paper.bib

@ARTICLE{Caballero-Gaudes2019-lv,
  title    = "A deconvolution algorithm for multi-echo functional {MRI}:
              Multi-echo Sparse Paradigm Free Mapping",
  author   = "Caballero-Gaudes, C{\'e}sar and Moia, Stefano and Panwar, Puja
              and Bandettini, Peter A and Gonzalez-Castillo, Javier",
  abstract = "This work introduces a novel algorithm for deconvolution of the
              BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm
              Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD
              percent signal change on the echo time (TE) and using
              sparsity-promoting regularized least squares estimation, ME-SPFM
              yields voxelwise time-varying estimates of the changes in the
              apparent transverse relaxation ($\Delta$R) without prior
              knowledge of the timings of individual BOLD events. Our results
              in multi-echo fMRI data collected during a multi-task
              event-related paradigm at 3 Tesla demonstrate that the maps of R
              changes obtained with ME-SPFM at the times of the stimulus trials
              show high spatial and temporal concordance with the activation
              maps and BOLD signals obtained with standard model-based
              analysis. This method yields estimates of $\Delta$R having
              physiologically plausible values. Owing to its ability to blindly
              detect events, ME-SPFM also enables us to map $\Delta$R
              associated with spontaneous, transient BOLD responses occurring
              between trials. This framework is a step towards deciphering the
              dynamic nature of brain activity in naturalistic paradigms,
              resting-state or experimental paradigms with unknown timing of
              the BOLD events.",
  journal  = "Neuroimage",
  volume   =  202,
  pages    = "116081",
  month    =  nov,
  year     =  2019,
  keywords = "BOLD fMRI; Deconvolution; Multi-echo; Single-trial",
  language = "en",
  doi = {10.1016/j.neuroimage.2019.116081}
}

@ARTICLE{Kundu2013-xm,
  title    = "Integrated strategy for improving functional connectivity mapping
              using multiecho {fMRI}",
  author   = "Kundu, Prantik and Brenowitz, Noah D and Voon, Valerie and Worbe,
              Yulia and V{\'e}rtes, Petra E and Inati, Souheil J and Saad, Ziad
              S and Bandettini, Peter A and Bullmore, Edward T",
  abstract = "Functional connectivity analysis of resting state blood oxygen
              level-dependent (BOLD) functional MRI is widely used for
              noninvasively studying brain functional networks. Recent findings
              have indicated, however, that even small ($\leq$1 mm) amounts of
              head movement during scanning can disproportionately bias
              connectivity estimates, despite various preprocessing efforts.
              Further complications for interregional connectivity estimation
              from time domain signals include the unaccounted reduction in
              BOLD degrees of freedom related to sensitivity losses from high
              subject motion. To address these issues, we describe an
              integrated strategy for data acquisition, denoising, and
              connectivity estimation. This strategy builds on our previously
              published technique combining data acquisition with multiecho
              (ME) echo planar imaging and analysis with spatial independent
              component analysis (ICA), called ME-ICA, which distinguishes BOLD
              (neuronal) and non-BOLD (artifactual) components based on linear
              echo-time dependence of signals-a characteristic property of BOLD
              T*2 signal changes. Here we show for 32 control subjects that
              this method provides a physically principled and nearly
              operator-independent way of removing complex artifacts such as
              motion from resting state data. We then describe a robust
              estimator of functional connectivity based on interregional
              correlation of BOLD-independent component coefficients. This
              estimator, called independent components regression, considerably
              simplifies statistical inference for functional connectivity
              because degrees of freedom equals the number of independent
              coefficients. Compared with traditional connectivity estimation
              methods, the proposed strategy results in fourfold improvements
              in signal-to-noise ratio, functional connectivity analysis with
              improved specificity, and valid statistical inference with
              nominal control of type 1 error in contrasts of connectivity
              between groups with different levels of subject motion.",
  journal  = "Proceedings of the National Academy of Sciences of the United States of America",
  volume   =  110,
  number   =  40,
  pages    = "16187--16192",
  month    =  oct,
  year     =  2013,
  keywords = "human neuroimaging; resting state fMRI; time series",
  language = "en",
  doi = {10.1073/pnas.1301725110}
}

@ARTICLE{Kundu2012-bq,
  title    = "Differentiating {BOLD} and {non-BOLD} signals in {fMRI} time
              series using multi-echo {EPI}",
  author   = "Kundu, Prantik and Inati, Souheil J and Evans, Jennifer W and
              Luh, Wen-Ming and Bandettini, Peter A",
  abstract = "A central challenge in the fMRI based study of functional
              connectivity is distinguishing neuronally related signal
              fluctuations from the effects of motion, physiology, and other
              nuisance sources. Conventional techniques for removing nuisance
              effects include modeling of noise time courses based on external
              measurements followed by temporal filtering. These techniques
              have limited effectiveness. Previous studies have shown using
              multi-echo fMRI that neuronally related fluctuations are Blood
              Oxygen Level Dependent (BOLD) signals that can be characterized
              in terms of changes in R(2)* and initial signal intensity (S(0))
              based on the analysis of echo-time (TE) dependence. We
              hypothesized that if TE-dependence could be used to differentiate
              BOLD and non-BOLD signals, non-BOLD signal could be removed to
              denoise data without conventional noise modeling. To test this
              hypothesis, whole brain multi-echo data were acquired at 3 TEs
              and decomposed with Independent Components Analysis (ICA) after
              spatially concatenating data across space and TE. Components were
              analyzed for the degree to which their signal changes fit models
              for R(2)* and S(0) change, and summary scores were developed to
              characterize each component as BOLD-like or not BOLD-like. These
              scores clearly differentiated BOLD-like ``functional network''
              components from non BOLD-like components related to motion,
              pulsatility, and other nuisance effects. Using non BOLD-like
              component time courses as noise regressors dramatically improved
              seed-based correlation mapping by reducing the effects of high
              and low frequency non-BOLD fluctuations. A comparison with
              seed-based correlation mapping using conventional noise
              regressors demonstrated the superiority of the proposed technique
              for both individual and group level seed-based connectivity
              analysis, especially in mapping subcortical-cortical
              connectivity. The differentiation of BOLD and non-BOLD components
              based on TE-dependence was highly robust, which allowed for the
              identification of BOLD-like components and the removal of non
              BOLD-like components to be implemented as a fully automated
              procedure.",
  journal  = "Neuroimage",
  volume   =  60,
  number   =  3,
  pages    = "1759--1770",
  month    =  apr,
  year     =  2012,
  language = "en",
  doi = {10.1016/j.neuroimage.2011.12.028}
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@MISC{Heunis2020-bd,
  title     = "The effects of multi-echo {fMRI} combination and rapid
               T2*-mapping on offline and real-time {BOLD} sensitivity",
  author    = "Heunis, S and Breeuwer, M and Caballero-Gaudes, C and Hellrung, Lydia and Huijbers, Willem and Jansen, Jacobus FA and Lamerichs, Rolf and Zinger, Svitlana  and Aldenkamp, Albert P",
  abstract  = "A variety of strategies are used to combine multi-echo
               functional magnetic resonance imaging (fMRI) data, yet recent
               literature lacks a systematic comparison of the available
               options. Here we compare six different approaches derived from
               multi-echo data and …",
  journal   = "bioRxiv",
  url       = {https://doi.org/10.1101/2020.12.08.416768},
  doi       = {10.1101/2020.12.08.416768},
  year      =  2020
}

@BOOK{Penny2011-vi,
  title     = "Statistical Parametric Mapping: The Analysis of Functional Brain
               Images",
  author    = "Penny, William D and Friston, Karl J and Ashburner, John T and
               Kiebel, Stefan J and Nichols, Thomas E",
  abstract  = "In an age where the amount of data collected from brain imaging
               is increasing constantly, it is of critical importance to
               analyse those data within an accepted framework to ensure proper
               integration and comparison of the information collected. This
               book describes the ideas and procedures that underlie the
               analysis of signals produced by the brain. The aim is to
               understand how the brain works, in terms of its functional
               architecture and dynamics. This book provides the background and
               methodology for the analysis of all types of brain imaging data,
               from functional magnetic resonance imaging to
               magnetoencephalography. Critically, Statistical Parametric
               Mapping provides a widely accepted conceptual framework which
               allows treatment of all these different modalities. This rests
               on an understanding of the brain's functional anatomy and the
               way that measured signals are caused experimentally. The book
               takes the reader from the basic concepts underlying the analysis
               of neuroimaging data to cutting edge approaches that would be
               difficult to find in any other source. Critically, the material
               is presented in an incremental way so that the reader can
               understand the precedents for each new development. This book
               will be particularly useful to neuroscientists engaged in any
               form of brain mapping; who have to contend with the real-world
               problems of data analysis and understanding the techniques they
               are using. It is primarily a scientific treatment and a didactic
               introduction to the analysis of brain imaging data. It can be
               used as both a textbook for students and scientists starting to
               use the techniques, as well as a reference for practicing
               neuroscientists. The book also serves as a companion to the
               software packages that have been developed for brain imaging
               data analysis. An essential reference and companion for users of
               the SPM software Provides a complete description of the concepts
               and procedures entailed by the analysis of brain images Offers
               full didactic treatment of the basic mathematics behind the
               analysis of brain imaging data Stands as a compendium of all the
               advances in neuroimaging data analysis over the past decade
               Adopts an easy to understand and incremental approach that takes
               the reader from basic statistics to state of the art approaches
               such as Variational Bayes Structured treatment of data analysis
               issues that links different modalities and models Includes a
               series of appendices and tutorial-style chapters that makes even
               the most sophisticated approaches accessible",
  publisher = "Elsevier",
  month     =  apr,
  year      =  2011,
  language  = "en",
  ISBN = {9780123725608}
}

@ARTICLE{Esteban2020-ul,
  title    = "Analysis of task-based functional {MRI} data preprocessed with
              {fMRIPrep}",
  author   = "Esteban, Oscar and Ciric, Rastko and Finc, Karolina and Blair,
              Ross W and Markiewicz, Christopher J and Moodie, Craig A and
              Kent, James D and Goncalves, Mathias and DuPre, Elizabeth and
              Gomez, Daniel E P and Ye, Zhifang and Salo, Taylor and
              Valabregue, Romain and Amlien, Inge K and Liem, Franziskus and
              Jacoby, Nir and Stoji{\'c}, Hrvoje and Cieslak, Matthew and
              Urchs, Sebastian and Halchenko, Yaroslav O and Ghosh, Satrajit S
              and De La Vega, Alejandro and Yarkoni, Tal and Wright, Jessey and
              Thompson, William H and Poldrack, Russell A and Gorgolewski,
              Krzysztof J",
  abstract = "Functional magnetic resonance imaging (fMRI) is a standard tool
              to investigate the neural correlates of cognition. fMRI
              noninvasively measures brain activity, allowing identification of
              patterns evoked by tasks performed during scanning. Despite the
              long history of this technique, the idiosyncrasies of each
              dataset have led to the use of ad-hoc preprocessing protocols
              customized for nearly every different study. This approach is
              time consuming, error prone and unsuitable for combining datasets
              from many sources. Here we showcase fMRIPrep
              (http://fmriprep.org), a robust tool to prepare human fMRI data
              for statistical analysis. This software instrument addresses the
              reproducibility concerns of the established protocols for fMRI
              preprocessing. By leveraging the Brain Imaging Data Structure to
              standardize both the input datasets (MRI data as stored by the
              scanner) and the outputs (data ready for modeling and analysis),
              fMRIPrep is capable of preprocessing a diversity of datasets
              without manual intervention. In support of the growing popularity
              of fMRIPrep, this protocol describes how to integrate the tool in
              a task-based fMRI investigation workflow.",
  journal  = "Nature Protocols",
  volume   =  15,
  number   =  7,
  pages    = "2186--2202",
  month    =  jul,
  year     =  2020,
  language = "en",
  doi = {10.1038/s41596-020-0327-3}
}

@ARTICLE{Cox1996-up,
  title    = "{AFNI}: software for analysis and visualization of functional
              magnetic resonance neuroimages",
  author   = "Cox, R W",
  abstract = "A package of computer programs for analysis and visualization of
              three-dimensional human brain functional magnetic resonance
              imaging (FMRI) results is described. The software can color
              overlay neural activation maps onto higher resolution anatomical
              scans. Slices in each cardinal plane can be viewed
              simultaneously. Manual placement of markers on anatomical
              landmarks allows transformation of anatomical and functional
              scans into stereotaxic (Talairach-Tournoux) coordinates. The
              techniques for automatically generating transformed functional
              data sets from manually labeled anatomical data sets are
              described. Facilities are provided for several types of
              statistical analyses of multiple 3D functional data sets. The
              programs are written in ANSI C and Motif 1.2 to run on Unix
              workstations.",
  journal  = "Computers and Biomedical Research",
  volume   =  29,
  number   =  3,
  pages    = "162--173",
  month    =  jun,
  year     =  1996,
  language = "en",
  doi = {10.1006/cbmr.1996.0014}
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Gonzalez-Castillo2016-tj,
  title    = "Evaluation of multi-echo {ICA} denoising for task based {fMRI}
              studies: Block designs, rapid event-related designs, and
              cardiac-gated {fMRI}",
  author   = "Gonzalez-Castillo, Javier and Panwar, Puja and Buchanan, Laura C
              and Caballero-Gaudes, Cesar and Handwerker, Daniel A and Jangraw,
              David C and Zachariou, Valentinos and Inati, Souheil and
              Roopchansingh, Vinai and Derbyshire, John A and Bandettini, Peter
              A",
  abstract = "Multi-echo fMRI, particularly the multi-echo independent
              component analysis (ME-ICA) algorithm, has previously proven
              useful for increasing the sensitivity and reducing false
              positives for functional MRI (fMRI) based resting state
              connectivity studies. Less is known about its efficacy for
              task-based fMRI, especially at the single subject level. This
              work, which focuses exclusively on individual subject results,
              compares ME-ICA to single-echo fMRI and a voxel-wise T2(⁎)
              weighted combination of multi-echo data for task-based fMRI under
              the following scenarios: cardiac-gated block designs, constant
              repetition time (TR) block designs, and constant TR rapid
              event-related designs. Performance is evaluated primarily in
              terms of sensitivity (i.e., activation extent, activation
              magnitude, percent detected trials and effect size estimates)
              using five different tasks expected to evoke neuronal activity in
              a distributed set of regions. The ME-ICA algorithm significantly
              outperformed all other evaluated processing alternatives in all
              scenarios. Largest improvements were observed for the
              cardiac-gated dataset, where ME-ICA was able to reliably detect
              and remove non-neural T1 signal fluctuations caused by
              non-constant repetition times. Although ME-ICA also outperformed
              the other options in terms of percent detection of individual
              trials for rapid event-related experiments, only 46\% of all
              events were detected after ME-ICA; suggesting additional
              improvements in sensitivity are required to reliably detect
              individual short event occurrences. We conclude the manuscript
              with a detailed evaluation of ME-ICA outcomes and a discussion of
              how the ME-ICA algorithm could be further improved. Overall, our
              results suggest that ME-ICA constitutes a versatile, powerful
              approach for advanced denoising of task-based fMRI, not just
              resting-state data.",
  journal  = "Neuroimage",
  volume   =  141,
  pages    = "452--468",
  month    =  nov,
  year     =  2016,
  keywords = "Block design; ME-ICA; Multi-echo fMRI; Rapid event related;
              Sensitivity",
  language = "en",
  doi = {10.1016/j.neuroimage.2016.07.049}
}

@ARTICLE{Logothetis2002-af,
  title     = "The neural basis of the blood--oxygen--level--dependent
               functional magnetic resonance imaging signal",
  author    = "Logothetis, Nikos K",
  journal   = "Philosophical Transactions of the Royal Society B: Biological Sciences",
  publisher = "The Royal Society",
  volume    =  357,
  number    =  1424,
  pages     = "1003--1037",
  year      =  2002,
  doi = {10.1098/rstb.2002.1114}
}

@ARTICLE{Posse1999-lt,
  title    = "Enhancement of {BOLD-contrast} sensitivity by single-shot
              multi-echo functional {MR} imaging",
  author   = "Posse, S and Wiese, S and Gembris, D and Mathiak, K and Kessler,
              C and Grosse-Ruyken, M L and Elghahwagi, B and Richards, T and
              Dager, S R and Kiselev, V G",
  abstract = "Improved data acquisition and processing strategies for blood
              oxygenation level-dependent (BOLD)-contrast functional magnetic
              resonance imaging (fMRI), which enhance the functional
              contrast-to-noise ratio (CNR) by sampling multiple echo times in
              a single shot, are described. The dependence of the CNR on T2*,
              the image encoding time, and the number of sampled echo times are
              investigated for exponential fitting, echo summation, weighted
              echo summation, and averaging of correlation maps obtained at
              different echo times. The method is validated in vivo using
              visual stimulation and turbo proton echoplanar spectroscopic
              imaging (turbo-PEPSI), a new single-shot multi-slice MR
              spectroscopic imaging technique, which acquires up to 12
              consecutive echoplanar images with echo times ranging from 12 to
              213 msec. Quantitative T2*-mapping significantly increases the
              measured extent of activation and the mean correlation
              coefficient compared with conventional echoplanar imaging. The
              sensitivity gain with echo summation, which is computationally
              efficient provides similar sensitivity as fitting. For all data
              processing methods sensitivity is optimum when echo times up to
              3.2 T2* are sampled. This methodology has implications for
              comparing functional sensitivity at different magnetic field
              strengths and between brain regions with different magnetic field
              inhomogeneities.",
  journal  = "Magnetic Resonance in Medicine",
  volume   =  42,
  number   =  1,
  pages    = "87--97",
  month    =  jul,
  year     =  1999,
  language = "en",
  doi = {10.1002/(sici)1522-2594(199907)42:1<87::aid-mrm13>3.0.co;2-o}
}

@ARTICLE{Cohen2021-ep,
  title    = "Improved resting state functional connectivity sensitivity and
              reproducibility using a multiband multi-echo acquisition",
  author   = "Cohen, Alexander D and Yang, Baolian and Fernandez, Brice and
              Banerjee, Suchandrima and Wang, Yang",
  abstract = "Recent advances in functional MRI techniques include multiband
              (MB) imaging and multi-echo (ME) imaging. In MB imaging multiple
              slices are acquired simultaneously leading to significant
              increases in temporal and spatial resolution. Multi-echo imaging
              enables multiple echoes to be acquired in one shot, where the ME
              images can be used to denoise the BOLD time series and increase
              BOLD sensitivity. In this study, resting state fMRI (rs-fMRI)
              data were collected using a combined MBME sequence and compared
              to an MB single echo sequence. In total, 29 subjects were imaged,
              and 18 of them returned within two weeks for repeat imaging.
              Participants underwent one MBME scan with three echoes and one MB
              scan with one echo. Both datasets were processed using standard
              denoising and advanced denoising. Advanced denoising included
              multi-echo independent component analysis (ME-ICA) for the MBME
              data and ICA-AROMA for the MB data. Resting state functional
              connectivity (RSFC) was evaluated using both selective seed-based
              and whole grey matter (GM) region-of-interest (ROI) based
              approaches. The reproducibility of connectivity metrics was also
              analyzed in the repeat subjects. In addition, functional
              connectivity density (FCD), a data-driven approach that counts
              the number of significant connections, both within a local
              cluster and globally, with each voxel was analyzed. Regardless of
              the standard or advanced denoising technique, all seed-based RSFC
              was significantly higher for MBME compared to MB. Much more GM
              ROI combinations showed significantly higher RSFC for MBME vs.
              MB. Reproducibility, evaluated using the dice coefficient was
              significantly higher for MBME relative to MB data. Finally, FCD
              was also higher for MBME vs. MB data. This study showed higher
              RSFC for MBME vs. MB data using selected seed-based, whole GM
              ROI-based, and data-driven approaches. Reproducibility found also
              higher for MBME data. Taken together, these results indicate that
              MBME is a promising technique for rs-fMRI.",
  journal  = "Neuroimage",
  volume   =  225,
  pages    = "117461",
  month    =  jan,
  year     =  2021,
  keywords = "Functional connectivity density; Multi-echo; Multi-echo
              independent component analysis; Multiband; Reproducibility;
              Resting state functional MRI",
  language = "en",
  doi = {10.1016/j.neuroimage.2020.117461}
}

@ARTICLE{Lynch2020-tz,
  title    = "Rapid Precision Functional Mapping of Individuals Using
              {Multi-Echo} {fMRI}",
  author   = "Lynch, Charles J and Power, Jonathan D and Scult, Matthew A and
              Dubin, Marc and Gunning, Faith M and Liston, Conor",
  abstract = "Resting-state functional magnetic resonance imaging (fMRI) is
              widely used in cognitive and clinical neuroscience, but
              long-duration scans are currently needed to reliably characterize
              individual differences in functional connectivity (FC) and brain
              network topology. In this report, we demonstrate that multi-echo
              fMRI can improve the reliability of FC-based measurements. In
              four densely sampled individual humans, just 10 min of multi-echo
              data yielded better test-retest reliability than 30 min of
              single-echo data in independent datasets. This effect is
              pronounced in clinically important brain regions, including the
              subgenual cingulate, basal ganglia, and cerebellum, and is linked
              to three biophysical signal mechanisms (thermal noise, regional
              variability in the rate of T decay, and S-dependent artifacts)
              with spatially distinct influences. Together, these findings
              establish the potential utility of multi-echo fMRI for rapid
              precision mapping using experimentally and clinically tractable
              scan times and will facilitate longitudinal neuroimaging of
              clinical populations.",
  journal  = "Cell Reports",
  volume   =  33,
  number   =  12,
  pages    = "108540",
  month    =  dec,
  year     =  2020,
  keywords = "functional brain networks; multi-echo fMRI; precision functional
              mapping; test-retest reliability",
  language = "en",
  doi = {10.1016/j.celrep.2020.108540}
}

@ARTICLE{Chang2009-bj,
  title    = "Relationship between respiration, end-tidal {CO2}, and {BOLD}
              signals in resting-state {fMRI}",
  author   = "Chang, Catie and Glover, Gary H",
  abstract = "A significant component of BOLD fMRI physiological noise is
              caused by variations in the depth and rate of respiration. It has
              previously been demonstrated that a breath-to-breath metric of
              respiratory variation (respiratory volume per time; RVT),
              computed from pneumatic belt measurements of chest expansion, has
              a strong linear relationship with resting-state BOLD signals
              across the brain. RVT is believed to capture breathing-induced
              changes in arterial CO(2), which is a cerebral vasodilator;
              indeed, separate studies have found that spontaneous fluctuations
              in end-tidal CO(2) (PETCO(2)) are correlated with BOLD signal
              time series. The present study quantifies the degree to which RVT
              and PETCO(2) measurements relate to one another and explain
              common aspects of the resting-state BOLD signal. It is found that
              RVT (particularly when convolved with a particular impulse
              response, the ``respiration response function'') is highly
              correlated with PETCO(2), and that both explain remarkably
              similar spatial and temporal BOLD signal variance across the
              brain. In addition, end-tidal O(2) is shown to be largely
              redundant with PETCO(2). Finally, the latency at which PETCO(2)
              and respiration belt measures are correlated with the time series
              of individual voxels is found to vary across the brain and may
              reveal properties of intrinsic vascular response delays.",
  journal  = "Neuroimage",
  volume   =  47,
  number   =  4,
  pages    = "1381--1393",
  month    =  oct,
  year     =  2009,
  language = "en",
  doi = {10.1016/j.neuroimage.2009.04.048}
}

@ARTICLE{Peters2007-lc,
  title    = "T2* measurements in human brain at 1.5, 3 and 7 {T}",
  author   = "Peters, Andrew M and Brookes, Matthew J and Hoogenraad, Frank G
              and Gowland, Penny A and Francis, Susan T and Morris, Peter G and
              Bowtell, Richard",
  abstract = "Measurements have been carried out in six subjects at magnetic
              fields of 1.5, 3 and 7 T, with the aim of characterizing the
              variation of T2* with field strength in human brain. Accurate
              measurement of T2* in the presence of macroscopic magnetic field
              inhomogeneity is problematic due to signal decay resulting from
              through-slice dephasing. The approach employed here allowed the
              signal decay due to through-slice dephasing to be characterized
              and removed from data, thus facilitating an accurate measurement
              of T2* even at ultrahigh field. Using double inversion recovery
              turbo spin-echo images for tissue classification, an analysis of
              T2* relaxation times in cortical grey matter and white matter was
              carried out, along with an evaluation of the variation of T2*
              with field strength in the caudate nucleus and putamen. The
              results show an approximately linear increase in relaxation rate
              R2* with field strength for all tissues, leading to a greater
              range of relaxation times across tissue types at 7 T that can be
              exploited in high-resolution T2*-weighted imaging.",
  journal  = "Magnetic Resonance Imaging",
  volume   =  25,
  number   =  6,
  pages    = "748--753",
  month    =  jul,
  year     =  2007,
  language = "en",
  doi = {10.1016/j.mri.2007.02.014}
}

@ARTICLE{Jenkinson2012-eh,
  title    = "{FSL}",
  author   = "Jenkinson, Mark and Beckmann, Christian F and Behrens, Timothy E
              J and Woolrich, Mark W and Smith, Stephen M",
  abstract = "FSL (the FMRIB Software Library) is a comprehensive library of
              analysis tools for functional, structural and diffusion MRI brain
              imaging data, written mainly by members of the Analysis Group,
              FMRIB, Oxford. For this NeuroImage special issue on ``20 years of
              fMRI'' we have been asked to write about the history,
              developments and current status of FSL. We also include some
              descriptions of parts of FSL that are not well covered in the
              existing literature. We hope that some of this content might be
              of interest to users of FSL, and also maybe to new research
              groups considering creating, releasing and supporting new
              software packages for brain image analysis.",
  journal  = "Neuroimage",
  volume   =  62,
  number   =  2,
  pages    = "782--790",
  month    =  aug,
  year     =  2012,
  language = "en",
  doi = {10.1016/j.neuroimage.2011.09.015}
}

@ARTICLE{Silvennoinen2003-kg,
  title    = "Comparison of the dependence of blood {R2} and R2* on oxygen
              saturation at 1.5 and 4.7 Tesla",
  author   = "Silvennoinen, M J and Clingman, C S and Golay, X and Kauppinen, R
              A and van Zijl, P C M",
  abstract = "Gradient-echo (GRE) blood oxygen level-dependent (BOLD) effects
              have both intra- and extravascular contributions. To better
              understand the intravascular contribution in quantitative terms,
              the spin-echo (SE) and GRE transverse relaxation rates, R(2) and
              R(2)(*), of isolated blood were measured as a function of
              oxygenation in a perfusion system. Over the normal oxygenation
              saturation range of blood between veins, capillaries, and
              arteries, the difference between these rates, R'(2) = R(2)(*) -
              R(2), ranged from 1.5 to 2.1 Hz at 1.5 T and from 26 to 36 Hz at
              4.7 T. The blood data were used to calculate the expected
              intravascular BOLD effects for physiological oxygenation changes
              that are typical during visual activation. This modeling showed
              that intravascular DeltaR(2)(*) is caused mainly by R(2)
              relaxation changes, namely 85\% and 78\% at 1.5T and 4.7T,
              respectively. The simulations also show that at longer TEs (>70
              ms), the intravascular contribution to the percentual BOLD change
              is smaller at high field than at low field, especially for GRE
              experiments. At shorter TE values, the opposite is the case. For
              pure parenchyma, the intravascular BOLD signal changes originate
              predominantly from venules for all TEs at low field and for short
              TEs at high field. At longer TEs at high field, the capillary
              contribution dominates. The possible influence of partial volume
              contributions with large vessels was also simulated, showing
              large (two- to threefold) increases in the total intravascular
              BOLD effect for both GRE and SE.",
  journal  = "Magnetic Resonance in Medicine",
  volume   =  49,
  number   =  1,
  pages    = "47--60",
  month    =  jan,
  year     =  2003,
  language = "en",
  doi = {10.1002/mrm.10355}
}

@ARTICLE{Power2018-ca,
  title    = "Ridding {fMRI} data of motion-related influences: Removal of
              signals with distinct spatial and physical bases in multiecho
              data",
  author   = "Power, Jonathan D and Plitt, Mark and Gotts, Stephen J and Kundu,
              Prantik and Voon, Valerie and Bandettini, Peter A and Martin,
              Alex",
  abstract = "``Functional connectivity'' techniques are commonplace tools for
              studying brain organization. A critical element of these analyses
              is to distinguish variance due to neurobiological signals from
              variance due to nonneurobiological signals. Multiecho fMRI
              techniques are a promising means for making such distinctions
              based on signal decay properties. Here, we report that multiecho
              fMRI techniques enable excellent removal of certain kinds of
              artifactual variance, namely, spatially focal artifacts due to
              motion. By removing these artifacts, multiecho techniques reveal
              frequent, large-amplitude blood oxygen level-dependent (BOLD)
              signal changes present across all gray matter that are also
              linked to motion. These whole-brain BOLD signals could reflect
              widespread neural processes or other processes, such as
              alterations in blood partial pressure of carbon dioxide (pCO) due
              to ventilation changes. By acquiring multiecho data while
              monitoring breathing, we demonstrate that whole-brain BOLD
              signals in the resting state are often caused by changes in
              breathing that co-occur with head motion. These widespread
              respiratory fMRI signals cannot be isolated from neurobiological
              signals by multiecho techniques because they occur via the same
              BOLD mechanism. Respiratory signals must therefore be removed by
              some other technique to isolate neurobiological covariance in
              fMRI time series. Several methods for removing global artifacts
              are demonstrated and compared, and were found to yield fMRI time
              series essentially free of motion-related influences. These
              results identify two kinds of motion-associated fMRI variance,
              with different physical mechanisms and spatial profiles, each of
              which strongly and differentially influences functional
              connectivity patterns. Distance-dependent patterns in covariance
              are nearly entirely attributable to non-BOLD artifacts.",
  journal  = "Proceedings of the National Academy of Sciences of the United States of America",
  volume   =  115,
  number   =  9,
  pages    = "E2105--E2114",
  month    =  feb,
  year     =  2018,
  keywords = "fMRI; functional connectivity; motion artifact; multiecho;
              respiration",
  language = "en",
  doi = {10.1073/pnas.1720985115}
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Murphy2013-vo,
  title    = "Resting-state {fMRI} confounds and cleanup",
  author   = "Murphy, Kevin and Birn, Rasmus M and Bandettini, Peter A",
  abstract = "The goal of resting-state functional magnetic resonance imaging
              (fMRI) is to investigate the brain's functional connections by
              using the temporal similarity between blood oxygenation level
              dependent (BOLD) signals in different regions of the brain ``at
              rest'' as an indicator of synchronous neural activity. Since this
              measure relies on the temporal correlation of fMRI signal changes
              between different parts of the brain, any non-neural
              activity-related process that affects the signals will influence
              the measure of functional connectivity, yielding spurious
              results. To understand the sources of these resting-state fMRI
              confounds, this article describes the origins of the BOLD signal
              in terms of MR physics and cerebral physiology. Potential
              confounds arising from motion, cardiac and respiratory cycles,
              arterial CO₂ concentration, blood pressure/cerebral
              autoregulation, and vasomotion are discussed. Two classes of
              techniques to remove confounds from resting-state BOLD time
              series are reviewed: 1) those utilising external recordings of
              physiology and 2) data-based cleanup methods that only use the
              resting-state fMRI data itself. Further methods that remove noise
              from functional connectivity measures at a group level are also
              discussed. For successful interpretation of resting-state fMRI
              comparisons and results, noise cleanup is an often over-looked
              but essential step in the analysis pipeline.",
  journal  = "Neuroimage",
  volume   =  80,
  pages    = "349--359",
  month    =  oct,
  year     =  2013,
  keywords = "Functional connectivity; Functional magnetic resonance imaging
              (fMRI); Noise correction; Physiological noise; Resting-state",
  language = "en",
  doi = {10.1016/j.neuroimage.2013.04.001}
}

@ARTICLE{Caballero-Gaudes2017-ix,
  title    = "Methods for cleaning the {BOLD} {fMRI} signal",
  author   = "Caballero-Gaudes, C{\'e}sar and Reynolds, Richard C",
  abstract = "Blood oxygen-level-dependent functional magnetic resonance
              imaging (BOLD fMRI) has rapidly become a popular technique for
              the investigation of brain function in healthy individuals,
              patients as well as in animal studies. However, the BOLD signal
              arises from a complex mixture of neuronal, metabolic and vascular
              processes, being therefore an indirect measure of neuronal
              activity, which is further severely corrupted by multiple
              non-neuronal fluctuations of instrumental, physiological or
              subject-specific origin. This review aims to provide a
              comprehensive summary of existing methods for cleaning the BOLD
              fMRI signal. The description is given from a methodological point
              of view, focusing on the operation of the different techniques in
              addition to pointing out the advantages and limitations in their
              application. Since motion-related and physiological noise
              fluctuations are two of the main noise components of the signal,
              techniques targeting their removal are primarily addressed,
              including both data-driven approaches and using external
              recordings. Data-driven approaches, which are less specific in
              the assumed model and can simultaneously reduce multiple noise
              fluctuations, are mainly based on data decomposition techniques
              such as principal and independent component analysis.
              Importantly, the usefulness of strategies that benefit from the
              information available in the phase component of the signal, or in
              multiple signal echoes is also highlighted. The use of global
              signal regression for denoising is also addressed. Finally,
              practical recommendations regarding the optimization of the
              preprocessing pipeline for the purpose of denoising and future
              venues of research are indicated. Through the review, we
              summarize the importance of signal denoising as an essential step
              in the analysis pipeline of task-based and resting state fMRI
              studies.",
  journal  = "Neuroimage",
  volume   =  154,
  pages    = "128--149",
  month    =  jul,
  year     =  2017,
  keywords = "BOLD fMRI; Denoising methods; Motion artifacts; Multi-echo;
              Phase-based methods; Physiological noise",
  language = "en",
  doi = {10.1016/j.neuroimage.2016.12.018}
}

@MISC{asyraff2020stimulus,
  title  =    "Stimulus-independent neural coding of event semantics:
               Evidence from cross-sentence fMRI decoding",
  author =    "Asyraff, Aliff and Lemarchand, Rafael and Tamm, Andres and Hoffman, Paul",
  journal =   "bioRxiv",
  year    =   2020,
  doi = {10.1101/2020.10.06.327817},
  url = {https://www.biorxiv.org/content/10.1101/2020.10.06.327817v1}
}

@ARTICLE{Stocker2006-ae,
  title    = "Dependence of amygdala activation on echo time: results from
              olfactory {fMRI} experiments",
  author   = "St{\"o}cker, Tony and Kellermann, Thilo and Schneider, Frank and
              Habel, Ute and Amunts, Katrin and Pieperhoff, Peter and Zilles,
              Karl and Shah, N Jon",
  abstract = "Echo time dependence of the BOLD sensitivity is an important
              topic in fMRI whenever brain regions are considered where the EPI
              data quality suffers from susceptibility gradients. Here, an fMRI
              study is presented showing that a reduced echo time EPI sequence
              significantly enhances the statistical inference in subcortical
              (limbic) brain regions, with special focus on the amygdala. As a
              consequence, to facilitate whole-brain fMRI with optimal echo
              times, a sequence with slice-dependent echo time is demonstrated
              with a focus on structures suffering from susceptibility changes.
              The applicability of this method is shown in a second fMRI study
              aimed at both, cortical, and limbic brain regions. The results
              are in good agreement with theoretical descriptions of the BOLD
              sensitivity under the influence of susceptibility gradients.",
  journal  = "Neuroimage",
  volume   =  30,
  number   =  1,
  pages    = "151--159",
  month    =  mar,
  year     =  2006,
  language = "en",
  doi = {10.1016/j.neuroimage.2005.09.050}
}

@ARTICLE{Moia2020-bb,
  title    = "Voxelwise optimization of hemodynamic lags to improve regional
              {CVR} estimates in breath-hold {fMRI}",
  author   = "Moia, Stefano and Stickland, Rachael C and Ayyagari, Apoorva and
              Termenon, Maite and Caballero-Gaudes, Cesar and Bright, Molly G",
  abstract = "Cerebrovascular Reactivity (CVR), the responsiveness of blood
              vessels to a vasodilatory stimulus, is an important indicator of
              cerebrovascular health. Assessing CVR with fMRI, we can measure
              the change in the Blood Oxygen Level Dependent (BOLD) response
              induced by a change in CO2 pressure (\%BOLD/mmHg). However, there
              exists a temporal offset between the recorded CO2 pressure and
              the local BOLD response, due to both measurement and
              physiological delays. If this offset is not corrected for,
              voxel-wise CVR values will not be accurate. In this paper, we
              propose a framework for mapping hemodynamic lag in breath-hold
              fMRI data. As breath-hold tasks drive task-correlated head motion
              artifacts in BOLD fMRI data, our framework for lag estimation
              fits a model that includes polynomial terms and head motion
              parameters, as well as a shifted variant of the CO2 regressor
              ($\pm$9 s in 0.3 s increments), and the hemodynamic lag at each
              voxel is the shift producing the maximum total model R2 within
              physiological constraints. This approach is evaluated in 8
              subjects with multi-echo fMRI data, resulting in robust maps of
              hemodynamic delay that show consistent regional variation across
              subjects, and improved contrast-to-noise compared to methods
              where motion regression is ignored or performed earlier in
              preprocessing.Clinical Relevance- We map hemodynamic lag using
              breathhold fMRI, providing insight into vascular transit times
              and improving the regional accuracy of cerebrovascular reactivity
              measurements.",
  journal  = "Conference proceedings - IEEE engineering in medicine and biology society",
  volume   =  2020,
  pages    = "1489--1492",
  month    =  jul,
  year     =  2020,
  language = "en",
  doi = {10.1109/EMBC44109.2020.9176225}
}

@ARTICLE{Moia2021-ti,
  title    = "{ICA-based} Denoising Strategies in {Breath-Hold} Induced
              Cerebrovascular Reactivity Mapping with Multi Echo {BOLD} {fMRI}",
  author   = "Moia, Stefano and Termenon, Maite and Uru{\~n}uela, Eneko and
              Chen, Gang and Stickland, Rachael C and Bright, Molly G and
              Caballero-Gaudes, C{\'e}sar",
  abstract = "Performing a BOLD functional MRI (fMRI) acquisition during
              breath-hold (BH) tasks is a non-invasive, robust method to
              estimate cerebrovascular reactivity (CVR). However, movement and
              breathing-related artefacts caused by the BH can substantially
              hinder CVR estimates due to their high temporal collinearity with
              the effect of interest, and attention has to be paid when
              choosing which analysis model should be applied to the data. In
              this study, we evaluate the performance of multiple analysis
              strategies based on lagged general linear models applied on
              multi-echo BOLD fMRI data, acquired in ten subjects performing a
              BH task during ten sessions, to obtain subjectspecific CVR and
              haemodynamic lag estimates. The evaluated approaches range from
              conventional regression models including drifts and motion
              timecourses as nuisance regressors applied on singleecho or
              optimally-combined data, to more complex models including
              regressors obtained from multi-echo independent component
              analysis with different grades of orthogonalization in order to
              preserve the effect of interest, i.e. the CVR. We compare these
              models in terms of their ability to make signal intensity changes
              independent from motion, as well as the reliability as measured
              by voxelwise intraclass correlation coefficients of both CVR and
              lag maps over time. Our results reveal that a conservative
              independent component analysis model applied on the
              optimally-combined multi-echo fMRI signal offers the largest
              reduction of motion-related effects in the signal, while yielding
              reliable CVR amplitude and lag estimates, although a conventional
              regression model applied on the optimally-combined data results
              in similar estimates. This work demonstrate the usefulness of
              multi-echo based fMRI acquisitions and independent component
              analysis denoising for precision mapping of CVR in single
              subjects based on BH paradigms, fostering its potential as a
              clinically-viable neuroimaging tool for individual patients. It
              also proves that the way in which data-driven regressors should
              be incorporated in the analysis model is not straight-forward due
              to their complex interaction with the BH-induced BOLD response.
              \#\#\# Competing Interest Statement The authors have declared no
              competing interest.",
  journal = {NeuroImage},
  pages = {117914},
  year = {2021},
  issn = {1053-8119},
  doi = {10.1016/j.neuroimage.2021.117914},
  url = {https://www.sciencedirect.com/science/article/pii/S1053811921001919},
  language = "en"
}