Functional Brain Organization in Space and Time

The brain is a network functionally organized at many spatial and temporal scales. To understand how the brain processes information, controls behavior and dynamically adapts to an ever-changing environment, it is critical to have a comprehensive description of the constituent elements of this network and how relationships between these elements may change over time. Decades of lesion studies, anatomical tract-tracing, and electrophysiological recording have given insight into this functional organization. Recently, however, resting state functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for whole-brain non-invasive measurement of spontaneous neural activity in humans, giving ready access to macroscopic scales of functional organization previously much more difficult to obtain. This thesis aims to harness the unique combination of spatial and temporal resolution provided by functional MRI to explore the spatial and temporal properties of the functional organization of the brain. First, we establish an approach for defining cortical areas using transitions in correlated patterns of spontaneous BOLD activity (Chapter 2). We then propose and apply measures of internal and external validity to evaluate the credibility of the areal parcellation generated by this technique (Chapter 3). In chapter 4, we extend the study of functional brain organization to a highly sampled individual. We describe the idiosyncratic areal and systems-level organization of the individual relative to a standard group-average description. Further, we develop a model describing the reliability of BOLD correlation estimates across days that accounts for relevant sources of variability. Finally, in Chapter 5, we examine whether BOLD correlations meaningfully vary over the course of single resting-state scans

The Dynamics of Functional Brain Networks:Integrated Network States during Cognitive Task Performance

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions, however it is unclear how this mechanism manifests over time. In this study, we use time-resolved network analysis of functional magnetic resonance imaging data to demonstrate that the human brain traverses between two functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. The integrated state enables faster and more accurate performance on a cognitive task, and is associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Our data confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.Comment: 38 pages, 4 figure

Neural processes underpinning pain perception : genetic, temporal, and behavioral factors

Pain is an alarm system – warning us of dangers in the environment – yet becomes problematic when it transitions into chronic pain. It is defined, according to the International Association of Pain as “An unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage”. In advancing our knowledge of the underlying mechanisms of acute pain, it is relevant to understand sources of variability in pain perception. One such source is the genetic influence on brain function. This can be studied using a classic twin design to infer the proportion of variance in brain activation attributed to genetics. Another source of variation pertains to the temporal fluctuations in brain activity that could track pain processing. This was studied here using time-varying functional connectivity. Furthermore, since pain arises through large-scale interactions in the brain – the purpose here is to study pain and related processes through network neuroscience. Specifically, how functionally specialized – or segregated – neural structures of the brain integrate to shape pain

Approaches For Capturing Time-Varying Functional Network Connectivity With Application to Normative Development and Mental Illness

Since the beginning of medical science, the human brain has remained an unsolved puzzle; an illusive organ that controls everything- from breathing to heartbeats, from emotion to anger, and more. With the power of advanced neuroimaging techniques, scientists have now started to solve this nearly impossible puzzle, piece by piece. Over the past decade, various in vivo techniques, including functional magnetic resonance imaging (fMRI), have been increasingly used to understand brain functions. fMRI is extensively being used to facilitate the identification of various neuropsychological disorders such as schizophrenia (SZ), bipolar disorder (BP) and autism spectrum disorder (ASD). These disorders are currently diagnosed based on patients’ self-reported experiences, and observed symptoms and behaviors over the course of the illnesses. Therefore, efficient identification of biological-based markers (biomarkers) can lead to early diagnosis of these mental disorders, and provide a trajectory for disease progression. By applying advanced machine learning techniques on fMRI data, significant differences in brain function among patients with mental disorders and healthy controls can be identified. Moreover, by jointly estimating information from multiple modalities, such as, functional brain data and genetic factors, we can now investigate the relationship between brain function and genes. Functional connectivity (FC) has become a very common measure to characterize brain functions, where FC is defined as the temporal covariance of neural signals between multiple spatially distinct brain regions. Recently, researchers are studying the FC among functionally specialized brain networks which can be defined as a higher level of FC, and is termed as functional network connectivity (FNC, defined as the correlation value that summarizes the overall connection between brain ‘networks’ over time). Most functional connectivity studies have made the limiting assumption that connectivity is stationary over multiple minutes, and ignore to identify the time-varying and reoccurring patterns of FNC among brain regions (known as time-varying FNC). In this dissertation, we demonstrate the use of time-varying FNC features as potential biomarkers to differentiate between patients with mental disorders and healthy subjects. The developmental characteristics of time-varying FNC in children with typically developing brain and ASD have been extensively studies in a cross-sectional framework, and age-, sex- and disease-related FNC profiles have been proposed. Also, time-varying FNC is characterized in healthy adults and patients with severe mental disorders (SZ and BP). Moreover, an efficient classification algorithm is designed to identify patients and controls at individual level. Finally, a new framework is proposed to jointly utilize information from brain’s functional network connectivity and genetic features to find the associations between them. The frameworks that we presented here can help us understand the important role played by time-varying FNC to identify potential biomarkers for the diagnosis of severe mental disorders

Multivariate analysis of brain metabolism reveals chemotherapy effects on prefrontal cerebellar system when related to dorsal attention network

BACKGROUND: Functional brain changes induced by chemotherapy are still not well characterized. We used a novel approach with a multivariate technique to analyze brain resting state [(18) F]FDG-PET in patients with lymphoma, to explore differences on cerebral metabolic glucose rate between chemotherapy-treated and non-treated patients. METHODS: PET/CT scan was performed on 28 patients, with 14 treated with systemic chemotherapy. We used a support vector machine (SVM) classification, extracting the mean metabolism from the metabolic patterns, or networks, that discriminate the two groups. We calculated the correct classifications of the two groups using the mean metabolic values extracted by the networks. RESULTS: The SVM classification analysis gave clear-cut patterns that discriminate the two groups. The first, hypometabolic network in chemotherapy patients, included mostly prefrontal cortex and cerebellar areas (central executive network, CEN, and salience network, SN); the second, which is equal between groups, included mostly parietal areas and the frontal eye field (dorsal attention network, DAN). The correct classification membership to chemotherapy or not chemotherapy-treated patients, using only one network, was of 50% to 68%; however, when all the networks were used together, it reached 80%. CONCLUSIONS: The evidenced networks were related to attention and executive functions, with CEN and SN more specialized in shifting, inhibition and monitoring, DAN in orienting attention. Only using DAN as a reference point, indicating the global frontal functioning before chemotherapy, we could better classify the subjects. The emerging concept consists in the importance of the investigation of brain intrinsic networks and their relations in chemotherapy cognitive induced changes

Statistical methods for high-dimensional data with complex correlation structure applied to the brain dynamic functional connectivity studyDY

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

FUNCTIONAL NETWORK CONNECTIVITY IN HUMAN BRAIN AND ITS APPLICATIONS IN AUTOMATIC DIAGNOSIS OF BRAIN DISORDERS

The human brain is one of the most complex systems known to the mankind. Over the past 3500 years, mankind has constantly investigated this remarkable system in order to understand its structure and function. Emerging of neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have opened a non-invasive in-vivo window into brain function. Moreover, fMRI has made it possible to study brain disorders such as schizophrenia from a different angle unknown to researchers before. Human brain function can be divided into two categories: functional segregation and integration. It is well-understood that each region in the brain is specialized in certain cognitive or motor tasks. The information processed in these specialized regions in different temporal and spatial scales must be integrated in order to form a unified cognition or behavior. One way to assess functional integration is by measuring functional connectivity (FC) among specialized regions in the brain. Recently, there is growing interest in studying the FC among brain functional networks. This type of connectivity, which can be considered as a higher level of FC, is termed functional network connectivity (FNC) and measures the statistical dependencies among brain functional networks. Each functional network may consist of multiple remote brain regions. Four studies related to FNC are presented in this work. First FNC is compared during the resting-state and auditory oddball task (AOD). Most previous FNC studies have been focused on either resting-state or task-based data but have not directly compared these two. Secondly we propose an automatic diagnosis framework based on resting-state FNC features for mental disorders such as schizophrenia. Then, we investigate the proper preprocessing for fMRI time-series in order to conduct FNC studies. Specifically the impact of autocorrelated time-series on FNC will be comprehensively assessed in theory, simulation and real fMRI data. At the end, the notion of autoconnectivity as a new perspective on human brain functionality will be proposed. It will be shown that autoconnectivity is cognitive-state and mental-state dependent and we discuss how this source of information, previously believed to originate from physical and physiological noise, can be used to discriminate schizophrenia patients with high accuracy

Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury.

Cognitive processes require a functional interaction between specialized multiple, local and remote brain regions. Although these interactions can be strongly altered by an acquired brain injury, brain plasticity allows network reorganization to be principally responsible for recovery. The present work evaluates the impact of brain injury on functional connectivity patterns. Networks were calculated from resting-state magnetoencephalographic recordings from 15 brain injured patients and 14 healthy controls by means of wavelet coherence in standard frequency bands. We compared the parameters defining the network, such as number and strength of interactions as well as their topology, in controls and patients for two conditions: following a traumatic brain injury and after a rehabilitation treatment. A loss of delta- and theta-based connectivity and conversely an increase in alpha- and beta-band-based connectivity were found. Furthermore, connectivity parameters approached controls in all frequency bands, especially in slow-wave bands. A correlation between network reorganization and cognitive recovery was found: the reduction of delta-band-based connections and the increment of those based on alpha band correlated with Verbal Fluency scores, as well as Perceptual Organization and Working Memory Indexes, respectively. Additionally, changes in connectivity values based on theta and beta bands correlated with the Patient Competency Rating Scale. The current study provides new evidence of the neurophysiological mechanisms underlying neuronal plasticity processes after brain injury, and suggests that these changes are related with observed changes at the behavioural leve

The Role of Amygdala Subregions in the Neurobiology of Social Anxiety Disorder

Social anxiety is characterised by fear and/or avoidance of social situations in which an individual may be scrutinised by others. Social anxiety is thought to exist as a spectrum, with individuals on the high-end experiencing frequent and severe anxiety in the context of social situations. When severe social anxiety is accompanied by distress and functional impairment, a diagnosis of social anxiety disorder (SAD) can be made. SAD is a prevalent and debilitating disorder that can be unremitting and pervasive in the absence of intervention. Current psychotherapeutic and pharmacotherapeutic treatments for SAD demonstrate limited efficacy in remitting symptoms. Therefore, it is important to achieve a better understanding of the neurobiological mechanisms implicated in this disorder and identify potential neural treatment targets to develop more efficacious treatments. This thesis aimed to further investigate the neurobiological mechanisms implicated in SAD (vs. controls) and the associations between neural functioning and social anxiety as a dimensional symptom, with a focus on the amygdala and four of its subregions (the amygdalostriatal, basolateral, centromedial, and superficial subregions). This was due to previous findings in the neuroimaging literature in SAD having consistently implicated the amygdala, albeit with mixed findings of both increased and decreased functioning in those with SAD compared to controls. In the literature to date, however, most studies had examined the amygdala as a singular homogenous region due to methodological limitations in being able to examine the functionally and structurally distinct subnuclei that make up this region. By examining the amygdala subregions through the use of multiband functional magnetic resonance imaging (fMRI), this thesis additionally sought to determine whether the mixed findings in the literature to date may be a result of amygdala subregion-specific activity and connectivity patterns. This was achieved through three research studies. Firstly, Study 1 involved a comprehensive systematic review that summarised the literature on resting-state neuroimaging in SAD with a focus on fMRI studies and findings specific to the amygdala and its subregions (Chapter 3). This was followed by two empirical studies which investigated the role of the amygdala and its subregions during resting-state (Study 2) and emotion processing (Study 3) fMRI paradigms (Chapters 5 and 6, respectively). Findings from the systematic review (Study 1) highlighted the mixed findings in the resting-state neuroimaging literature in SAD to date, along with methodological limitations relating to neuroimaging acquisition and analysis. The empirical studies sought to address these limitations and demonstrated differing amygdala subregion activity and connectivity patterns at rest and during emotion processing. In the resting-state fMRI study (Study 2), there were no statistically significant differences in functional connectivity of the amygdala and its subregions in those with SAD compared to controls. However, social anxiety severity was found to be positively associated with connectivity between the superficial subregion and the supramarginal gyrus. The superficial subregion, along with the basolateral and centromedial subregions, were also implicated in the task-based emotion processing fMRI study (Study 3). In response to happy, angry, and fearful faces, those with SAD (vs. controls) had hyperactivation of the superficial subregion, hypoconnectivity between the superficial subregion and the precuneus, and hyperconnectivity between the basolateral subregion and broader brain regions (i.e., the pre/postcentral gyrus and the supramarginal gyrus). Additionally, social anxiety severity was positively associated with superficial and centromedial activation. Overall, the findings from this thesis provide novel information to the current understanding of the neurobiology of SAD by demonstrating amygdala subregion-specific alterations. This has important implications for research, theory, and clinical practice that are detailed in the thesis discussion (Chapter 7). Briefly, in terms of research, findings from the thesis provide support for the continuing investigation of SAD using both dimensional and categorical approaches. This was evident by the findings from the two empirical papers which demonstrated positive associations between subregional activity and connectivity patterns and social anxiety severity. With regards to theory, differences in neural patterns that were observed at rest (Study 2) and during emotion processing (Study 3) provide support for distinct neurobiological models to be constructed based on whether those with SAD are in the absence or presence of social stimuli. This is in contrast to the most recently proposed neurobiological model of SAD which was informed by a combination of resting-state and task-based fMRI data. Finally, with regards to clinical practice, the findings from this thesis provide preliminary evidence of the superficial, basolateral, and centromedial subregions of the amygdala as being potential treatment targets that can be used to inform the development of more efficacious treatments for SAD