Disambiguating the role of blood flow and global signal with partial information decomposition

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas

Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders

The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks

A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity

It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the WU-Minn-Ox HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The HCP-style neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium

Biological Mechanisms Linking Stress and Anhedonia

Evidence from research across species suggests that stress exposure is linked with anhedonia (loss of pleasure and/or decreased motivation). However, the mechanisms through which stress might impact anhedonia remain unclear. Chapters 1 and 2 of this dissertation review putative etiological pathways from stress to anhedonia and discuss stressor characteristics that could inform experimental models of stress-induced anhedonia. Chapter 3 describes an attempt to identify which types of stress are most associated with anhedonia using stress interview data from multiple datasets. Unexpectedly, we found no credible effects on anhedonic symptoms for stressor chronicity, severity, dependence on behavior, or interpersonal focus. Instead, number of stressors endorsed was the best predictor of anhedonic symptoms. Next, Chapters 4 and 5 report on two studies that tested possible biological mediators of the stress-anhedonia link. Chapter 4 describes an analysis of the UK Biobank dataset aimed at evaluating frontostriatal functional connectivity as a mechanism of stress-induced anhedonia. Although stress exposure predicted anhedonia, analyses uncovered no stable relation between frontostriatal connectivity and anhedonia, and no support for the proposed mediation model. Chapter 5 details a study that implemented a laboratory-based stressor to assess its potential impact on motivated behavior (thought to be a key component of anhedonia), and whether any such effects might be mediated by inflammatory responding. Low concentrations of salivary cytokines suggested questionable validity of inflammatory assessment, and no effect of stress on inflammatory responding was observed. Additionally, stress produced no measurable changes in motivated behavior. Thus, analyses revealed no evidence consistent with inflammation as a mechanism of stress-induced anhedonia. Finally, Chapter 6 discusses conclusions and implications of the current findings, and provides ideas for future directions

Using Movies to Probe the Neurobiology of Anxiety

Over the past century, research has helped us build a fundamental understanding of the neurobiological underpinnings of anxiety. Specifically, anxiety engages a broad range of cortico-subcortical neural circuitry. Core to this is a ‘defensive response network’ which includes an amygdala-prefrontal circuit that is hypothesized to drive attentional amplification of threat-relevant stimuli in the environment. In order to help prepare the body for defensive behaviors to threat, anxiety also engages peripheral physiological systems. However, our theoretical frameworks of the neurobiology of anxiety are built mostly on the foundations of tightly-controlled experiments, such as task-based fMRI. Whether these findings generalize to more naturalistic settings is unknown. To address this shortcoming, movie-watching paradigms offer an effective tool at the intersection of tightly controlled and entirely naturalistic experiments. Particularly, using suspenseful movies presents a novel and effective means to induce and study anxiety. In this thesis, I demonstrate the potential of movie-watching paradigms in the study of how trait and state anxiety impact the ‘defensive response network’ in the brain, as well as peripheral physiology. The key findings reveal that trait anxiety is associated with differing amygdala-prefrontal responses to suspenseful movies; specific trait anxiety symptoms are linked to altered states of anxiety during suspenseful movies; and states of anxiety during movies impact brain-body communication. Notably, my results frequently diverged from those of conventional task-based experiments. Taken together, the insights gathered from this thesis underscore the utility of movie-watching paradigms for a more nuanced understanding of how anxiety impacts the brain and peripheral physiology. These outcomes provide compelling evidence that further integration of naturalistic methods will be beneficial in the study of the neurobiology of anxiety

Mapping genome-wide neuropsychiatric mutation effects on functional brain connectivity : c opy number variants delineate dimensions contributing to autism and schizophrenia

Les recherches menées pour comprendre les troubles du spectre autistique (TSA) et la schizophrénie (SZ) ont communément utilisé une approche dite descendante, partant du diagnostic clinique pour investiguer des phénotypes intermédiaires cérébraux ainsi que des variations génétiques associées. Des études transdiagnostiques récentes ont remis en question ces frontières nosologiques, et suggèrent des mécanismes étiologiques imbriqués. L’approche montante propose de composer des groupes de porteurs d’un même variant génétique afin d’investiguer leur contribution aux conditions neuropsychiatriques (NPs) associées. Les variations du nombre de copies (CNV, perte ou gain d’un fragment d’ADN) figurent parmi les facteurs biologiques les plus associés aux NPs, et sont dès lors des candidats particulièrement appropriés. Les CNVs induisant un risque pour des conditions similaires, nous posons l’hypothèse que des classes entières de CNVs convergent sur des dimensions d’altérations cérébrales qui contribuent aux NPs. L’imagerie fonctionnelle au repos (rs-fMRI) s’est révélée un outil prometteur en psychiatrie, mais presqu’aucune étude n’a été menée pour comprendre l’impact des CNVs sur la connectivité fonctionnelle cérébrale (FC). Nos objectifs étaient de: 1) Caractériser l’effet des CNVs sur la FC; 2) Rechercher la présence des motifs conférés par ces signatures biologiques dans des conditions idiopathiques; 3) Tester si la suppression de gènes intolérants à l’haploinsuffisance réorganise la FC de manière indépendante à leur localisation dans le génome. Nous avons agrégé des données de rs-fMRI chez: 502 porteurs de 8 CNVs associées aux NPs (CNVs-NP), de 4 CNVs sans association établie, ainsi que de porteurs de CNVs-NPs éparses; 756 sujets ayant un diagnostic de TSA, de SZ, ou de trouble déficitaire de l’attention/hyperactivité (TDAH), et 5377 contrôles. Les analyses du connectome entier ont montré un effet de dosage génique positif pour les CNVs 22q11.2 et 1q21.1, et négatif pour le 16p11.2. La taille de l’effet des CNVs sur la FC était corrélée au niveau de risque psychiatrique conféré par le CNV. En accord avec leurs effets sur la cognition, l’effet des délétions sur la FC était plus élevé que celui des duplications. Nous avons identifié des similarités entre les motifs cérébraux conférés par les CNVs-NP, et l’architecture fonctionnelle des individus avec NPs. Le niveau de similarité était associé à la sévérité du CNV, et était plus fort avec la SZ et les TSA qu’avec les TDAH. La comparaison des motifs conférés par les délétions les plus sévères (16p11.2, 22q11.2) à l’échelle fonctionnelle, et d’expression génique, nous a confirmé l’existence présumée de relation entre les mutations elles-mêmes. À l’aide d’une mesure d’intolérance aux mutations (pLI), nous avons pu inclure tous les porteurs de CNVs disponibles, et ainsi identifier un profil d’haploinsuffisance impliquant le thalamus, le cortex antérieur cingulaire, et le réseau somato-moteur, associé à une diminution de mesure d’intelligence générale. Enfin, une analyse d’exploration factorielle nous a permis de confirmer la contribution de ces régions cérébrales à 3 composantes latentes partagées entre les CNVs et les NPs. Nos résultats ouvrent de nouvelles perspectives dans la compréhension des mécanismes polygéniques à l’oeuvre dans les maladies mentales, ainsi que des effets pléiotropiques des CNVs.Research on Autism Spectrum Disorder (ASD) and schizophrenia (SZ) has mainly adopted a ‘top-down’ approach, starting from psychiatric diagnosis, and moving to intermediate brain phenotypes and underlying genetic factors. Recent cross-disorder studies have raised questions about diagnostic boundaries and pleiotropic mechanisms. By contrast, the recruitment of groups based on the presence of a genetic risk factor allows for the investigation of molecular pathways related to a particular risk for neuropsychiatric conditions (NPs). Copy number variants (CNVs, loss or gain of a DNA segment), which confer high risk for NPs are natural candidates to conduct such bottom-up approaches. Because CNVs have a similar range of adverse effects on NPs, we hypothesized that entire classes of CNVs may converge upon shared connectivity dimensions contributing to mental illness. Resting-state functional MRI (rs-fMRI) studies have provided critical insight into the architecture of brain networks involved in NPs, but so far only a few studies have investigated networks modulated by CNVs. We aimed at 1) Delineating the effects of neuropsychiatric variants on functional connectivity (FC), 2) Investigating whether the alterations associated with CNVs are also found among idiopathic psychiatric populations, 3) Testing whether deletions reorganize FC along general dimensions, irrespective of their localization in the genome. We gathered rsfMRI data on 502 carriers of eight NP-CNVs (high-risk), four CNVs without prior association to NPs as well as carriers of eight scarcer NP-CNVs. We also analyzed 756 subjects with idiopathic ASD, SZ, and attention deficit hyperactivity disorder (ADHD), and 5,377 controls. Connectome-wide analyses showed a positive gene dosage effect for the 22q11.2 and 1q21.1 CNVs, and a negative association for the 16p11.2 CNV. The effect size of CNVs on relative FC (mean-connectivity adjusted) was correlated with the known level of NP-risk conferred by CNVs. Consistent with results on cognition, we also reported that deletions had a larger effect size on FC than duplications. We identified similarities between high-risk CNV profiles and the connectivity architecture of individuals with NPs. The level of similarity was associated with mutation severity and was strongest in SZ, followed by ASD, and ADHD. The similarity was driven by the thalamus, and the posterior cingulate cortex, previously identified as hubs in transdiagnostic psychiatric studies. These results raised questions about shared mechanisms across CNVs. By comparing deletions at the 16p11.2 and 22q11.2 loci, we identified similarities at the connectivity, and at the gene expression level. We extended this work by pooling all deletions available for analysis. We asked if connectivity alterations were associated with the severity of deletions scored using pLI, a measure of intolerance to haploinsufficiency. The haploinsufficiency profile involved the thalamus, anterior cingulate cortex, and somatomotor network and was correlated with lower general intelligence and higher autism severity scores in 3 unselected and disease cohorts. An exploratory factor analysis confirmed the contribution of these regions to three latent components shared across CNVs and NPs. Our results open new avenues for understanding polygenicity in psychiatric conditions, and the pleiotropic effect of CNVs on cognition and on risk for neuropsychiatric disorders