The default network (DN), and specifically its sub-networks default network A (DN A) and default network B (DN B), has been strongly implicated in social cognition. This study examined its role in predicting social behavior, and also differences that may exist across diagnostic groups that may explain discrepancies in social cognition and behavior. One of the popular methods of study is functional connectivity, or analyzing correlated activity in the brain. Autism Spectrum Disorder (ASD) is a disorder characterized by social impairment and abnormal social behavior. To date, much of the functional connectivity research in ASD has focused on global connectivity, or specific but large areas of the brain. This study adds to the body of that research in attempting to understand both global functional connectivity and the functional connectivity of specific networks (DN A and DN B) that are involved in social cognition and thus implicated in ASD. A sample of 75 individuals with ASD, 85 neurotypical individuals, and 505 individuals with varying other diagnoses was examined to determine the role of global functional connectivity and the role of DN A and DN B in social cognition by the predictive ability of brain features to determine behavioral outcomes. This analysis also aimed to determine if there are group differences in these same brain features. The features we examined included functional connectivity, or the comparison of timeseries of regions of interest, network surface area, and network similarity. This study found that there was no discernible difference across diagnostic group in global or network-specific functional connectivity for DN A. The majority of features for DN B did not differ across diagnostic group, but there was one connection that was significantly different between the autism group and the others. There was no global predictive ability of functional connectivity and brain topology for social cognition measures, nor was there predictive ability for DN A features. DN B features, however, were predictive of social cognition in the autism group, but not in the control group or the other diagnostic groups examined. This study adds to the current body of research by supporting findings already reported by others, and by adding new findings about the role of DN B in social cognition in autism.

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While the neural underpinnings of concrete semantic knowledge have been studied extensively, abstract conceptual knowledge remains enigmatic. In the first experiment, participants underwent a functional MRI scan while thinking deeply about abstract and concrete words. A functional connectivity analysis revealed a cortical network, including portions of the left temporal parietal cortex (TPC), that showed coordinated activity specific to abstract word processing. Alternatively, concrete words led to cooperation of a network in the inferior, middle and polar temporal lobes. In a second experiment, participants with focal lesions in the left TPC, as well as matched control participants, were tested on a spoken-to-written word matching task, in which they were asked to select either an abstract or concrete word, from an array of words that were related or unrelated to the target. The results revealed an interaction between concreteness and relatedness. Participants with lesions did not have an overall deficit for abstract words, relative to concrete words, in this task. However, their accuracy was significantly lower for abstract words in related arrays, compared to words in unrelated arrays. These results confirm that the TPC plays an important role in abstract concept representation, and that it is part of a larger network of functionally cooperative regions needed for abstract word processing. These results also provide converging evidence that abstract concepts rely on neural networks that are independent from those involved in concrete concepts, and have important implications for existing accounts of the neural representation of semantic memory. / Psychology

Resting-state functional MRI(rs-fMRI) represents a powerful means to assess brain functional connectivity in healthy subjects and in neuropsychiatric patients. Aberrant functional connectivity has been observed in subjects affected by Alcohol Use Disorders (AUD) and other forms of substance dependence, a major health issue worldwide with limited treatment options. Despite intense investigation, the specific neuronal substrates involved and the functional implications of aberrant connectivity in these patients remain unknown. Moreover, it is unclear whether treatment can reverse these alterations, and normalize functional connectivity. Several methodological and conceptual questions in the analysis of functional connectivity are still open, and contribute to this uncertainty. Functional connectivity is defined in terms of correlated MR-signal fluctuations, and in-scanner patient motion and other nuisance signals can introduce spurious correlations, thus representing substantial confounding factors. At a more general level, understanding the effects of complex conditions, like AUD, on brain connectivity and their functional implications requires a deep comprehension of the brain organizational principles at multiple scales, a tremendous challenge that is at the heart of modern neuroscience. In this PhD dissertation I address some of the outstanding questions in the analysis and interpretation of aberrant functional connectivity in AUD. To this end, I have embraced the formalism of graph-theory, a powerful framework to assess the effects of alcohol abuse on the local and global topological organization of resting state connectivity. On the methodological side, I have investigated the effects of subject’s motion on the structure of resting state networks, and compared efficacy of different approaches to remove motion-related confounds. Moreover, I demonstrate the importance of network sparsification to remove spurious connections from the graph while maximizing the structural information that can be extracted from the system. Leveraging these methodological developments, I have evaluated functional alterations in different samples of AUD patients. In two independent studies, I demonstrated specific alterations in the topological organization of the insular cortex and subcortical basal structures in recently detoxified alcoholics. Interestingly, protracted abstinence appears to partially normalize functional connectivity, thus suggesting that alcohol-induced alterations in connectivity may be amenable to treatment. Based on these findings, I have studied the effects on brain functional networks of a putative novel treatment based on deep Transcranial Magnetic Stimulation (TMS). Specifically, I analyzed resting state connectivity in AUD patients subjected to repetitive TMS of the bilateral insula and of the anterior cingulate cortex (ACC), and demonstrated treatment-induced changes that may underlie the efficacy of this potential treatment in surrogate clinical read-outs.

Autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD) have been difficult to differentiate in clinical settings, as these two disorders are phenotypically similar and both exhibit atypical attention and executive functioning. Mischaracterizations between these two disorders can lead to inappropriate medication regimes, significant delays in special services, and personal distress to families and caregivers. There is evidence that ASD and ADHD are biologically different for attentional and executive functioning mechanisms, as only half of individuals with co-occurring ASD and ADHD respond to stimulant medication. Further, neurobehavioral work has supported these biological differences for ASD and ADHD, with both shared and distinct functional connectivity. In specific, two brain networks have been implicated in these disorders: the salience network (SN) and frontoparietal network (FPN). The SN is a network anchored by bilateral anterior insula and the dorsal anterior cingulate cortex and has been implicated in “bottom-up” attentional processes for both internal and external events. The FPN is anchored by lateral prefrontal cortex areas and the parietal lobe and plays a roll in “top-down” executive processes. Functional connectivity subgroups differentiated ASD from ADHD with between SN-FPN connectivity patterns, but not by within-SN or within-FPN connectivity patterns. Further, subgroup differences in ASD+ADHD comorbidity vs. ASD only were found for within-FPN connectivity. / Master of Science / Autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD) have been difficult to differentiate in clinical settings, as these two disorders are similar and both exhibit attention and executive functioning difficulties. ASD and ADHD have shared and distinct functional brain network connectivity related to attention and executive functioning. Two brain networks have been implicated in these disorders: the salience network (SN) and frontoparietal network (FPN). The SN is a network that has been implicated in “bottom-up” attentional processes for both internal and external events. The FPN plays a roll in “top-down” executive processes. This study found that functional connectivity patterns between the SN and FPN differentiated ASD from ADHD. Further, connectivity patterns in children with co-occurring ASD and ADHD were characterized by within-FPN connectivity.

Evidence of networks in the resting-brain reflecting the spontaneous brain activity is perhaps the most significant discovery to understand intrinsic brain functionality. Moreover, subsequent detection of dynamics in these networks can be milestone in differentiating the normal and disordered brain functions. However, capturing the correct dynamics is a challenging task since no ground truths' are present for comparison of the results. The change points of these networks can be different for different subjects even during normal brain functions. Even for the same subject and session, dynamics can be different at the start and end of the session based on the fatigue level of the subject scanned. Despite the absence of ground truths, studies have analyzed these dynamics using the existing methods and some of them have developed new algorithms too. One of the most commonly used method for this purpose is sliding window correlation. However, the result of the sliding window correlation is dependent on many parameters and without the ground truth there is no way of validating the results. In addition, most of the new algorithms are complicated, computationally expensive, and/or focus on just one aspect on these dynamics. This study applies the algorithms and concepts from signal processing, image processing, video processing, information theory, and machine learning to analyze the results of the sliding window correlation and develops a novel algorithm to detect change points of these networks adaptively. The findings in this study are divided into three parts: 1) Analyzing the extent of variability in well-defined networks of rodents and humans with sliding window correlation applying concepts from information theory and machine learning domains. 2) Analyzing the performance of sliding window correlation using simulated networks as ground truths for best parameters’ selection, and exploring its dependence on multiple frequency components of the correlating signals by processing the signals in time and Fourier domains. 3) Development of a novel algorithm based on image similarity measures from image and video processing that maybe employed to identify change points of these networks adaptively.

Alzheimer's disease (AD) is developing into the single greatest healthcare challenge in the coming decades. The development of early and effective treatments that can prevent the pathological damage responsible for AD-related dementia is of utmost priority for healthcare authorities. The role of the APOE-ε4 genotype, which has been shown to increase an individual's risk of developing AD, is of central interest to this goal. Understanding the mechanism by which possession of this gene modulates brain function, leading to a predisposition towards AD is an active area of research. Functional connectivity (FC) is an excellent candidate for linking APOE-related differences in brain function to sites of AD pathology. Magnetoencephalography (MEG) is a neuroimaging tool that can provide a unique insight into the electrophysiology underpinning resting-state networks (RSNs) - whose dysfunction is postulated to lead to a predisposition to AD. This thesis presents a range of methods for measuring functional connectivity in MEG data. We first develop a set of novel adaptations for preprocessing MEG data and performing source reconstruction using a beamformer (chapter 3). We then develop a range of analyses for measuring FC through correlations in the slow envelope oscillations of band-limited source-space MEG data (chapter 4). We investigate the optimum time scales for detecting FC. We then develop methods for extracting single networks (using seed-based correlation) and multiple networks (using ICA). We proceed to develop a group-statistical framework for detecting spatial differences in RSNs and present a preliminary finding for APOE-genotype-dependent differences in RSNs (chapter 5). We also develop a statistical framework for quantifying task-locked temporal differences in functional networks during task-positive experiments (chapter 6). Finally, we demonstrate a data-driven parcellation and network analysis pipeline that includes a novel correction for signal leakage between parcels. We use this framework to show evidence of stationary cross-frequency FC (chapter 7).

Soon after the advent of signal-recording techniques in the brain, functional connectivity (FC), a measure of interregional neural interactions, became an important tool to assess brain function and its relation to structure. It was discovered that certain groups of regions in the brain corresponding to behavioural domains are organized into intrinsic networks of connectivity (ICNs). These networks were shown to exhibit high FC during rest, and also during task. ICNs are not only delineated by areas which correspond to various behaviours, but can be modulated in the long and short-term in their connectivity by disease conditions, learning, and task performance. The significance of changes in FC, permanent and transient, is poorly understood with respect to even the simplest ICNs corresponding to motor and visual regions. A better grasp on how to interpret these changes could elucidate the mechanisms and implications of patterns in FC changes during therapy and basic tasks. The aim of this work is to examine long-term changes in the connectivity of several ICNs as a result of modulation by stroke and rehabilitation, and to assess short term changes due to simple, continuous task performance in healthy volunteers. To explore long-term changes in ICN connectivity, fifteen hemiparetic stroke patients underwent resting state scanning and behavioural testing before and after a two-week session of Constraint Induced Movement Therapy (CIMT). It was found that therapy led to localized increases in FC within the sensorimotor ICN. To assess transient changes in FC with task, sixteen healthy volunteers underwent a series of scans during rest, continuous performance of a non-demanding finger-tapping task, viewing of a continuous visual stimulus, and a combined (but uncoupled) visual and motor task. Group Independent Component Analysis (ICA) revealed that canonical ICNs remained robustly connected during task conditions as well as during rest, and dual regression/seed analyses showed that visual and sensorimotor ICNs showed divergent patterns of changes in FC, with the former showing increased intra-network connectivity and the latter decreased intra-network connectivity. Additionally, it was found that task activation within ICNs has a relationship to these changes in FC. Overall, these results suggest that modulation of functional connectivity is a valuable and informative tool in the study of disease recovery and task performance.

Explorations into the organisation of spontaneous activity within the brain have demonstrated the existence of networks of temporally correlated activity, consisting of brain areas that share similar cognitive or sensory functions. These so-called resting state networks (RSNs) emerge spontaneously during rest and disappear in response to overt stimuli or cognitive demands. In recent years, the study of RSNs has emerged as a valuable tool for probing brain function, both in the healthy brain and in disorders such as schizophrenia, Alzheimer’s disease and Parkinson’s disease. However, analyses of these networks have so far been limited, in part due to assumptions that the patterns of neuronal activity that underlie these networks remain constant over time. Moreover, the majority of RSN studies have used functional magnetic resonance imaging (fMRI), in which slow fluctuations in the level of oxygen in the blood are used as a proxy for the activity within a given brain region. In this thesis we develop the use of magnetoencephalography (MEG) to study resting state functional connectivity. Unlike fMRI, MEG provides a direct measure of neuronal activity and can provide novel insights into the temporal dynamics that underlie resting state activity. In particular, we focus on the application of non- stationary analysis methods, which are able to capture fast temporal changes in activity. We first develop a framework for preprocessing MEG data and measuring interactions within different RSNs (Chapter 3). We then extend this framework to assess temporal variability in resting state functional connectivity by applying time- varying measures of interactions and show that within-network functional connectivity is underpinned by non-stationary temporal dynamics (Chapter 4). Finally we develop a data driven approach based on a hidden Markov model for inferring short lived connectivity states from resting state and task data (Chapter 5). By applying this approach to data from multiple subjects we reveal transient states that capture short lived patterns of neuronal activity (Chapter 6).

Despite the success of available medical and psychosocial treatments, a sizable subgroup of individuals with commonly co-occurring disorders, generalized anxiety disorder (GAD) and major depressive disorder (MDD), fail to make sufficient treatment gains thereby prolonging their deficits in life functioning and satisfaction. Clinically, these patients often display temperamental features reflecting heightened sensitivity to underlying motivational systems related to threat/safety and reward/loss (e.g., somatic anxiety) as well as inordinate negative self-referential processing (e.g., worry, rumination). This profile may reflect disruption in two important neural networks associated with emotional/motivational salience (e.g., salience network) and self-referentiality (e.g., default network, DN). Emotion Regulation Therapy (ERT) was developed to target this hypothesized profile and its neurobehavioral markers. In the present study, 22 GAD patients (with and without MDD) completed resting state MRI scans before receiving 16 sessions of ERT. To test study these hypotheses, we examined the associations between baseline patterns of intrinsic functional connectivity (iFC) of the insula and of hubs within the DN (anterior and dorsal medial prefrontal cortex [MPFC] and posterior cingulate cortex [PCC]) and treatment-related changes in worry, somatic anxiety symptoms and decentering. Results suggest that greater treatment linked reductions in worry were associated with iFC clusters in both the insular and parietal cortices. Greater treatment linked gains in decentering, a metacognitive process that involves the capacity to observe items that arise in the mind with healthy psychological distance that is targeted by ERT, was associated with iFC clusters in the anterior and posterior DN. The current study adds to the growing body of research implicating disruptions in the default and salience networks as promising targets of treatment for GAD with and without co-occurring MDD.