My doctoral work has addressed the anatomy-function relationship by illustrating 1) the unique topology of brain anatomy for biologically plausible functional connectome to exist, 2) a higher vulnerability of neuroanatomy against the genetic control for a brain hub region, whereas lower genetic influence on its functional fingerprints, and 3) a global functional plasticity following a local injury beyond its anatomical boundaries. All together, the work here has demonstrated the interplay between brains structure and function, as well as the impact of familial relatedness (heritability) on these measures.

<p>The human brain is possibly the most complicated structure in the known world. It contains 100 billion neurons, each making contact with 1,000 to 10,000 other neurons. The neurons themselves have hundreds of excitatory and inhibitory neurotransmitters / receptors with which to use to activate or de-activate other neighbouring neurons. Ultimately these neurons are organized into locally defined functional neural networks, which in turn connect with other neural networks to create non-localized highly-complex brain circuits. These brain circuits are responsible for the higher order functions such as perception, emotions, learning, language and conscious thought. In healthy brain states, these networks and circuits are communicating and signalling appropriately. With mental illness, or in an intoxicated brain state, however, this network and circuit functioning can become disrupted: either in a very specific manner, or in a generalized form. Alcohol (in the form of ethanol) is one of the oldest and most widely used psychoactive drugs in the world. In recreational doses, it can have drastic effects on how a person thinks and behaves. Small doses will lead to feelings of euphoria, increased sociability, and impaired judgement, while larger doses will lead to impaired memory and comprehension, and at extreme doses will lead from confusion and stupor to coma and death. The current understanding of alcohol’s effects on the brain is that it acts in a very specific way on a variety of brain enzymes and receptors. This in turn affects specific brain circuitries, making alcohol intoxication a promising model of pharmacological neural network and brain circuit modulation. Obsessive-compulsive disorder (OCD) is a major psychiatric disorder that affects many (lifetime prevalence of between 1-2.5%) and can cause significant disability and impairment in one’s life. The onset of OCD is usually during childhood and adolescence, with more than 50% of adults with OCD reporting its onset occurring before the age of 18. The etiological origin of OCD lies ultimately in specific neuropathological processes, with current theories postulating either a neurochemical model that emphasizes the dysfunction of the serotonergic and possibly dopaminergic systems; or a neuroanatomical model that emphasizes the dysfunction of a specific corticostriatal pathway; or both. Magnetic resonance imaging (MRI), with its safe, non-invasive ability to image both anatomy, brain function and brain metabolism, provides a unique tool with which to probe brain circuit changes, such as in healthy subjects under a pharmacological challenge (ethyl alcohol), or in medication naïve children with OCD. In the ethanol intoxicated brain, functional MRI can be used to probe specific resting state networks (RSN; functionally connected networks that exhibit low frequency blood oxygen level dependent (BOLD) fluctuations), measure brain BOLD time-signal complexity using fractal analysis, and to correlate these findings with measured alcohol levels in the brain in vivo using magnetic resonance spectroscopy (MRS). In subjects with OCD, functional MRI can be used to once again probe RSNs, and as well, MRS can be used to look at potential differences in brain metabolites in axonal projections that connect OCD relevant brain circuits. It is the purpose of this thesis to show how brain circuits and neural networks in atypical brain states (such as intoxication or mental illness) can be probed and better understood using advanced MRI techniques, such as resting state fMRI. Over a series of three studies, one involving MRI scans of healthy male adults before and after drinking a substantial amount of ethanol, and two others comparing RSNs in children with OCD versus healthy matched controls, and MRS differences in prefrontal white matter between the same two groups, we examined brain circuit changes using advanced MRI techniques. In the alcohol study, evidence was found of brain signal complexity decreasing after 60min and 90min post alcohol consumption. Simultaneously, a mixture of increased and decreased functional connectivity in the default mode network was found after 60min post alcohol consumption, which became general decreased functional connectivity after 90min post alcohol consumption. These changes took place while alcohol in the brain increased substantially after 60min. These findings may help provide insight into the neurofunctional underpinnings of the cognitive and behavioural changes observed during acute alcohol intoxication. In the first study on medication naïve children with OCD, we observed increased connectivity (OCD>control) in the right section of Brodmann area 43 of the auditory cortex, as well as decreased connectivity in the right section of Brodmann area 8 and Brodmann area 40 in the cingulate network. In the second study looking at medication naïve children with OCD, we observed higher levels of N-acetyl-aspartate (NAA) and choline in the right prefrontal white matter (RPFWM) in children with OCD compared to healthy controls, as well as a positive correlation of creatine, NAA, and myo-inositol levels in the RPFWM and OCD symptom severity. Both studies lend further support to the cortico-striatal-thalamiccortical hypothesis of OCD, while the first study further implicates other regions of the brain outside of the CSTC. Both of these OCD studies further demonstrated the differences in brain circuits of neuropsychiatric disorders between children and adults.</p> / Doctor of Philosophy (PhD)

Neurobiological theories of bipolar spectrum disorder (BSD) propose that the emotional dysregulation characteristic of BSD stems from disrupted prefrontal control over subcortical limbic structures (Strakowski et al., 2012; Depue & Iacono, 1989). However, existing neuroimaging research on functional connectivity between frontal and limbic brain regions remains inconclusive, and is unable to adequately characterize global functional network dynamics. Graph theoretical analysis provides a framework for understanding the local and global connections of the brain and comparing these connections between groups (Sporns et al., 2004). The purpose of this study was to investigate resting state functional connectivity in individuals at low and high risk for BSD based on moderate versus high reward sensitivity, both with and without a BSD diagnosis, using graph theoretical network analysis. Results demonstrated decreased connectivity in a cognitive control region (dorsolateral prefrontal cortex), but increased connectivity of a brain region involved in the detection and processing of reward (bilateral orbitofrontal cortex), among participants at high risk for BSD. Participants with BSD showed increased inter-module connectivity of the dorsal anterior cingulate cortex (ACC). Reward sensitivity was associated with decreased global and local efficiency, and interacted with BSD risk group status to predict inter-module connectivity. Findings are discussed in relation to neurobiological theories of BSD. / Psychology

The default mode network (DMN) is active in the absence of task demands and during self-referential thought. Considerable evidence suggests that the DMN is involved in normative aspects of social cognition, and as such, disruptions in the function of DMN would be expected in disorders characterized by alterations in social function. Consistent with this notion, work in autism spectrum disorder (ASD) and social anxiety disorder (SAD) has demonstrated altered activation of several core regions of the DMN relative to neurotypical controls. Despite emergent evidence for alterations within the same brain systems in SAD and ASD, as well as a behavioral continuum of social impairments, no study to date has examined what is unique and what is common to the brain systems within these disorders. Therefore, the primary aim of the current study is to precisely characterize the topology of neural connectivity within the DMN in SAD and ASD and neurotypical controls in order to test the following hypotheses through functional and structural connectivity analyses of the DMN. Our analyses demonstrate increased coavtivation of the dorsomedial prefrontal cortex in ASD and SAD compared to controls, as well as over and under connectivity in structural brain connectivity in ASD. These results may reflect general deficits in social function at rest, and disorder specific alterations in structural connectivity in ASD. / Master of Science

The objective of this study was to expand on a method previously established in the lab for predicting subcortical structures using functional magnetic resonance imaging (fMRI) data restricted to the cortical surface. Our goal is to enhance the utility of low cost, portable imaging modalities, such as functional near infrared spectroscopy (fNIRS), which is limited in signal penetration depth. Previous work in the lab successfully employed functional connectivity to predict ten resting state networks and six anatomically de fined structures from the outer 10 mm layer of cortex using resting state fMRI data. The novelty of this study was two-fold: we chose to predict the functionally de fined region fusiform face area (FFA), and we utilized the functional connectivity of both resting state and task activation. Right FFA was identi ed for 27 subjects using a general linear model of a functional localizer tasks, and the average time series were extracted from right FFA and used as training and testing labels in support vector regression (SVR) models. Both resting state and task data decoded activity in right FFA above chance, both within and between run types. Our method is not specific to resting state, potentially broadening the scope of research questions depth-limited techniques can address. We observed a similarity in our accuracy cross-validation to previous work in the lab. We characterized this relationship between prediction accuracy and spatial signal-to-noise (SNR). We found that this relationship varied between resting state and task, as well as the functionality of features included in SVR modeling. / Master of Science / We used functional magnetic resonance imaging (fMRI) to predict activity in a deep brain region based on activity along the brain surface. This would increase the type of brain function a person could study using alternative methods that are less costly and easier to use, but can only detect signals along the surface. We were able to use this method to predict the fusiform face are, a region in the brain that responds more strongly to face images than other types of images. We also found a relationship between the quality of spatial information in the brain and the accuracy of predictions. This relationship differed depending on the types of brain regions were used to build the models, as well as whether the subjects were performing a task or rest scan.

Resting state functional magnetic resonance imaging (fMRI) is currently at the forefront of research on cognition and the brain’s large-scale organization. Patterns of hemodynamic activity that it records have been strongly linked to certain behaviors and cognitive pathologies. These signals are widely assumed to reflect local neuronal activity but our understanding of the exact relationship between them remains incomplete. Researchers often address this using multimodal approaches, pairing fMRI signals with known measures of neuronal activity such as electroencephalography (EEG). It has long been thought that infraslow (< 0.1 Hz) fMRI signals, which have become so important to the study of brain function, might have a direct electrophysiological counterpart. If true, EEG could be positioned as a low-cost alternative to fMRI when fMRI is impractical and therefore could also become much more influential in the study of functional brain networks. Previous works have produced indirect support for the fMRI-EEG relationship, but until recently the hypothesized link between them had not been tested in resting humans. The objective of this study was to investigate and characterize their relationship by simultaneously recording infraslow fMRI and EEG signals in resting human adults. We present evidence strongly supporting their link by demonstrating significant stationary and dynamic correlations between the two signal types. Moreover, functional brain networks appear to be a fundamental unit of this coupling. We conclude that infraslow electrophysiology is likely playing an important role in the dynamic configuration of the resting state brain networks that are well-known to fMRI research. Our results provide new insights into the neuronal underpinnings of hemodynamic activity and a foundational point on which the use of infraslow EEG in functional connectivity studies can be based.

Diffusion weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) perfusion-weighted imaging (PWI) are commonly employed in clinical practice and in research to give pathophysiological information for patients with acute ischemic stroke. DWI is thought to roughly reflect the severely damaged infarct core, while DSC-PWI reflects the area of hypoperfusion. The volumetric difference between DWI and DSC-PWI is termed the PWI/DWI-mismatch, and has been suggested as an MRI surrogate of the ischemic penumbra. However, due to the application of a contrast agent, which has potentially severe side-effects (e.g., nephrogenic systemic fibrosis), the DSC-PWI precludes repetitive examinations for monitoring purposes. New approaches are being sought to overcome this shortcoming. BOLD (blood oxygen-level dependent) signal can reflect the metabolism of blood oxygen in the brain and hemodynamics can be assessed with resting-state fMRI. The aim of this thesis was to use resting-state fMRI as a new approach to give similar information as DSC-PWI. This thesis comprises two studies: In the first study (see Chapter 2), two resting-state fMRI methods, local methods which compare low frequency amplitudes between two hemispheres and a k-means clustering approach, were applied to investigate the functional damage of patients with acute ischemic stroke both in the time domain and frequency domain. We found that the lesion areas had lower amplitudes than contralateral homotopic healthy tissues. We also differentiated the lesion areas from healthy tissues using a k-means clustering approach. In the second study (see Chapter 3), time-shift analysis (TSA), which assesses time delays of the spontaneous low frequency fluctuations of the resting-state BOLD signal, was applied to give similar pathophysiological information as DSC-PWI in the acute phase of stroke. We found that areas which showed a pronounced time delay to the respective mean time course were very similar to the hypoperfusion area. In summary, we suggest that the resting-state fMRI methods, especially the time-shift analysis (TSA), may provide comparable information to DSC-PWI and thus serve as a useful diagnostic tool for stroke MRI without the need for the application of a contrast agent.

La complexité de la connaissance humaine est révèlée dans l'organisation spatiale et temporelle de la dynamique du cerveau. Nous pouvons connaître cette organisation grâce à l'analyse des signaux dépendant du niveau d'oxygène sanguin (BOLD), lesquels sont obtenus par l'imagerie par résonance magnétique fonctionnelle (IRMf). Nous observons des dépendances statistiques entre les régions du cerveau dans les données BOLD. Ce phénomène s' appelle connectivité fonctionnelle (CF). Des modèles computationnels sont développés pour reproduire la connectivité fonctionnelle (CF). Comme les études expérimentales précédantes, ces modèles assument que la CF est stationnaire, c'est-à-dire la moyenne et la covariance des séries temporelles BOLD utilisées par la CF sont constantes au fil du temps. Cependant, des nouvelles études expérimentales concernées par la dynamique de la CF à différentes échelles montrent que la CF change dans le temps. Cette caractéristique n'a pas été reproduite dans ces modèles computationnels précédants. Ici on a augmenté la non-linéarité de la dynamique locale dans un modèle computationnel à grande échelle. Ce modèle peut reproduire la grande variabilité de la CF observée dans les études expérimentales. / The complexity of human cognition is revealed in the spatio-temporal organization of brain dynamics. We can gain insight into this organization through the analysis of blood oxygenation-level dependent (BOLD) signals, which are obtained from functional magnetic resonance imaging (fMRI). In BOLD data we can observe statistical dependencies between brain regions. This phenomenon is known as functional connectivity (FC). Computational models are being developed to reproduce the FC of the brain. As in previous empirical studies, these models assume that FC is stationary, i.e. the mean and the covariance of the BOLD time series used for the FC are constant over time. Nevertheless, recent empirical studies focusing on the dynamics of FC at different time scales show that FC is variable in time. This feature is not reproduced in the simulated data generated by some previous computational models. Here we have enhanced the non-linearity of local dynamics in a large-scale computational model. By enhancing this non-linearity, our model is able to reproduce the variability of the FC found in empirical data.

Orientador: Prof. Dr. João Ricardo Sato / The brain is a complex system organized in structurally segregated and functionally specialized regions. The brain areas are composed of neuronal networks interconnected by axonal pathways that integrate through correlated neural activity. Recent studies on neural connectivity using graph theoretical analysis have revealed that brain networks interact through densely connected regions with high topological value called hubs. Previous studies of Default Mode Network (DMN), one of the most important resting-state networks, have improved the understanding of the intrinsic neuronal activity and the dynamics of the human brain. Spontaneous brain activity and Resting-State Functional Connectivity (RSFC) patterns of Resting-State Network (RSN) are essential for the comprehension of the brain function. Neuroimaging techniques such as functional Near Infrared Spectroscopy (fNIRS) make these studies possible. Thus, the main objective of this study was to investigate the RSFC using Eigenvector Centrality (EVC) measure of graph theory in fNIRS data. This work has demonstrated the effectiveness of the graph analysis for detection of hubs and mcommunities, and identified brain regions associated with rich-club, that integrates highly interconnected hubs and plays a central role in the flow and integration of Information throughout the brain. One can also conclude from the RSFC analysis the existence of functional hubs associated with DMN. / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2017. / O cérebro é um sistema complexo organizado em regiões segregadas estruturalmente e especializadas funcionalmente que são compostas por redes neuronais interconectadas por vias axonais que se integram através de atividade neural correlacionada. Estudos recentes sobre conectividade neural usando teoria de grafos revelaram que as redes cerebrais interagem através de regiões densamente conectadas e com alto valor topológico denominadas hubs. Dentre as redes existentes destaca-se, por sua contribuição para a melhor compreensão do funcionamento do cérebro humano, a rede de modo padrão (Default Mode Network, DMN). A atividade espontânea do cérebro e os padrões de conectividade funcional (Resting-State Functional Connectivity, RSFC) das redes cerebrais na condição de repouso (Resting-State Network, RSN) também se tornam essenciais nos estudos que visam compreender a função desse órgão, estudos esses possibilitados graças às técnicas de neuroimagem destacando-se a espectroscopia funcional no infravermelho próximo (functional Near Infrared Spectroscopy, fNIRS). Assim, o objetivo principal deste estudo foi investigar a RSFC usando a medida de centralidade do autovetor (Eigenvector Centrality, EVC), técnica pertencente à teoria de grafos, em dados de fNIRS. Este estudo pode demonstrar a eficácia da metodologia empregada para analisar a RSFC além de revelar a existência de um núcleo estrutural, denominado hub complex, densamente conectado (rich-club), que integra hubs altamente interligados e desempenha papel central no fluxo e integração da informação ao longo do cérebro. Pode-se também concluir a partir da análise da RSFC a existência de hubs funcionais associados à DMN.

Major depressive disorder (MDD) will soon represent the most costly and debilitating disorder in the world. Yet, a clear model of the mechanisms underlying MDD remains elusive. This lack of clarity obscures efforts to prevent and treat MDD more effectively. This dissertation seeks to advance an integrated model of the mechanisms underlying MDD across cognitive, neural, and genetic levels of analysis. Building on the empirical foundation of cognitive theories of MDD, the dissertation includes three studies that help address questions about the cognitive mechanisms underlying depression vulnerability and maintenance. Specifically, the three studies focus on identifying 1) how elaborative processing biases, including attentional biases and rumination, give rise to specific symptoms of MDD and 2) elucidating biological mechanisms that may give rise to these biases. Together, these studies help advance an integrated model of MDD that, ultimately, may help facilitate the prevention and treatment of this costly and debilitating disorder. / text