The brain consists of spatially distinct areas, which underlie different aspects of human behavior. Using advanced neuroimaging technology and neurocomputational analysis methods, researchers have been able to uncover the functional roles of many of these areas and how they are interconnected both structurally and functionally to produce actions, sensations and cognitions which allow us to navigate our lives. In more recent years, it has been discovered that these brain networks also underlie the healthy functioning of the brain while it is at rest, i.e. awake but not performing any explicit or goal-directed tasks. Changes in these resting-state networks (RSNs) have been implicated in a number of neurological and psychiatric disorders, indicating that their degradation may play a role in the diverse loss of sensory, motor or cognitive functions associated with these.In this thesis, we introduce some new guidelines for capturing the electrophysiology of RSN structures using magnetoencephalography (MEG), a non-invasive neuroimaging technique which directly measures the magnetic fields associated with the synchronized electrical neural activity underlying these connections. Using MEG, we are able to consider these complex communication structures with great spatial and temporal resolution and probe how they are altered in multiple sclerosis (MS), a disease defined in part by both the degradation of the structures connecting different brain areas and by impairments across a wide spectrum of cognitive functions. However, in order to achieve this, there are methodological and analytical issues that must be dealt with.This thesis is separated into three introductory chapters and three research chapters. The introductory chapters outline the relevant theoretical bases that are not covered in the specific research chapters, while each of the research chapters contain a study undertaken as part of the thesis. Additionally, some research chapters start with an additional introductory prologue which expands on relevant ideas or concepts that are used but not fully explained in the corresponding papers.This thesis contains three empirical studies. In the first, we investigated the differential impact of source reconstruction methods and MEG system type on resting state functional connectivity (rsFC). The results showed that the choice of source reconstruction algorithm has a substantial impact on the uncovered rsFC in the posterior default mode network (DMN). Specifically, this was shown to be due to a suppression of the source activity in this region when using a Beamformer rather than minimum norm estimation (MNE) for source reconstruction. Through exploring this effect this we also made a novel discovery about a linear synchronization structure within the posterior DMN. This also led us to recommend the use of MNE when conducting MEG rsFC studies involving the DMN, representing a novel and important result regarding best practice recommendations for the field as a whole and for the subsequent studies in this thesis.In the second study, we set out to distinguish intrinsic, i.e. task-invariant, and extrinsic, i.e. task-dependent, functional connectivity (FC) using a large data set containing MEG data from more than a hundred participants acquired during several different tasks with multiple task levels, as well as during rest, We were able to demonstrate that the human brain operates using two distinct modes of neuronal integration in parallel, i.e. intrinsic FC in the form of amplitude FC and extrinsic FC in the form of phase FC. These results are important both in that they establish a new conceptual framework for functional integration in the human brain and in that they highlight a potentially fuzzy distinction between resting-state and task-related FC, which can be better approached using this novel intrinsic/extrinsic formulation. Having established the existence of an intrinsic functional integration structure in amplitude FC among brain regions, in the third study we investigated how amplitude rsFC is altered in brain disease, here represented by patients with MS. We showed that patients with MS display specific alterations in amplitude FC, particularly involving the DMN and sensorimotor (SMN) networks, compared to healthy participants. Additionally, we showed that the degree of disease-related physical disability was associated with specific motor-related amplitude rsFC changes, and that variations in cognitive task performance and neuropsychological scores were different between patients and healthy subjects on scores which were significantly different between the groups. These results demonstrate the ability of intrinsic/amplitude FC to characterize functional changes in clinical populations that are associated with specific disability-related and neuropsychological outcomes. / Le cerveau se compose de différentes zones fonctionnelles spatialement distinctes, qui sous-tendent différents aspects du comportement humain. En utilisant une technologie avancée de neuroimagerie et des méthodes d'analyse neurocomputationnelle, les neuroscientifiques ont caractérisé les rôles fonctionnels d’un bon nombre de structures cérébrales (i.e. la spécialisation fonctionnelle) et comment elles sont interconnectées à la fois structurellement et fonctionnellement (i.e. l’intégration fonctionnelle) pour produire les actions motrices, les sensations et les fonctions cognitives qui nous permettent de naviguer dans nos vies. Ces dernières années, les techniques de neuroimagerie ont également démontré que ces réseaux cérébraux fonctionnels sous-tendent également le bon fonctionnement du cerveau lorsqu'il est « au repos », c'est-à-dire qu'il n'effectue aucune tâche explicite ou ciblée. Des modifications de ces réseaux « de l’état de repos » (RSN) ont été impliquées dans un certain nombre de pathologies neurologiques ou psychiatriques, indiquant que leur altération peut jouer un rôle dans les déficits de fonctions sensorielles, motrices ou cognitives présentées par les patients.Dans cette thèse, nous introduisons de nouvelles lignes directrices pour investiguer l'électrophysiologie des RSN à l'aide de la magnétoencéphalographie (MEG), une technique de neuroimagerie non invasive qui mesure directement les champs magnétiques associés à l'activité neuronale électrique. Nous avons premièrement déterminé comment les choix méthodologiques au niveau de la reconstruction de sources en MEG influence les résultats de l’estimation de l’intégration fonctionnelle cérébrale. Ensuite, nous avons été en mesure d’étudier l’intégration fonctionnelle au sein des RSNs avec une grande résolution spatiale et temporelle, et ainsi, de déterminer les processus neurophysiologiques à l’origine de l’intégration fonctionnelle « intrinsèque » (i.e. indépendante d’une tâche ou de ce que le sujet fait) et « extrinsèque » (i.e. influencée ou modulée par une tâche). Nous avons démontré que l’intégration fonctionnelle intrinsèque repose sur le couplage de l’enveloppe (ou amplitude) de l’activité rythmique cérébrale alors que l’extrinsèque repose sur le couplage de phase de cette activité. Enfin, nous avons déterminé comment l’intégration fonctionnelle intrinsèque est altérée dans la sclérose en plaques (SEP), une maladie caractérisée en partie par la dégradation des connexions reliant différentes zones cérébrales et par des altérations variables des fonctions cognitive. Nous avons pu démontrer que le handicap moteur et certains troubles cognitifs (fatigue, cognitiven fluence verbale) sont associés à des altérations de l’intégration fonctionnelle intrinsèque de RSNs spécifiques. / Doctorat en Sciences biomédicales et pharmaceutiques (Médecine) / info:eu-repo/semantics/nonPublished

Neurological abnormalities are associated with emotion processing deficits seen in children with neurodevelopmental disorders. Research suggests that inflammatory mechanisms can negatively impact brain structure and function and are thought to play a role in these processing atypicalities. Children with chromosome 22q11.2 deletion syndrome (22q11.2DS) exhibit emotion processing impairments and associated neural abnormalities. We investigated the roles of inflammatory factors and structural connectivity in relation to emotion processing deficits in 28 children with 22q11.2DS and 33 typically developing children (M = 11.12 years old; SD = 2.17). Results indicate poorer social skills and significantly lower emotion recognition scores in children with 22q11.2DS compared to controls. Additionally, children with 22q11.2DS had higher anisotropic diffusion in right amygdala to fusiform gyrus white matter pathways and lower serum IL-3 concentrations than their typically developing peers. Right amygdala to fusiform gyrus FA values partially mediated the relationship between 22q11.2DS and social skills, as well as the relationship between 22q11.2DS and emotion recognition accuracy. However, there was no indication that IL-3 mediated the relationship between diagnosis and abnormal connectivity. Future studies should employ longitudinal methods to characterize how these connectivity patterns influence social-emotional development as the child ages.

La schizophrénie est un trouble mental, chronique et invalidant caractérisé par divers symptômes tels que des hallucinations, des épisodes délirants ainsi que des déficiences dans les fonctions cognitives. Au fil des ans, l'Imagerie par Résonance Magnétique (IRM) a été de plus en plus utilisée pour mieux comprendre les anomalies structurelles et fonctionnelles inhérentes à ce trouble. Les progrès récents en apprentissage automatique et l'apparition de larges bases de données ouvrent maintenant la voie vers la découverte de biomarqueurs pour le diagnostic/ pronostic assisté par ordinateur. Compte tenu des limitations des algorithmes actuels à produire des signatures prédictives stables et interprétables, nous avons prolongé les approches classiques de régularisation avec des contraintes structurelles provenant de la structure spatiale du cerveau afin de: forcer la solution à adhérer aux hypothèses biologiques, produisant des solutions interprétables et plausibles. De telles contraintes structurelles ont été utilisées pour d'abord identifier une signature neuroanatomique de la schizophrénie et ensuite une signature fonctionnelle des hallucinations chez les patients atteints de schizophrénie. / Schizophrenia is a disabling chronic mental disorder characterized by various symptoms such as hallucinations, delusions as well as impairments in high-order cognitive functions. Over the years, Magnetic Resonance Imaging (MRI) has been increasingly used to gain insights on the structural and functional abnormalities inherent to the disorder. Recent progress in machine learning together with the availability of large datasets now pave the way to capture complex relationships to make inferences at an individual level in the perspective of computer-aided diagnosis/prognosis or biomarkers discovery. Given the limitations of state-of-the-art sparse algorithms to produce stable and interpretable predictive signatures, we have pushed forward the regularization approaches extending classical algorithms with structural constraints issued from the known biological structure (spatial structure of the brain) in order to force the solution to adhere to biological priors, producing more plausible interpretable solutions. Such structured sparsity constraints have been leveraged to identify first, a neuroanatomical signature of schizophrenia and second a neuroimaging functional signature of hallucinations in patients with schizophrenia. Additionally, we also extended the popular PCA (Principal Component Analysis) with spatial regularization to identify interpretable patterns of the neuroimaging variability in either functional or anatomical meshes of the cortical surface.

Over the last 20 years, there has been increasing research on the negative effects of discrimination on the mental and physical health of people of color. As mental health has an important relationship with the functional connectivity of brain networks, it is vital to further understand this. One way to measure functional brain connectivity is by observing the activity of the brain’s resting state networks (RSN) while a participant is at rest. Previous studies investigating connectivity have demonstrated a relationship between altered connectivity of RSNs and neuropsychiatric disorders, including Alzheimer’s disease, depression, and anxiety. The RSN of interest in this analysis is the salience network (SN). This network, anchored in the anterior insula and dorsal anterior cingulate cortex, is involved in the responses to “salient” stimuli that are infrequent in space or time, compete for an individual’s attention, and are surprising or emotionally engaging, such as an act of discrimination. The aim of this study was to use a seed-based correlation analysis to examine the relationship between perceived discrimination and the functional connectivity of the SN in black and white participants and to evaluate the differences in SN functional connectivity between black and white participants. Resting state functional connectivity was measured by using the functional magnetic resonance imaging (fMRI) data collected from 18 healthy older adults partaking in two different studies investigating aging, cognition, and the accompanying changes in neuroanatomy. The Analysis of Functional NeuroImages (AFNI) software was used to examine the correlations in activation in the primary nodes of the SN with activation in clusters in the other primary nodes. Perceived discrimination was measured using the Experiences of Discrimination Scale (EOD), a self-report measuring the frequency of instances of discrimination and the perceived reason behind the discrimination. Preliminary results from this analysis demonstrated that black participants, when compared to the white participants, demonstrated greater functional connectivity between the left and right insula and decreased functional connectivity between the right anterior cingulate cortex and the right insula. Black participants demonstrated a positive association between perceived overall discrimination and functional connectivity between the right and left insula and a negative association between perceived overall discrimination and functional connectivity between the right anterior cingulate cortex and the left insula. The white participants demonstrated a negative association between perceived overall discrimination and functional connectivity between the left and the right insula. Considering the inability for these results to survive correction for multiple comparisons, a larger sample size is necessary to obtain true statistical significance. Although existing research has implicated functional connectivity changes in the regions of the salience network in populations experiencing social exclusion, anxiety, and depression, further analyses are necessary to expand the limited research available regarding the effects of overall and race-based discrimination on the resting state functional connectivity of neural networks involved in emotional processing.

La qualité du sommeil se modifie avec l’âge, et les troubles du sommeil seraient associés au déclin cognitif et à un risque accru de développer une maladie d’Alzheimer (MA). Cependant, les mécanismes cérébraux sous-tendant cette association restent mal compris. L’objectif de cette thèse était de contribuer à une meilleure compréhension des corrélats cérébraux structuraux, fonctionnels et moléculaires des principales modifications objectives du sommeil dans le vieillissement, et d’explorer les liens avec les performances cognitives. Nos résultats montrent que les altérations des premiers cycles de sommeil et de l’activité à ondes lentes sont associées à un hypométabolisme, une hypoperfusion et/ou une diminution du volume de substance grise au niveau des aires fronto-cingulaires et hippocampiques. De plus, la présence d’un syndrome d’apnées obstructive du sommeil et l’altération de la microstructure du sommeil paradoxal étaient significativement associés à une augmentation de la charge amyloïde, respectivement au niveau du cortex cingulaire postérieur et du précunéus, ou de manière plus diffuse. En revanche, les liens avec la cognition restaient subtils voire absents, certaines modifications cérébrales étant asymptomatiques. Ainsi, le sommeil pourrait être un facteur de résilience face aux premières altérations neuropathologiques de la MA. Ces résultats supportent la nécessité de dépister et traiter les pathologies du sommeil dans le vieillissement, avant l’apparition des premiers déficits cognitifs, dans l’espoir de ralentir le déclin cognitif. / Sleep changes are a major feature of the ageing process, and sleep disturbances are increasingly recognized as a risk factor for cognitive decline and Alzheimer’s disease (AD). However, the brain mechanisms underlying this association are still unclear. The objective of this thesis was to deepen our understanding about brain structural, functional and molecular correlates of the main objective sleep changes in ageing, and to assess the potential links with cognitive performance. Our results demonstrate that the fragmentation of the first sleep cycles and the alteration of slow wave activity, are associated with reduced gray matter metabolism, perfusion and/or volume in fronto-cingulate and hippocampal areas. Moreover, sleep-disordered breathing and rapid eye movement sleep microstructure alterations were related to increased amyloid burden respectively in the posterior cingulate cortex and precuneus, or more widespread neocortical areas. However, associations with cognitive performance remained subtle or inexistent, suggesting early and asymptomatic associations between sleep and brain changes. Therefore, sleep may contribute to resilience processes and may help to cope with early neuropathological changes in AD. These results support the need to screen and treat sleep disturbances in older adults, before the onset of the first cognitive signs, in order to slow cognitive decline.

BACKGROUND: Idiopathic psychotic disorders, which include schizophrenia, schizoaffective and bipolar disorder with psychosis, are debilitating disorders affecting about 3% of the world’s population. Neurodevelopmental and neurodegenerative hypotheses have been proposed in psychosis, but the literature is mixed in regards to whether psychosis pathogenesis involves one or both of these processes. Since the visual system matures early in development, studying visual pathway abnormalities stratified by disease onset may further inform our understanding of psychosis pathogenesis. OBJECTIVE: The objective of this thesis is to determine whether disease onset, independent of illness duration, has a differential effect on visual cortical abnormalities in psychosis. We examined visual cortical measures for thickness, surface area, and volume using a pseudo-longitudinal study design of first episode psychosis-schizophrenia (FEP-SZ), FEP-non-schizophrenia (FEP-NSZ), early onset psychosis (EOP, <15 years of age), adult onset psychosis (OP, >15 and <30 years of age), and late onset psychosis (LOP, >30 years of age) groups. Relationships between visual cortical metrics and clinical or functional outcomes were performed. METHODS: The FEP sample (n= 102) included healthy controls (n= 44), FEP-SZ (n= 36), and FEP-NSZ (n= 22). The chronic psychosis data included healthy controls (n= 311) and psychosis probands (n=510). Psychosis probands was stratified by disease onset: EOP (n=213), OP (n=257), and LOP (n=40). Propensity matching was performed to match healthy controls (HC) according to age, sex and race. Linear regression models were performed comparing the means of visual cortical measures between groups. Partial Spearman correlations controlling for confounding factors were performed between visual cortical regions and clinical data. For FEP, clinical outcomes were assessed using Clinical Global Impression scale (CGI), Scale of Positive Symptoms (SAPS), and Scale of Negative Symptoms (SANS). For onset groups, clinical and functional outcomes were assessed using Positive and Negative Syndrome Scale (PANSS), Montgomery–Åsberg Depression Rating Scale (MADRS), Brief Assessment of Cognition (BACS), Wecshler Memory Scale (WMS) spatial span, anti-saccade error rates, dot expectancy pattern test, emotion recognition test, and Birchwood Social Functioning Scale (SFS). Multiple comparisons were performed using the Benjamini-Hochberg procedure. RESULTS: FEP-SZ was associated with smaller V1 and V2 areas, higher MT area and lower MT thickness compared to HCs. Lower MT thickness was associated with worse negative symptoms. Compared to HC, patients with chronic psychosis had lower V1, V2, and MT areas, as well as smaller MT thickness. V1 and V2 area and MT thickness were lower in the EOP group in comparison to matched HC. OP and LOP had a thinner MT region compared to matched HC. Of particular note, it was observed that EOP had greater area differences as compared to thickness reductions in the LOP group. Increased hallucinations and delusions were associated with a thinner MT region in the EOP group. CONCLUSION: When stratified by disease onset, FEP, EOP, OP, and LOP appear to have different pathogenic mechanisms and the severity of visual cortex neuroanatomical abnormalities are dependent on when the disease onset occurs. EOP occurs earlier in neurodevelopment resulting in greater severity in symptom and visual cortical measures as compared to OP. On the contrary, LOP appears to have a neurodegenerative mechanism which is evidenced by accelerated visual cortical thinning. / 2022-06-01T00:00:00Z

Many human neurological and psychiatric disorders are substantially heritable and there is growing inter-est in searching for genetic variants explaining variability in disease-induced alterations of brain anatomy and function, as measured using neuroimaging techniques. The standard analysis approach in genetic neuroimaging is the mass-univariate linear modeling approach, which is disadvantageous, since it cannot account for dependencies among collinear variables and has to be corrected for multiple testing. In con-trast, multivariate methods include combined information from multiple variants simultaneously into the analysis, and can therefore account for the correlation structure in both the neuroimaging and the genetic data. Partial Least Squares Analysis and Canonical Correlation Analysis are common multivariate ap-proaches and different variants have been established for genetic neuroimaging. However, a compre-hensive comparison with respect to data characteristics and strengths and weaknesses of these methods was missing to date. This thesis elaborately compared three multivariate techniques, Sparse Canonical Correlation Analysis (Sparse CCA), Bayesian Inter-Battery Factor Analysis (Bayesian IBFA) and Partial Least Squares Corre-lation (PLSC) in order to express a clear statement on which method in to choose for analysis in genetic neuroimaging. It was shown that for highly collinear neuroimaging data, Bayesian IBFA could not be recommended, since additional post-processing steps were required to differentiate between causal and non-informative components. In contrast, Sparse CCA and PLSC were suitable for genetic neuroimaging data. Among the two, the use of Sparse CCA was recommended in situations with relatively low-dimensional neuroimaging and genetic data, since its predictive power was higher when data dimension-ality was below 400 times sample size. For higher dimensionalities, the predictive power of PLSC ex-ceeded that of Sparse CCA. Thus, for multivariate modeling of high-dimensional neuroimaging-genetics-associations, a preference for the usage of PLSC was indicated. The remainder of this thesis dealt with the improvement of the computational efficiency of multivariate statistics in genetic neuroimaging, since it can be expected that there will be a growth in cost- and time-efficient DNA sequencing as well as neuroimaging techniques in the coming years, which will result in excessively long computation times due to increasing data dimensionality. To accommodate this large number of variables, a new and computational efficient statistical approach named PLSC-RP was pro-posed, which incorporates a method for dimensionality reduction named Random projection (RP) into traditional PLSC in order to represent the originally high-dimensional data in lower dimensional spaces. Subsequently, PLSC is used for multivariate analysis of compressed data sets. Finally, the results are transformed back to the original spaces to enable the interpretation of original variables. It was demon-strated that the usage of PLSC-RP reduced computation times from hours to seconds compared to its state-of-the-art counterpart PLSC. Nonetheless, the accuracy of the results was not impaired, since the results of PLSC-RP and PLSC were statistically equivalent. Furthermore, PLSC-RP could be used for inte-grative analysis of data sets containing high-dimensional neuroimaging data, high-dimensional genetic data or both, and was therefore shown to be independent of the statistical data type. Thus, PLSC-RP opens up a wide range of possible applications.

Obesity is an important global health factor due to associated comorbidities and its pervasive occurrence. In my thesis, I aimed to contribute to a better understanding of the mechanisms that underlie obesity development as well as its adverse consequences in the brain. Both studies analyzed subsets of the LIFE-Adult study, a deeply-phenotyped, cross-sectional cohort study based on the general population of Leipzig. In the first study, I investigated the association of compulsive eating, a specific type of obesity-associated eating behavior, body mass index (BMI) and gray matter structure. Compulsive eating behavior, was selectively associated with cortical thickness of the right lateral orbitofrontal cortex, a region important for impulse control. Higher BMI was related to widespread reductions of cortical thickness. Orbitofrontal cortex structure may therefore predict compulsive eating behavior, while higher BMI is associated with decreased cortical thickness even in young and middle-aged adults. In the second study, I focused on the link between BMI, functional brain networks and cognitive function in older adults. Here, higher BMI was related to reduced connectivity of the default mode network, a network of brain regions known as a biomarker of aging and dementia. This finding demonstrates that functional connectivity may be an biomarker allowing to detect obesity-associated brain changes at an early stage. Taken together, these findings support the view of obesity as a risk factor for brain health. Yet, more longitudinal studies are needed to confirm this and reveal the underlying mechanisms.:List of Abbreviations i Table of Figures ii 1. Introduction 1 1.1 Central regulation of food intake 1 1.2 Characteristics of addictive-like eating behavior 3 1.3 Obesity as a risk factor for brain damage and cognitive decline 5 1.4 Assessment of brain structure and function with magnetic resonance imaging 8 1.4.1 A very short introduction to MRI 8 1.4.2 Structural MRI: Measuring the size and shape of the brain 8 1.4.3 Resting state functional MRI: Investigating the intrinsic brain architecture 10 2. Published studies 12 2.1 Neuroanatomical correlates of food addiction symptoms and body mass index in the general population 12 2.2 Higher body mass index is associated with reduced posterior default mode connectivity in older adults 24 3. Summary 38 References 43 Appendix 54 Supplementary Information for “Neuroanatomical correlates of food addiction symptoms and body mass index in the general population” 54 Author contributions to the publication “Neuroanatomical correlates of food addiction and body mass index in the general population” 59 Author contributions to the publication “Higher body mass index is associated with reduced posterior default mode connectivity in older adults” 60 Declaration of Authenticity 61 Curriculum Vitae 62 List of publications 63 List of conference contributions 65 Acknowledgments 66

The human visual system is tasked with transforming variations in light within our environment into a coherent percept, typically described using properties such as luminance and contrast. The experiments described in this dissertation examine how the human visual cortex responds to each of these stimulus properties at the population-level, and explores the degree to which contrast adaptation can alter these response properties. The first set of experiments (Chapter 2) demonstrate how saturating sigmoidal contrast response functions can be captured using human fMRI by leveraging sustained contrast adaptation to reduce the heterogeneity of response profiles across neural populations. The results obtained using this methodology have the potential to rectify the qualitatively different findings reported across visual neuroscience, when comparing electrophysiological and population-based neuroimaging measures. The second set of experiments (Chapter 3) demonstrate how under certain conditions a well-established visuocortical response property, contrast response, can also reflect luminance encoding, challenging the idea that luminance information plays no significant role in supporting visual perception. Specifically, these results show that the mean luminance information of a visual signal persists within visuocortical representations, even after controlling for pupillary dynamics, and potentially reflects an inherent imbalance of excitatory and inhibitory components. The final set of experiments (Chapter 4) examine how the time course of population activity during initial periods of adaptation differs across seemingly slightly different adapter conditions. The degree to which stimulus adapter orientation bias (radial vs. concentric orientation) or stimulus adapter luminance (2409 cd/m2 vs. 757.3 cd/m2) can alter adaptation time course dynamics is examined in detail, as well as investigating the prevalence of any retinotopic bias. In an effort to coalesce the findings across all three chapters, the shape and efficacy of the initial adaptation time course is ultimately compared against the contrast and luminance response function parameters reported in previous chapters. As a whole, the findings reported in this dissertation challenge some common assumptions about how the early human visual cortex adjusts and responds to the environment, provide methodological tools and stimulus design caveats vision neuroscientists will need to consider, and play a significant role in cortical models of vision.

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