The rapid advancements in the field of machine learning and artificial intelligence has led to the emergence of technologies like the Brain Computer Interface (BCI), which has revolutionized rehabilitation protocols. However, given the neural basis of BCIs and the dependence of its performance on cognitive factors, BCIs may be used to characterize the functional capacity of the user. A resting state segment can also be considered for characterization of the functional network integrity, creating a two part framework that probes the functional networks and their cognitive manifestations. This thesis explores such a two part framework using a simultaneous EEG-fMRI setup on a healthy population. The BCI accuracies for all subjects increased over the course of the scan and is thought to be due to learning processes on the subject's part. Since such learning processes require cognitive faculties such as attention and working memory, these factors might modulate the BCI performance profile, making it a potential metric for the integrity of such cognitive factors. The resting state analysis identified four EEG Microstates that have been previously found to be associated with verbal, visual, saliency and attention reorientation tasks. The proportion of each microstate that composed the corresponding fMRI resting state networks (RSN) were identified, opening up the potential for predicting fMRI-based RSN information, from EEG microstates alone. The developed protocol can be used to diagnose potential conditions that negatively affect the functional capacity of the user by using the results from this study as healthy control data. This is the first known BCI based system for characterization of the user's functional integrity, opening up the possibility of using BCIs as a metric for diagnosing a neuropathology. / Thesis / Master of Applied Science (MASc)

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).

This thesis investigated transcranial direct current stimulation (tDCS) as applied to the motor system, and its ability to modulate underlying cortical processes and resultant motor behaviours. Functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) were employed to assess the extent to which tDCS induces quantifiable changes in neural structure and function in controls and stroke patients. Modifications in the connectivity of intrinsic functional networks following tDCS application were examined using resting state fMRI. Polarity-specific changes were found: cathodal (inhibitory) tDCS increased the strength of the default mode network and increased functional coupling between major nodes within the motor network. No significant effects were found following anodal (excitatory) tDCS. Although anodal tDCS elicited only subtle changes in resting activity, it is known to produce robust modifications of behaviour. Single and paired-pulse TMS were used to investigate the neurophysiological underpinnings of these changes. Consistent with the theory of homeostatic plasticity, anodal tDCS applied prior to task performance increased GABAA-mediated cortical inhibition and worsened behaviour. The specificity of these changes suggests a central role for the mechanism of surround inhibition. A longitudinal clinical trial in chronic stroke patients was conducted to determine the utility of tDCS as an adjunct in motor rehabilitation. Serial MRI scans revealed that, when combined with motor training, anodal tDCS increased functional activity and grey matter in primarily ipsilesional motor areas. These brain changes were correlated with behavioural improvements in the stroke-affected upper limb. The laterality of connectivity at baseline, as measured by resting state activity and corticospinal tract integrity, was predictive of response to the rehabilitation program, particularly in those stroke patients who received tDCS. Asymmetry favouring the contralesional hemisphere predicted greater behavioural gains. Such results underscore the importance of re-normalisation of structure and functional activity toward the lesioned hemisphere in stroke rehabilitation.

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Moreover, potent postnatal brain plasticity engenders increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Recently, resting-state functional magnetic resonance imaging (fMRI) emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Its application has expanded to infant populations in the past decade, providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal/ disease states. However, rapid extension of the resting-state technique to infant populations leaves many methodological issues need to be resolved prior to standardization of the technique. The purpose of this thesis is to describe a protocol for intrinsic functional connectivity analysis, and extraction of resting-state networks in infants <12 months of age using the data-driven approach independent component analysis (ICA). To begin, we review the evolution of resting-state fMRI application in infant populations, including the biological premise for neural networks. Next, we present a protocol designed such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature. Presented protocol provides detailed, albeit basic framework for RSN analysis, with interwoven discussion of basic theory behind each technique, as well as the rationale behind selecting parameters. The overarching goal is to catalyze efforts towards development of robust, infant-specific acquisition and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used. Finally, we review the literature, current methodological challenges and potential future directions for the field of infant resting-state fMRI.

Manual sleep scoring, executed by visual inspection of the EEG, is a very time consuming activity, with an inherent subjective decisional component. Automatic sleep scoring could ease the job of the technicians, because faster and more accurate. Frequency information characterizing the main brain rhythms, and consequently the sleep stages, needs to be extracted from the EEG data. The approach used in this study involves a wavelet filter bank for the EEG frequency features extraction. The wavelet packet analysis tool in MATLAB has been employed and the frequency information subsequently used for the automatic sleep scoring by means of an artificial neural network. Finally, the automatic sleep scoring has been employed for epoching the fMRI data, thus allowing for studying brain resting state networks during sleep. Three resting state networks have been inspected; the Default Mode Network, The Attentional Network and the Salience Network. The networks functional connectivity variations have been inspected in both healthy and narcoleptic subjects. Narcolepsy is a neurobiological disorder characterized by an excessive daytime sleepiness, whose aetiology may be linked to a loss of neurons in the hypothalamic region.

Complex network models of functional connectivity have emerged as a paradigm shift in brain mapping over the past decade. Despite significant attention within the neuroimaging and cognitive neuroscience communities, these approaches have hitherto not been extensively explored in neurosurgery. The aim of this thesis is to investigate how the field of connectomics can contribute to understanding the effects of focal brain lesions and to functional brain mapping in neurosurgery. This datasets for this thesis include a clinical population with focal brain tumours and a cohort focused on healthy adolescent brain development. Multiple network analyses of increasing complexity are performed based upon resting state functional MRI. In patients with focal brain tumours, the full complement of resting state networks were apparent, while also suggesting putative patterns of network plasticity. Connectome analysis was able to identify potential signatures of node robustness and connections at risk that could be used to individually plan surgery. Focal lesions induced the formation of new hubs while down regulating previously established hubs. Overall these data are consistent with a dynamic rather than a static response to the presence of focal lesions. Adolescent brain development demonstrated discrete dynamics with distinct gender specific and age-gender interactions. Network architecture also became more robust, particularly to random removal of nodes and edges. Overall these data provide evidence for the early vulnerability rather than enhanced plasticity of brain networks. In summary, this thesis presents a combined analysis of pathological and healthy development datasets focused on understanding the functional effects of focal brain lesions at a network level. The coda serves as an introduction to a forthcoming study, known as Connectomics and Electrical Stimulation for Augmenting Resection (CAESAR), which is an evolution of the results and methods herein.

No description available.

La neuromodulation apporte une extraordinaire richesse à la dynamique des réseaux de neurones. Parmi les neuromodulateurs du système nerveux central, la noradrénaline permettrait de faciliter l'adaptation du comportement face aux variations des contraintes environnementales en modulant l'attention, cette fonction au coeur de la cognition qui nous permet de sélectionner l'information la plus pertinente en fonction de notre but. Ce processus complexe qui opère à chaque instant à la fois dans l'espace et le temps, constitue une étape essentielle dans cette adaptation comportementale. Cependant, à ce jour, les mécanismes par lesquels ce neuromodulateur exerce ses effets sur le cerveau sain demeurent mal connus. Mon travail de thèse a pour objectif d'examiner les déterminants comportementaux et les marqueurs neuronaux de l'effet stimulant des agonistes noradrénergiques. La question posée était : "Comment la noradrénaline agit-elle pour optimiser l'attention?" Pour répondre à cette question, j'ai choisi de combiner la pharmacologie, l'analyse du comportement, et l'imagerie par résonnance magnétique fonctionnelle chez le singe. Un des principaux résultats de mes travaux est que l'administration d'agents noradrénergiques induit une large réorganisation des réseaux cérébraux, qui pourrait être à l'origine de l'optimisation des réponses comportementales observées parallèlement / Neuromodulation provides an extraordinary wealth to the dynamics of neural networks. Among the neuromodulators of the central nervous system, noradrenaline would facilitate behavioral adaptation facing variations of environmental constraints by modulating attention, this function at the heart of cognition that allows us to select the most relevant information based our goal. This complex process that operates at every moment both in space and time, is an essential step in this behavioral adaptation. However, to date, the mechanisms by which this neuromodulator exerts its effects on healthy brain remain unknown. My thesis aims to examine the behavioral and neural markers of the boosting effect of noradrenergic agonists. The question asked was: "How does noradrenaline optimize attention?" To answer this question, I chose to combine pharmacology, behavior analysis, and functional Magnetic Resonance Imaging in monkeys. One of the main results of my work is that the administration of noradrenergic agents induced a large-scale brain networks reorganization, which could be responsible for optimizing behavioral responses observed in parallel