Merge pull request #2 from OnnoKampman/corrections

Final adjustments

README.md

@@ -6,11 +6,24 @@ Thesis defended on December 1st, 2022.
 
 This thesis investigates how to robustly estimate time-varying functional connectivity (TVFC), a construct in neuroimaging research that looks at changes in functional coupling (correlation between time series) between brain regions during a functional magnetic resonance imaging (fMRI) scan, and how it can be used as a lens through which to study depression as a functional disorder.
 
-Unfortunately, the field of TVFC is still riddled with uncertainty, especially regarding its estimation. This is mainly due to the absence of a ground truth. Without resolving this first, the value of any study, including this depression study, is significantly undermined and conclusions made therein less trustworthy. Therefore, I propose a novel and principled method for estimating TVFC, based on the Wishart process (WP), a covariance matrix stochastic process that has recently become computationally tractable, and introduce a comprehensive benchmarking framework based on machine learning principles to make sure it performs better than existing methods in the field. These benchmarks include simulations, subject phenotype prediction, test-retest studies, brain state analyses, external task prediction, and a range of qualitative method comparisons. Furthermore, I introduce a benchmark based on cross-validation, that can be run on any data set. The WP model is found to outperform other common estimation methods, such as sliding-windows (SW) approaches and dynamic conditional correlation (DCC).
-
-Returning to the depression study, several differences are found between depressed and healthy control cohorts. The study is run on thousands of participants from the UK Biobank, yielding unprecedented statistical power and robustness. I investigate connectivity between individual brain regions as well as functional networks (FNs). Depressed participants show decreased global connectivity, and increased connectivity instability (as measured by the temporal characteristics of estimated TVFC). By defining multiple depression phenotypes, I find that brain dynamics are affected especially when patients have been professionally diagnosed or indicated to be depressed during their fMRI scan, but were less or not at all affected based on self-reported past instances and genetic predisposition. I show that choosing a different TVFC estimation method would have changed our scientific conclusions. This sensitivity to seemingly arbitrary researcher choices highlights the need for robust method development and the importance of community-approved benchmarking.
+Unfortunately, the field of TVFC is still riddled with uncertainty, especially regarding its estimation.
+This is mainly due to the absence of a ground truth.
+Without resolving this first, the value of any study, including this depression study, is significantly undermined and conclusions made therein less trustworthy.
+Therefore, I propose a novel and principled method for estimating TVFC, based on the Wishart process (WP), a covariance matrix stochastic process that has recently become computationally tractable, and introduce a comprehensive benchmarking framework based on machine learning principles to make sure it performs better than existing methods in the field.
+These benchmarks include simulations, subject phenotype prediction, test-retest studies, brain state analyses, external task prediction, and a range of qualitative method comparisons.
+Furthermore, I introduce a benchmark based on cross-validation, that can be run on any data set.
+The WP model is found to outperform other common estimation methods, such as sliding-windows (SW) approaches and dynamic conditional correlation (DCC).
+
+Returning to the depression study, several differences are found between depressed and healthy control cohorts.
+The study is run on thousands of participants from the UK Biobank, yielding unprecedented statistical power and robustness.
+I investigate connectivity between individual brain regions as well as functional networks (FNs).
+Depressed participants show decreased global connectivity, and increased connectivity instability (as measured by the temporal characteristics of estimated TVFC).
+By defining multiple depression phenotypes, I find that brain dynamics are affected especially when patients have been professionally diagnosed or indicated to be depressed during their fMRI scan, but were less or not at all affected based on self-reported past instances and genetic predisposition.
+I show that choosing a different TVFC estimation method would have changed our scientific conclusions.
+This sensitivity to seemingly arbitrary researcher choices highlights the need for robust method development and the importance of community-approved benchmarking.
 
 I wrap up this thesis with a discussion of results and how this style of work fits into the bigger picture of neuroscientific research, reflect on what has been learned about depression, and posit promising directions for future work.
+
 ## Source code for experiments
 
 https://github.com/OnnoKampman/neuro-dynamic-covariance
@@ -25,6 +38,8 @@ This document is generated using `Latexmk` version 4.77 and Biber version 2.17.
 
 You may need to clear the cache by running `rm -rf $(biber --cache)` if you encounter issues with compiling.
 
+I use Skim to view the generated PDF.
+
 ## Inspiration
 
 [1] https://github.com/kks32/phd-thesis-template

appendix/03_extra_benchmarking_results.tex

@@ -331,3 +331,66 @@ \subsection{Trivariate}
     Means and standard deviations are shown across $T = 200$ trials.
   }\label{fig:results-sim-d3s-200-no-noise-all-correlation-matrix-RMSE}
 \end{figure}
+
+
+\clearpage
+\section{HCP: Extra subject measure predictions}\label{appendix:hcp-more-results}
+%%
+
+
+\begin{figure}[h]
+  \centering
+  \includegraphics[width=\textwidth]{fig/hcp/d15/subject_measure_prediction/social-emotional/morphometricity_all_TVFC_summary_measures}
+  \caption{
+    HCP benchmark subject social-emotional measures prediction morphometricity scores (with standard error).
+    Run on TVFC summary measures of mean (top), variance (middle), and rate-of-change (bottom row).
+    sFC is added for reference to the TVFC mean plot.
+  }\label{fig:results-subject-measures-prediction-social-emotional}
+\end{figure}
+
+
+\begin{figure}[h]
+  \centering
+  \includegraphics[width=\textwidth]{fig/hcp/d15/subject_measure_prediction/other/morphometricity_all_TVFC_summary_measures}
+  \caption{
+    HCP benchmark subject other measures prediction morphometricity scores (with standard error).
+    Run on TVFC summary measures of mean (top), variance (middle), and rate-of-change (bottom row).
+    sFC is added for reference to the TVFC mean plot.
+  }\label{fig:results-subject-measures-prediction-other}
+\end{figure}
+
+
+%%
+\clearpage
+\section{HCP: Brain states}\label{appendix:hcp-brain-states}
+%%
+
+
+\begin{figure}[ht]
+  \centering
+  \includegraphics[width=\textwidth, trim={0.0cm 0cm 0.0cm 0cm}, clip]{fig/hcp/d15/brain_states/k03/brain_states_SVWP_joint}
+  \caption{
+    Brain states extracted from SVWP TVFC estimates on HCP data ($D = 15$ time series).
+    Brain states across the four scans look very similar.
+  }\label{fig:hcp-results-brain-states-svwp}
+\end{figure}
+
+
+\begin{figure}[ht]
+  \centering
+  \includegraphics[width=\textwidth, trim={0.0cm 0cm 0.0cm 0cm}, clip]{fig/hcp/d15/brain_states/k03/brain_states_DCC_joint}
+  \caption{
+    Brain states extracted from DCC TVFC estimates on HCP data ($D = 15$ time series).
+    Brain states are low contrast in general, and the four scans look very similar.
+    This aligns with our intuition when looking at the DCC model predictions.
+  }\label{fig:hcp-results-brain-states-dcc}
+\end{figure}
+
+
+\begin{figure}[ht]
+  \centering
+  \includegraphics[width=\textwidth, trim={0.0cm 0cm 0.0cm 0cm}, clip]{fig/hcp/d15/brain_states/k03/brain_states_SW_cross_validated}
+  \caption{
+    Brain states extracted from SW-CV TVFC estimates on HCP data ($D = 15$ time series).
+  }\label{fig:hcp-results-brain-states-sw-cv}
+\end{figure}

ch/1_Introduction/0_Introduction.tex

@@ -3,7 +3,7 @@ \chapter{Introduction}\label{ch:introduction}
 
 \info[inline]{Paragraph: Direct summary of what this thesis is about.}
 This thesis introduces novel approaches for robust estimation of \gls{tvfc} from \gls{fmri} neuroimaging data.
-\Gls{tvfc} is a construct that studies the time-varying nature of interaction between brain regions.
+\Gls{tvfc} is a construct that studies the time-varying nature of the interaction between brain regions.
 Estimation approaches are compared and evaluated to existing ones through a proposed benchmarking framework.
 %
 Afterwards, we use the best performing method to investigate how brain dynamics differ between depressed and healthy (control) subjects.
@@ -29,7 +29,7 @@ \chapter{Introduction}\label{ch:introduction}
 Some look at small circuits of neurons, some look at whole-brain structures, some investigate emergent brain waves, and some try to model human brain function with animal or computational models and speculate how these relate to human brain function (sometimes referred to as comparative cognition).
 Neuroscientists have long considered that the brain is modular in its organization~\parencite{Prinz2006}.
 Many would therefore specialize in a particular brain \gls{roi} to study its function and relationship to other regions.
-Even though the view of the brain as a collection of entirely distinct modules has been abandoned, this tradition persists to some degree, and is often still (approximately) valid~\parencite{Genon2018}.
+Even though the view of the brain as a collection of entirely distinct modules has been abandoned, this tradition persists to some degree and is often still (approximately) valid~\parencite{Genon2018}.
 Moreover, contemporary neuroscientists have argued for a more sophisticated view of \emph{hierarchy} in such modularity.
 %
 The division between psychology and neuroscience has blurred and merged in recent years.
@@ -60,20 +60,20 @@ \chapter{Introduction}\label{ch:introduction}
 
 \info[inline]{Paragraph: Explain our point-of-view of the human brain.}
 The particular point-of-view of the human brain in this work is the following.
-We abstract away implementational level details, and consider the human brain as a complex, dynamic system organ that is divided into distinct yet interacting regions.\footnote{Brain regions and components (or `nodes', borrowing from graph theoretic jargon) can be defined in several ways, which will be addressed later.}
+We abstract away implementational level details and consider the human brain as a complex, dynamic system organ that is divided into distinct yet interacting regions.\footnote{Brain regions and components (or `nodes', borrowing from graph theoretic jargon) can be defined in several ways, which will be addressed later.}
 We will not go into depth on any neurobiological signatures of behavior and disorders.
 %
 This characterization is based on a historical trend.
 Early modern neuroscientists discovered that the brain can be \emph{segregated} into distinct cortical (and subcortical) regions with distinct functions.
 Naturally, it followed that neuroscientists became interested in how these regions connect and communicate with one another.
-This is often referred to as the `functional' architecture of the brain, in contrast with the better understood \emph{structural} or \emph{anatomical} brain architecture.
-Higher-order cognition and complex behavior is made possible by the spatiotemporal integration, re-organization, and segregation of brain regions~\parencite{Deco2011}.
+This is often referred to as the `functional' architecture of the brain, in contrast with the better-understood \emph{structural} or \emph{anatomical} brain architecture.
+Higher-order cognition and complex behavior are made possible by the spatiotemporal integration, re-organization, and segregation of brain regions~\parencite{Deco2011}.
 Over the years a plethora of studies has painted a picture of the brain as a complex, distributed, and adaptive network of anatomical and functional segregation and integration that re-organizes itself at different time scales to process information and address a given task.
 Brain constituent parts also exhibit complex system structures, such as modular and hierarchical topology~\parencite{Meunier2009, Deco2015}.
 Human brains have been found to rely on higher degrees of synergistic interactions compared to nonhuman primates, highlighting their importance to complex cognition~\parencite{Luppi2022}.
 %
-Anatomical organization and connectivity in the brain has been studied by a range of imaging methods, including \gls{mri}, which uses powerful magnetic fields and radio waves to produce high resolution images of the inside of the body, and \gls{dti}, which images axons in white matter tracts.
-Functional interactions, often referred to as `connectivity' too, can be characterized using neuroimaging methods such as \gls{fmri}~\parencite{Soares2016}, \gls{pet}, which requires an injection of positron emitting isotopes, \gls{nirs}, \gls{eeg}, and \gls{meg}~\parencite{Rossini2019}.
+Anatomical organization and connectivity in the brain has been studied by a range of imaging methods, including \gls{mri}, which uses powerful magnetic fields and radio waves to produce high-resolution images of the inside of the body, and \gls{dti}, which images axons in white matter tracts.
+Functional interactions, often referred to as `connectivity' too, can be characterized using neuroimaging methods such as \gls{fmri}~\parencite{Soares2016}, \gls{pet}, which requires an injection of positron-emitting isotopes, \gls{nirs}, \gls{eeg}, and \gls{meg}~\parencite{Rossini2019}.
 Crucially, such connectivity is usually not directly observed, and needs to be estimated.
 
 \info[inline]{Paragraph: Discuss confusing terminology of `connectivity'.}

ch/1_Introduction/1_Functional_connectivity.tex

@@ -8,7 +8,7 @@ \section{Functional connectivity}
 Such connectivity depends on statistical dependencies between activity or \emph{activation} in brain regions, which can be measured by various neuroimaging modalities, most commonly \gls{fmri}, \gls{eeg}~\parencite[e.g.][]{Tagliazucchi2012, Chang2013}, and \gls{meg}~\parencite[e.g.][]{Baker2014, Vidaurre2018}.\footnote{As discussed later, modeling dependencies between random variables is also an important problem in machine learning and statistics.}
 Sometimes multiple concurrent modalities are used, such as \gls{fmri} and \gls{eeg} (but it is not possible to combine \gls{fmri} with \gls{meg}).
 %
-Neuroimaging methods can be divided into those that capture \emph{structural} and those capture \emph{functional} signals.
+Neuroimaging methods can be divided into those that capture \emph{structural} and those that capture \emph{functional} signals.
 The former aims to map out the location and molecular properties of brain tissue.
 The latter aims to capture more dynamic signals, related to the activity of brain tissue.
 Structural and functional (including \gls{fc}) analyses are complementary in building a holistic understanding of the brain, as they capture complementary (and disparate) information~\parencite{Lang2012}.
@@ -59,7 +59,7 @@ \section{Functional connectivity}
 
 Preprocessing pipelines, which take raw \gls{fmri} data and output node time series of interest, impact study results and conclusions~\parencite{Caballero-Gaudes2017}.
 As such, preprocessing steps also heavily influence subsequently extracted \gls{fc}~\parencite{Aquino2022}.
-An example of an impactful preprocessing step is \gls{gsr}, which can improve spatial localization of networks, but can introduce artificial anticorrelations into the data~\parencite{Murphy2009}.
+An example of an impactful preprocessing step is \gls{gsr}, which can improve the spatial localization of networks, but can introduce artificial anticorrelations into the data~\parencite{Murphy2009}.
 
 \info[inline]{Paragraph: Describe fMRI functional connectivity.}
 In the context of \gls{fmri}, `connectivity' is typically (i.e.~traditionally) characterized as the Pearson correlation coefficient between brain region \gls{bold} measurement component time series~\parencite{Zalesky2012}.

ch/1_Introduction/2_Functional_networks.tex

@@ -7,7 +7,7 @@ \section{Functional networks}\label{sec:functional-brain-networks}
 Cognitive tasks are not just performed by isolated brain regions, but rather by such networks, i.e.~linked collections of brain regions~\parencite{Bressler2010}.
 %
 These networks have been identified from \gls{fmri} \gls{bold} signals from scanning subjects presented with external tasks.
-Moreover, it has been shown that such large-scale networks exist at rest, and that these strongly resemble those found in task paradigms~\parencite{Smith2009}.\footnote{In neuroimaging, `rest' usually refers to the absence of an external stimulus or task, leaving mental activity relatively unconstrained. Signals measured at rest are sometimes referred to as `intrinsic' or `spontaneous', but rather confusingly in neuroimaging this sometimes refers to an actual signal of interest and sometimes to just noise.}
+Moreover, it has been shown that such large-scale networks exist at rest and that these strongly resemble those found in task paradigms~\parencite{Smith2009}.\footnote{In neuroimaging, `rest' usually refers to the absence of an external stimulus or task, leaving mental activity relatively unconstrained. Signals measured at rest are sometimes referred to as `intrinsic' or `spontaneous', but rather confusingly in neuroimaging this sometimes refers to an actual signal of interest and sometimes to just noise.}
 In such cases, these networks are referred to as \glspl{rsn} or \glspl{icn}.
 However, this name can cause confusion, because networks with similar extents can be found during task executions.
 Therefore, we opt to simply use the term `functional network'~\parencite[FN; see also][]{Finn2021}.