5xjPrlj1Z+BHPsRI/wrW1MtfmR7UKP8qP6T38GNfRe7kO19WqXJcf12r8ONfT6lyP6+t9XJ8b6P3cgBvqA9yQG+mD3Igb60PcmJvow9yEm2oNbsrNtCY34+Zai5vzv2mSxnMLx/OKZ9M1e7LwE55XbX6CW+ij3IKf1Mf4SW6pdbglP6V1+SlupY9zK26t9bg1t9H63Iaf1gb8ND+jDfkZbquNuC0/q435WW6nTbgdt9em3J6f02b8HD+vzfl5/idN0ng+4Xgu8Qy6ZE8WfsFzacEv8Iv6JL/IL2lLfolf1qf4ZX5FW/Er/Kq25lf5NW3Dr3EHfZo7cEd9hjvy69qWX+c39Fl+gztpO+7EnbU9d+Yu+hx34a76PHflfWmSxnMIx/nHWXfOnizczfm/yN24u77E3bmHvsw9uKe+wj25l77Kvbi3vsa9uY924D7cVztyX+6nr3M/7q9vcH8eoJ14AA/UzjyQB2kXHsSDtSsP5r1pksZ5h+Oc40w7ZU8WHuKcu/MQHqo9eCgP0548jIdrLx7OI7Q3j+CR2odH8ijty6N4tPbj0TxG+/MYHqsDeCyP04E8jsfrIB7PE3QwT+C/0ySNcw3HecbZvZE9WXii8xzKE3mSDuNJ/KYO5zd5so7gyTxFR/IUnqqjeCpP09E8jafrGJ7OM3Qsz+CZOo5n8iwdz7N4tk7g2bwnTdI4v3CcW5zR69mThd9ybpP4LZ6jb/IcnquTeS6/rVP4bX5Hp/I7/K5O43f5PZ3O7/H7OoPf5w90Jn/AH+os/pA/0tn8Ee9OkzTOKRznE2fRMXuy8MfOZw5/zJ/oXP6EP9W3+VP+TN/hz/hzfZc/5y/0Pf6Cv9T3+Uv+Sj/gr/hr/ZC/5m/0I/6Gd6VJGucRjnOIe+6QPVn4W+fwCX/L3+mn/B1/r5/x9/yDfs4/8I/6Bf/I8/RLnsc/6Vf8E/+sX/PP/It+w7/wzjRJ477Dcb9xb69lTxae736/4/m8QL/nBbxQf+CFvEh/5EW8WOfxYl6iP/ESXqo/81Jepr/wMt6RJmncXzjuK+7h1ezJwsvd1wJezr/qQv6VV+giXsG/6WL+jVfqEl7Jq3Qpr+LVuoxX819pksZ9hOP641pfyZ4svMb1/8pr+Hddwb/zWv2N1/I6XcnreL2u4vX8h67mP3h7mqRxveG4zriml7MnC//pOn/nP3mDruUNvFHX8UbepOt5E2/WP3gzb0uTNK4rHNcT3/1S9mThLa5nA2/hrbqRt/I23cTbeLtu5u28NU3S+P5wfG98x4vZk4X/8r1b+S/eodt4B+/U7byTt6RJGt8Tjs+Pz3ohe7LwLp+/g3fxbt3Ju3lzmqTxeeH4nPiZ57MnC+/xObt5D29KkzR+Lhz/Pv7uuezJwhvTJI2/D8efR6/2+0vx+yIjMiQL/50jaT+OhfdqX46F92kfjoX/0d4cC/+rvTgW3q89ORY+oD04Fj6o3TkWPqTdOBY+rF05Fj6iXTgWPqqdORY+pp04Fj6ub3AsfEJf51j4pHbkWPiUduBY+LS+xrHwGX2VY+H/9BWOhc/qyxwLn9OXOBY+ry/yef/ZuqAv8AX/Obioz/NF7/IlfY4veU9zpE0a72M43sN454ZnSBa+7D3cy5f5iu7jK5ySM+k/HAun0X85Fr5K93MsfLUe4Fj4Gj3IsXBaPcSx8LV6mGPh6/QIx8Lp9CjHwtfrMY6Fb9DjHAun1xMcC9+oJzkWvklPcSx8s57mWPgWPcOx8K36H8fCGfQsx8K36TmOhTPqeY6FU/UCx8KZ9CLHwpn1EsfC2dMmjfcuHO9bvFvDMiQLZ8mZ9ArHwlk13q2sFs6maTgbZ9erODvn0Ks5B+fUazgn59K0nItz67Wcm/PodZyH82o6zsv59HrOx/n1Bs7Pt2t6vp3v0Bv5Dr5Tb+I7uYDezAW4oN7CBfkuvZXv4kKagQtxYb2NC3MRzchFuKimclEuppm4GBfXzFycs6VNGu9XON6reIeGZkgWLpEzaVYuwSU1G5fkUpqdS3FpzcGluYzm5DJcVnNxWS6nubkcl9c8XJ4raF6uwBU1H1fkuzU/382V9HauxJX1Dq7MVfROrsL3aAG+h+/VgnwvV9W7uCpX00JcjatrYa7O92kRvo/v16J8Pz+gxfgBflCL84OcNW3SeI/C8f7EuzIkQ7LwQzmTluSH+GEtxQ9zDS3NNbimluGaXEvLci1+RMvxI1xby3NtflQr8KP8mFbkx7iO3s11uK5W4rr8uFbmx7meVuF6XF/v4frcQO/lBtxQq3JDbqTVuBE31urcmJvofdyEm+r93JSb6QPcjJvrg9ycs6RNGu9LON6TeCcGZ0gWfiJn0of5CW6hNbgFP6k1+UluqbW4JT+lj/BT3EprcyturY9ya26jj3Ebflrr8NP8jNblZ7itPs5t+Vmtx89yO63P7bi9NuD2/Jw25Of4eW3Ez/ML2phf4Be1Cb/IL2lTfolf1mb8Mr+izfkVzpw2abwX4Xgf4tkPypAs/Kr3oQW/yq/pk/wad9CW3IE76lPckV/XVvw6v6Gt+Q3upG24E3fWp7kzd9FnuAt31bbclbvps9yNu2s77s49tD334J76HPfkXvo89+Le+gL35j76IvfhvvoS9+V++jL34/76CvfnTGmTxvMPx3OPZzwwQ7LwAM/9NR7AA7UDD+RB2pEH8WB9nQfzEH2Dh/BQ7cRDeZh25mE8XLvwcB6hXXkEj9RuPJJHaXcexaO1B4/mMdqTx/BY7cVjeZz25nE8XvvweJ6gfXkCT9R+PJEnaX+exKlpk8ZzDsfz/b/nmcH+1296vgP5TZ6sg3gyT9HBPIWn6hCeytN0KE/j6TqMp/MMHc4zeKaO4Jk8S0fyLJ6to3g2v6Wj+S2eo2N4Ds/VsTyX39Zx/Da/o+P5HX5XJ/C7/J5O5Pf4fZ3E73PGtEnjeYbjOcYz658hWfgDz3Eyf8Af6hT+kD/SqfwRf6zT+GP+RKfzJ/ypzuBP+TOdyZ/x5zqLP+cvdDZ/wV/qW/wlf6Vz+Cv+Wufy1/yNvs3f8Lf6Dn/L3+m7/B1/r+/x9/yDvs8/8G1pk8ZzC8fzimfTL0Oy8I+e14f8I8/Tj3ge/6Qf80/8s37CP/Mv+in/wvP1M57PC/RzXsAL9QteyIv0S17Ei/UrXsxL9Gtewkv1G17Ky/RbXsbL9Ttezr/q9/wrr9AfeAVnSJs0nk84nks8g74ZkoV/81zm8W+8Un/ilbxKf+ZVvFp/4dW8RufzGv5dF/DvvFYX8lpep4t4Ha/Xxbye/9Al/Af/qUv5T96gy3gDb9TlvJE36a+8iTfrCt7Mt6ZNGs8hHOcfZ90nQ7LwFue/krfwVl3FW3mbruZtvF3X8Hb+S3/nv3iHruUdvFPX8U7epet5F+/WP3g379E/eQ//rRv4b96rG3kv79NNvI//0c38D9+SNmmcdzjOOc60d4Zk4X+d81b+l/frNt7PB3Q7H+CD+hcf5EO6gw/xYd3Jh/mI7uIjfFR381E+pnv4GB/Xv/k4n9C9fIJP6j4+yaf0Hz7FN6dNGucajvOMs+uVIVn4tPPcz6f5jB7gM/yfHuT/+Kwe4rN8Tg/zOT6vR/g8X9CjfIEv6jG+yJf0OF/iy3qCL/MVPclXOCVX0lMcC9+UNmmcXzjOLc6oZ4Zk4TS5kp7hWPgq/Y9j4av1LMfC1+g5joXT6nmOha/VCxwLX6cXORZOp5c4Fr5eL3MsfINe4Vg4vcYZpbfwjWmTxjmF43ziLHpkSBa+0flcxTfy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diff --git a/template/csf.nii b/template/csf.nii
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diff --git a/template/grey.nii b/template/grey.nii
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diff --git a/template/hcp_cortex_seeds.mat b/template/hcp_cortex_seeds.mat
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diff --git a/template/parcellations/Conte69.L.inflated.8k_fs_LR.surf.gii b/template/parcellations/Conte69.L.inflated.8k_fs_LR.surf.gii
new file mode 100644
index 0000000..33387bf
--- /dev/null
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diff --git a/template/parcellations/Conte69.L.midthickness.8k_fs_LR.surf.gii b/template/parcellations/Conte69.L.midthickness.8k_fs_LR.surf.gii
new file mode 100644
index 0000000..99895de
--- /dev/null
+++ b/template/parcellations/Conte69.L.midthickness.8k_fs_LR.surf.gii
@@ -0,0 +1,110 @@
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diff --git a/template/parcellations/Conte69.R.inflated.8k_fs_LR.surf.gii b/template/parcellations/Conte69.R.inflated.8k_fs_LR.surf.gii
new file mode 100644
index 0000000..20bde8f
--- /dev/null
+++ b/template/parcellations/Conte69.R.inflated.8k_fs_LR.surf.gii
@@ -0,0 +1,110 @@
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diff --git a/template/parcellations/Conte69.R.midthickness.8k_fs_LR.surf.gii b/template/parcellations/Conte69.R.midthickness.8k_fs_LR.surf.gii
new file mode 100644
index 0000000..25e1393
--- /dev/null
+++ b/template/parcellations/Conte69.R.midthickness.8k_fs_LR.surf.gii
@@ -0,0 +1,110 @@
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eJw03HW0VeXa8GEQwQ12g3Qq3d0gnXYr0t0d0t1ISJd0d5ciHSqI2N2NIor1vfeZ13fGuMfvGnvDWnM+z/OPh7lWqlSpUt3wf3OvCT+jN/Iz/KzexM/yc3ozP8fN9BZuxs/rrfw8N9fbuDm30Nu5BbfUO7glt9I7uRW31ru4NbfRu7kNt9WM3JbbaSZux+31Hm7PHTQzd+COmoU7cifNyp24s2bjztxFs3MX7qo5uCt305zcjbtrLu7OPTQ39+Cemod7ci/Ny724t+bj3lxDr+M4h3Hm7jPhPvos9+G++hz35X7ajPtxf32e+/MAbc4DeKC24IE8SFvyIH5BW/ELPFhb82Aeom14CA/VtjyUh2k7HsbDtT0P5xHagUfwSO3II3mUduJRPFo782geo114DI/VrjyWx2k3HsfjtTuP5wnagyfwRO3JE3mS9uJJPFl782SuqTU4zlucrfwmPEX78hSeqv14Kk/T/jyNX9QB/CJP14E8nWfoIJ7BM/UFnsmzdDDP4pd0CL/Es3Uoz+Y5Oozn8FwdznN5no7geTxfR/J8XqCjeAEv1NG8kBfpGF7Ei3UsL+YlOo6X8FIdz0v5ZZ3AL/MyncjLeLlO4uW8QifzCr5fa3KcqzhDBUx4pU7llbxKp/EqXq0v8mpeo9N5Da/VGbyW1+lMXsfrdRav5w36Em/gjTqbN/ImncObeLPO5c28RefxFt6q83krb9MFvI2360Lezjt0Ee/gnbqYd/IuXcK7eLcu5d28R1/mPbxXl/Fe3qfLeR/v1xW8n2vp/RznJ85KQRM+oKv4AB/U1XyQD+kaPsSv6Fp+hV/VdfwqH9b1fJhf0w38Gh/RjXyEj+omPsrHdDMf4+O6hY/zCd3KJ/ikbuOTfEq38yk+rTv4NJ/RnXyGz+ouPsuv625+nd/QPfwGv6l7+U0+p/v4HJ/X/Xyea2stjnMSZ6KQCb+lB/ktvqCH+AK/ra/w23xRX+WL/I4e5nf4XX2N3+X39Ai/x+/rUX6fP9Bj/AF/qMf5Q/5IT/BH/LGe5I/5Ez3Fn/Cnepo/5c/0DH/Gn+tZ/py/0Nf5C/5S3+Av+St9k7/ir/Ucf83f6Hn+hutobY7zEHtf2IS/1Qv8LX+nb/N3/L1e5O/5B32Hf+Af9V3+kX/S9/gn/lnf55/5F/2Af+FL+iFf4l/1I/6Vf9OP+Te+rJ/wZf5dP+Xf+Yp+xlf4D/2c/+A/9Qv+k6/ql3yV/9Kv+C/+W7/mv/kf/Yb/4bpahwvb4yIm/K9+x//yf/o9/8epUif9gWPCqfVHjglfoz9xTDiN/swx4Wv1F44Jp9VLHBNOp79yTPg6/Y1jwil6mWPC6fV3jgln0CscE75e/+CY8A36J8eEb9SrHBO+Sf/imPDN+jfHhG/RfzgmXM/P6nIRe1nUhG9NnfQ/jgnfprGXt5nw7Zqab+c79Bq+g+/UNHwn36XX8l18t6bluzmjpuOMnEmv40x8j6bwPZxZ03NmzqIZOAtn1es5K2fTGzgbZ9cbOTvn0Js4B+fUmzkn59JbOBfXt8exn/XtY+xZsVTJhHPbx9s4N+fR2zkP59U7OC/n0zs5H9+rd/G9fJ/ezfdxfs3I+bmAZuICXFDv4YJcSDNzIS6sWbgwF9GsXISLajYuysU0Oxfj4pqDi3MJzckluKTm4pLcIFXS2LcG9iv2pniqZMKl7FceLsWlNS+X5jKaj8twWb2Xy3I5vY/LcXnNz+W5ghbgClxRC3JFrqSFuBJX1sJcmatoEa7CVbUoV+VqWoyrcXUtztW5hpbgGlxTS3JNbpgqaexPQ/sSe1AiVTLh++1Lab6fa2kZrsW1tSzX5jpajutwXS3PdbmeVuB6XF8rcn1uoJW4ATfUytyQG2kVbsSNtSo35iZajZtwU63OTfkBrcEP8INakx/kRqmSxj40sv6x1iVTJRN+yPrX4of4Ya3ND/MjWocf4Ue1Lj/Kj2k9fowf1/r8OD+hDfgJflIb8pP8lDbip/hpbcxP8zPahJ/hZ7UpP8vP6QP8HDfTB7kZN06VNNa7sXWONS2VKpnw89b5YX6em+sj3Jxb6KPcglvqY9ySW+nj3Ipb6xPcmtvok9yG2+pT3Jbb6dPcjtvrM9yeO+iz3IE76nPckTtpM+7ETVIljXVtYj1j7UqnSibc2Xo2587cRVtwF+6qLbkrd9NW3I27a2vuzj20DffgntqWe3Ivbce9uLe2597cRztwH+6rHbkv99NO3I+bpkoa69fUusUalUmVTLi/devC/XmAduUBPFC78UAepN15EL+gPfgFHqw9eTAP0V48hIdqbx7Kw7QPD+Ph2peH8wjtxyP4gVRJY50esD6xFmVTJRMeaX0G8EgepQN5FI/WQTyax+gLPIbH6mAey+N0CI/j8TqUx/MEHcYTeKIO54k8SUfwJH4wVdJYjwetQ9xzuVTJhCdbh1E8mafoaJ7CU3UMT+VpOpan8Ys6jl/k6Tqep/MMncAzeKZO5Jk8SyfxLH4oVdK474fcb9xb+VTJhF9yv1P4JZ6tU3k2z9FpPIfn6os8l+fpdJ7H83UGz+cFOpMX8EKdxQv54VRJ4/4edl9xDxVSJRNe5L5m8yJerHN4MS/RubyEl+o8Xsov63x+mZfpAl7Gy3UhL+dHUiWN+3jE9ce1VkyVTHiF61/MK3ilLuGVvEqX8iperS/zal6jy3gNr9XlvJYfTZU0rvdR1xnXVClVMuF1rnMlr+P1uorX8wZdzRt4o67hjbxJ1/ImfixV0riux1xPvHflVMmEN7ue9byZt+gG3sJbdSNv5W26ibfx46mSxvs/7n3jPaqkSia83ftu4e28Q7fyDt6p23gnP5EqabzPE14/XqtqqmTCu7z+Dt7Fu3Un7+YnUyWN13vS68TfqZYqmfAer7Ob9/BTqZLG33vKn4/fVU+VTPhpjd8/7efRlFTJ//amTp5XuoEvpU66jy/xr7qff+Xf9AD/xpf1IF/m3/UQ/85X9BW+wn/oq/wH/6mH+U++qq/xVf5Lj/Bf/Lce5b/5Hz3G//C/epz/5f/0BP/Hqa5JepJjwqn1FMeEr9HTHBNOo2c4JnytnuWYcFp9nWPC6fQNjglfp29yTDhFz3FMOL2e55hwBn2LY8LX6wWOCb/tZ2lSJY5zGGcunpW7kW+4JumvHBO+UX/jmPBNepljwjfr7xwTvkWvcEz4Vv2DY8K36Z8cE75dr3JM+A79i2PCd+rfHBO+S//hmPDd+i/HhDPqfxwTzqRx5jKZ8D2amu/hzHoNZ+YsmoazcFa9lrNyNk3L2Ti7puPsnEOv4xycU1M4J+fS9JyLc2sGzs159HrOwxedxTh3F525OFvxPOZNnNd5u5Hzcj69ifPxvXoz38v36S18H+fXWzk/F9DbuAAX1Nu5IBfSO7gQF9Y7uTAX0bu4CBfVu7koF9OMXIyLayYuziX0Hi7BJTUzl+RSmoVLcWnNyqW5jGbjMlxWs3NZLqc5uByX15xcnitoLq7AFTU3V+RKmocr8Tupk8b5Cse5ijMUz/bezJWvSZqPK3MVvZercFW9j6tyNc3P1bi6FuDqXEMLcg2uqYW4Jt+vhfl+rqVFuBbX1qJcm+toMa7DdbU41+V6WoLrcX0tyfW5gZbiBtxQS3NDbqRluBE31rLcmJtoOW7CTbU8N+UHtAI/wA9qRX6QH9JK/BC/mzppnKNwnJ84K/E8+C388DVJq/DD/IhW5Uf4Ua3Gj/JjWp0f48e1Bj/OT2hNfoKf1Pv5SX5Ka/FT/LTW5qf5Ga3Dz/CzWpef5ee0Hj/HzbQ+N+PntQE/z821ITfnFtqIW3BLbcwtuZU24VbcWptya26jD3AbbqsPcltupw9xO34vddI4L+E4J3Em4rMCt3L7a5I+wu25gz7KHbijPsYduZM+zp24sz7BnbmLPslduKs+xV25mz7N3bi7PsPduYc+yz24pz7HPbmXNuNe3Fuf597cR5tzH+6rLbgv99OW3I/7ayvuzwO0NQ/ggdqGB/IgbcuD+AVtxy/w+6mTxrkIx3mIvY/Pi9zGg52HDjyYh2hHHsJDtRMP5WHamYfxcO3Cw3mEduURPFK78Ugepd15FI/WHjyax2hPHsNjtReP5XHam8fxeO3D43mC9uUJPFH78USepP15Ek/WATyZp+hAnsJTdRBP5Wn6Ak/jD1Injf0Px77HHsdng27nF+37EH6Rp+tQns4zdBjP4Jk6nGfyLB3Bs/glHckv8WwdxbN5jo7mOTxXx/BcnqdjeR7P13E8nxfoeF7AC3UCL+RFOpEX8WKdxIt5iU7mJbxUp/BSflmn8su8TKfxMv4wddLY53Dsb+xlfN7rDl5uf6fzcl6hM3gFr9SZvJJX6Sxexav1JV7Na3Q2r+G1OofX8jqdy+t4vc7j9bxB5/MG3qgLeCNv0oW8iTfrIt7MW3Qxb+GtuoS38jZdytt4u77M23mHLuMd/FHqpLGf4djH2LP4rN6dvNM+ruCdvEtX8i7erat4N+/R1byH9+oa3sv7dC3v4/26jvfzAV3PB/igbuCDfEg38iF+RTfxK/yqbuZX+bBu4cP8mm7l1/iIbuMjfFS381E+pjv4GH+cOmnsWzj2K/YmPk95Fx+3X7v4OJ/Q3XyCT+oePsmndC+f4tO6j0/zGd3PZ/isHuCz/Loe5Nf5DT3Eb/Cb+gq/yef0VT7H5/Uwn+e39DV+iy/oEb7Ab+tRfpsv6jG+yJ+kThr7E459iT2Iz7beze/YlxP8Dr+rJ/ldfk9P8Xv8vp7m9/kDPcMf8Id6lj/kj/R1/og/1jf4Y/5E3+RP+FM9x5/yZ3qeP+PP9S3+nL/QC/wFf6lv85f8lV7kr/jT1EljH8Kx/rHW8XnijPy19X+Xv+Zv9D3+hr/V9/lb/k4/4O/4e/2Qv+cf9CP+gX/Uj/lH/kk/4Z/4Z/2Uf+Zf9DP+hS/p53yJf9Uv+Ff+Tb/k3/iyfsWX+bPUSWO9w7HOsabxWe1M/Lt1/oZ/5yv6LV/hP/Q7/oP/1O/5T76qP/BV/kt/5L/4b/2J/+Z/9Gf+h//VX/hf/k8v8X8c/1hxyZr+7x8u0iROrb9xTPgavcwx4c9TJ411Dcd6xtrF593v4TRpkl7hmPC1+gfHhNPqnxwTTqdXOSZ8nf7FMeEU/Ztjwun1H44JZ9B/OSZ8vf7HMeEbNNbuBhO+UVPzjXyTXsM38Repk8b6hWPdYo3iuwEy883W7Vq+mW/RtHwL36rp+Fa+Ta/j2/h2TeHb+Q5Nz3fwnZqB7+S79Hq+i+/WG/huzqg3ckbOpDdxJv4yddJYp3CsT6xFfF9CFr7H+tzC93BmvZUzcxa9jbNwVr2ds3I2vYOzcXa9k7NzDr2Lc3BOvZtzci7NyLk4t2bi3PxV6qSxHuFYh7jn+K6IrJzHOmTmPJxXs3BezqdZOR/fq9n4Xr5Ps/N9nF9zcH4uoDm5ABfUXFyQC2luLsRfp04a9x2O+417i+/DyMaF3W9eLsxFNB8X4aJ6LxflYnofF+Pimp+LcwktwCW4pBbkklxKC3Ep/iZ10ri/cNxX3EN8t0d2Lu2+inBpLqNFuQyX1WJclstpcS7H5bUEl+cKWpIrcEUtxRX529RJ4z7Ccf1xrTlSJROu5PrLcCWurGW5MlfRclyFq2p5rsrVtAJX4+pakavzd6mTxvWG4zrjmnKmSiZcw3VW5hpcU6twTb5fq/L9XEurcS2urdW5Nn+fOmlcVziuJ947V6pkwnVcT02uw3X1fq7L9bQW1+P6Wpvr8w+pk8b7h+N94z1yp0om3MD71uUG3FDrcUNupPW5Ef+YOmm8TzheP14rvm8mDzf2+g25MTfRRtyEf0qdNF4vHK8Tfye+qyYvN/U6Tbgp/5w6afy9cPz5+F18n00+/iV10vh9OH4evc7zSw+kSZ5XigmPTpP0QR7NY/QhHsNj9WEey+P0ER7H4/VRHs8T9DGewBP1cZ7Ik/QJnsST9UmezFP0KZ7CU/VpnsrT9Bmexi/qs/wiT9fneDrP0GY8g2fq8zyTZ2lznsUvaQt+iWdrS57Nc7QVz+G52prn8jxtw/N4vrbl+bxA2/ECXqjteSEv0g68iDtqPGYTjnMYZy6eldvHi9MkHcOLeYmO5SW8VMfxUn5Zx/PLvEwn8DJerhN5Oa/QSbyCV+pkXsmrdAqv4tU6lVfzGp3Ga3itvshreZ1O53W8Xmfwet6gM3kDb9RZvJE36Uu8iTfrbN7MW3QOb+GtOpe38jadx9t4u87n7bxDF/AO3qkLeSfv0kW8iztpR47zFmcrnsfcz7vTJF3Cu3mPLuU9vFdf5r28T5fxPt6vy3k/H9AVfIAP6ko+yId0FR/iV3Q1v8Kv6hp+lQ/rWj7Mr+k6fo2P6Ho+wkd1Ax/lY7qRj/Fx3cTH+YRu5hN8UrfwST6lW/kUn9ZtfJrP6HY+w2d1B5/l13Unv85v6C5+gztrJ45zFWconu09wG+mSbqH3+RzupfP8Xndx+f5Ld3Pb/EFPcAX+G09yG/zRT3EF/kdfYXf4Xf1VX6X39PD/B6/r6/x+/yBHuEP+EM9yh/yR3qMP+KP9Th/zJ/oCf6EP9WT/Cl/pqf4M/5cT/Pn/IWe4S/4Sz3LX/JX+jp/xV/rG/w1d9HOHOcnzko8D36Qv0mT9Bx/w9/qef6Wv9O3+Dv+Xi/w9/yDvs0/8I96kX/kn/Qd/ol/1nf5Z/5F3+Nf+JK+z5f4V/2Af+Xf9EP+jS/rR3yZf9eP+Xe+op/wFf5DP+U/+E/9jP/kq/o5X+W/9Av+i//WL/lv/ke/4n/4X/2a/+Wu2oXjnMSZiM8KHOL/0iT9lv/jVNcm/Y5jwqn1e44JX6M/cEw4jf7IMeFr9SeOCafVnzkmnE5/4ZjwdXqJY8Ip+ivHhNPrbxwTzqCXOSZ8vf7OMeEb9ArHhG/UPzgmfJP+yTHhm/Uqx4Rv0b84Jnyr/s0x4dv0H44J367/cky4m5915TgPsffxeZFX+I5rk8be32HCd2pqvpPv0mv4Lr5b0/DdnFGv5YycSdNyJr5H0/E9nFmv48ycRVM4C2fV9JyVs2kGzsbZ9XrOzjn0Bs7BOfVGzsm59CbOxbn1Zs7NefQWzsN59VbOy/n0Ns7H9+rtfC93dyZi/7vb+9jj+GzQq3yffb+T7+P8ehfn5wJ6NxfggpqRC3IhzcSFuLDew4W5iGbmIlxUs3BRLqZZuRgX12xcnEtodi7BJTUHl+RSmpNLcWnNxaW5jObmMlxW83BZLqd5uRyX13xcnivovVyBe6RJGvscjv2NvYzPex3mivY3P1fkSlqAK3FlLciVuYoW4ipcVQtzVa6mRbgaV9eiXJ1raDGuwTW1ONfk+7UE38+1tCTX4tpaimtzHS3NdbiuluG6XE/Lcj2ur+W4PjfQ8tyAG2oFbsg90ySN/QzHPsaexWf1XuNG9rESN+LGWpkbcxOtwk24qVblpvyAVuMH+EGtzg/yQ1qDH+KHtSY/zI/o/fwIP6q1+FF+TGvzY/y41uHH+Qmty0/wk1qPn+SntD4/xU9rA36an9GG/Az3SpM09i0c+xV7E5+nPMLP2q/G/Cw/p034OW6mTbkZP68P8PPcXB/k5txCH+IW3FIf5pbcSh/hVtxaH+XW3EYf4zbcVh/nttxOn+B23F6f5PbcQZ/iDtxRn+aO3Emf4U7cO03S2J9w7EvsQXy29Sh3ti/PcWfuos24C3fV57krd9Pm3I27awvuzj20JffgntqKe3Ivbc29uLe24d7cR9tyH+6r7bgv99P23I/7awfuzwO0Iw/ggdqJB3KfNEljH8Kx/rHW8XniYzzI+nfhQfyCduUXeLB248E8RLvzEB6qPXgoD9OePIyHay8eziO0N4/gkdqHR/Io7cujeLT249E8RvvzGB6rA3gsj9OBPI77pkka6x2OdY41jc9qH+fx1vkFHs8TdDBP4Ik6hCfyJB3Kk3iyDuPJPEWH8xSeqiN4Kk/TkTyNX9RR/CJP19E8nWfoGJ7BM3Usz+RZOo5ncb80SWNdw7GesXbxefcT/JL1nMAv8WydyLN5jk7iOTxXJ/NcnqdTeB7P16k8nxfoNF7AC/VFXsiLdDov4sU6gxfzEp3JS3ipzuKl3D9N0li/cKxbrFF8N8BJftm6zeaXeZnO4WW8XOfycl6h83gFr9T5vJJX6QJexat1Ia/mNbqI1/BaXcxreZ0u4XW8Xpfyeh6QJmmsUzjWJ9Yivi/hFG+wPst4A2/U5byRN+kK3sSbdSVv5i26irfwVl3NW3mbruFtvF3X8nbeoet4B+/U9byTB6ZJGusRjnWIe47vijjNu6zDRt7Fu3UT7+Y9upn38F7dwnt5n27lfbxft/F+PqDb+QAf1B18kA/pTj7Eg9IkjfsOx/3GvcX3YZzhV9zvbn6FX9U9/Cof1r18mF/TffwaH9H9fISP6gE+ysf0IB/j43qIj/MLaZLG/YXjvuIe4rs9zvIJ9/Uqn+CTephP8il9jU/xaT3Cp/mMHuUzfFaP8Vl+XY/z6zw4TdK4j3Bcf1xrfB/J6/yG6z/Jb/Cbeorf5HN6ms/xeT3D5/ktPctv8QV9nS/wkDRJ43r/59SuK7X5P7/tOt/kt/minuOL/I6e53f4XX2L3+X39AK/x0PTJI3rCsf1/O+9UycTft/1XOT3+QN9hz/gD/Vd/pA/0vf4Ix6WJmm8fzjeN94jvhvmHH/sfT/gj/kT/ZA/4U/1I/6Uh6dJGu8TjteP14rvmznPn3n9T/gz/lw/5c95RJqk8XrheJ34O/FdNW/xF17nc/6CR6ZJGn8vHH8+fhffZ3OBR6VJGr8Px8+j8X9JxzMjX16b/Pt9TDhL2qRfcUw4q37NMeFs+g3HhLPrtxwTzqHfcUw4p37PMeFc+gPHhHPrjxwTzqM/cUw4r/7MMeF8+gvHhO/VSxwTvk9/5Zhwfv2NY8IF9DLHhAvq7xwTLqRXOCZcWP/gmHAR/ZNjwkX1KseEi+lfHBMurn9zTLiE/sMx4ZL6L8eES+l/HBMuranSJo4Jp9Z4FD8c5zDOXPx7fycu4xxm5TJcVrNxWS6n2bkcl9ccXJ4raE6uwBU1F1fkSpqbK3FlzcOVuYrm5SpcVfNxVa6m93I1rq73cXWuofm5BtfUAlyT79eCfD/X0kJci2trYa7NdbQI1+G6WpTrcj0txvW4vhbn+txAS3ADbqgluSE30lLciBtraW7M12hqjvMWZyueD+jMTdImLctNuKmW46b8gJbnB/hBrcAP8kNakR/ih7USP8yPaGV+hB/VKvwoP6ZV+TF+XKvx4/yEVucn+EmtwU/yU1qTn+Kn9X5+mp/RWvwMP6u1+Vl+Tuvwc9xM63Izfl7r8fPcXOtzc26hDbgFt9SG3JJbaSNuxa21MbfmNHoNx7mKMxTPE3ThNmmTNuU23FYf4LbcTh/kdtxeH+L23EEf5g7cUR/hjtxJH+VO3Fkf487cRR/nLtxVn+Cu3E2f5G7cXZ/i7txDn+Ye3FOf4Z7cS5/lXtxbn+Pe3EebcR/uq89zX+6nzbkf99cW3J8HaEsewAO1FQ/kQdqaB/G1mobj/MRZ6eoZhPALaZO25Rd4sLbjwTxE2/MQHqodeCgP0448jIdrJx7OI7Qzj+CR2oVH8ijtyqN4tHbj0TxGu/MYHqs9eCyP0548jsdrLx7PE7Q3T+CJ2ocn8iTty5N4svbjyTxF+/MUnqoDeCpP04E8jV/UQfwip9VrOc5JnIlunlkJT0+bdDBP5xk6hGfwTB3KM3mWDuNZ/JIO55d4to7g2TxHR/IcnqujeC7P09E8j+frGJ7PC3QsL+CFOo4X8iIdz4t4sU7gxbxEJ/ISXqqTeCm/rJP5ZV6mU3gZL9epvJxX6DRewSv1RV7J6TQtx3mIve/uWZbwqrRJZ/AqXq0zeTWv0Vm8htfqS7yW1+lsXsfrdQ6v5w06lzfwRp3HG3mTzudNvFkX8Gbeogt5C2/VRbyVt+li3sbbdQlv5x26lHfwTn2Zd/IuXca7eLcu5928R1fwHt6rK3kvX6fpOPY99riHZ1zC++z7at7H+3UN7+cDupYP8EFdxwf5kK7nQ/yKbuBX+FXdyK/yYd3Eh/k13cyv8RHdwkf4qG7lo3xMt/ExPq7b+Tif0B18gk/qTj7Jp3QXn+LTuptP8xndw2f4rO7ls5yi13Hsb+xlT8++hF+3v/v5dX5DD/Ab/KYe5Df5nB7ic3xeX+Hz/Ja+ym/xBT3MF/htfY3f5ot6hC/yO3qU3+F39Ri/y+/pcX6P39cT/D5/oCf5A/5QT/GH/JGe5o/4Yz3DH/MnepY/4fSawrGPsWe9PBMT/tQ+vsGf8mf6Jn/Gn+s5/py/0PP8BX+pb/GX/JVe4K/4a32bv+Zv9CJ/w9/qO/wtf6fv8nf8vb7H3/MP+j7/wD/qB/wj/6Qf8k/8s37EP/Mv+jH/wpf0E77EGTQ9x37F3vT2rEz4V/v1Gf/Kv+nn/Btf1i/4Mv+uX/LvfEW/4iv8h37Nf/Cf+g3/yVf1W77Kf+l3/Bf/rd/z3/yP/sD/8L/6I//L/+lP/B+nSpf0Z44Jp9ZfOCZ8jV7imPD1fpaBY19iD/p4hiacJl3S3zgmfK1e5phwWv2dY8Lp9ArHhK/TPzgmnKJ/ckw4vV7lmHAG/Ytjwtfr3xwTvkH/4Zjwjfovx4Rv0v84Jnyzxh7cbMK3aGq+hW/Va/hWvsHexD7cYA9irft6tiZ8m/W/lm/j2zUt3853aDq+g+/U6/hOvktT+C6+W9Pz3ZxRM3BGzqTXcya+R2/geziz3siZOYvexFk4q97MWTmb3sLZOLveytn5xrRJY73Dsc6xpv08cxPOYZ1v5xycU+/gnJxL7+RcnFvv4tycR+/mPJxXM3JezqeZOB/fq/fwvXyfZub7OL9m4fxcQLNyAS6o2bggF9LsXIhvSps01jUc6xlr19+zOOHC1jMnF+YimouLcFHNzUW5mObhYlxc83JxLqH5uASX1Hu5JJfS+7gUl9b8XJrLaAEuw2W1IJflclqIy/HNaZPG+oVj3WKNBnhGJ1zeuhXh8lxBi3IFrqjFuCJX0uJciStrCa7MVbQkV+GqWoqrcjUtzdW4upbh6lxDy3INrqnluCbfkjZprFM41ifWYqBnd8L3W58KfD/X0opci2trJa7NdbQy1+G6WoXrcj2tyvW4vlbj+txAq3MDbqg1uCE30prciG9NmzTWIxzrEPc8yDM94cbWoRY35iZam5twU63DTfkBrcsP8INajx/kh7Q+P8QPawN+mB/RhvwIP6qN+FG+LW3SuO9w3G/c2wue9Qk/5n6b8GP8uDblx/kJfYCf4Cf1QX6Sn9KH+Cl+Wh/mp/kZfYSf4Wf1UX6Wb0+bNO4vHPcV9zDYM0Dh59zX4/wcN9MnuBk/r0/y89xcn+Lm3EKf5hbcUp/hltxKn+VWfEfapHEf4bj+uNYhng0Kt3b9zbg1t9HnuQ231ebclttpC27H7bUlt+cO2oo78J1pk8b1huM645qGemYo3NF1tuGO3EnbcifurO24M3fR9tyFu2oH7sp3pU0a1xWO64n3HuZZonA319OJu3F37czduYd24R7cU7tyT747bdJ4/3C8b7zHcM8YhXt53+7ci3trD+7NfbQn9+GMaZPG+4Tj9eO1Rnj2KNzX6/fmvtxP+3A/zpQ2abxeOF4n/s5IzySF+3udftyf70mbNP5eOP58/G6UZ5XCmdMmjd+H4+fR///80oB0yfNKMeFN6ZIO5E28WQfxZt6iL/AW3qqDeStv0yG8jbfrUN7OO3QY7+CdOpx38i4dwbt4t47k3bxHR/Ee3qujeS/v0zG8j/frWN7PB3QcH+CDOp4P8iGdwIf4FZ3Ir/CrOolf5cM6mQ/zazqFX+MjOpWP8FGdxkf5mL7Ix/i4TufjfEJn8AneqPGft+E4h3HmvvIMXfhkuqSb+SSf0i18ik/rVj7NZ3Qbn+Gzup3P8uu6g1/nN3Qnv8Fv6i5+k8/pbj7H53UPn+e3dC+/xRd0H1/gt3U/v80X9QBf5Hf0IL/D7+ohfpff01f4PX5fX+X3+QM9zB/wh/oaf8gf6RH+iD/Wo/wxf6LH+BP+VI/zp/yZnuDPeINu5Dhvcba+9pxm+PN0SU/x5/yFnuYv+Es9w1/yV3qWv+Kv9XX+mr/RN/gb/lbf5G/5Oz3H3/H3ep6/5x/0Lf6Bf9QL/CP/pG/zT/yzXuSf+Rd9h3/hS/ouX+Jf9T3+lX/T9/k3vqwf8GX+XT/k3/mKfsRX+A/9mP/gP/UT/pOv6qd8lf/Sz/gvXq8bOM5VnKFvPPMb/jtd0i/4b/5Hv+R/+F/9iv/l//Rr/o/jyxaj33BMOLV+yzHha/Q7jgmn0e85Jnyt/sAx4bT6I8eE0+lPHBO+Tn/mmHCK/sIx4fR6iWPCGfRXjglfr79xTPgGvcwx4Rv1d44J36RXOCZ8s/7BMeFb9E+OCd+qVzkmfJv+xTHhdX62nuP8xFn51nPi4duvS/oPx4Tv0H85Jnyn/scx4bs0zspdJny3pua7OaNewxk5k6bhTHyPXsv3cGZNy5k5i6bjLJxVr+OsnE1TOBtn1/ScnXNoBs7BOfV6zsm59AbOxbn1Rs7NefQmzsN59WbOy/n0Fs7H9+qtfC/fp7fxfbzWGYrzstZZiTPxnc8QhPM7J3dwfi6gd3IBLqh3cUEupHdzIS6sGbkwF9FMXISL6j1clItpZi7GxTULF+cSmpVLcEnNxiW5lGbnUlxac3BpLqM5uQyX1Vxclstpbi7H5TUPl+cKmpcrcEXNxxW5kt7Llbiy3seVeU26pHEuwnEeYu+/9zmScBXnoQBX4apakKtyNS3E1bi6FubqXEOLcA2uqUW5Jt+vxfh+rqXFuRbX1hJcm+toSa7DdbUU1+V6WprrcX0tw/W5gZblBtxQy3FDbqTluRE31grcmJtoRW7CTbUSN+UHtDI/wKvTJY39D8e+xx7/4DND4Qfte1V+kB/SavwQP6zV+WF+RGvwI/yo1uRH+TG9nx/jx7UWP85PaG1+gp/UOvwkP6V1+Sl+Wuvx0/yM1udn+FltwM/yc9qQn+Nm2oib8fPamJ/n5tqEm3MLbcotuKU+wC15Vbqksc/h2N/Yyx99Dizcyv4+xK24tT7MrbmNPsJtuK0+ym25nT7G7bi9Ps7tuYM+wR24oz7JHbmTPsWduLM+zZ25iz7DXbirPstduZs+x924uzbj7txDn+ce3FObc0/upS24F/fWltybV6ZLGvsZjn2MPfvJZ/jCfexja+7DfbUN9+V+2pb7cX9tx/15gLbnATxQO/BAHqQdeRC/oJ34BR6snXkwD9EuPISHalceysO0Gw/j4dqdh/MI7cEjeKT25JE8SnvxKB6tvXk0r0iXNPYtHPsVe/Ozz1mGx9ivvjyGx2o/HsvjtD+P4/E6gMfzBB3IE3iiDuKJPElf4Ek8WQfzZJ6iQ3gKT9WhPJWn6TCexi/qcH6Rp+sIns4zdCTP4Jk6imfyLB3Ns3h5uqSxP+HYl9iDX3zmNfySfRnLL/FsHcezeY6O5zk8VyfwXJ6nE3kez9dJPJ8X6GRewAt1Ci/kRTqVF/FincaLeYm+yEt4qU7npfyyzuCXeZnO5GW8XGfxcl6Wzs/SJY71j7W+5HPG4RXWfzav4JU6h1fyKp3Lq3i1zuPVvEbn8xpeqwt4La/ThbyO1+siXs8bdDFv4I26hDfyJl3Km3izvsybeYsu4y28VZfzVn45XdJY73Csc6zprz7DHd5mnVfyNt6uq3g779DVvIN36hreybt0Le/i3bqOd/MeXc97eK9u4L28TzfyPt6vm3g/H9DNfIAP6hY+yId0Kx/ipemSxrqGYz1j7X7zOfjwK9ZzO7/Cr+oOfpUP604+zK/pLn6Nj+huPsJHdQ8f5WO6l4/xcd3Hx/mE7ucTfFIP8Ek+pQf5FJ/WQ3yal6RLGusXjnWLNbrsOwPCZ6zbq3yGz+phPsuv62v8Or+hR/gNflOP8pt8To/xOT6vx/k8v6Un+C2+oCf5Ar+tp/htvqin+SIvTpc01ikc6xNr8bvvUQi/Y33O8jv8rr7O7/J7+ga/x+/rm/w+f6Dn+AP+UM/zh/yRvsUf8cd6gT/mT/Rt/oQ/1Yv8KS9KlzTWIxzrEPd8xXdIhD+zDu/yZ/y5vsef8xf6Pn/BX+oH/CV/pR/yV/y1fsRf8zf6MX/D3+on/C1/p5/yd7wwXdK473Dcb9zbH74nI/y9+/2cv+cf9Av+gX/UL/lH/km/4p/4Z/2af+Zf9Bv+hS/pt3yJf9Xv+FdekC5p3F847ivu4U/f+RH+zX39wL/xZf2RL/Pv+hP/zlf0Z77Cf+gv/Af/qZf4T76qv/JVnp8uadxHOK4/rvWq7ykJ/+X6L/Nf/Lf+zn/zP3qF/+F/9Q/+l//TP/k/TpWS9CrHhOelSxrX+z9f67p8h0o4dUrSvzkmfI3+wzHhNPovx4Sv1f84JpxW45rSmvDcdEnjusJxPf97b9/jEk7neq7hdHydpuHrOEWv5RROr2k5Pc9JlzTePxzvG+/xj++MCWfwvtdxBr5eU/h6vkHT8w08O13SeJ9wvH681r++hyZ8o9e/nm/km/QGvolfSpc0Xi8crxN/5z/fYRO+2evcxDfzrHRJ4++F48/H7/73MEjaxDPTJY3fh+Pn0Wv8sVv+73cFTfh5vZWf5+Z6GzfnFno7t+CWege35FZ6J7fi1noXt+Y2eje34baakdtyO83E7bi93sPtuYNm5g7cUbNwR+6kWbkTd9Zs3Jm7aHbuwl01B3flbpqTu3F3zcXduYfm5h7cU/NwT+6lebkX99Z83Jv76L3ch/vqfdyX+2l+7sf9tQD359qaPlXiOIdx5gqZ8ABtzgN4oLbggTxIW/IgfkFb8Qs8WFvzYB6ibXgID9W2PJSHaTsexsO1PQ/nEdqBR/BI7cgjeZR24lE8WjvzaB6jXXgMj9WuPJbHaTcex+O1O4/nCdqDJ/BE7ckTeZL24kk8WXvzZJ6ifXgKT9W+PJWnaT+exi9qf36R62htjvMWZ6uwCU/XgTydZ+ggnsEz9QWeybN0MM/il3QIv8SzdSjP5jk6jOfwXB3Oc3mejuB5PF9H8nxeoKN4AS/U0byQF+kYXsSLdSwv5iU6jpfwUh3PS/llncAv8zKdyMt4uU7i5bxCJ/MKXqlTeCWv0qm8ilfrNF7Na/RFXsN1tQ7HuYozVMSE1+oMXsvrdCav4/U6i9fzBn2JN/BGnc0beZPO4U28WefyZt6i83gLb9X5vJW36QLextt1IW/nHbqId/BOXcw7eZcu4V28W5fybt6jL/Me3qvLeC/v0+W8j/frCt7PB3QlH+CDuooP8iFdzYf4FV3Dr3A9rctxfuKsFDXhV3Udv8qHdT0f5td0A7/GR3QjH+GjuomP8jHdzMf4uG7h43xCt/IJPqnb+CSf0u18ik/rDj7NZ3Qnn+GzuovP8uu6m1/nN3QPv8Fv6l5+k8/pPj7H53U/n+e39AC/xRf0IF/gt/UQv80X9RW+yPW1Hsc5iTNRzITf0cP8Dr+rr/G7/J4e4ff4fT3K7/MHeow/4A/1OH/IH+kJ/og/1pP8MX+ip/gT/lRP86f8mZ7hz/hzPcuf8xf6On/BX+ob/CV/pW/yV/y1nuOv+Rs9z9/wt/oWf8vf6QX+jr/Xt/l7/kEv8g/cQOtzMXtf3IR/1Hf5R/5J3+Of+Gd9n3/mX/QD/oUv6Yd8iX/Vj/hX/k0/5t/4sn7Cl/l3/ZR/5yv6GV/hP/Rz/oP/1C/4T76qX/JV/ku/4r/4b/2a/+Z/9Bv+h//Vb/lf/k+/4/84/kPqO3v/v/+oSp84tf7AMeGGftaAi9vjEiZ8TfqkP3FMOI3+zDHha/UXjgmn1UscE06nv3JM+Dr9jWPCKXqZY8Lp9XeOCWfQKxwTvl7/4JjwDfonx4Rv1KscE75J/+KY8M36N8eEb9F/OCZ8q/7LMeHb9D+OCd+usce3m/Admprv4Eb2Pva5kf2NvSyZkkz4Tvubhu/ku/Ravovv1rR8N2fUdJyRM+l1nInv0RS+hzNres7MWTQDZ+Gsej1n5Wx6A2fj7HojZ+ccehPn4Jx6M+fkXHoL5+Lceivn5jx6G+fhvHo75+V8egfn48YpSWM/G9vH2LNSKcmE77WPd/G9fJ/ezfdxfs3I+bmAZuICXFDv4YJcSDNzIS6sWbgwF9GsXISLajYuysU0Oxfj4pqDi3MJzckluKTm4pJcSnNzKS6tebg0l9G8XIbLaj4uy01Sksa+NbFfsTelU5IJl7Nf93E5Lq/5uTxX0AJcgStqQa7IlbQQV+LKWpgrcxUtwlW4qhblqlxNi3E1rq7FuTrX0BJcg2tqSa7J92spvp9raWmuxbW1DNfmOlqW63DTlKSxP03tS+xBmZRkwnXtS3muy/W0Atfj+lqR63MDrcQNuKFW5obcSKtwI26sVbkxN9Fq3ISbanVuyg9oDX6AH9Sa/CA/pPfzQ/yw1uKH+RGtzY/wo1qHH+UHUpLGPjxg/WOty6YkE37M+tfjx/hxrc+P8xPagJ/gJ7UhP8lPaSN+ip/Wxvw0P6NN+Bl+Vpvys/ycPsDPcTN9kJvx8/oQP8/N9WFuzi30EW7BLfVRbskPpiSN9X7QOsealktJJtzKOj/Orbi1PsGtuY0+yW24rT7FbbmdPs3tuL0+w+25gz7LHbijPscduZM2407cWZ/nztxFm3MX7qotuCt305bcjR9KSRrr+pD1jLUrn5JMuLv1bM3duYe24R7cU9tyT+6l7bgX99b23Jv7aAfuw321I/flftqJ+3F/7cz9eYB24QE8ULvyQB6k3XgQP5ySNNbvYesWa1QhJZnwC9atB7/Ag7UnD+Yh2ouH8FDtzUN5mPbhYTxc+/JwHqH9eASP1P48kkfpAB7Fo3Ugj+YxOojH8CMpSWOdHrE+sRYVU5IJj7U+g3ksj9MhPI7H61AezxN0GE/giTqcJ/IkHcGTeLKO5Mk8RUfxFJ6qo3kqT9MxPI0fTUka6/GodYh7rpSSTPhF6zCOX+TpOp6n8wydwDN4pk7kmTxLJ/Esfkkn80s8W6fwbJ6jU3kOz9VpPJcfS0ka9/2Y+417q5ySTHie+53O83i+zuD5vEBn8gJeqLN4IS/Sl3gRL9bZvJiX6Bxewkt1Li/lx1OSxv097r7iHqqkJBN+2X3N55d5mS7gZbxcF/JyXqGLeAWv1MW8klfpEl7Fq3Upr+YnUpLGfTzh+uNaq6YkE17j+pfxGl6ry3ktr9MVvI7X60pezxt0FW/gjbqaN/KTKUnjep90nXFN1VKSCW9ynWt5E2/WdbyZt+h63sJbdQNv5W26kbfxUylJ47qecj3x3tVTkglvdz2beTvv0C28g3fqVt7Ju3Qb7+KnU5LG+z/tfeM9aqQkE97tfXfwbt6jO3kP79VdvJefSUka7/OM14/XqpmSTHif19/D+3i/7uX9/GxK0ni9Z71O/J37U5IJH/A6+/kAP5eSNP7ec/58/K5WSjLhZhq/b+bn0Qypkjn4f7+7bMK3Zkh6iGPCt+krHBO+XV/lmPAdephjwnfqaxwTvkuPcEz4bj3KMeGMeoxjwpn0OMeE79ETHBPOrCc5JpxFT3FMOKue5phwNj3DMeHsepZjwjn0dY4J59Q3OCacS9/kmHBuPccx4Tx6nmPCefUtjgnn0wscE75X3+aY8H16kWPC+fUdjgkX0Hc5JnyLXp8q8WXn7rf0yYQLOoe3cUEupLdzIS6sd3BhLqJ3chEuqndxUS6md3MxLq4ZuTiX0ExcgkvqPVySS2lmLsWlNQuX5jKalctwWc3GZbmcZudyXF5zcHmuoDm5AlfUXFyRK2lursSVNQ9X5iqal6twVc3HVbma3svVuLrex9W5hubnGlxTC3BNvllv4ThvcbZ+TZ9M+P4MSQvx/VxLC3Mtrq1FuDbX0aJch+tqMa7L9bQ41+P6WoLrcwMtyQ24oZbihtxIS3MjbqxluDE30bLchJtqOW7KD2h5foAf1Ar8ID+kFfkhflgr8cP8iFbmR/hRrcKP8mNalR/jx7UaP85PaHV+gp/UGvwkP6U1+Sm+SW/mOFdxhi6lTyb8dIaktfhpfkZr8zP8rNbhZ/k5rcvPcTOtx834ea3Pz3NzbcDNuYU25BbcUhtxS26ljbkVt9Ym3JrbaFNuw231AW7L7fRBbsft9SFuzx30Ye7AHfUR7sid9FHuxJ31Me7MXfRx7sJd9Qnuyt30Se7G3fUp7s436k0c5yfOyi/pkwn3yJD0Ge7BPfVZ7sm99Dnuxb21GffmPvo89+G+2pz7cj9twf24v7bk/jxAW/EAHqiteSAP0jY8iF/QtvwCD9Z2PJiHaHsewkO1Aw/lYdqRh/Fw7cTDeYR25hE8UrvwSB6lXXkUj9ZuPJrHaHcewzfojRznJM7Ez+mTCY/NkLQnj+Vx2ovH8XjtzeN5gvbhCTxR+/JEnqT9eBJP1v48mafoAJ7CU3UgT+VpOoin8Yv6Ar/I03UwT+cZOoRn8EwdyjN5lg7jWfySDueXeLaO4Nk8R0fyHJ6ro3guz9PRPI/n6xiez9frDRznIfb+p/TJhBdkSDqOF/BCHc8LeZFO4EW8WCfyYl6ik3gJL9XJvJRf1in8Mi/TqbyMl+s0Xs4r9EVewSt1Oq/kVTqDV/FqncmreY3O4jW8Vl/itbxOZ/M6Xq9zeD1v0Lm8gTfqPN7Im3Q+b+IMej3Hvsce/5g+mfBm+76QN/MWXcRbeKsu5q28TZfwNt6uS3k779CXeQfv1GW8k3fpct7Fu3UF7+Y9upL38F5dxXt5n67mfbxf1/B+PqBr+QAf1HV8kA/pej7Er+gGfoVf1Y38Kh/WTXyY02sGjv2NvfwhfTLh1+zvFn6Nj+hWPsJHdRsf5WO6nY/xcd3Bx/mE7uQTfFJ38Uk+pbv5FJ/WPXyaz+hePsNndR+f5dd1P7/Ob+gBfoPf1IP8Jp/TQ3yOz+srfJ7f0lf5Lb6gh/kCp2h6jn2MPfs+fTLht+3jEX6bL+pRvsjv6DF+h9/V4/wuv6cn+D1+X0/y+/yBnuIP+EM9zR/yR3qGP+KP9Sx/zJ/o6/wJf6pv8Kf8mb7Jn/Hneo4/5y/0PH/BX+pb/CV/pRf4K75OUzj2K/bmu/TJhL+2Xxf5a/5G3+Fv+Ft9l7/l7/Q9/o6/1/f5e/5BP+Af+Ef9kH/kn/Qj/ol/1o/5Z/5FP+Ff+JJ+ypf4V/2Mf+Xf9HP+jS/rF3yZf9cv+Xe+ol/xFU6n13HsS+zBt+mTCf9hX77hP/hP/Zb/5Kv6HV/lv/R7/ov/1h/4b/5Hf+R/+F/9if/l//Rn/o/j/3z92R787/+IvT5xar3EMeFr9FeOCafR3zgmfK1e5phwWv2dY8Lp9ArHhNP+/59xrP//1jp9MuHrrk/6J8eEU/Qqx4TT618cE86gf3NM+Hr9h2PCN+i/HBO+Uf/jmPBNGmt9kwnfrKn5Zr5Fr+Fb+FZNw7fybXot38a3a1q+ne/QdHwHX2sPYr2vtdaxpl+nTyZ8p3VO4Tv5Lk3Pd/HdmoHv5ox6PWfkTHoDZ+J79Ea+hzPrTZyZs+jNnIWz6i2clbPprZyNs+ttnJ1z6O2cg3PqHZyT02RIGusajvWMtfsqfTLhXNbzLs7FufVuzs15NCPn4byaifNyPr2H8/G9mpnv5fs0C9/H+TUr5+cCmo0LcEHNzgW5kObgQlxYc3JhviZD0li/cKxbrNGX6ZMJF7FuubkIF9U8XJSLaV4uxsU1HxfnEnovl+CSeh+X5FKan0txaS3ApbmMFuQyXFYLcVkup4W5HKfOkDTWKRzrE2vxRfpkwuWtT1EuzxW0GFfgilqcK3IlLcGVuLKW5MpcRUtxFa6qpbkqV9MyXI2ra1muzjW0HNfg+EfwctYmHOsQ9/x5+mTCNa1DBa7J92tFvp9raSWuxbW1MtfmOlqF63Bdrcp1uZ5W43pcX6tzff5/NN1luFVV27BhFd2CSINdoCIoJSCldHd3d6dIg9jd3YpKdzdKN0gq3SAgFlICfs/9zvP7cR/XeezNXmvOMcY/51pW13Jcnf9LkzTu+z/3G/d2OE0y4RrutwLX4JpakWtyLa3Etbi2VubaXEercB2uq1W5LtfTalyP62t1rs9X0ySN+7vqvuIeDqVJJtzAfdXkBtxQa3FDbqS1uRE31jrcmJtoXW7CTbUeN+VmWp+b8ZU0SeM+rrj+uNaDaZIJN3f9Dbk5t9BG3IJbamNuya20Cbfi1tqUW3MbbcZt+HKapHG9l11nXNOBNMmE27rOFtyW22lLbsfttRW35w7amjtwR23DHfnfNEnjuv51PfHe+9MkE+7ketpxJ+6s7bkzd9EO3IW7akfuypfSJI33v+R94z32pUkm3M37duZu3F27cHfuoV25B19MkzTe56LXj9famyaZcE+v3517ci/twb34Qpqk8XoXvE78zZ40yYR7e51e3JvPp0kaf3fev4/f7U6TTPicxu/P+Xk0vorp+v/Nq//73fcmPFpf4dE8Rl/mMTxWX+KxPE5f5HE8Xl/g8TxBn+cJPFGf44k8SZ/lSTxZn+HJPEVH8hSeqk/zVJ6mI3gaT9fhPJ1n6DCewTN1KM/kWTqEZ/FsHcyzeY4O4jk8VwfyXJ6nA3gez9eneD4v0P68gBfqk7yQF2k/XsSLtS8v5iXah5dw91RJ4/9lGI5zGGfuOxP+QcfwD/yjjuUfeamO46W8TMfzMl6uE3g5r9CJvIJX6iReyat0Mq/i1TqFV/MancpreK1O47W8TqfzOl6vM3g9b9CZvIE36izeyJt0Nm/izTqHN/NPOpd/4i06j7fwVp3PW3mbLuBtvF0X8nbeoYt4B+/UxbyTf9Yl/DN3S5U0zl04zlucrW9N+Bf9kX/hXbqUd/FuXca7eY8u5z28V1fwXt6nK3kf79dVvJ8P6Go+wAd1DR/kQ7qWD/FhXceH+Yiu5yN8VDfwUT6mG/kYH9dNfJxP6GY+wb/qT/wrn9QtfJJP6VY+xad1G5/m33Q7/8ZndAef4d91J//Of+jP/Ad3TZU0zlc4zlWcoVEm/Kfu4j/5L93Nf/Hfuof/5rO6l8/yP7qP/+Fzup/P8Xk9wOf5gh7kC3xRD/FFvqSH+RL/q0f4X76sR/kyX9FjfIWv6nG+yv/pCf6Pr7k56a8cE75WT3JM+Do9xTHhVHqaY8LX628cE75Bz3BMOEV/55jwjfoHx4S7pEoa5yg8yln5xoRT35z0L44Jp9G/OSZ8k57lmHBa/YdjwjfrOY4Jp9PzHBNOrxc4JpxBL3JMOKNe4phwJv2XY8KZ9TLHhLPoFY4JZ9WrHBPOpv9xTPgWjbNyiwnfqtfyrXybXse38e2aim/nO/R6voPv1Bv4Tr5LU/guvltv5Lu5c6qkcV7C3zgXX6dNJnyPc5KG7+F79Sa+l+/TtHwfZ9ebOTvn0HScg+/X9Hw/P6AZ+AF+UDPyg5xTM3FOfkgz80OcS7NwLs6tWTk3P6zZ+GF+RG/hRziP3sp5OK/exnk5n97O+Ti/3sH5uYDeyQX4Ub2LH+WCejcX5E6pksa5CMd5iL3/Km0y4ULOw71ciAvrfVyYH9Ps/BgX0RxchIvq/VyUi+kDXIyL64NcnEtoTi7Bj+tD/Dg/obn4CS6pubkkl9KHuRSX1ke4NJfRPFyGy2peLsvlNB+X4/Kan8tzBS3AFbiiPsoVuZIW5ErcMVXS2P9w7Hvs8ZdpkwlXtu+FuTJX0ce4ClfVIlyVq2lRrsbVtRhX5xpanGtwTS3BNbmWPs61uLY+wbW5jpbkOlxXS3FdrqeluR7X1zJcnxtoWW7ADbUcN+RGWp4bcWOtwI25iVbkJtxUK3FT7pAqaexzOPY39vKLtMmEm9nfKtyMm2tVbs4ttBq34JZanVtyK63Brbi11uTW3EZrcRtuq7W5LbfTOtyO22tdbs8dtB534I5anztyJ23AnbizNuTO3EUbcRfuqo25K3fTJtyNu2tT7s7tUyWN/QzHPsaefZ42mXAP+9ice3BPbcE9uZe25F7cW1txb+6jrbkP99U23Jf7aVvux09qO36S+2t77s9PaQd+igdoRx7AA7UTD+RB2pkH8WDtwoN5iHblITxUu/FQHqbdeRi3S5U09i0c+xV781naZMLD7VdPHs4jtBeP4Ke1Nz/NI7UPj+RntC8/w89qP36Wn9Mn+Tl+Xvvz8/yCPsUv8Is6gF/kl3Qgv8Qv6yB+mV/RwfwKv6pD+FV+TYfya/y6DuPXuW2qpLE/4diX2INP0yYTfsO+jOA3+E19mt/kt3Qkv8Vv6zP8Nr+jz/I7/K4+x+/ye/o8v8fv6wv8Pn+gL/IH/KG+xB/yR/oyf8Qf6yv8MX+ir/In/Km+xp/yZ/o6f8ZtUiWNfQjH+sdaf5I2mfDn1v9N/py/0Lf4C/5S3+Yv+St9h7/ir/Vd/pq/0ff4Gx6l7/Mo/lY/4G/5O/2Qv+Pv9SP+nkfrxzyax+gnPIbH6qc8lsfpZzyOW6dKGusdjnWONf04bTLh8db5Cx7PE/RLnsAT9SueyJP0a57Ek/UbnsxTdBRP4an6LU/lafodT+Pp+j1P5xk6mmfwTB3DM3mWjuVZPFvH8WxulSpprGs41jPW7qO0yYTnWM8JPIfn6kSey/N0Es/j+TqZ5/MCncILeKFO5YW8SKfxIl6s03kxL9EZvIR/0Jn8A/+os/hHXqqzeSm3TJU01i8c6xZr9GHaZMLLrNtcXsbLdR4v5xU6n1fwSl3AK3mVLuRVvFoX8Wpeo4t5Da/VJbyW1+kPvI7X64+8njfoUt7ALVIljXUKx/rEWnyQNpnwRuuznDfyJl3Bm3izruTN/JOu4p94i67mLbxV1/BW3qZreRtv13W8nXfoet7BO3UD7+TmqZLGeoRjHeKe30+bTPhn67CJf+ZfdDP/wrv0J97Fu3UL7+Y9upX38F7dxnt5n27nfbxfd/B+PqA7+QA3S5U07jsc9xv39l7aZMIH3e8vfJAP6S4+xId1Nx/mI7qHj/BR3ctH+Zju42N8XPfzcT6hB/gEN02VNO4vHPcV9/Bu2mTCv7qvQ/wrn9TDfJJP6RE+xaf1KJ/m3/QY/8Zn9Dif4d/1BP/OTVIljfsIx/XHtb6TNpnwH67/JP/Bf+op/pP/0tP8F/+tv/HffFbP8Fn+R3/nf7hxqqRxveG4zrimt9MmEz7nOv/kc3xe/+LzfEH/5gt8Uc/yRb6k//AlbpQqaVxXOK4n3vuttMmE/3U95/lfvqwX+DJf0Yt8ha/qJb7KDVMljfcPx/vGe7yZNpnwf973Mv/H16RLeoVjwtfqVY4JN0iVNN4nHK8fr/VG2mTC16VLGq91nQmn0ms5FddPlTReLxyvE3/zetpkwtd7nVR8PddLlTT+Lhz/Pn73WtpkwnVTJY3fh+Pn0Ws8v3RDuuR5pZjwfemSpvB9nF1v5OycQ1NzDr5f0/D9/IDexA/wg5qWH+ScejPn5Ic0HT/EuTQ95+LcmoFz88OakR/mRzQTP8J5NDPn4byahfNyPs3K+Ti/ZuP8XEBv4QL8qN7Kj3JBvY0LciG9nQtxYb2DC/Njeic/xkX0Li7CRfVuLsrF9B4uxsX1Xi7OH6Qkjf+XYTjOYZy5eFbuFS6RLml2LsGPaw5+nJ/Q+/kJLqkPcEkupQ9yKS6tObk0l9GHuAyX1Vxclstpbi7H5fVhLs8V9BGuwBU1D1fkSpqXK3FlzceVuYrm5ypcVQtwVa6mj3I1rq4FuTrX0EJcg2tqYa7JtfQxrsW1tQjX5jpalOtwXS3GdbmeFud6/H5K0jh34ThvcbbiecyXuX66pI9zfW6gT3ADbqgluSE30lLciBtraW7MTbQMN+GmWpabcjMtx824uZbn5txCK3ALbqkVuSW30krciltrZW7NbbQKt+G2WpXbcjutxu24vVbn9txBa3AH7qg1uSN30lrciTtrbe7MXbQOd+GuWpe7cjetx934vZSkcb7Cca7iDMWzvS9x93RJG3B37qENuQf31Ebck3tpY+7FvbUJ9+Y+2pT7cF9txn25nzbnfvyktuAnub+25P78lLbip3iAtuYBPFDb8EAepG15EA/WdjyYh2h7HsJDtQMP5WHakYfxcO3Ew3mEduYR/LR24ad5pHblkfyMduNn+N2UpHGOwnF+4qzE8+Av8rPpkvbgZ/k57cnP8fPai5/nF7Q3v8Avah9+kV/SvvwSv6z9+GV+RZ/kV/hV7c+v8mv6FL/Gr+sAfp3f0IH8Br+pg/hNfksH81v8tg7ht/kdHcrv8Ls6jN/l93Q4v8fv6wh+nz/Qp/kD/lBH8of8kT7DH/E7KUnjvITjnMSZiM8KvMAfp0v6HH/Mn+jz/Al/qi/wp/yZvsif8ef6En/OX+jL/AV/qa/wl/yVvspf8df6Gn/N3+jr/A2P0jd4FH+rb/K3/J2+xd/x9/o2f8+j9R0ezWP0XR7DY/U9Hsvj9H0ex+P1Ax7PE/RDnsAT9SOeyG+nJI1zEY7zEHsfnxd5nic5D5/wJJ6sn/JknqKf8RSeqp/zVJ6mX/A0nq5f8nSeoV/xDJ6pX/NMnqXf8CyeraN4Ns/Rb3kOz9XveC7P0+95Hs/X0TyfF+gYXsALdSwv5EU6jhfxYh3Pi3mJTuAl/INO5B/4rZSksf/h2PfY4/hs0HP8o32fzD/yUp3CS3mZTuVlvFyn8XJeodN5Ba/UGbySV+lMXsWrdRav5jU6m9fwWp3Da3mdzuV1vF7n8XreoPN5A2/UBbyRN+lC3sSbdRFv5p90Mf/EW3QJb+Gt+gNv5TdTksY+h2N/Yy/j817P8jb7u5S38XZdxtt5hy7nHbxTV/BO/llX8s/8i67iX3iXruZdvFvX8G7eo2t5D+/VdbyX9+l63sf7dQPv5wO6kQ/wQd3EB/mQbuZDfFh/4sN8RLfwET6qW/kov5GSNPYzHPsYexaf1XuGj9nH7XyMj+sOPs4ndCef4F/1Z/6VT+ovfJJP6S4+xad1N5/m33QP/8ZndC+f4d91H//Of+h+/oP/1AP8J/+lB/kv/lsP8d98Vg/zWf5Hj/A/fE6P8jl+PSVp7Fs49iv2Jj5POZLP26/jfJ4v6Am+wBf1V77Il/QkX+J/9RT/y5f1NF/mK/obX+Greoav8n/6O//H16RP+gfHhK/VPzkmfJ3+xTHhVPo3x4Sv17McE75B/+GYcIqe45jwaylJY3/CsS+xB/HZ1qf5xvRJL3BMOLVe5JhwGr3EMeGb9F+OCafVyxwTvlmvcEw4nV7lmHB6/Y9jwhk09iCDCWfUazkjZ9LrOBNn1lScmbPo9ZyFs+oNnJWzaQpn41dTksY+hGP9Y63j88Qj+Bbrn5pv4Vs1Dd/Kt+lNfBvfrmn5dr5Db+Y7+E5Nx3fyXZqe7+K7NQPfzfdoRr6H79VMfC/fp5n5Ps6uWTg759CsnIPv12x8P7+SkjTWOxzrHGsan9Uezg9Y51v5AX5Qb+MHOafezjn5Ib2DH+Jceifn4tx6F+fmh/Vufpgf0Xv4Ec6j93Iezqv3cV7Op9k5H+fXHJyfC+j9XIBfTkka6xqO9Yy1i8+7D+NHreeD/CgX1JxckAvpQ1yIC2suLsyPaW5+jIvow1yEi+ojXJSLaR4uxsU1LxfnEpqPS/Djmp8f5ye0AD/BL6UkjfULx7rFGsV3AwzlktatIJfkUlqIS3FpLcyluYw+xmW4rBbhslxOi3I5Lq/FuDxX0OJcgStqCa7IlfRxrsSV9QmuzC+mJI11Csf6xFrE9yUM4SrWpxRX4apamqtyNS3D1bi6luXqXEPLcQ2uqeW5JtfSClyLa2tFrs11tBLX4bpamevyCylJYz3CsQ5xz/FdEYO5nnWoyvW4vlbj+txAq3MDbqg1uCE30prciBtrLW7MTbQ2N+GmWoebcjOty834+ZSkcd/huN+4t/g+jEHc3P3W5+bcQhtwC26pDbklt9JG3Ipba2NuzW20CbfhttqU23I7bcbt+LmUpHF/4bivuIf4bo+B3N59teD23EFbcgfuqK24I3fS1tyJO2sb7sxdtC134a7ajrvysylJ4z7Ccf1xrfF9JAO4m+vvwN24u3bk7txDO3EP7qmduSf30i7ci3trV+7Nz6QkjesNx3XGNcV3pTzFfVxnd+7DfbUH9+V+2pP78ZPai5/k/tqb+/PIlKRxXeG4nnjv+L6W/vyU6+nLT/EA7ccDeKA+yQN5kPbnQfx0StJ4//9z2uQ94rthnuTB3ncAD+YhOpCH8FAdxEN5RErSeJ9wvH68VnzfTD8e5vWH8DAerkN5+P93StJ4vXC8TvxNfFdNXx7hdYbzCB6W4rVSEse/j9/F99n04aEp/j4lcfw8eq3nl55OnzyvFBP+NH3Skfwpf6bP8Gf8uT7Ln/MX+hx/wV/q8/wlf6Uv8Ff8tb7IX/M3+hJ/w6P0ZR7F3+or/C1/p6/yd/y9vsbf82h9nUfzGH2Dx/BYfZPH8jh9i8fxeH2bx/MEfYcn8ER9lyfyJH2PJ/FkfZ8n8xT9gKfwVP2Qp/I0/Yin8XT9mKfzDP2EZ/DDqZOmuSZxnMM4c/GsXArPTJ/0M57Js/RznsWz9QuezXP0S57Dc/Urnsvz9Guex/P1G57PC3QUL+CF+i0v5EX6HS/ixfo9L+YlOpqX8A86hn/gH3Us/8hLdRwv5WU6npfxcp3Ay3mFTuQVvFIn8UpepZN5Fa/WKbya1+hUXsNrdRqv5XU6ndfxep3B6zl36qRx7sJx3uJsxfOYN/KG9Eln8QbeqLN5I2/SObyJN+tc3sw/6Tz+ibfofN7CW3UBb+VtupC38XZdxNt5hy7mHbxTl/BO/ll/4J/5F/2Rf+FdupR38W5dxrt5jy7nPbxXV/Be3qcreR/v11W8nw/oaj7AB3UNH+RDupYP8WFdx4f5iK7nI5wrddI4X+E4V3GG4tne1Hw0fdKNfJSP6SY+xsd1Mx/nE/oTn+BfdQv/yid1K5/kU7qNT/Fp3c6n+Tfdwb/xGd3JZ/h3/Zl/5z/0F/6D/9Rd/Cf/pbv5L/5b9/DffFb38ln+R/fxP3xO9/M5Pq8H+Dxf0IN8gS/qIb7Il/QwX+J/9Qj/yw+lThrnKBznJ85KPA+ehi+nT3qML/MVPc5X+Kqe4Kv8n/7K//E1GZKe5JjwtXqKY8LX6WmOCafS3zgmfL2e4ZjwDfo7x4RT9A+OCd+of3JMOLX+xTHhNPo3x4Rv0rMcE06r/3BM+GY9xzHhdHqeY8Lp9QLHhDPoRY4JZ9RLHBPOpP9yTDhn6qRxXsJxTuJMxGcFbuLMGZJe4ZhwFr3KMeGs+h/HhLNpnIlsJnyLXsu38K16Hd/Kt2kqvo1v1+v5dr5Db+A7+E5N4Tv5Lr2R7+K7NTXfzfdoGr6H79Wb+F6+T9PyfZxdb+bsnEPTcQ6+X9Pz/fyAZuAH+EHNyA9yTs3EOfnB1H6WOnGch9j7+LxIWn7IecjCD3Euzcq5OLdm49z8sN7CD/Mjeis/wnn0Ns7DefV2zsv59A7Ox/n1Ts7PBfQuLsCP6t38KBfUe7ggF9J7uRAX1vu4MD+m2fkxLqI5uAgX1fu5KBfTB7gYF9cHuTiX0Jxcgh9InTT2Pxz7Hnscnw26mR+377n4cX5Cc/MTXFIf5pJcSh/hUlxa83BpLqN5uQyX1Xxclstpfi7H5bUAl+cK+ihX4IpakCtyJS3ElbiyFubKXEUf4ypcVYtwVa6mRbkaV9diXJ1raHGuwTW1BNfk+1MnjX0Ox/7GXsbnvdJxLfv7BNfi2lqSa3MdLcV1uK6W5rpcT8twPa6vZbk+N9By3IAbanluyI20AjfixlqRG3MTrcRNuKlW5qbcTKtwM26uVbk5t9Bq3IJbanVuya20Brfi1lqTW3OO1EljP8Oxj7Fn8Vm99NzGPtbmNtxW63Bbbqd1uR2313rcnjtofe7AHbUBd+RO2pA7cWdtxJ25izbmLtxVm3BX7qZNuRt312bcnXtoc+7BPbUF9+Re2pJ7cW9txb25j7bmPpw9ddLYt3DsV+xNfJ4yA/e1X225L/fTdtyPn9T2/CT31w7cn5/SjvwUD9BOPIAHamceyIO0Cw/iwdqVB/MQ7cZDeKh256E8THvwMB6uPXk4j9BePIKf1t78NI/UPjyS70udNPYnHPsSexCfbc3Iz9iXfvwMP6tP8rP8nPbn5/h5fYqf5xd0AL/AL+pAfpFf0kH8Er+sg/llfkWH8Cv8qg7lV/k1Hcav8es6nF/nN3QEv8Fv6tP8Jr+lI/ktvjd10tiHcKx/rHV8njgTv239n+W3+R19jt/hd/V5fpff0xf4PX5fX+T3+QN9iT/gD/Vl/pA/0lf4I/5YX+WP+RN9jT/hT/V1/pQ/0zf4M/5c3+TP+Qt9i7/ge1InjfUOxzrHmsZntTPzl9b5Hf6Sv9J3+Sv+Wt/jr/kbfZ+/4VH6AY/ib/VD/pa/04/4O/5eP+bvebR+wqN5jH7KY3isfsZjeZx+zuN4vH7B4/nu1EljXcOxnrF28Xn3LDzBen7FE3iifs0TeZJ+w5N4so7iyTxFv+UpPFW/46k8Tb/naTxdR/N0nqFjeAbP1LE8k2fpOJ7Fs3U8z+a7UieN9QvHusUaxXcDZOU51m0iz+G5Oonn8jydzPN4vk7h+bxAp/ICXqjTeCEv0um8iBfrDF7MS3QmL+EfdBb/wD/qbP6R70ydNNYpHOsTaxHfl5CNl1qfubyUl+k8XsbLdT4v5xW6gFfwSl3IK3mVLuJVvFoX82peo0t4Da/VH3gtr9MfeR3fkTpprEc41iHuOb4r4hZebx2W8XreoMt5A2/UFbyRN+lK3sSbdRVv5p90Nf/EW3QNb+Gtupa38jZdx9v49tRJ477Dcb9xb/F9GLfydve7gbfzDt3IO3inbuKd/LNu5p/5F/2Jf+FduoV38W7dyrt5j27jPXxb6qRxf+G4r7iH+G6P23iv+9rBe3mf7uR9vF9/5v18QH/hA3xQd/FBPqS7+RAf1j18mG9NnfT/7iN1cv1xrfF9JLfzEde/j4/wUd3PR/mYHuBjfFwP8nE+oYf4BP+qh/lXviV10rjecFxnXFN8V8odfNJ1HuWTfEqP8Sk+rcf5NP+mJ/g3PqO/8hnOljppXFc4rifeO76v5U7+3fWc4t/5Dz3Nf/Cf+hv/yX/pGf6Ls6ZOGu8fjveN94jvhrmL//a+f/DffFb/5LP8j/7F/3CW1EnjfcLx+vFa8X0zd/M5r3+Wz/F5/YfPc+bUSeP1wvE68TfxXTX38AWvc54vcKbUSePvwvHv43fxfTb3csbUSeP34fh5NL7vJuaR/3lqSjLhrBmT5uGYcDbNyzHhWzQfx4Rv1fwcE75NC3BM+HZ9lGPCd2hBjgnfqYU4JnyXFuaY8N36GMeE79EiHBO+V4tyTPg+LcYx4exanGPCObQEx4Tv18c5JvyAPsEx4Qe1JMeEc2opjgk/pKU5JpxLy3BMOLeW5Zjww1qOY8KPaHmOCefRChwTzqsVOSacReN/PxuOcxhnbkpKMuF8zmE2zsf59RbOzwX0Vi7Aj+pt/CgX1Nu5IBfSO7gQF9Y7uTA/pnfxY1xE7+YiXFTv4aJcTO/lYlxc7+PiXEKzcwl+XHPw4/yE3s9PcEl9gEtyKX2QS3FpzcmluYw+xGW4rObislxOc3M5Lq8Pc3muoI9wBa6oebgiV9K8XIkzaxaO8xZna3JKMuHKGZPm58pcRQtwFa6qj3JVrqYFuRpX10JcnWtoYa7BNfUxrsm1tAjX4tpalGtzHS3GdbiuFue6XE9LcD2ur49zfW6gT3ADbqgluSE30lLciBtraW7MTbQMN+GmWpabcjMtx824uZbn5txCK3ALbqkVuSW30krcijNpZo5zFWdoUkoy4dYZk1bh1txGq3IbbqvVuC230+rcjttrDW7PHbQmd+COWos7cietzZ24s9bhztxF63IX7qr1uCt30/rcjbtrA+7OPbQh9+Ce2oh7ci9tzL24tzbh3txHm3If7qvNuC/30+bcj5/UFvwk99eW3J+f0lb8FGfUTBznJ87KxJRkwgMyJm3DA3igtuWBPEjb8SAerO15MA/RDjyEh2pHHsrDtBMP4+HamYfzCO3CI/hp7cpP80jtxiP5Ge3Oz/Cz2oOf5ee0Jz/Hz2svfp5f0N78Ar+offhFfkn78kv8svbjl/kVfZJf4Ve1P7/Kr+lT/Bpn0Iwc5yTOxISUZMKvZ0w6kF/nN3QQv8Fv6mB+k9/SIfwWv61D+W1+R4fxO/yuDud3+T0dwe/x+/o0v88f6Ej+gD/UZ/hD/kif5Y/4Y32OP+ZP9Hn+hD/VF/hT/kxf5M/4c32JP+cv9GX+gr/UV/hL/kpf5a/4a32Nv+b0moHjPMTej09JJvxNxqRv8Dc8St/kUfytvsXf8nf6Nn/H3+s7/D2P1nd5NI/R93gMj9X3eSyP0w94HI/XD3k8T9CPeAJP1I95Ik/ST3gST9ZPeTJP0c94Ck/Vz3kqT9MveBpP1y95Os/Qr3gGz9SveSan0/Qc+x57PC4lmfAs+z6KZ/Fs/ZZn8xz9jufwXP2e5/I8Hc3zeL6O4fm8QMfyAl6o43ghL9LxvIgX6wRezEt0Ii/hH3QS/8A/6mT+kZfqFF7Ky3QqL+PlOo2X8wqdzit4pc7glbxKZ/IqvlnTcexv7OXYlGTCq+3vbF7Na3QOr+G1OpfX8jqdx+t4vc7n9bxBF/AG3qgLeSNv0kW8iTfrYt7MP+kS/om36A+8hbfqj7yVt+lS3sbbdRlv5x26nHfwTl3BO/lnXck/8y+6in/htHozxz7Gno1JSSa8yz6u4V28W9fybt6j63gP79X1vJf36Qbex/t1I+/nA7qJD/BB3cwH+ZD+xIf4sG7hw3xEt/IRPqrb+Cgf0+18jI/rDj7OJ3Qnn+Bf9Wf+lU/qL3ySb9K0HPsVezM6JZnwKfu1m0/xad3Dp/k33cu/8Rndx2f4d93Pv/MfeoD/4D/1IP/Jf+kh/ov/1sP8N5/VI3yW/9Gj/A+f02N8js/rcT7PF/QEX+CL+itf5Et6ki9xGr2JY19iD75PSSb8r305zf/yZf2NL/MVPcNX+Kr+zlf5P/2D/+NrMiX9k2PC1+pfHBO+Tv/mmHAqPcsx4ev1H44J36DnOCacouc5JnyjXuCYcGq9yDHhNHqJY8Kp///PONY/1vq7lGTCN2VKepljwmn1CseEb9arHBNOp/9xTDi9xlqnN+EMei1n4Ix6HWfkTJqKM3FmvZ4zcxa9gbNwVk3hrJxNb+RsfIum5lv4Vk3Dt/KN9iDW+0ZrHWv6bUoy4dusc1q+jW/Xm/l2vkPT8R18p6bnO/kuzcB38d2ake/mezQT38P3ama+l+/TLHwfZ9esnJ1zaDbOwffrLXw/P6C38gOckjFprGs41jPWblRKMuEHreft/CDn1Ds4Jz+kd/JDnEvv4lycW+/m3Pyw3sMP8yN6Lz/CefQ+zsN5NTvn5Xyag/Nxfr2f83MBfYAL8A0Zk8b6hWPdYo2+SUkm/Kh1y8mPckF9iAtyIc3Fhbiw5ubC/Jg+zI9xEX2Ei3BRzcNFuZjm5WJcXPNxcS6h+bkEP64F+HG+PmPSWKdwrE+sxdcpyYSfsD4F+QkuqYW4JJfSwlyKS+tjXJrLaBEuw2W1KJflclqMy3F5Lc7luYKW4ApcUR/nipwqY9JYj3CsQ9zzVynJhCtZh5JciStrKa7MVbQ0V+GqWoarcjUty9W4upbj6lxDy3MNrqkVuCbX0opci6/LmDTuOxz3G/f2ZUoy4drutzLX5jpahetwXa3KdbmeVuN6XF+rc31uoDW4ATfUmtyQG2ktbsTXZkwa9xeO+4p7+CIlmXBj91WHG3MTrctNuKnW46bcTOtzM26uDbg5t9CG3IJbaiNuyddkTBr3EY7rj2v9PCWZcCvX34RbcWttyq25jTbjNtxWm3NbbqctuB2315bcnv/LkDSuNxzXGdf0WUoy4Q6uszV34I7ahjtyJ23LnbiztuPO3EXbcxe+miFpXFc4rife+9OUZMJdXU9H7srdtBN34+7ambtzD+3CPfhKhqTx/uF433iPT1KSCff0vt24J/fS7tyLe2sP7s2XMySN9wnH68drfZySTLiP1+/Ffbiv9ua+/G+GpPF64Xid+JuPUpIJ9/M6fbkfX8qQNP4uHP8+fvdhSjLhixmSxu/D8fPoddckz4z8kyb57/cx4TczJT3LMeG39G+OCb+tf3FM+B39k2PC7+ofHBN+T3/nmPD7eoZjwh/obxwT/lBPc0z4Iz3FMeGP9STHhD/RXzkm/Kme4JjwZ3qcY8Kf6zGOCX+hRzkm/KUe4ZjwV3qYY8Jf6yGOCX+jBzkmPEoPcEz4W93PMeHvdB/HhL/XvRwTHq17OCY8RndzTPgN/d+x/D/HOYwzF/+9PzOPdQ7f4rE8Tt/mcTxe3+HxPEHf5Qk8Ud/jiTxJ3+dJPFk/4Mk8RT/kKTxVP+KpPE0/5mk8XT/h6TxDP+UZPFM/45k8Sz/nWTxbv+DZPEe/5Dk8V7/iuTxPv+Z5PF+/4fm8QEfxAl6o3/JCXqTf8SJerN/zYl6io3kJ/6Bj+Ad+Xd/gOG9xtuL5gEz8Y6ak4/hHXqrjeSkv0wm8jJfrRF7OK3QSr+CVOplX8iqdwqt4tU7l1bxGp/EaXqvTeS2v0xm8jtfrTF7PG3QWb+CNOps38iadw5t4s87lzfyTzuOfeIvO5y28VRfwVt6mC3kbb9dFvJ136GLewTt1Ce/kn/UH/plf09c5zlWcoXieICP/kinpUv6Fd+ky3sW7dTnv5j26gvfwXl3Je3mfruJ9vF9X834+oGv4AB/UtXyQD+k6PsSHdT0f5iO6gY/wUd3IR/mYbuJjfFw383E+oT/xCf5Vt/CvfFK38kk+pdv4FJ/W7Xyaf9Md/Buf0Z18hn/Xn/l3flVf4zg/cVbi+YMM/EempLv4D/5Td/Of/Jfu4b/4b93Lf/NZ3cdn+R/dz//wOT3A5/i8HuTzfEEP8QW+qIf5Il/SI3yJ/9Wj/C9f1mN8ma/ocb7CV/UEX+X/9Ff+j6/JnPQkx4Sv1VMcE75OT3NMOJX+xjHh6/UMx4Rv0N85JvyKn73KcU7iTMTzCuk5JXPSPzkmfKP+xTHh1Po3x4TT6FmOCd+k/3BMOK2e45jwzXqeY8Lp9ALHhNPrRY4JZ9BLHBPOqP9yTDiTXuaYcGa9wjHhLHqVY8JZ9T+OCWfTOBPZTPgWvZZv4Vv1Or6Vb9NUfBvfrtfz7XyH3sB38MvOSpyLl52J2Pt4viEd3+k83Mh38l2amu/iuzUN38336E18D9+raflevk9v5vs4u6bj7JxD03MOvl8z8P38gGbkB/hBzcQPck7NzDn5Ic3CD3Euzcq5OLdm49z8sN7CD/Mjeis/wnn0Ns7DefV2zsv59A7Oxy9lShr7H459jz2O5yFu5vz2/S7OzwX0bi7Aj+o9/CgX1Hu5IBfS+7gQF9bsXJgf0xz8GBfR+7kIF9UHuCgX0we5GBfXnFycS+hDXIIf11z8OD+hufkJLqkPc0kupY9wKS6tebg0l9G8XIbLaj4uyy9mShr7HI79jb2M5yfScjn7W4DLcXl9lMtzBS3IFbiiFuKKXEkLcyWurI9xZa6iRbgKV9WiXJWraTGuxtW1OFfnGlqCa3BNfZxrci19gmtxbS3JtbmOluI6XFdLc12up2W4HtfXslyfX8iUNPYzHPsYexbPW9zEDexjeW7ADbUCN+RGWpEbcWOtxI25iVbmJtxUq3BTbqZVuRk312rcnFtodW7BLbUGt+RWWpNbcWutxa25jdbmNtxW63Bbbqd1uR2313rcnjtofe7Az2dKGvsWjv2KvUnjGY1wR/vVkDtyJ23EnbizNubO3EWbcBfuqk25K3fTZtyNu2tz7s49tAX34J7akntyL23Fvbi3tube3EfbcB/uq225L/fTdtyPn9T2/CT31w7cn5/LlDT2Jxz7EnuQ2jM04afsSyd+igdoZx7AA7ULD+RB2pUH8WDtxoN5iHbnITxUe/BQHqY9eRgP1148nEdobx7BT2sffppHal8eyc9oP36Gn9Un+Vl+Tvvzc/xsJj/LlDjWP9b6Rs/WhJ+3/gP4eX5BB/IL/KIO4hf5JR3ML/HLOoRf5ld0KL/Cr+owfpVf0+H8Gr+uI/h1fkOf5jf4TR3Jb/Jb+gy/xW/rs/w2v6PP8Tv8TKaksd7hWOdY0xTP3ITftc4v8Lv8nr7I7/H7+hK/zx/oy/wBf6iv8If8kb7KH/HH+hp/zJ/o6/wJf6pv8Kf8mb7Jn/Hn+hZ/zl/o2/wFf6nv8Jc8MlPSWNdwrGes3Q2exQl/ZT3f46/4a32fv+Zv9AP+hkfphzyKv9WP+Fv+Tj/m7/h7/YS/59H6KY/mMfoZj+Gx+jmP5XH6BY/j8folj+enMyWN9QvHusUaXe8ZnfAE6/Y1T+CJ+g1P5Ek6iifxZP2WJ/MU/Y6n8FT9nqfyNB3N03i6juHpPEPH8gyeqeN4Js/S8TyLR2RKGusUjvWJtUjl2Z3wbOszkWfzHJ3Ec3iuTua5PE+n8Dyer1N5Pi/QabyAF+p0XsiLdAYv4sU6kxfzEp3FS3h4pqSxHuFYh7jn6zzTE/7BOszhH/hHncs/8lKdx0t5mc7nZbxcF/ByXqELeQWv1EW8klfpYl7Fq3UJr+ZhmZLGfYfjfuPervWsT3iN+/2R1/BaXcpreZ0u43W8Xpfzet6gK3gDb9SVvJE36SrexJt1NW/moZmSxv2F477iHq7xDFD4J/e1ln/iLbqOt/BWXc9beZtu4G28XTfydt6hm3gH79TNvJOHZEoa9xGO649rjedvYsI/u/4t/DP/olv5F96l23gX79btvJv36A7ew3t1J+/lwZmSxvWG4zrjmq56Zii8z3X+wvt4v+7i/XxAd/MBPqh7+CAf0r18iAdlShrXFY7rife+4lmi8GHXs58P8xE9wEf4qB7ko3xMD/ExHpgpabx/ON433uOyZ4zCx73vET7OJ/Qon+Bf9Rj/ygMyJY33Ccfrx2v969mj8Emvf4JP8in9lU/xU5mSxuuF43Xiby55Jil82uuc4tPcP1PS+Ltw/Pv43UXPKoWfzJQ0fh+On0fj+aV4ZuRcPAOUOZnw/VmSnueY8AN6gWPCD+pFjgnn1EscE35I/+WYcC69zDHh3HqFY8IP61WOCT+i/3FMOI/GQ1vhmHBevZZjwvn0Oo4J59dUHBMuoNdzTPhRvYFjwgU1hWPChfRGjgkX1tQcE35M03BMuIjexDHhopqWY8LF9GaOCRfXdBwTLqHpOSb8uGbgmPATmpFjwjk07TWJ4xzGmTuTOZlwSefwAS7JpfRBLsWlNSeX5jL6EJfhspqLy3I5zc3luLw+zOW5gj7CFbii5uGKXEnzciWurPm4MlfR/FyFq2oBrsrV9FGuxtW1IFfnGlqIa3BNLcw1uZY+xrW4thbh2lxHi3IdrqvFuC7X0+Jcj+trCa7PDfRxbsAN9QluyNk1B8d5i7P1e+Zkwo2yJC3FjbixlubG3ETLcBNuqmW5KTfTctyMm2t5bs4ttAK34JZakVtyK63Erbi1VubW3EarcBtuq1W5LbfTatyO22t1bs8dtAZ34I5akztyJ63Fnbiz1ubO3EXrcBfuqnW5K3fTetyNu2t97s49tAH34J7akHvyfZqd41zFGfojczLhXlmSNuZe3FubcG/uo025D/fVZtyX+2lz7sdPagt+kvtrS+7PT2krfooHaGsewAO1DQ/kQdqWB/FgbceDeYi25yE8VDvwUB6mHXkYD9dOPJxHaGcewU9rF36aR2pXHsnPaDd+hp/V7vwsP6c9+Dl+Xnvy83yv3sdxfuKs/Jk5mfALWZL25hf4Re3DL/JL2pdf4pe1H7/Mr+iT/Aq/qv35VX5Nn+LX+HUdwK/zGzqQ3+A3dRC/yW/pYH6L39Yh/Da/o0P5HX5Xh/G7/J4O5/f4fR3B7/MH+jR/wB/qSP6QP9Jn+CP+WJ/lj/kTfY4/4U/1ef6U79F7Oc5JnIm/MicT/ixL0hf5M/5cX+LP+Qt9mb/gL/UV/pK/0lf5K/5aX+Ov+Rt9nb/hUfoGj+Jv9U3+lr/Tt/g7/l7f5u95tL7Do3mMvstjeKy+x2N5nL7P43i8fsDjeYJ+yBN4on7EE3mSfsyTeLJ+wpN5in7KU/huvYfjPMTe/505mfDULEk/56k8Tb/gaTxdv+TpPEO/4hk8U7/mmTxLv+FZPFtH8Wyeo9/yHJ6r3/Fcnqff8zyer6N5Pi/QMbyAF+pYXsiLdBwv4sU6nhfzEp3AS/gHncg/8I86iX/kpTqZl/IyncLL+C69m2PfY4/PZk4mvNy+T+PlvEKn8wpeqTN4Ja/SmbyKV+ssXs1rdDav4bU6h9fyOp3L63i9zuP1vEHn8wbeqAt4I2/ShbyJN+si3sw/6WL+ibfoEt7CW/UH3srb9Efextt1KW/nHbqMd/CdehfH/sZe/pM5mfBO+7uCd/LPupJ/5l90Ff/Cu3Q17+LduoZ38x5dy3t4r67jvbxP1/M+3q8beD8f0I18gA/qJj7Ih3QzH+LD+hMf5iO6hY/wUd3KR/mYbuNjfFy383E+oTv4BN+hd3LsY+zZuczJhH+1jz/zr3xSf+GTfEp38Sk+rbv5NP+me/g3PqN7+Qz/rvv4d/5D9/Mf/Kce4D/5Lz3If/Hfeoj/5rN6mM/yP3qE/+FzepTP8Xk9xuf5gh7nC3xRT/BFvl3v4Niv2JvzmZMJX7JfJ/kS/6un+F++rKf5Ml/R3/gKX9UzfJX/09/5P74ma9I/OCZ8rf7JMeHr9C+OCafSvzkmfL2e5ZjwDfoPx4RT9BzHhG/U8xwTTq0XOCacRi9yTPg2P7udY19iDy5kTiZ8U9ak/3JMOK1e5pjwzXqFY8Lp9CrHhNPrfxwTzqCxBxlMOKNeyxk5k17HmTizpuLMnEWv5yycVW/grJxNUzgb36I38i18q6bmW/k2TcO38a32JvbhVnsQa30xczLh261/Wr6d79Cb+Q6+U9PxnXyXpue7+G7NwHfzPZqR7+F7NRPfy/dpZr6Ps2sWzs45NCvn4Ps1G9/PD+gt/AA/qLfyg5xTb+OcfEuWpLHe4VjnWNNLmZMJP2Sd7+CHOJfeybk4t97FuflhvZsf5kf0Hn6E8+i9nIfz6n2cl/Npds7H+TUH5+cCej8X4Ef1AX6UC+qDXJALaU4uxNmyJI11Dcd6xtr9mzmZcGHrmYsL82Oamx/jIvowF+Gi+ggX5WKah4txcc3LxbmE5uMS/Ljm58f5CS3AT3BJfZRLciktyKW4tBbi0pw1S9JYv3CsW6zR5czJhMtYt8e4DJfVIlyWy2lRLsfltRiX5wpanCtwRS3BFbmSPs6VuLI+wZW5ipbkKlxVS3FVrqaluRpnyZI01ikc6xNrcSVzMuHq1qcsV+caWo5rcE0tzzW5llbgWlxbK3JtrqOVuA7X1cpcl+tpFa7H9bUq1+cGWo0bcOYsSWM9wrEOcc9XMycTbmgdanBDbqQ1uRE31lrcmJtobW7CTbUON+VmWpebcXOtx825hdbnFtxSG3BLzpQladx3OO437u2/zMmEW7nfRtyKW2tjbs1ttAm34bbalNtyO23G7bi9Nuf23EFbcAfuqC25I2fMkjTuLxz3FfdwTZZkwp3cV2vuxJ21DXfmLtqWu3BXbcdduZu2527cXTtwd+6hHbkHZ8iSNO4jg+uPa702SzLhnq6/M/fkXtqFe3Fv7cq9uY924z7cV7tzX+6nPbgfp8+SNK43veuMa7ouSzLhJ11nL36S+2tv7s9PaR9+igdoXx7AA7UfD+R0WZLGdaVzPfHeqbIkEx7kevrzIB6sT/FgHqIDeAgP1YE8lG/OkjTe/2bvG+9xfZZkwsO872AexsN1CA/nETqUR3DaLEnjfdJ6/XitG7IkE37a6w/np3mkjuCRfFOWpPF6N3md+JuULMmEn/E6I/kZTpMlafxdGv8+fndjlmTCqTV+n9rP/6/XJM+MfP6/380x4a36GW/lbfopb+Pt+glv5x36Me/gnfoR7+Sf9UP+mX/RD/gX3qXv8y7ere/xbt6j7/Ie3qvv8F7ep2/zPt6vb/F+PqBv8gE+qG/wQT6kr/MhPqyv8WE+oq/yET6qr/BRPqYv8zE+ri/xcT6hL/IJ/lVf4F/5pD7PJ/mUPsen+LQ+y6d5i8ajZeE4h3HmZpvwb7qNf+Mzup3P8O+6g3/nP3Qn/8F/6s/8J/+lv/Bf/Lfu4r/5rO7ms/yP7uF/+Jzu5XN8Xvfxeb6g+/kCX9QDfJEv6UG+xP/qIf6XL+thvsxX9Ahf4at6lK/yf3qM/+NrsiU9zjHha/UEx4Sv0185JpxKT3JM+Ho9xTHhG/Q0x4R/8rMtHOctztYsE07JlvQMx4Rv1N85Jpxa/+CYcBr9k2PCN+lfHBNOq39zTPhmPcsx4XT6D8eE0+s5jgln0PMcE86oFzgmnEkvckw4s17imHAW/Zdjwln1MseEs+kVjgnfolc5Jnyr/scx4ds0ztZtJny7Xsu38x16Hd/Bd2oqvpPv0uv5Lr5bb+C7ebMzF+drs3MVZ2hm1mTC9zhXN/I9fK+m5nv5Pk3D93F2vYmzcw5Nyzn4fr2Z7+cHNB0/wA9qen6Qc2oGzskPaUZ+iHNpJs7FuTUz5+aHNQs/zI9oVn6E82g2zsN59RbOy/n0Vs7H+fU2zs8F9HYuwI/qHfwoF9Q7uSAX0ru4EBfWu7kwb8qaNM7RJucnzsqMrMmEH8uW9F5+jIvofVyEi2p2LsrFNAcX4+J6PxfnEvoAl+DH9UF+nJ/QnPwEl9SHuCSX0lxciktrbi7NZfRhLsNl9REuy+U0D5fj8pqXy3MFzccVuKLm54pcSQtwJa6sj3JlrqIFuQpX1UJclatpYa7GG7MmjfOy0TmJMzE9azLh6tmSFuHqXEOLcg2uqcW4JtfS4lyLa2sJrs119HGuw3X1Ca7L9bQk1+P6WorrcwMtzQ24oZbhhtxIy3IjbqzluDE30fLchJtqBW7KzbQiN+PmWombcwutzC24pVbhltxKq3Irbq3VuDVvyJo0zsUG5yH2flrWZMJtnIca3Ibbak1uy+20Frfj9lqb23MHrcMduKPW5Y7cSetxJ+6s9bkzd9EG3IW7akPuyt20EXfj7tqYu3MPbcI9uKc25Z7cS5txL+6tzbk399EW3If7akvuy/20FffjJ7U1P8nrsyaN/V9v32OPp2ZNJtzfvrfl/vyUtuOneIC25wE8UDvwQB6kHXkQD9ZOPJiHaGcewkO1Cw/lYdqVh/Fw7cbDeYR25xH8tPbgp3mk9uSR/Iz24mf4We3Nz/Jz2oef4+e1Lz/PL2g/foFf1Cf5RV6XNWns8zr7G3s5JWsy4Zfs71P8Er+sA/hlfkUH8iv8qg7iV/k1Hcyv8es6hF/nN3Qov8Fv6jB+k9/S4fwWv60j+G1+R5/md/hdHcnv8nv6DL/H7+uz/D5/oM/xB/yhPs8f8kf6An/EH+uL/DGvzZo09nOtfYw9m5w1mfAn9vFl/oQ/1Vf4U/5MX+XP+HN9jT/nL/R1/oK/1Df4S/5K3+Sv+Gt9i7/mb/Rt/oZH6Ts8ir/Vd/lb/k7f4+/4e32fv+fR+gGP5jH6IY/hsfoRj+Vx+jGP4zVZk8a+rbFfsTeTsiYTHm+/PuXxPEE/4wk8UT/niTxJv+BJPFm/5Mk8Rb/iKTxVv+apPE2/4Wk8XUfxdJ6h3/IMnqnf8Uyepd/zLJ6to3k2z9ExPIfn6liey/N0HM/j1VmTxv6sti+xBxOzJhOeb18m8HxeoBN5AS/USbyQF+lkXsSLdQov5iU6lZfwDzqNf+AfdTr/yEt1Bi/lZTqTl/FyncXLeYXO5hW8UufwSl6lc3kVr9Z5vJpXZfWzrIlj/f9vrbMmE15j/RfwGl6rC3ktr9NFvI7X62Jezxt0CW/gjfoDb+RN+iNv4s26lDfzT7qMf+Itupy38FZdwVt5m67kbbxdV/H2/1fTfYf9WP99HL9USmnJZW8V2XuEQhMpskcSSrSLsvfem2yivfdS2SQjlC0ryt6y3ff7Ph/3H6/j+Thwfb/n+TnP3x+/43cdx4836HLewEtTk8Z5L3XOcaYfpCYLb3TOK3gjb9LfeBNv1pW8mbfoKt7CW3U1b+Vtuoa38Xb9nbfzX7qW/+Iduo538E5dzzt5l/7Bu3i3/sm7eY9u4D28JDVpnOsS5xln935qsvDfznMT/817dTPv5X26hffxP7qV/+F/dRv/y/t1O+/nA/oXH+CDuoMP8iHdyYf4sO7iw3xEd/MRPqp7+CgvTk0a57fYucUZvZeaLHzMue3lY3xc9/FxPqH/8Ak+qf/yST6l+/kUn9YDfJrP6EE+w//pIf6Pz+phPsvn9Aif4/N6lM/zotSkcU6LnE+cxbupycIXnM9xvsAX9QRf5Et6ki/xZT3Fl/mKnuYrnJI56RmOhdPofxwLX6VnORa+Ws9xLHyNnudYeGFq0jiPhc4h7vmd1GThtJmTXuRY+Fq9xLHwdXqZY+F0eoVj4es17vl6C9+gafgGTq9XcXq+Ua/mG/kmvYZv4gWpSeO+F7jfuLe3U5OFb3a/1/LNfItex7fwrZqOb+UMej1n4Nv0Br6NM2p6zsipeiOncia9iTPx/NSkcX/z3Vfcw9zUZOHM7usWzsxZ9FbOwlk1A2flbHobZ+PsmpGzcw5N5RycUzNxTv4lNWncxy+uP651TmqycC7Xn4VzcW7Nyrk5j2bjPJxXs3Nezqc5OB/n15ycn39OTRrX+7PrjGt6KzVZ+HbXmZtv5zs0D9/Bd2pevpMLaD4uwAU1Pxfkn1KTxnX95Hriu2enJgvf5Xru4Lu4kN7JhbiwFuDCXEQLchGel5o0vn+e743vmJWaLFzU9xbiolxMC3MxLq5FuDj/mJo0vudHnx+fNTM1WbiEzy/GJbikFueS/ENq0vi8H3xO/MyM1GThUj6nJJfi71OTxs9979/H301PTRb+TuPvv/Pn0XQpye+MTL45+d/vY+FHMyd9k2Phx3QSx8J1dCLHwnV1AsfCj+t4joXr6TiOhevrWI6FG+gYjoUb6miOhRvpKI6FG+tIjoWb6AiOhZvqcI6Fm+kwjoWb61COhZ/QIRwLt9DBHAs/qYM4Fm6pAzkWfkoHcCzcSvtzLNxa+3Es3Eb7ciz8tPbhWPgZ7c2xcFvtxbFwab0mJXG8h/HOxf/ev5af9R4+xs9yO63D7bi91uX2/Jw+zs/x81qPn+cXtD6/wC9qA36RX9KG/BK/rI34ZX5FG/Mr/Ko24Vf5NW3Kr3EHbcYduKM25478uj7Br/Mb2oLf4E76JHfiztqSO3MXfYq7cFdtxV25m7bmbtxd23B37qFPcw/uqc9wT+6lbbkXl9HSHO9bvFvx+wG/c+/MSdtxb+6j7bkP99XnuC/30+e5H/fXF7g/D9AXeQAP1Jd4IA/Sl3kQD9ZXeDAP0Vd5CA/V13goD9MOPIyHa0ceziP0dR7BI/UNHsmjtBOP4tHamUfzGO3CY3isduWxPE678Tger915PE/QHjyBJ2pPnsiTtBdP4rJahuO9incofp9gDb+ZOWkffpMna1+ezFO0H0/hqdqfp/I0HcDTeLoO5Ok8QwfxDJ6pg3kmz9IhPItn61CezW/pMH6L5+hwnsNzdQTP5bd1JL/N7+gofoff1dH8Lr+nY/g9fl/H8vv8gY7jD/hDHc8f8kc6gT/ij3Uif8yf6CT+hMtpWY73J96V+P2D1fxp5qST+VP+TKfwZ/y5TuXP+Qudxl/wlzqdv+SvdAZ/xV/rTP6av9FZ/A1/q7P5W/5O3+Lv+Hudw9/zDzqXf+Af9W3+kefpOzyPf9J3+Sf+Wd/jn/kXfZ9/4fn6Ac/nBfohL+CF+hEv5EX6MS/ixfoJL+byWo7jPYl3In5fYRUvyZz0M17CS/VzXsrL9Atexsv1S17Ov+pX/Cuv0K95Bf+m3/BvvFK/5ZW8Sr/jVbxav+fVvEZ/4DX8u/7Iv/NancdreZ3+xOt4vf7M6/kP/YX/4D91Pv/JG3QBb+CNupA38iZdxJt4sy7mzVxBy3O8D/Hs4/cbVvKWzEmX8hbeqst4K2/T5byNt+uvvJ3/0hX8F+/Q33gH79SVvJN36Srexbt1Ne/mPbqG9/Df+jv/zXt1Le/lfbqO9/E/up7/4X/1D/6X9+ufvJ8P6AY+wAd1Ix/kQ7qJD/Fh3cyHuaJW4Hju8Yzj9yF+4yOe+1Y+wkd1Gx/lY7qdj/Fx/YuP8wndwSf4pO7kk3xKd/EpPq27+TSf0T18hv/Tv/k/Pqt7+Syf0318js/rP3yeL+i/fIEv6n6+yJf0AF/iy3qQL/MVPcRXOCVL0sMcC9/tzypyPN94lvH7Eys4TZakRzkWvkqPcSx8tR7nWPgaPcGxcFo9ybHwtXqKY+Hr9DTHwun0DMfC1+t/HAvfoGc5Fk6v5zgWvlHPcyx8k17gWPhmvcix8C16iWPhW/Uyx8IZ9ArHwrdpPMvbLFzJn8XzrORZxjOL37f4lTN6jldxRk7VqzmVM+k1nIkza1rOzFn0Ws7CWfU6zsrZNB1n4+x6PWfnHHoD5+Ccmp5zci69kXNxbr2Jc3MevZnzcF69hfNyPr2V83F+zcD5+Xa9jW/nyp5rPLdwPK94NvH7Gcv5Ds8rle/gOzUT38kFNDMX4IKahQvyXZqV7+JCmo0LcWHNzoW5iObgIlxUc3JRLqa5uBgX19xcnEtoHi7BJTUvl+RSmo9LcWnNz6W5jN7OZbhK5qTxfMLxXOIZLPM7NOGynsudXJbLaQEux+W1IJfnCnoXV+CKWogr8t1amO/mSlqEK3FlLcqVuYoW4yp8jxbne/heLcH3clUtyVW5mpbialxdS3N1vk/L8H18T+ak8RzCcf5x1kv9bk34fudfju/nB7Q8P8APagV+kB/SivwQP6x388NcQytxDa6plbkm19IqXIsf0Xv4Ea6t93JtflSr8qP8mFbjx7iOVuc6XFfv47p8b+akcd7hOOc40yV+5yb8uHN+gB/nevog1+P6+hDX5wb6MDfghlqDG3IjrcmNuLHW4sbcRB/hJtxUa3NTbqaPcjNuro9xc35C6/AT3ELrcguumjlpnGs4zjPObrHfxQk/6Tzr8ZPcUutzS35KG/BT3EobciturY24NbfRxtyGn9Ym/DQ/o035GW6rzbgtP6vN+Vlup09wO26vLbg9V8ucNM4vHOcWZ7TI7+iEn3NuLfk5fl6f4uf5BW3FL/CL2ppf5Je0Db/EL+vT/DK/os/wK/yqtuVX+TV9ll/jDtqOO3BHbc8duXrmpHFO4TifOIuFfncn/LrzeZ5f5zf0BX6DO+mL3Ik760vcmbvoy9yFu+or3JW76avcjbvra9yde2gH7sE9tSP35PsyJ43zCMc5xD0v8Ds94V7O4Q3uxb21E/fmPtqZ+3Bf7cJ9uZ925X7cX7txfx6g3XkAD9QePJAHaU8exPdnThr3HY77jXub73d9woPdb28ezEO0Dw/hodqXh/Iw7cfDeLj25+E8QgfwCB6pA3kkj9JBPIofyJw07i8c9xX38IvfAQqPdl9DeDSP0aE8hsfqMB7L43Q4j+PxOoLH8wQdyRN4oo7iifxg5qRxH+G4/rjWn/1uUHiS6x/Dk/hNHctv8mQdx5N5io7nKTxVJ/BUnqYTeRo/lDlpXG84rjOu6Se/MxSe7jrf5Ok8QyfzDJ6pU3gmz9KpPItn6zSezQ9nThrXFY7rie+e53eJwm+5nhn8Fs/RmTyH5+osnstv62x+m2tkThrfH47vje/40e8Yhd/xvXP4HX5X5/K7/J6+ze9xzcxJ43vC8fnxWT/43aPw+z7/XX6fP9D3+AOulTlpfF44Pid+5nu/kxT+0Od8wB/yI5mTxs+F49/H333nd5TCtTMnjb8Px59H4/eXYuHqKcnCH/mZR/gjP/ux1uKPfe8nWpM/cS+fag3+1Ll9pg/zZ57d5/oQf+69+kIf5Fj4S32AY+Gv9H6Ohb/W+zgW/karcyz8rVbjWPg7rcqx8Pd6L8fCP+g9HAv/qFU4Fp6nlTkW/kkrcSz8s97NsfAvWpFj4flagWPhBVqeY+GFWo5j4UValmPhxVqGY+ElWppj4bfTJ43fXwrHexjvXLWUZOGl3sOPeSkv0094GS/XT3k5/6qf8a+8Qj/nFfybfsG/8Ur9klfyKv2KV/Fq/ZpX8xr9htfw7/ot/85r9Ttey+v0e17H6/UHXs9/6I/8B/+p8/hP3qA/8QbeqD/zRt6kv/Am3qzzeTNv0QW8hbfqQt7K23QRb+Ptupi381+6hP/iuemTxnsXjvct3q2qKcnCO7IkXcY7eKcu5528S3/lXbxbV/Bu3qO/8R7+W1fy37xXV/Fe3qereR//o2v4H/5Xf+d/eb+u5f18QNfxAT6o6/kgH9I/+BAf1j/5MB/RDXyEj+pGPsrHdBMf4+O6mY/zCd3CJ/ikbuWTfEq38Sk+rdv5NJ/Rv/gMz0mfNN6vcLxX8Q7dm5Is/F+WpDv5Pz6ru/gsn9PdfI7P6x4+zxf0b77AF3UvX+RLuo8v8WX9hy/zFf2Xr3BK1qT7ORZOowc4Fr5KD3IsfLUe4lj4Gj3MsXBaPcKx8LV6lGPh6/QYx8Lp9DjHwtfrCY6Fb9CTHAun11McC9+opzkWvknPcCz8Vvqk8R6F4/2Jd+WelGThm7MmPcux8C16jmPhW/U8x8IZ9ALHwrfpRY6FM+oljoVT9TLHwpn0CsfCmTXelcwWzqJpOAtn1as4K2fTqzkbZ9drODvn0LScg3PqtZyTc+l1nItzazrOzXn0es7DefUGzsv5ND3n4/x6I+fn2/Umvp1np08a70s43pN4J6qkJAvf4T25he/gO/VWvpMLaAYuwAX1Ni7Id2lGvosLaSoX4sKaiQtzEc3MRbioZuGiXEyzcjEurtm4OJfQ7FyCS2oOLsmlNCeX4tKai0tzGc3NZbis5uGyXE7zcjkur/m4PFfQ/FyBK+rtXJFnpU8a70U43od49pVTkoXv9j7cyXdzJS3AlbiyFuTKXEXv4ip8jxbie/heLcz3clUtwlW5mhblalxdi3F1vk+L8318v5bg+/kBLckP8INaih/kh7Q0P8QPaxl+mGtoWa7BNbUc1+RaWp5r8SNagR/h2lqRa/PM9Enj+YfjucczrpSSLPyo516JH+XHtDI/xnW0CtfhunoP1+XH9V5+nOtpVa7H9bUa1+cGWp0bcEO9jxtyI72fG3FjfYAbcxN9kJtwU32Im3IzfZibcXOtwc35Ca3JT3ALrcUt+El9hJ/kllqbW/KM9EnjOYfj+f7fs01JFn7K832Mn+JWWodbcWuty625jT7ObfhprcdP8zNan5/httqA2/Kz2pCf5XbaiNtxe23M7fk5bcLP8fPalJ/nF7QZv8AvanN+kV/SJ/glfllb8Mv8ij7Jr/Cr2pJf5enpk8bzDMdzjGdWMSVZ+DXPsRW/xh20NXfgjtqGO/Lr+jS/zm/oM/wGd9K23Ik767PcmbtoO+7CXbU9d+Vu+hx34+76PHfnHvoC9+Ce+iL35F76Evfi3voy9+Y++gr34b76KvflaemTxnMLx/OKZ1MhJVm4n+fVgftxf+3I/XmAvs4DeKC+wQN5kHbiQTxYO/NgHqJdeAgP1a48lIdpNx7Gw7U7D+cR2oNH8EjtySN5lPbiUTxae/NoHqN9eAyP1b48lqemTxrPJxzPJZ5B+ZRk4XGeS38ex+N1AI/nCTqQJ/BEHcQTeZIO5kn8pg7hN3myDuXJPEWH8RSeqsN5Kk/TETyNp+tIns4zdBTP4Jk6mmfyLB3Ds3i2juXZPCV90ngO4Tj/OOtyKcnCbzn/8fwWz9EJPIfn6kSey2/rJH6b39E3+R1+Vyfzu/yeTuH3+H2dyu/zBzqNP+APdTp/yB/pDP6IP9aZ/DF/orP4E/5UZ/OnPDl90jjvcJxznGnZlGThz5zzHP6MP9e5/Dl/oW/zF/ylvsNf8lf6Ln/FX+t7/DV/o+/zN/ytfsDf8nf6IX/H3+tH/D3/oB/zD/yjfsI/8jz9lOfxm+mTxrmG4zzj7MqkJAv/5Dw/55/4Z/2Cf+Zf9Ev+hefrVzyfF+jXvIAX6je8kBfpt7yIF+t3vJiX6Pe8hJfqD7yUl+mPvIyX6zxezpPSJ43zC8e5xRmVTkkW/tW5/cy/8gr9hVfwbzqff+OVuoBX8ipdyKt4tS7i1bxGF/Ma/l2X8O+8VpfyWl6ny3gdr9flvJ4npk8a5xSO84mzKJWSLPyH81nBf/Cf+hv/yRt0JW/gjbqKN/ImXc2beLOu4c28RX/nLbxV1/JW3qbreBtv1/W8nSekTxrnEY5ziHsumZIs/Jdz+JP/4h26gXfwTt3IO3mXbuJdvFs3827eo1t4D/+tW/lv3qvbeC/v0+28j8enTxr3HY77jXsrkZIs/I/73cH/8L+6k//l/bqL9/MB3c0H+KDu4YN8SP/mQ3xY9/JhPqL7+AiPS5807i8c9xX3UDwlWfio+/qXj/Ix3c/H+Lge4ON8Qg/yCT6ph/gkn9LDfIpP6xE+zWPTJ437CMf1x7UWS0kWPuP6j/EZ/k+P8398Vk/wWT6nJ/kcn9dTfJ4v6Gm+wGPSJ43rDcd1xjUVTUkWvug6/+OLfEnP8iW+rOf4Ml/R83yFU7IlvcCx8Oj0SeO6wnE98d1FUpKF02RLeolj4av0MsfCV+sVjoWv0fjuayw8Kn3S+P5wfG98R+GUZOG0vvcqTsvX6tV8LV+n1/B1PDJ90viecHx+fFahlGThdD7/Wk7H1+t1fD2PSJ80Pi8cnxM/c1dKsvANPud6voGHp08aPxeOfx9/VzAlWXhY+qTx9+H482iK//+4+P/ryp8xWTh9tqTfcCx8o37NsfBN+hXHwjfrlxwL36JfcCx8q37OsXAG/Yxj4dv0U46FM+onHAun6sccC2fSjzgWzqwfciycRT/gWDirvs+xcDZ9j2Ph7Poux8I59B2OhXPq2xwL59K5HAvn1jkcC+fRtzgWzquzORbOp7M4Fs6vMzkWvl1ncCx8h07nWPiJlKTxu3TheA/jncuXMVn4Tu/hjXwnF9CbuAAX1Ju5IN+lt/BdXEhv5UJcWDNwYS6it3ERLqoZuSgX01QuxsU1ExfnEpqZS3BJzcIluZRm5VJcWrNxaS6j2bkMl9UcXJbLaU4ux+U1F5fnCpqbK3BFzcMV+W7Ny3dzJc3Hlbiy5ufKXEVv5yp8j97B93DzlKTx3oXjfYt3K2/GZOF7syUtwPdyVS3IVbma3sXVuLoW4up8nxbm+/h+LcL38wNalB/gB7UYP8gPaXF+iB/WEvww19CSXINraimuybW0NNfiR7QMP8K1tSzX5ke1HD/Kj2l5fozraAWuw3W1Itflx/VufpzraSWux/W1MtfnBlqFG3BDvYcbcrOUpPF+heO9incoT8Zk4UbZklblRtxYq3FjbqLVuQk31fu4KTfT+7kZN9cHuDk/oQ/yE9xCH+IW/KQ+zE9yS63BLfkprclPcSutxa24tT7CrbmN1uY2/LQ+yk/zM/oYP8NttQ635We1Lj/L7fRxbsfttR635+e0Pj/Hz2sDfp5f0Ib8AjdNSRrvUTjen3hXcmdMFn4xW9LG/CK/pE34JX5Zm/LL/Io241f4VW3Or/Jr+gS/xh20BXfgjvokd+TXtSW/zm/oU/wGd9JW3Ik7a2vuzF20DXfhrvo0d+Vu+gx34+7alrtzD32We3BPbcc9uZe2517cW5/j3txHn+c+3Fdf4L7cJCVp05TE8Z7EO5ErY7Jwv2xJX+J+3F9f5v48QF/hATxQX+WBPEhf40E8WDvwYB6iHXkID9XXeSgP0zd4GA/XTjycR2hnHsEjtQuP5FHalUfxaO3Go3mMducxPFZ78Fgepz15HI/XXjyeJ2hvnsATtQ9P5Enalydx45Sk8V6E432IZ58zY7Lwm96H/vwmT9YBPJmn6ECewlN1EE/laTqYp/F0HcLTeYYO5Rk8U4fxTJ6lw3kWz9YRPJvf0pH8Fs/RUTyH5+ponstv6xh+m9/RsfwOv6vj+F1+T8fze/y+TuD3+QOdyB/whzqJP+RGKUnj+YfjucczzpExWfgjz30yf8Qf6xT+mD/RqfwJf6rT+FP+TKfzZ/y5zuDP+QudyV/wlzqLv+SvdDZ/xV/rW/w1f6Nz+Bv+Vufyt/ydvs3f8ff6Dn/PP+i7/AP/qO/xjzxP3+d5/JN+wD/xz/oh/8wNU5LGcw7H841nmT1jsvAvnu/H/AvP1094Pi/QT3kBL9TPeCEv0s95ES/WL3gxL9EveQkv1a94KS/Tr3kZL9dveDn/qt/yr7xCv+MV/Jt+z7/xSv2BV/Iq/ZFX8Wqdx6t5jf7Ea/h3/Zl/5wYpSeN5huM5xjPLljFZeK3nOJ/X8jpdwOt4vS7k9fyHLuI/+E9dzH/yBl3CG3ijLuWNvEmX8SberMt5M2/RX3kLb9UVvJW36W+8jbfrSt7Of+kq/ot36GrewTt1De/kXfo77+L6KUnjuYXjecWzyZoxWXi357WOd/MeXc97+G/9g//mvfon7+V9uoH38T+6kf/hf3UT/8v7dTPv5wO6hQ/wQd3KB/mQbuNDfFi382E+on/xET6qO/goH9OdfIyP6y4+zvVSksbzCcdziWeQJWOy8AnPZQ+f4JP6N5/kU7qXT/Fp3cen+Yz+w2f4P/2X/+Ozup/P8jk9wOf4vB7k83xBD/EFvqiH+SJf0iN8iS/rUb7MV/QYX+GU7EmPcyz8eErSeA7hOP8468wZk4XTZE96kmPhq/QUx8JX62mOha/RMxwLp9X/OBa+Vs9yLHydnuNYOJ2e51j4er3AsfANepFj4fR6iWPhG/Uyx8I36RWOhW/WOOubLVw3JWmcdzjOOc40U8Zk4Vuc81V8C9+qV/OtnEGv4Qx8m6bl2zijXssZOVWv41TOpOk4E2fW6zkzZ9EbOAtn1fSclbPpjZyNs+tNnJ1z6M2cg+ukJI1zDcd5xtmlZkwWzuk8b+WcnEszcC7Orbdxbs6jGTkP59VUzsv5NBPn4/yamfPz7ZqFb+c7NCvfwXdqNr6TC2h2LsAFNQcX5MdSksb5hePc4owyZkwWvsu55eK7uJDm5kJcWPNwYS6iebkIF9V8XJSLaX4uxsX1di7OJfQOLsEl9U4uyaW0AJfi0lqQS/OjKUnjnMJxPnEWt2VMFi7jfApxGS6rhbksl9MiXI7La1EuzxW0GFfgilqcK/LdWoLv5kpakitxZS3FlbmKluYqXDslaZxHOM4h7jlDxmThe5xDWb6H79VyfC9X1fJclatpBa7G1bUiV+f79G6+j+/XSnw/P6CV+QF+UKvwg/xIStK473Dcb9zbrRmThR9yv/fyQ/ywVuWHuYZW4xpcU6tzTa6l93EtfkTv50e4tj7AtflRfZAf5VopSeP+wnFfcQ+3ZEwWfsx9PcyPcR2twXW4rtbkuvy41uLHuZ4+wvW4vtbm+txAH+UGXDMladxHOK4/rvXmjMnCDV1/HW7IjbQuN+LG+jg35iZaj5twU63PTbmZNuBmXCMlaVxvOK4zrummjMnCzV1nI27OT2hjfoJbaBNuwU9qU36SW2ozbskPpySN6wrH9cR335gxWfgp1/MEP8WttAW34tb6JLfmNtqS2/BDKUnj+8PxvfEd6TMmCz/te1vx0/yMtuZnuK224bb8YErS+J5wfH581g0Zk4Wf9fnP8LPcTttyO34gJWl8Xjg+J37m+ozJwu19Tjtuz/enJI2fC8e/j79LlzFZ+L6UpPH34fjz6HX/u2v/d4dvS0npnz1ZeIAe4Vh4oB7lWHiQHuNYeLAe51h4iJ7gWHionuRYeJie4lh4uJ7mWHiEnuFYeKT+x7HwKD3LsfBoPcex8Bg9z7HwWL3AsfA4vcix8Hi9xLHwBL3MsfBEvcKx8CRNyZg4Fn5T03AsPFmv4lh4il7NsfBUvYZj4WmalmPh6Xotx8Iz9DqOhY+nSRrvY7i/d66fhWfqQJ7Js3QQz+LZOphn81s6hN/iOTqU5/BcHcZz+W0dzm/zOzqC3+F3dSS/y+/pKH6P39fR/D5/oGP4A/5Qx/KH/JGO44/4Yx3PH/MnOoE/4U91In/Kn+kk/ow/1zf5c/5CJ/MX/KVO4S/5K53KX/HXOo2/5m90On/D3+oM/paPpUka71043rd4t/pa+Dudxd/x9zqbv+cf9C3+gX/UOfwjz9O5PI9/0rf5J/5Z3+Gf+Rd9l3/h+foez+cF+j4v4IX6AS/kRfohL+LF+hEv5iX6MS/hpfoJL+Vl+ikv4+X6GS/nX/Vz/pVX6Be8gn/TL/k3Xqlf8UpepV/zKl6t3/BqXqPf8ho+miZpvF/heK/iHepj4d/1e/6d1+oPvJbX6Y+8jtfrPF7Pf+hP/Af/qT/zn7xBf+ENvFHn80bepAt4E2/WhbyZt+gi3sJbdTFv5W26hLfxdl3K2/kvXcZ/8Q5dzjt4p/7KO3mXruBdvFt/4928R1fyHv5bV/HfvFdX817ep2t4Hx9JkzTeo3C8P/Gu9LbwP7qW/+F/dR3/y/t1Pe/nA/oHH+CD+icf5EO6gQ/xYd3Ih/mIbuIjfFQ381E+plv4GB/XrXycT+g2PsEndTuf5FP6F5/i07qDT/MZ3cln+D/dxf/xWd3NZ/mc7uFzfF7/5vN8QffyBb6o+/giH06TNN6XcLwn8U70svAl/Zcv8WXdz5f5ih7gK5ySI+lBjoXT6CGOha/SwxwLX61HOBa+Ro9yLJxWj3EsfK0e51j4Oj3BsXA6Pcmx8PV6imPhG/Q0x8Lp9QzHwjfqfxwL36RnORa+Wc9xLHyLnudY+Fa9wLFwBr3IsfChNEnjvQj38ux7Wvi2HEkvcyycUa9wLJyq8exTLZxJ03AmzqxXcWbOoldzFs6q13BWzqZpORtn12s5O+fQ6zgH59R0nJNz6fWci3PrDZyb82h6zsN59UbOy/n0Js7H+fVmzs+36y18O9+ht/IdfKdm4Dv5YJqk8fzDPT3nHtmThQt47hm5ABfUVC7Id2kmvosLaWYuxIU1CxfmIpqVi3BRzcZFuZhm52JcXHNwcS6hObkEl9RcXJJLaW4uxaU1D5fmMpqXy3BZzcdluZzm53JcXm/n8lxB7+AKXFHv5Ip8IE3SeM7heL7xLLtnTxa+2/MtyHdzJb2LK3FlLcSVuYoW5ip8jxbhe/heLcr3clUtxlW5mhbnalxdS3B1vk9L8n18v5bi+/kBLc0P8INahh/kh7QsP8QPazl+mGtoea7BNbUC1+RaWpFr8f40SeN5huM5xjPrlj1Z+BHPsRI/wrW1MtfmR7UKP8qP6T38GNfRe7kO19WqXJcf12r8ONfT6lyP6+t9XJ8b6P3cgBvqA9yQG+mD3Igb60PcmJvow9yEm2oNbsrNtCY34+Zai5vzv2mSxnMLx/OKZ9M1e7LwE55XbX6CW+ij3IKf1Mf4SW6pdbglP6V1+SlupY9zK26t9bg1t9H63Iaf1gb8ND+jDfkZbquNuC0/q435WW6nTbgdt9em3J6f02b8HD+vzfl5/idN0ng+4Xgu8Qy6ZE8WfsFzacEv8Iv6JL/IL2lLfolf1qf4ZX5FW/Er/Kq25lf5NW3Dr3EHfZo7cEd9hjvy69qWX+c39Fl+gztpO+7EnbU9d+Yu+hx34a76PHflfWmSxnMIx/nHWXfOnizczfm/yN24u77E3bmHvsw9uKe+wj25l77Kvbi3vsa9uY924D7cVztyX+6nr3M/7q9vcH8eoJ14AA/UzjyQB2kXHsSDtSsP5r1pksZ5h+Oc40w7ZU8WHuKcu/MQHqo9eCgP0548jIdrLx7OI7Q3j+CR2odH8ijty6N4tPbj0TxG+/MYHqsDeCyP04E8jsfrIB7PE3QwT+C/0ySNcw3HecbZvZE9WXii8xzKE3mSDuNJ/KYO5zd5so7gyTxFR/IUnqqjeCpP09E8jafrGJ7OM3Qsz+CZOo5n8iwdz7N4tk7g2bwnTdI4v3CcW5zR69mThd9ybpP4LZ6jb/IcnquTeS6/rVP4bX5Hp/I7/K5O43f5PZ3O7/H7OoPf5w90Jn/AH+os/pA/0tn8Ee9OkzTOKRznE2fRMXuy8MfOZw5/zJ/oXP6EP9W3+VP+TN/hz/hzfZc/5y/0Pf6Cv9T3+Uv+Sj/gr/hr/ZC/5m/0I/6Gd6VJGucRjnOIe+6QPVn4W+fwCX/L3+mn/B1/r5/x9/yDfs4/8I/6Bf/I8/RLnsc/6Vf8E/+sX/PP/It+w7/wzjRJ477Dcb9xb69lTxae736/4/m8QL/nBbxQf+CFvEh/5EW8WOfxYl6iP/ESXqo/81Jepr/wMt6RJmncXzjuK+7h1ezJwsvd1wJezr/qQv6VV+giXsG/6WL+jVfqEl7Jq3Qpr+LVuoxX819pksZ9hOP641pfyZ4svMb1/8pr+Hddwb/zWv2N1/I6XcnreL2u4vX8h67mP3h7mqRxveG4zriml7MnC//pOn/nP3mDruUNvFHX8UbepOt5E2/WP3gzb0uTNK4rHNcT3/1S9mThLa5nA2/hrbqRt/I23cTbeLtu5u28NU3S+P5wfG98x4vZk4X/8r1b+S/eodt4B+/U7byTt6RJGt8Tjs+Pz3ohe7LwLp+/g3fxbt3Ju3lzmqTxeeH4nPiZ57MnC+/xObt5D29KkzR+Lhz/Pv7uuezJwhvTJI2/D8efR6/2+0vx+yIjMiQL/50jaT+OhfdqX46F92kfjoX/0d4cC/+rvTgW3q89ORY+oD04Fj6o3TkWPqTdOBY+rF05Fj6iXTgWPqqdORY+pp04Fj6ub3AsfEJf51j4pHbkWPiUduBY+LS+xrHwGX2VY+H/9BWOhc/qyxwLn9OXOBY+ry/yef/ZuqAv8AX/Obioz/NF7/IlfY4veU9zpE0a72M43sN454ZnSBa+7D3cy5f5iu7jK5ySM+k/HAun0X85Fr5K93MsfLUe4Fj4Gj3IsXBaPcSx8LV6mGPh6/QIx8Lp9CjHwtfrMY6Fb9DjHAun1xMcC9+oJzkWvklPcSx8s57mWPgWPcOx8K36H8fCGfQsx8K36TmOhTPqeY6FU/UCx8KZ9CLHwpn1EsfC2dMmjfcuHO9bvFvDMiQLZ8mZ9ArHwlk13q2sFs6maTgbZ9erODvn0Ks5B+fUazgn59K0nItz67Wcm/PodZyH82o6zsv59HrOx/n1Bs7Pt2t6vp3v0Bv5Dr5Tb+I7uYDezAW4oN7CBfkuvZXv4kKagQtxYb2NC3MRzchFuKimclEuppm4GBfXzFycs6VNGu9XON6reIeGZkgWLpEzaVYuwSU1G5fkUpqdS3FpzcGluYzm5DJcVnNxWS6nubkcl9c8XJ4raF6uwBU1H1fkuzU/382V9HauxJX1Dq7MVfROrsL3aAG+h+/VgnwvV9W7uCpX00JcjatrYa7O92kRvo/v16J8Pz+gxfgBflCL84OcNW3SeI/C8f7EuzIkQ7LwQzmTluSH+GEtxQ9zDS3NNbimluGaXEvLci1+RMvxI1xby3NtflQr8KP8mFbkx7iO3s11uK5W4rr8uFbmx7meVuF6XF/v4frcQO/lBtxQq3JDbqTVuBE31urcmJvofdyEm+r93JSb6QPcjJvrg9ycs6RNGu9LON6TeCcGZ0gWfiJn0of5CW6hNbgFP6k1+UluqbW4JT+lj/BT3EprcyturY9ya26jj3Ebflrr8NP8jNblZ7itPs5t+Vmtx89yO63P7bi9NuD2/Jw25Of4eW3Ez/ML2phf4Be1Cb/IL2lTfolf1mb8Mr+izfkVzpw2abwX4Xgf4tkPypAs/Kr3oQW/yq/pk/wad9CW3IE76lPckV/XVvw6v6Gt+Q3upG24E3fWp7kzd9FnuAt31bbclbvps9yNu2s77s49tD334J76HPfkXvo89+Le+gL35j76IvfhvvoS9+V++jL34/76CvfnTGmTxvMPx3OPZzwwQ7LwAM/9NR7AA7UDD+RB2pEH8WB9nQfzEH2Dh/BQ7cRDeZh25mE8XLvwcB6hXXkEj9RuPJJHaXcexaO1B4/mMdqTx/BY7cVjeZz25nE8XvvweJ6gfXkCT9R+PJEnaX+exKlpk8ZzDsfz/b/nmcH+1296vgP5TZ6sg3gyT9HBPIWn6hCeytN0KE/j6TqMp/MMHc4zeKaO4Jk8S0fyLJ6to3g2v6Wj+S2eo2N4Ds/VsTyX39Zx/Da/o+P5HX5XJ/C7/J5O5Pf4fZ3E73PGtEnjeYbjOcYz658hWfgDz3Eyf8Af6hT+kD/SqfwRf6zT+GP+RKfzJ/ypzuBP+TOdyZ/x5zqLP+cvdDZ/wV/qW/wlf6Vz+Cv+Wufy1/yNvs3f8Lf6Dn/L3+m7/B1/r+/x9/yDvs8/8G1pk8ZzC8fzimfTL0Oy8I+e14f8I8/Tj3ge/6Qf80/8s37CP/Mv+in/wvP1M57PC/RzXsAL9QteyIv0S17Ei/UrXsxL9Gtewkv1G17Ky/RbXsbL9Ttezr/q9/wrr9AfeAVnSJs0nk84nks8g74ZkoV/81zm8W+8Un/ilbxKf+ZVvFp/4dW8RufzGv5dF/DvvFYX8lpep4t4Ha/Xxbye/9Al/Af/qUv5T96gy3gDb9TlvJE36a+8iTfrCt7Mt6ZNGs8hHOcfZ90nQ7LwFue/krfwVl3FW3mbruZtvF3X8Hb+S3/nv3iHruUdvFPX8U7epet5F+/WP3g379E/eQ//rRv4b96rG3kv79NNvI//0c38D9+SNmmcdzjOOc60d4Zk4X+d81b+l/frNt7PB3Q7H+CD+hcf5EO6gw/xYd3Jh/mI7uIjfFR381E+pnv4GB/Xv/k4n9C9fIJP6j4+yaf0Hz7FN6dNGucajvOMs+uVIVn4tPPcz6f5jB7gM/yfHuT/+Kwe4rN8Tg/zOT6vR/g8X9CjfIEv6jG+yJf0OF/iy3qCL/MVPclXOCVX0lMcC9+UNmmcXzjOLc6oZ4Zk4TS5kp7hWPgq/Y9j4av1LMfC1+g5joXT6nmOha/VCxwLX6cXORZOp5c4Fr5eL3MsfINe4Vg4vcYZpbfwjWmTxjmF43ziLHpkSBa+0flcxTfyTXo138Q36zV8M9+iafkWvlWv5Vs5g17HGfg2Tce3cUa9njNyqt7AqZxJ03Om/z+btEnjPMJxDnHP3TMkC2d2DjdxZs6iN3MWzqq3cFbOprdyNs6uGTg759DbOAfn1Iyck3NpKufi3JqJc///eaR1DmkTx/3GvXXLkCycx/1m4TycV7NyXs6n2Tgf59fsnJ9v1xx8O9+hOfkOvlNz8Z1cQHNzAb4+bdK4v3DcV9xD1wzJwgXdV14uyHdpPr6LC2l+LsSF9XYuzEX0Di7CRfVOLsrFtAAX43Rpk8Z9hOP641q7ZEgWLu767+LiXEILcQkuqYW5JJfSIlyKS2tRLs1ltBiX4evSJo3rDcd1xjV1zpAsXNZ1luCyXE5Lcjkur6W4PFfQ0lyBK2oZrsjXpk0a1xWO64nv7pQhWfhu11OO7+ZKWp4rcWWtwJW5ilbkKpw2bdL4/nB8b3zHGxmShe/xvZX4Hr5XK/O9XFWrcFW+Jm3S+J5wfH581usZkoWr+fx7uRpX16pcna9OmzQ+LxyfEz/TMUOy8H0+pzrfx1elTRo/F45/H3/XIUOycJq0SePvw/Hn0av8/8dtuT4lZVpqsvD9uZJu5lj4Ad3EsfCDupFj4Yd0A8fCD+ufHAvX0D84Fq6p6zkWrqXrOBZ+RNdyLFxbf+dY+FFdw7HwY7qaY+E6uopj4bq6kmPhx/U3joXr6QqOhevrrxwLN9DlHAs31GUcCzfSpRwLN9YlHAs30cUcCzfVRRwLN9OFHAs31wUcCz+h8zkWfipd0htSEsd7GO/c1NRk4Rbewwe4BT+pD/KT3FIf4pb8lD7MT3ErrcGtuLXW5NbcRmtxG35aH+Gn+Rmtzc9wW32U2/Kz+hg/y+20Drfj9lqX2/Nz+jg/x89rPX6eX9D6/AK/qA34RX5JG/JL/LI24pf5FW3Mr/Cr2oRf5de0Kb/GHbQZd+CO2pw78uv6BL/OLdMljfcuHO9bvFtTUpOF38iV9El+gztpS+7EnfUp7sxdtBV34a7amrtyN23D3bi7Ps3duYc+wz24p7blntxLn+Ve3FvbcW/uo+25D/fV57gv99PnuR/31xe4Pw/QF3kAD9SXeCAP0pd5EA/WV3gwD9FXeQgP1dd4KA/TDjyMh2tHHs4j9HUewU+mSxrvVzjeq3iHJqcmC4/MlbQTj+RR2plH8WjtwqN5jHblMTxWu/FYHqfdeRyP1x48nidoT57AE7UXT+RJ2psn8Zvah9/kydqXJ/MU7cdTeKr256k8TQfwNJ6uA3k6z9BBPINn6mCeybN0CM/i2TqUZ/NbOozf4jk6nOfwXB3Bc7lFuqTxHoXj/Yl35c3UZOG3cyUdxW/zOzqa3+F3dQy/y+/pWH6P39dx/D5/oOP5A/5QJ/CH/JFO5I/4Y53EH/Mn+iZ/wp/qZP6UP9Mp/Bl/rlP5c/5Cp/EX/KVO5y/5K53BX/HXOpO/5m90Fn/D3+ps/pa/07f4O/5e5/D3/IPO5R/4iXRJ430Jx3sS78Sk1GThH3MlfYd/5Hn6Ls/jn/Q9/ol/1vf5Z/5FP+BfeL5+yPN5gX7EC3ihfswLeZF+wot4sX7Ki3mJfsZLeKl+zkt5mX7By3i5fsnL+Vf9in/lFfo1r+Df9Bv+jVfqt7ySV+l3vIpX6/e8mtfoD7yGm6dLGu9FON6HePYTU5OFf/c+zOPfea3+xGt5nf7M63i9/sLr+Q+dz3/wn7qA/+QNupA38EZdxBt5ky7mTbxZl/Bm3qJLeQtv1WW8lbfpct7G2/VX3s5/6Qr+i3fob7yDd+pK3sm7dBXv4t26mnfzHl3De7hZuqTx/MPx3OMZT0hNFv7bc1/Lf/NeXcd7eZ+u5338j/7B//C/+if/y/t1A+/nA7qRD/BB3cQH+ZBu5kN8WLfwYT6iW/kIH9VtfJSP6XY+xsf1Lz7OJ3QHn+CTupNP8indxaf4tO7m03xG9/AZbpouaTzncDzfeJbjU5OF//N89/J/fFb38Vk+p//wOT6v//J5vqD7+QJf1AN8kS/pQb7El/UQX+YrepivcErupEc4Fk6jRzkWvkqPcSx8tR7nWPgaPcGxcFo9ybHwtXqKY+Hr9DTHwun0DMfCTdIljecZjucYz2xcarLw9bmTnuVY+AY9x7Fwej3PsfCNeoFj4Zv0IsfCN+sljoVv0cscC9+qVzgWzqDxzDJY+DZNw7dxRr2KM3KqXs2pnEmv4UycWdNyZs6i13IWzqrXcVbOpuk4GzdOlzSeWzieVzybsanJwtk9rxs4O+fQ9JyDc+qNnJNz6U2ci3PrzZyb8+gtnIfz6q2cl/NpBs7H+fU2zs+3a0a+ne/QVL6D79RMfCcX0MxcgAtqFi7Id2lWvosLaTYuxI3SJY3nE47nEs9gTGqycGHPJQcX5iKak4twUc3FRbmY5uZiXFzzcHEuoXm5BJfUfFySS2l+LsWl9XYuzWX0Di7DZfVOLsvltACX4/JakMtzBb2LK3BFLcQVuWG6pPEcwnH+cdajU5OF73b+RfhurqRFuRJX1mJcmatoca7C92gJvofv1ZJ8L1fVUlyVq2lprsbVtQxX5/u0LN/H92s5vp8f0PL8AD+oFfhBfkgr8kPcIF3SOO9wnHOc6ajUZOGHnXMlfphraGWuwTW1CtfkWnoP1+JH9F5+hGtrVa7Nj2o1fpQf0+r8GNfR+7gO19X7uS4/rg/w41xPH+R6XF8f4vr/73RJ41zDcZ5xdiNTk4UbOM8a3IAbak1uyI20FjfixvoIN+YmWpubcFN9lJtyM32Mm3FzrcPN+Qmty09wC32cW/CTWo+f5JZan1tyvXTONF3iOLc4oxGpycJPObeG/BS30kbciltrY27NbbQJt+GntSk/zc9oM36G22pzbsvP6hP8LLfTFtyO2+uT3J6f05b8HD+ezjmmSxznE2cxPDVZ+Hnn04qf5xe0Nb/AL2obfpFf0qf5JX5Zn+GX+RVty6/wq/osv8qvaTt+jTtoe+7AHfU57sh10yWN8wjHOcQ9D0tNFn7dObzAr/Mb+iK/wZ30Je7EnfVl7sxd9BXuwl31Ve7K3fQ17sbdtQN35x7akXtwnXRJ477Dcb9xb0NTk4V7ut83uCf30k7ci3trZ+7NfbQL9+G+2pX7cj/txv24v3bn/jxAe/AAfixd0ri/cNxX3MOQ1GThge6rFw/kQdqbB/Fg7cODeYj25SE8VPvxUB6m/XkYD9cBPJwfTZc07iMc1x/XOjg1WXiE6x/EI3ikDuaRPEqH8CgerUN5NI/RYTyGx+pwHsu10yWN6w3Hdf7fNaUmC49znSN5HI/XUTyeJ+honsATdQxP5Ek6lifxI+mSxnWF43riuwemJgu/6XrG85s8WSfwZJ6iE3kKT9VJPJVrpUsa3x+O743vGJCaLDzN907maTxdp/B0nqFTeQbXTJc0viccnx+f1T81WXimz5/OM3mWzuBZXCNd0vi8cHxO/Ey/1GTh2T5nFs/mh9MljZ8Lx7+Pv+ubmiz8ULqk8ffh+PPo9f/7X/3/B4uKgTg=
+
+
diff --git a/template/parcellations/ExpAtlases_README.txt b/template/parcellations/ExpAtlases_README.txt
new file mode 100644
index 0000000..f5238ad
--- /dev/null
+++ b/template/parcellations/ExpAtlases_README.txt
@@ -0,0 +1,137 @@
+This file describes the experimental atlas files
+
+expFM800AtlasFile.mat
+expFM400AtlasFile.mat
+expFM200AtlasFile.mat
+expConte69AtlasFile.mat
+
+which contain various different parcellations.
+
+RANDOM PARCELLATIONS
+================================================
+The files
+expFM800AtlasFile.mat
+expFM400AtlasFile.mat
+expFM200AtlasFile.mat
+
+contain random parcellation of the 164K conte69 template from Caret
+in 800,400,200 parcels using the Fast marching method from the
+Fast Marching toolbox
+(https://www.ceremade.dauphine.fr/~peyre/teaching/manifold/tp3.html)
+
+The above numbers refer to the entire cortical sheet.
+The number per hemisphere is half of these numbers.
+The parcels have been created on the left hemisphere
+and then the matched voxels were used in the right hemisphere
+as in the Conte69 atlas the left and right hemisphere voxels
+are supposed to be more or less matched. Thus these atlases are symmetric.
+
+In each of these files you ll find a matlab variable
+'atlasinfo' which contains a single structure.
+
+
+
+atlasinfo{1}
+
+ans =
+
+ parent: 'experimental'
+ name: 'FastMarching800'
+ mnem: 'FM800'
+ L: [1x1 struct]
+ R: [1x1 struct]
+
+The field 'parent' mimics the Conte69 atlases,
+as there each atlas belongs to a parent family.
+The 'name' is a name I have come up with and
+'mnem' is a mnemonic I have come up with.
+
+The fields 'L','R' contain the actual atlas information PER HEMISPHERE.
+
+ atlasinfo{1}.L
+
+ans =
+
+ data: [163842x1 double]
+ label: {1x400 cell}
+ key: [1x400 double]
+ rgb: [400x3 double]
+ centpos: [400x3 double]
+ parcelarea: [400x1 double]
+ parcelNvoxs: [400x1 double]
+
+
+Fields
+data: contains the actual parcel index. This index is PER HEMISPHERE.
+ So for the 800 parcel random parcellation the values in this field
+ take values between 1 and 400 in the Left hemisphere and 1 and 400
+ in the right hemisphere as well.
+label: label of the parcels, They are labeled 'L_1','L_2'... for left and
+ 'R_1','R_2'... for the right hemisphere.
+key: This just contains the key values for each parcel. Here jsut between
+ 1 and 400.
+rgb: This contains a color code for each parcel.
+centpos: This contains the position of the central voxel of the parcel.
+ This is the parcel based on which the Voronoi distances were computed.
+parcelarea: The area of each parcel.
+parcelNvoxs: The number of voxels in each parcel.
+
+
+
+
+CONTE69 atlases
+==============================================
+The file
+expConte69AtlasFile.mat
+
+contains 6 of the parcellations of the Conte69 atlas.
+
+In this file you ll find a matlab variable
+'atlasinfo' which contains 6 structures, one for each parcellation scheme.
+
+These parcellations scheme have the following code names in the Conte69 atlas:
+
+
+
+ atlasinfo{1}.name: 'CaretCompParc' : Caret Composite Parcellation. Contains Visuotopic areas, some brodmann especially sensorimotor and orbitofrontal areas.
+ atlasinfo{2}.name: 'CaretBrodmann' : Brodmann areas
+ atlasinfo{3}.name: 'CaretRSN7' : 7 Resting state network parcels
+ atlasinfo{4}.name: 'CaretRSN17' : 17 Resting state network parcels
+ atlasinfo{5}.name: 'CaretComCons' : Caret Common Consensus. Parcels of the brain where researchers have assigned some kind of functionality.
+ atlasinfo{6}.name: 'CaretComConsFilled' : Same as above but with the unassigned cortex assigned.
+
+
+Similarly to the random parcellations these atlases have a 'L' and a 'R' fiels which contain:
+
+atlasinfo{1}.L
+
+ans =
+
+ data: [163842x1 int32]
+ label: {1x55 cell}
+ key: [1x55 double]
+ rgba: [55x4 double]
+
+
+'data',and 'key' are similar as for the random parcellations.
+the field 'name' is similat but does NOT have the prefix 'L_' and 'R_' so for example
+area V1 is called 'V1' in both hemispheres.
+Here instad of the field 'rgb' there is the field 'rgba' which has an
+additional column which is supposed to represent oppacity.The reason
+this is here is because these colors are extracted from Caret files
+where this additional column exists.
+
+Again all the atlas information is defined PER HEMISPHERE.
+
+
+
+
+
+
+
+
+
+
+
+
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diff --git a/template/parcellations/RSN-networks.L.164k_fs_LR.label.gii b/template/parcellations/RSN-networks.L.164k_fs_LR.label.gii
new file mode 100755
index 0000000..a0780fa
--- /dev/null
+++ b/template/parcellations/RSN-networks.L.164k_fs_LR.label.gii
@@ -0,0 +1,251 @@
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diff --git a/template/parcellations/RSN-networks.L.8k_fs_LR.label.gii b/template/parcellations/RSN-networks.L.8k_fs_LR.label.gii
new file mode 100644
index 0000000..aa902f5
--- /dev/null
+++ b/template/parcellations/RSN-networks.L.8k_fs_LR.label.gii
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diff --git a/template/parcellations/RSN-networks.R.164k_fs_LR.label.gii b/template/parcellations/RSN-networks.R.164k_fs_LR.label.gii
new file mode 100755
index 0000000..e242ffb
--- /dev/null
+++ b/template/parcellations/RSN-networks.R.164k_fs_LR.label.gii
@@ -0,0 +1,255 @@
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diff --git a/template/parcellations/RSN-networks.R.8k_fs_LR.label.gii b/template/parcellations/RSN-networks.R.8k_fs_LR.label.gii
new file mode 100644
index 0000000..30db2be
--- /dev/null
+++ b/template/parcellations/RSN-networks.R.8k_fs_LR.label.gii
@@ -0,0 +1,191 @@
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+
+
diff --git a/template/parcellations/RSN-networks_164k.mat b/template/parcellations/RSN-networks_164k.mat
new file mode 100644
index 0000000..7c6578a
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diff --git a/template/parcellations/RSN-networks_32k.mat b/template/parcellations/RSN-networks_32k.mat
new file mode 100644
index 0000000..3e6497f
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diff --git a/template/parcellations/RSN-networks_8k.mat b/template/parcellations/RSN-networks_8k.mat
new file mode 100644
index 0000000..8f6e0d3
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diff --git a/template/parcellations/expConte69AtlasFile.mat b/template/parcellations/expConte69AtlasFile.mat
new file mode 100644
index 0000000..ef9fe57
Binary files /dev/null and b/template/parcellations/expConte69AtlasFile.mat differ
diff --git a/template/parcellations/expFM200AtlasFile.mat b/template/parcellations/expFM200AtlasFile.mat
new file mode 100644
index 0000000..27b43c2
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diff --git a/template/parcellations/expFM200AtlasFile_164k.mat b/template/parcellations/expFM200AtlasFile_164k.mat
new file mode 100644
index 0000000..2fd3d89
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diff --git a/template/parcellations/expFM200AtlasFile_8k.mat b/template/parcellations/expFM200AtlasFile_8k.mat
new file mode 100644
index 0000000..866cdc6
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diff --git a/template/parcellations/expFM400AtlasFile.mat b/template/parcellations/expFM400AtlasFile.mat
new file mode 100644
index 0000000..86edd0c
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diff --git a/template/parcellations/expFM400AtlasFile_164k.mat b/template/parcellations/expFM400AtlasFile_164k.mat
new file mode 100644
index 0000000..7738cb3
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diff --git a/template/parcellations/expFM400AtlasFile_8k.mat b/template/parcellations/expFM400AtlasFile_8k.mat
new file mode 100644
index 0000000..9138421
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diff --git a/template/parcellations/expFM800AtlasFile.mat b/template/parcellations/expFM800AtlasFile.mat
new file mode 100644
index 0000000..f9953bb
Binary files /dev/null and b/template/parcellations/expFM800AtlasFile.mat differ
diff --git a/template/parcellations/expFM800AtlasFile_164k.mat b/template/parcellations/expFM800AtlasFile_164k.mat
new file mode 100644
index 0000000..9dec57f
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diff --git a/template/parcellations/expFM800AtlasFile_8k.mat b/template/parcellations/expFM800AtlasFile_8k.mat
new file mode 100644
index 0000000..ada19fc
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diff --git a/template/parcellations/orig/ExpAtlases_README.txt b/template/parcellations/orig/ExpAtlases_README.txt
new file mode 100644
index 0000000..f5238ad
--- /dev/null
+++ b/template/parcellations/orig/ExpAtlases_README.txt
@@ -0,0 +1,137 @@
+This file describes the experimental atlas files
+
+expFM800AtlasFile.mat
+expFM400AtlasFile.mat
+expFM200AtlasFile.mat
+expConte69AtlasFile.mat
+
+which contain various different parcellations.
+
+RANDOM PARCELLATIONS
+================================================
+The files
+expFM800AtlasFile.mat
+expFM400AtlasFile.mat
+expFM200AtlasFile.mat
+
+contain random parcellation of the 164K conte69 template from Caret
+in 800,400,200 parcels using the Fast marching method from the
+Fast Marching toolbox
+(https://www.ceremade.dauphine.fr/~peyre/teaching/manifold/tp3.html)
+
+The above numbers refer to the entire cortical sheet.
+The number per hemisphere is half of these numbers.
+The parcels have been created on the left hemisphere
+and then the matched voxels were used in the right hemisphere
+as in the Conte69 atlas the left and right hemisphere voxels
+are supposed to be more or less matched. Thus these atlases are symmetric.
+
+In each of these files you ll find a matlab variable
+'atlasinfo' which contains a single structure.
+
+
+
+atlasinfo{1}
+
+ans =
+
+ parent: 'experimental'
+ name: 'FastMarching800'
+ mnem: 'FM800'
+ L: [1x1 struct]
+ R: [1x1 struct]
+
+The field 'parent' mimics the Conte69 atlases,
+as there each atlas belongs to a parent family.
+The 'name' is a name I have come up with and
+'mnem' is a mnemonic I have come up with.
+
+The fields 'L','R' contain the actual atlas information PER HEMISPHERE.
+
+ atlasinfo{1}.L
+
+ans =
+
+ data: [163842x1 double]
+ label: {1x400 cell}
+ key: [1x400 double]
+ rgb: [400x3 double]
+ centpos: [400x3 double]
+ parcelarea: [400x1 double]
+ parcelNvoxs: [400x1 double]
+
+
+Fields
+data: contains the actual parcel index. This index is PER HEMISPHERE.
+ So for the 800 parcel random parcellation the values in this field
+ take values between 1 and 400 in the Left hemisphere and 1 and 400
+ in the right hemisphere as well.
+label: label of the parcels, They are labeled 'L_1','L_2'... for left and
+ 'R_1','R_2'... for the right hemisphere.
+key: This just contains the key values for each parcel. Here jsut between
+ 1 and 400.
+rgb: This contains a color code for each parcel.
+centpos: This contains the position of the central voxel of the parcel.
+ This is the parcel based on which the Voronoi distances were computed.
+parcelarea: The area of each parcel.
+parcelNvoxs: The number of voxels in each parcel.
+
+
+
+
+CONTE69 atlases
+==============================================
+The file
+expConte69AtlasFile.mat
+
+contains 6 of the parcellations of the Conte69 atlas.
+
+In this file you ll find a matlab variable
+'atlasinfo' which contains 6 structures, one for each parcellation scheme.
+
+These parcellations scheme have the following code names in the Conte69 atlas:
+
+
+
+ atlasinfo{1}.name: 'CaretCompParc' : Caret Composite Parcellation. Contains Visuotopic areas, some brodmann especially sensorimotor and orbitofrontal areas.
+ atlasinfo{2}.name: 'CaretBrodmann' : Brodmann areas
+ atlasinfo{3}.name: 'CaretRSN7' : 7 Resting state network parcels
+ atlasinfo{4}.name: 'CaretRSN17' : 17 Resting state network parcels
+ atlasinfo{5}.name: 'CaretComCons' : Caret Common Consensus. Parcels of the brain where researchers have assigned some kind of functionality.
+ atlasinfo{6}.name: 'CaretComConsFilled' : Same as above but with the unassigned cortex assigned.
+
+
+Similarly to the random parcellations these atlases have a 'L' and a 'R' fiels which contain:
+
+atlasinfo{1}.L
+
+ans =
+
+ data: [163842x1 int32]
+ label: {1x55 cell}
+ key: [1x55 double]
+ rgba: [55x4 double]
+
+
+'data',and 'key' are similar as for the random parcellations.
+the field 'name' is similat but does NOT have the prefix 'L_' and 'R_' so for example
+area V1 is called 'V1' in both hemispheres.
+Here instad of the field 'rgb' there is the field 'rgba' which has an
+additional column which is supposed to represent oppacity.The reason
+this is here is because these colors are extracted from Caret files
+where this additional column exists.
+
+Again all the atlas information is defined PER HEMISPHERE.
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/template/parcellations/orig/expConte69AtlasFile.mat b/template/parcellations/orig/expConte69AtlasFile.mat
new file mode 100644
index 0000000..ef9fe57
Binary files /dev/null and b/template/parcellations/orig/expConte69AtlasFile.mat differ
diff --git a/template/parcellations/orig/expFM200AtlasFile.mat b/template/parcellations/orig/expFM200AtlasFile.mat
new file mode 100644
index 0000000..27b43c2
Binary files /dev/null and b/template/parcellations/orig/expFM200AtlasFile.mat differ
diff --git a/template/parcellations/orig/expFM400AtlasFile.mat b/template/parcellations/orig/expFM400AtlasFile.mat
new file mode 100644
index 0000000..86edd0c
Binary files /dev/null and b/template/parcellations/orig/expFM400AtlasFile.mat differ
diff --git a/template/parcellations/orig/expFM800AtlasFile.mat b/template/parcellations/orig/expFM800AtlasFile.mat
new file mode 100644
index 0000000..f9953bb
Binary files /dev/null and b/template/parcellations/orig/expFM800AtlasFile.mat differ
diff --git a/template/parcellations/parcellations_VGD11b.L.164k_fs_LR.label.gii b/template/parcellations/parcellations_VGD11b.L.164k_fs_LR.label.gii
new file mode 100755
index 0000000..97694c8
--- /dev/null
+++ b/template/parcellations/parcellations_VGD11b.L.164k_fs_LR.label.gii
@@ -0,0 +1,289 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ LOBE.OCCIPITAL
+1 Sept 2006 - PHT00 column replaced with newly registered Resampled_Macaque.PHT00.R.REGISTER-with-F99.41218.spec.deform_map
+Appended File: fsaverage.LR.composite_13may11.164k_fs_LR.paint
+Added Brodmann, renamed OFP03 column (dve 13may11)
+Added ER (dve 29mar11)
+revised borders, paint entries (dve 23oct10)
+Deformed from: ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Deformed with: fsaverage.R.registered-to-fs_LR.164k_fs_LR.deform_map
+Appended File: /Users/vanessen/BRAIN_MAP_DATA/PALS_B12_HUMAN/FREESURFER-to-PALS/FREESURFER_AVG-to-PALS/FS.R/ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Medial wall created using surface: ROI; medial wall border generated by Donna, revised by DVE (14 march 09)
+Load sphere, rotate to see medial wall; Surface: Transform: Apply current view; then project to plane, positive z.
+
+Then select nodes inside border = medial wall;
+This also assigns nodes on the far side. Remove these by drawing temporary border = ??? on the very inflated surface, with unwanted regions off to side of desired MW. Draw 3D closed border, assign paint to enclosed nodes.
+DVE 14 march 09
+
+Appended File: fsaverage.LR.composite_7june11.164k_fs_LR.paint
+7june11 (DVE) added retinotopic and OMPFC to left hem fsaverage architectonic parcellation
+Added Brodmann, renamed OFP03 column (dve 13may11)
+Added ER (dve 29mar11)
+revised borders, paint entries (dve 23oct10)
+Deformed from: ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Deformed with: fsaverage.R.registered-to-fs_LR.164k_fs_LR.deform_map
+Appended File: /Users/vanessen/BRAIN_MAP_DATA/PALS_B12_HUMAN/FREESURFER-to-PALS/FREESURFER_AVG-to-PALS/FS.R/ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Medial wall created using surface: ROI; medial wall border generated by Donna, revised by DVE (14 march 09)
+Load sphere, rotate to see medial wall; Surface: Transform: Apply current view; then project to plane, positive z.
+Then select nodes inside border = medial wall;
+This also assigns nodes on the far side. Remove these by drawing temporary border = ??? on the very inflated surface, with unwanted regions off to side of desired MW. Draw 3D closed border, assign paint to enclosed nodes.
+DVE 14 march 09
+Appended File: fsaverage.R.MedialWall.164k_fs_r.paint
+Appended File: fsaverage.R.MedialWall.164k_fs_r.paint
+
+16 may 10 (DVE) cleaned up extraneous MT(V5) nodes in medial wall, elsewhere; created LVE00 and PHT00 paint columns with just candidate homologues to human architectonic, visuotopic areas.
+9 Sept 2006 - updated references (URL's) in column comments - DVE
+8 Sept - LOBE.OCCIP -> LOBE.OCCIPITAL
+1 Sept 2006 - PHT00 column replaced with newly registered Resampled_Macaque.PHT00.R.REGISTER-with-F99.41218.spec.deform_map
+Appended File: fsaverage.LR.composite_13may11.164k_fs_LR.paint
+Added Brodmann, renamed OFP03 column (dve 13may11)
+Added ER (dve 29mar11)
+revised borders, paint entries (dve 23oct10)
+Deformed from: ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Deformed with: fsaverage.R.registered-to-fs_LR.164k_fs_LR.deform_map
+Appended File: /Users/vanessen/BRAIN_MAP_DATA/PALS_B12_HUMAN/FREESURFER-to-PALS/FREESURFER_AVG-to-PALS/FS.R/ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Medial wall created using surface: ROI; medial wall border generated by Donna, revised by DVE (14 march 09)
+Load sphere, rotate to see medial wall; Surface: Transform: Apply current view; then project to plane, positive z.
+
+Then select nodes inside border = medial wall;
+This also assigns nodes on the far side. Remove these by drawing temporary border = ??? on the very inflated surface, with unwanted regions off to side of desired MW. Draw 3D closed border, assign paint to enclosed nodes.
+DVE 14 march 09
+]]>
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diff --git a/template/parcellations/parcellations_VGD11b.L.8k_fs_LR.label.gii b/template/parcellations/parcellations_VGD11b.L.8k_fs_LR.label.gii
new file mode 100644
index 0000000..e4a8fae
--- /dev/null
+++ b/template/parcellations/parcellations_VGD11b.L.8k_fs_LR.label.gii
@@ -0,0 +1,205 @@
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diff --git a/template/parcellations/parcellations_VGD11b.R.164k_fs_LR.label.gii b/template/parcellations/parcellations_VGD11b.R.164k_fs_LR.label.gii
new file mode 100755
index 0000000..9bfe537
--- /dev/null
+++ b/template/parcellations/parcellations_VGD11b.R.164k_fs_LR.label.gii
@@ -0,0 +1,286 @@
+
+
+
+
+
+
+
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+
+
+ LOBE.OCCIPITAL
+1 Sept 2006 - PHT00 column replaced with newly registered Resampled_Macaque.PHT00.R.REGISTER-with-F99.41218.spec.deform_map
+Appended File: fsaverage.LR.composite_13may11.164k_fs_LR.paint
+Added Brodmann, renamed OFP03 column (dve 13may11)
+Added ER (dve 29mar11)
+revised borders, paint entries (dve 23oct10)
+Deformed from: ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Deformed with: fsaverage.R.registered-to-fs_LR.164k_fs_LR.deform_map
+Appended File: /Users/vanessen/BRAIN_MAP_DATA/PALS_B12_HUMAN/FREESURFER-to-PALS/FREESURFER_AVG-to-PALS/FS.R/ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Medial wall created using surface: ROI; medial wall border generated by Donna, revised by DVE (14 march 09)
+Load sphere, rotate to see medial wall; Surface: Transform: Apply current view; then project to plane, positive z.
+
+Then select nodes inside border = medial wall;
+This also assigns nodes on the far side. Remove these by drawing temporary border = ??? on the very inflated surface, with unwanted regions off to side of desired MW. Draw 3D closed border, assign paint to enclosed nodes.
+DVE 14 march 09
+
+Appended File: fsaverage.LR.composite_7june11.164k_fs_LR.paint
+7june11 (DVE) added retinotopic and OMPFC to left hem fsaverage architectonic parcellation
+Added Brodmann, renamed OFP03 column (dve 13may11)
+Added ER (dve 29mar11)
+revised borders, paint entries (dve 23oct10)
+Deformed from: ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Deformed with: fsaverage.R.registered-to-fs_LR.164k_fs_LR.deform_map
+Appended File: /Users/vanessen/BRAIN_MAP_DATA/PALS_B12_HUMAN/FREESURFER-to-PALS/FREESURFER_AVG-to-PALS/FS.R/ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Medial wall created using surface: ROI; medial wall border generated by Donna, revised by DVE (14 march 09)
+Load sphere, rotate to see medial wall; Surface: Transform: Apply current view; then project to plane, positive z.
+Then select nodes inside border = medial wall;
+This also assigns nodes on the far side. Remove these by drawing temporary border = ??? on the very inflated surface, with unwanted regions off to side of desired MW. Draw 3D closed border, assign paint to enclosed nodes.
+DVE 14 march 09
+Appended File: fsaverage.R.MedialWall.164k_fs_r.paint
+Appended File: fsaverage.R.MedialWall.164k_fs_r.paint
+
+16 may 10 (DVE) cleaned up extraneous MT(V5) nodes in medial wall, elsewhere; created LVE00 and PHT00 paint columns with just candidate homologues to human architectonic, visuotopic areas.
+9 Sept 2006 - updated references (URL's) in column comments - DVE
+8 Sept - LOBE.OCCIP -> LOBE.OCCIPITAL
+1 Sept 2006 - PHT00 column replaced with newly registered Resampled_Macaque.PHT00.R.REGISTER-with-F99.41218.spec.deform_map
+Appended File: fsaverage.LR.composite_13may11.164k_fs_LR.paint
+Added Brodmann, renamed OFP03 column (dve 13may11)
+Added ER (dve 29mar11)
+revised borders, paint entries (dve 23oct10)
+Deformed from: ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Deformed with: fsaverage.R.registered-to-fs_LR.164k_fs_LR.deform_map
+Appended File: /Users/vanessen/BRAIN_MAP_DATA/PALS_B12_HUMAN/FREESURFER-to-PALS/FREESURFER_AVG-to-PALS/FS.R/ProbabilisticArchitectonic_FRB08.R.164k_fs_r.paint
+Medial wall created using surface: ROI; medial wall border generated by Donna, revised by DVE (14 march 09)
+Load sphere, rotate to see medial wall; Surface: Transform: Apply current view; then project to plane, positive z.
+
+Then select nodes inside border = medial wall;
+This also assigns nodes on the far side. Remove these by drawing temporary border = ??? on the very inflated surface, with unwanted regions off to side of desired MW. Draw 3D closed border, assign paint to enclosed nodes.
+DVE 14 march 09
+]]>
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diff --git a/template/parcellations/parcellations_VGD11b.R.8k_fs_LR.label.gii b/template/parcellations/parcellations_VGD11b.R.8k_fs_LR.label.gii
new file mode 100644
index 0000000..0f4808b
--- /dev/null
+++ b/template/parcellations/parcellations_VGD11b.R.8k_fs_LR.label.gii
@@ -0,0 +1,206 @@
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diff --git a/template/parcellations/parcellations_VGD11b_164k.mat b/template/parcellations/parcellations_VGD11b_164k.mat
new file mode 100644
index 0000000..4cacad7
Binary files /dev/null and b/template/parcellations/parcellations_VGD11b_164k.mat differ
diff --git a/template/parcellations/parcellations_VGD11b_32k.mat b/template/parcellations/parcellations_VGD11b_32k.mat
new file mode 100644
index 0000000..383b29b
Binary files /dev/null and b/template/parcellations/parcellations_VGD11b_32k.mat differ
diff --git a/template/parcellations/parcellations_VGD11b_8k.mat b/template/parcellations/parcellations_VGD11b_8k.mat
new file mode 100644
index 0000000..9225b35
Binary files /dev/null and b/template/parcellations/parcellations_VGD11b_8k.mat differ
diff --git a/template/white.nii b/template/white.nii
new file mode 100644
index 0000000..d014e0a
Binary files /dev/null and b/template/white.nii differ
diff --git a/trial_functions/trialfun_Motort.m b/trial_functions/trialfun_Motort.m
index 053506c..d2127fb 100644
--- a/trial_functions/trialfun_Motort.m
+++ b/trial_functions/trialfun_Motort.m
@@ -1,4 +1,7 @@
function [trl,trlInfoColDescr, trialSummary, scanStartSamp, scanEndSamp, warninfo] = trialfun_Motort( cfg )
+%% This is a wrapper function for the Motor core trial definition function trialfun_Motort_BaseExtractAll.m
+% See the help notes of this core function for information on input and
+% output variables.
% Copyright (C) 2011-2013 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
%
diff --git a/trial_functions/trialfun_Motort_BaseExtractAll.m b/trial_functions/trialfun_Motort_BaseExtractAll.m
index 61e6b94..bc7c514 100644
--- a/trial_functions/trialfun_Motort_BaseExtractAll.m
+++ b/trial_functions/trialfun_Motort_BaseExtractAll.m
@@ -1,63 +1,100 @@
function[trl,trlInfoColDescr,trialSummary,scanStartSamp,scanEndSamp,warninfo] = trialfun_Motort_BaseExtractAll( cfg )
-
-% This is the Trial Definition Function for Motor Task experiment.
-% The trigger is derived from the PhotoDiode transients on the trigger channle
-%__________________________________________________________________________
-%% Input:
-% cfg: Structure with fields that contain parameters for extracting trials
-% cfg.datafile: Char string represents filename of raw input MEG data
-% cfg.trialdef.stimulCode: Respresting the desired stimulus type to be extracted from the data with following integer values:
-% 1 -> Left Hand
-% 2 -> Left Foot
-% 3 -> Tongue
-% 4 -> Right Hand
-% 5 -> Right Foot
-% 6 -> Fixation
-% cfg.trialdef.TrigBasedOnEMG = 'yes' or 'no' (default = 'yes')
-% cfg.trialdef.cutMode = 'trials' or 'blocks' (default = 'trials')
-% cfg.trialdef.prestimTime: Desired time interval (in seconds) before Trigger onset.
-% Must be positive. In 'blocks' mode, prestimTime w.r.t. first event.
-% cfg.trialdef.poststimTime: Desired time interval (in seconds) after Trigger onset.
-% Must be positive. In 'blocks' mode, poststimTime w.r.t. last event.
-% cfg.trialdef.plotresults = 'yes' or 'no' (default = 'no')
-% cfg.trialdef.summaryfile: name of text file in order to save a sumamry of
-% the trial information as this is read from the triggers. It is used as a
-% quality check. If is empty nothing is printed.
-%__________________________________________________________________________
-%% Output:
-% trl: Ntrials-by-3 matrix with the trial definition
-% Column 1: Start sample for trials
-% Column 2: End sample for trials
-% Column 3: Offset of beginning of each trial from Trigger onset in Samples (i.e. -100).
-%__________________________________________________________________________
-%% === Example 1:
-% cfg = [];
-% cfg.datafile = 'xxx\c,rfDC';% xxx is directory of the raw MEG scan
-% cfg.trialfun = 'trialfun_Motort_Base';
-% cfg.trialdef.stimulCode = 4; % 1->Left Hand, 2->Left Foot, 3->Tongue, 4->Right Hand, 5->Right Foot, 6->Fixation
-% cfg.trialdef.prestimTime = 0.1;
-% cfg.trialdef.poststimTime = 0.5;
-% %cfg.trialdef.TrigBasedOnEMG = 'yes';%(default = 'yes')
-% %cfg.trialdef.cutMode = 'trials';%(default = 'trials')
-% %cfg.trialdef.plotresults = 'no';%(default = 'no')
-% cfgDefTr = ft_definetrial(cfg);
-% cfgDefTr.dataformat = '4d';
-% cfgDefTr.headerformat = '4d';
-% dataRaw = ft_preprocessing(cfgDefTr);
-%% === Example 2:
-% cfg = [];
-% cfg.datafile = 'xxx\c,rfDC';% xxx is directory of the raw MEG scan
-% cfg.trialfun = 'trialfun_Motort_Base';
-% cfg.trialdef.stimulCode = 2; % 1->Left Hand, 2->Left Foot, 3->Tongue, 4->Right Hand, 5->Right Foot, 6->Fixation
-% cfg.trialdef.prestimTime = 0;
-% cfg.trialdef.poststimTime = 0.5;
-% cfg.trialdef.TrigBasedOnEMG = 'no';%(default = 'yes')
-% cfg.trialdef.cutMode = 'blocks';%(default = 'trials')
-% cfg.trialdef.plotresults = 'yes';%(default = 'no')
-% cfgDefTr = ft_definetrial(cfg);
-% cfgDefTr.dataformat = '4d';
-% cfgDefTr.headerformat = '4d';
-% dataRaw = ft_preprocessing(cfgDefTr);
+%% This is the Trial Definition Function for Motor experiment.
+% It extracts the trial definition for ALL trials within each of different
+% datagroup.
+% There are 2 different data groups for Motor task.
+%
+% DATA GROUPS
+%----------------------------------
+% 1. mnemonic: TFLA. description: Onset of flashing cross that instructs
+% subject to perform movement by hand or foot.
+% 2. mnemonic: TEMG. description: Onset of the emg signal from hand or
+% foot recorded muscles.
+%
+%
+%
+% INPUT VARIABLE
+%----------------------------------
+% cfg : This is a structure containing information required for extracting
+% the trials for either of the 2 data groups described above.
+% Fields:
+% .datafile: This is the filename of the raw data file.
+% .trialdef: This is a structure containing the parameters
+% required to split the data into the trials of either data
+% group.
+% Fields:
+% .trialdef.TrigBasedOnEMG = 'yes' or 'no'; % Defines the 0 reference time of eahc trial
+% according to the desired data group.
+% 'yes' for TEMG,'no' for TFLA data group
+%
+% .trialdef.cutMode = 'trials'; % This representes that data will be cut in trials
+% % and not in blocks. The same for both data groups
+% .trialdef.preStimTime = 1.2; % Time interval prior to 0 reference point for each trial
+% .trialdef.postStimTime = 1.2; % Time interval after the 0 reference point for each trial
+% .trialdef.montage % This is the montage containing the emg channels. This
+% variable is constructed by the hcp_exgmontage.m function.
+% In .labelnew subfield , the expected
+% emg channels names are expected to be
+% {'EMG_LH','EMG_LF','EMG_RH','EMG_RF'}
+%
+%
+%
+% OUTPUT VARIABLES
+%-----------------------------------
+% trl: This is a numerical matrix. Each column corresponds to a trial and
+% each column to a specific condition of piece of information, quantified numerically , regarding
+% each trial. The first 3 Columns describe the trial start sample, end sample and time offset
+% of the start of the trial relative to the 0 reference point. These 3
+% columns are used by fieldtrip as the necessary information required
+% to extract the data for each trial.
+% The rest of the columns encode various types of information about
+% each trial. This part of the trl Matrix , from column 4 to the last
+% column, should be refered to as 'trialinfo' part of the trl matrix.
+%
+% trlInfoColDescr: This is a cell array. Each element is a string
+% describing the type of information encoded by the corresponding column of
+% the 'trialinfo' part of trl matrix described above.
+%
+% trialSummary: This is a structure containing an overview of the breakdown of the data
+% in trials of the main conditions for BOTH data groups.
+% This is used for Quality Control purposes in order to
+% ensure that the correct number of trials for each of
+% the main conditions is present in the data and identifying any
+% discrepances due to problems in the stimulus presentation protocol.
+%
+% scanStartSamp: This is the sample number of the onset of the first
+% stimulus block.
+% scanEndSamp: This is the sample number of the offset of the last
+% stimulus block.
+% warninfo: This is cell variable used for Quality Control , in
+% order to catch problems with unexpected trigger values.
+%
+%
+% Below is presented, for convenience of the reader, the description of
+% each column of the 'trialinfo' part of trl matrix described above and
+% contained in the trlInfoColDescr output variable.
+%
+%
+% Trialinfo Column Description as presented in trlInfoColDescr output variable
+%-------------------------------------------------------------------------------
+%========================================================================
+% data group: TFLA
+%------------------
+% ' 1. Block Index '
+% ' 2. Block Stim Code: 1-Left Hand, 2 - Left Foot, 4 - Right Hand. 5 - Right Foot, 6 - Fixation'
+% ' 3. Trial Index in Block
+% ' 4. Trial Onset Sample'
+% ' 5. prev. Block Stim Code'};
+%========================================================================
+% data group: TEMG
+%------------------
+% ' 1. Block Index '
+% ' 2. Block Stim Code: 1-Left Hand, 2 - Left Foot, 4 - Right Hand. 5 - Right Foot, 6 - Fixation'
+% ' 3. Trial Index in Block (This is derived by finding the flash cross onset just before the EMG onset)'
+% ' 4. Trial EMG Onset Sample'
+% ' 5. prev. Block Stim Code'
+% ' 6. Time from EMG onset to the previous Flashing Cross'
+%========================================================================
% Copyright (C) 2011-2013 by the Human Connectome Project, WU-Minn Consortium (1U54MH091657)
%
@@ -491,13 +528,17 @@
% 6. Time from EMG onset to the previous Flashing Cross
motorBlockColumnDescription={''}; % IF BLOCKS ARE ANALYZED AS A WHOLE ADD DESCRIPTION
-motorTrlColumnDescription={' 1. Block Index '
+motorTrlColumnDescriptionEMG={' 1. Block Index '
' 2. Block Stim Code: 1-Left Hand, 2 - Left Foot, 4 - Right Hand. 5 - Right Foot, 6 - Fixation'
' 3. Trial Index in Block (This is derived by finding the flash cross onset just before the EMG onset)'
- ' 4. Trial Onset EMG Sample'
+ ' 4. Trial EMG Onset Sample'
' 5. prev. Block Stim Code'
' 6. Time from EMG onset to the previous Flashing Cross'};
-
+motorTrlColumnDescription={' 1. Block Index '
+ ' 2. Block Stim Code: 1-Left Hand, 2 - Left Foot, 4 - Right Hand. 5 - Right Foot, 6 - Fixation'
+ ' 3. Trial Index in Block '
+ ' 4. Trial Onset Sample'
+ ' 5. prev. Block Stim Code'};
motorBlockInfo=allBlockInfo((allBlockInfo(:,2)~=6),:);
@@ -550,13 +591,28 @@
% end
%==================================================
%-- Fuse with trial info from flashing cross
- NemgTrials=size(tmpInfoEMG);
-
+ %-------------------------------------------------------------------------
+ NemgTrials=size(tmpInfoEMG,1);
+ indPreEmpty=[];
+ for iTrial=1:NemgTrials,
+ iTrial
+ tmpIndx=find(motorTrialInfo(:,4)25);
+ if length(indDiffLong)>1
+ error('There are more than one extra up-down photodiode events');
+ end
+ if indDiffLong~=1,
+ error('There is one extra up-down photodiode event but is not at the beginnin of the scan');
+ end
+ indPhoto_UPDOWN(1:2)=[];
+ indPhoto_UP(1)=[];
+ indPhoto_DOWN(1)=[];
+ NeventsPhoto=length(indPhoto_UPDOWN);
+
+ else
+ error('The image onsets-offsets is different in the photodiode and parallel port triggers');
+ end
end
end