Childhood maltreatment can have detrimental consequences on individual well-being and cognitive functioning. One type of childhood maltreatment that remains stigmatized and under-researched among men is child sexual abuse (CSA). Research examining the neurophysiological consequences of CSA in males is limited even further. This dissertation presents three original research articles which provide preliminary evidence of the lasting neurophysiological impact of CSA in men. We recruited all male participants, of those who experienced CSA, some had PTSD (CSA+PTSD) others did not have PTSD (CSA-PTSD) allowing for the examination of differences in males with histories of CSA (but no PTSD) versus those who have CSA histories and PTSD. We also recruited control males with no CSA histories nor PTSD. Three functional MRI tasks and one resting state functional scan were obtained. The letter n-back, and an emotional picture n-back task were used in the first study as a measure of working memory and emotional processing. The first study highlights the lasting impact CSA can have on men, regarding brain activity during working memory, and working memory when negative emotional stimuli are involved. The second study examined how negative/traumatic memories are re-experienced. Results from the second study demonstrate that CSA impacts the neurophysiology of autobiographical memory for traumatic experiences. In the final study, resting state functional connectivity was examined within the default mode, salience and limbic networks, and differences in functional connectivity within the networks were observed. Together, these findings highlight the long-term neural impact of CSA and can validate the experience of men who have lived through CSA. They can also guide researchers and clinicians to potential avenues of support for the well-being of these men. These studies highlight the need for more research with men who have experienced CSA so we can fully understand their altered neurophysiological responses, and how this knowledge can be used to support their mental health and continued wellness throughout their lives.

BACKGROUND: Cognitive deficits are a core component of schizophrenia and among the strongest determinants of functional disability in psychotic illnesses. In particular, impairment in information processing speed has been demonstrated to be among the most significant in patients. Poor processing speed not only frequently occurs prior to psychosis onset during the prodromal or clinical high risk phase of psychotic illness, it has also been found to be a strong predictor of conversion to psychosis. However, the neurobiological basis of impaired processing speed in the clinical high risk population is not well understood. Functional connectivity during resting state fMRI provides useful insights into the organization and communication between brain regions that may elucidate the brain circuit basis underlying processing speed. OBJECTIVE: To identify the strongest link between brain functional connectivity and a measure of information processing speed in individuals at clinical high risk for conversion to psychosis by utilizing a data-driven analysis. METHODS: Cognitive and resting state fMRI data were collected from 198 clinical high risk participants and 123 neurotypical controls in the second phase of the North American Prodromal Longitudinal Study. Processing speed was measured by the Brief Assessment of Cognition in Schizophrenia Symbol Coding task. A multivariate pattern analysis was used to identify, at the individual voxel level, how functional connectivity correlates with information processing speed. RESULTS: Clinical high risk participants demonstrated significantly reduced processing speed, relative to neurotypical controls. Similarly, at risk patients who later converted to psychosis (n=17) also showed poorer performance on the BACS Symbol Coding task compared to non-converters. The strongest whole-brain link between connectivity and processing speed within the clinical high risk population was the bilateral amygdala. Specifically, connectivity between the bilateral amygdala and a functional brain network known as the salience network correlated with processing speed. CONCLUSIONS: Functional connectivity between the bilateral amygdala and the salience network was linked to individual variation in processing speed in the clinical high risk population. This affirmed a growing literature that implicates amygdala involvement in cognitive function and provides a potential biomarker for psychosis risk prior to diagnosis.

La compréhension des effets cérébraux sous-tendant l’impact de la stimulation magnétique transcrânienne répétée (rTMS) est un a priori nécessaire à la connaissance de la prise en charge thérapeutique des patients bénéficiant de ces traitements. A posteriori, elle permet en plus de comprendre les processus physiopathologiques responsables des symptômes cliniques propres aux troubles mentaux. Nous nous sommes ici intéressés aux effets de la rTMS sur le cerveau des patients souffrant de schizophrénie (SZ), au travers des deux principes fondamentaux du fonctionnement cérébral que sont la ségrégation et l’intégration fonctionnelle. En premier lieu, nous avons analysé l’effet de la rTMS visant le cortex préfrontal médian (CPM) sur le principe de ségrégation fonctionnelle chez des SZ avec trouble de la cognition sociale. Pour ce faire nous avons développé un paradigme d’imagerie par résonance magnétique fonctionnelle (IRMf) écologique et novateur de cognition sociale. Un prérequis aux études longitudinales en IRMf est la reproductibilité du signal d’activation. Nous avons alors démontré la fiabilité de notre paradigme entre deux sessions, puis à l’aide de ce paradigme, nous avons étudié l’effet de la rTMS sur la ségrégation à partir de la variation du signal BOLD et des taux de N-AcetylAspartate et de glutamate. Puis nous avons étudié l’impact de la rTMS ciblant le sillon temporal supérieur gauche (STSg) sur les hallucinations auditivo-verbales (AVH) en termes de connectivité fonctionnelle statique (intégration fonctionnelle). Nous avons ainsi mis en évidence l’effet bénéfique de la rTMS sur le trouble de cognition sociale et sur les AVH. En outre, quand la rTMS cible le STSg, il ne semble pas avoir d’effet sur la connectivité fonctionnelle statique du réseau cérébral du langage observé. Cependant, focalisé au niveau du CPM, elle permettrait d’augmenter la concentration de N-acétylaspartate des SZ. L’absence d’effet de la rTMS illustrerait plutôt des profils d’organisation cérébrale différents des SZ, et ce par des variabilités interindividuelles, suggérant qu’il serait à l’avenir bénéfique de déterminer les caractéristiques optimales de la stimulation sur une base individuelle afin de mieux moduler les processus anormaux du cerveau dans les schizophrénies. / The understanding of the brain effects underlying the impact of repeated transcranial magnetic stimulation (rTMS) is a necessary a priori necessary concerning patients treatments. A posteriori, it also helps to understand the pathophysiological processes responsible for the clinical symptoms of mental disorders. Hither, we are interested in the effects of rTMS on the brain of patients with schizophrenia (SZ), through the two fundamental principles of cerebral functioning: segregation and functional integration. First, we have analyzed the effect of rTMS on the medial prefrontal cortex (MPFC) according to functional segregation in SZ with social cognition disorder. To do this we have developed an ecological and innovative social cognition paradigm for functional magnetic resonance imaging (fMRI). A prerequisite for longitudinal studies in fMRI is the reproducibility of the activation signal, we have then demonstrated the reliability of our paradigm between two sessions. Using this paradigm, we have studied the effect of rTMS on segregation from the variation of the BOLD signal and the levels of N-Acetyl Aspartate and glutamate. Then, we studied the impact of rTMS targeting the left temporal sulcus (STS) on auditory-verbal hallucinations (AVH) in terms of functional connectivity (functional integration). We have thus demonstrated the beneficial effect of rTMS on social cognition disorder and on AVH. Moreover, when the rTMS targets the STS, it does not seem to have an effect on the static functional connectivity within the listening language network. However, focused on the MPFC, it would increase the N-acetylaspartate concentration of SZ. The absence of the effect of the rTMS would rather illustrate different brain organization patterns of the SZ, due to inter-individual variability, suggesting that it would be in the future beneficial to determine optimal characteristics of stimulation on an individual basis in order to best modulate abnormal processes of the brain in schizophrenias.

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgements

Intrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modi cation. Most analyses of functional magnetic resonance imaging (fMRI) data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. Bayesian Intrinsic Connectivity Network (BICNet) captures the ICN structure with We propose a BICNet model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling resting-state fMRI (rfMRI) and task-related fMRI (tfMRI); (3) Compared to independent component analysis (ICA)-based methods, it can quantify the difference of ICNs amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the Human Connectome Project (HCP) and identify several ICNs related to distinct language processing functions.

Indiana University-Purdue University Indianapolis (IUPUI) / A popular non-invasive brain activity measurement method is based on the functional magnetic resonance imaging (fMRI). Such data are frequently used to study functional connectivity (FC) defined as statistical association among two or more anatomically distinct fMRI signals (Friston, 1994). FC has emerged in recent years as a valuable tool for providing a deeper understanding of neurodegenerative diseases and neuropsychiatric disorders, such as Alzheimer's disease and autism. Information about complex association structure in high-dimensional fMRI data is often discarded by a calculating an average across complex spatiotemporal processes without providing an uncertainty measure around it. First, we propose a non-parametric approach to estimate the uncertainty of dynamic FC (dFC) estimates. Our method is based on three components: an extension of a boot strapping method for multivariate time series, recently introduced by Jentsch and Politis (2015); sliding window correlation estimation; and kernel smoothing. Second, we propose a two-step approach to analyze and summarize dFC estimates from a task-based fMRI study of social-to-heavy alcohol drinkers during stimulation with avors. In the first step, we apply our method from the first paper to estimate dFC for each region subject combination. In the second step, we use semiparametric additive mixed models to account for complex correlation structure and model dFC on a population level following the study's experimental design. Third, we propose to utilize the estimated dFC to study the system's modularity defined as the mutually exclusive division of brain regions into blocks with intra-connectivity greater than the one obtained by chance. As a result, we obtain brain partition suggesting the existence of common functionally-based brain organization. The main contribution of our work stems from the combination of the methods from the fields of statistics, machine learning and network theory to provide statistical tools for studying brain connectivity from a holistic, multi-disciplinary perspective.

Indiana University-Purdue University Indianapolis (IUPUI) / The social cognitive ability to identify another’s internal state and social affective ability to share another’s emotional experience, known as empathy, are integral to healthy social functioning. During tasks, neural systems active when adolescents empathize include cognitive (medial prefrontal cortex and posterior cingulate cortex with the dorsolateral prefrontal cortex) and affective (anterior insula and anterior cingulate cortex) regions that are consistent with the adult task-based literature implicating the default mode, salience, and frontoparietal networks. However, task-based studies are limited to examining neural regions probed by the task; thus, do not capture broader patterns of information processing associated with complex processes, such as empathy. Methods of functional connectivity capture broader patterns of information processing at the level of network connectivity. Although it has clear advantages in identifying neural vulnerabilities to disorder, functional connectivity has yet to be used in adolescent investigations of empathy. Via parent- and self-report, deficits in either cognitive or affective processes central to empathy associate with the most widely agreed on classifications of behavioral disorders in adolescents – transdiagnostic symptoms of internalizing and externalizing. However, this evidence relies exclusively on self-report measures and research has yet to examine the neural connectivity underlying transdiagnostic symptoms in relation to cognitive and affective empathy. What has yet to be known is (1) how the social cognitive and affective processes of empathy are functionally connected across a heterogeneous sample of adolescents and (2) the association of cognitive, affective, and imbalanced empathy with transdiagnostic symptoms. Addressing these gaps in knowledge is an important incremental step for specifying vulnerabilities not fully captured via subjective report alone. This information can be used to improve prevention and intervention strategies. The present study will examine the functional connectivity of neural networks underlying empathy in early to mid-adolescents and their association with transdiagnostic symptoms.

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