L’imagerie par résonance magnétique fonctionnelle (IRMf) est un outil de choix pour cartographier d’une manière non invasive l’activité du cortex, donnant ainsi un accès à l’organisation fonctionnelle cérébrale. Cette organisation des aires cérébrales en réseaux complexes reste encore un vaste sujet d’étude, autant dans le domaine de la recherche fondamentale, pour mieux comprendre le développement et le fonctionnement du cerveau, que dans le domaine clinique, à des fins diagnostiques par exemple. Les réseaux cérébraux dits de repos, chez un sujet donné, peuvent être observés lors d’études IRMf lorsqu’aucune tâche motrice ou cognitive n’est imposée au sujet imagé. La première partie de cette thèse a permis le développement d’une méthode automatique d’identification de ces réseaux. Réalisée à l’échelle du sujet, cette méthode permet de sélectionner tous les réseaux spécifiques au sujet ce qui s’avère nécessaire dans un cadre diagnostique où l’individu prime. Au delà de la détection et de l’identification de ces réseaux, l’étude de leurs modes d’interaction dans l’espace et dans le temps et plus généralement l’analyse de la dynamique de la connectivité fonctionnelle (DCF) fait l’objet d’un intérêt grandissant. Cette analyse nécessite le développement de méthodes innovantes de traitement du signal et de l’image qui, pour l’heure, sont encore de nature exploratoire. La deuxième partie de cette thèse présente donc de nouvelles approches pour caractériser la DCF en utilisant le cadre probabiliste de modèles de Markov cachés multidimensionnels. Les mécanismes conversationnels entre réseaux cérébraux peuvent ainsi être identifiés et caractérisés à l’échelle de la seconde. Deux applications, au niveau du sujet puis du groupe, ont permis de mettre en avant les modifications des propriétés dynamiques des interactions entre réseaux sous certaines conditions ou pathologies. / The functional magnetic resonance imaging (fMRI) is a perfect tool for mapping in a non- invasive manner the activity of the cortex, giving access to the functional organization of the brain. This organization of brain areas into complex networks remains a large topic of study, both from a fundamental research perspective, to better understand the development and function of the brain, and from a clinical perspective, for diagnostic purposes for instance. The resting-state networks in a given subject can be observed in fMRI studies where no motor or cognitive tasks are imposed to the subject. The first part of this thesis focused on the development of an automatic identification method of these networks. Performed at the subject level, this method selects all the resting-state networks proper to the subject. Beyond the detection and identification of these networks, the study of interactions between these networks in space and time, and more generally the analysis of the dynamic functional connectivity (DFC), is the subject of growing interest. This analysis requires the development of innovative methods of signal or image processing that, for now, are still exploratory. The second part of this thesis thus presents new approaches to characterize the DFC using the probabilistic framework of multidimensional hidden Markov models. Conversational mechanisms between brain networks can be identified and characterized at the resolution of the second. Two applications, first on a single subject then on a group, helped to highlight the changes of dynamic properties of interaction between networks under certain conditions or diseases.

Functional connectivity is a field within neuroscience where measurements of co-activation between brain regions are used to test various hypotheses or explore how the brain activates depending on a given situation or task. After analysis, the underlying data in the field consists of a n by n adjacency matrix where each cell represents a correlation value between two regions in the brain. Depending on the research question the number of regions and matrices incorporated varies and new visualizations are needed in order to portray them. In this thesis the design of an interactive web based visualization tool for functional connectivity was explored through an iterative design process. The design of the tool was based on existing guidelines, interviews and best practices in data visualization as well as an analysis of current visualization solutions used in functional connectivity. The final concept and prototype uses a network plot for functional connectivity called the connectogram as well as a grouped bar graph to provide an intuitive and accessible way of comparing functional connectivity data by interacting with and highlighting networks and specific network data through direct manipulation. Results of qualitative evaluations of a prototype using data from a concurrent scientific project is presented. The prototype was found to be useful, engaging, easily perceivable and offered an easy and quick way of exploring data sets.

Cartographier la connectivité fonctionnelle du cerveau à partir des donnés d'IRMf est devenu un champ de recherche très actif. Cependant, les outils théoriques et pratiques sont limités et plusieurs tâches importantes, telles que la définition empirique de réseaux de connexion cérébrale, restent difficiles en l’absence d'un cadre pour la modélisation statistique de ces réseaux. Nous proposons de développer au niveau des populations, des modèles joints de connectivité anatomique et fonctionnelle et l'alignement inter-sujets des structures du cerveau. Grâce à une telle contribution, nous allons développer des nouvelles procédures d'inférence statistique afin de mieux comparer la connectivité fonctionnelle entre différents sujets en présence du bruit (bruit scanner, bruit physiologique, etc.). / Mapping the functions of the human brain using fMRI data has become a very active field of research. However, the available theoretical and practical tools are limited and many important tasks like the empirical definition of functional brain networks, are difficult to implement due to lack of a framework for statistical modelling of such networks. We propose to develop at the population level, models that jointly perform estimation of functional connectivity and alignment the brain data across the different individuals / subjects in the population. Building upon such a contribution, we will develop new methods for statistical inference to help compare functional connectivity across different individuals in the presence of noise (scanner noise, physiological noise, etc.).

Background: Methamphetamine (MA) abuse is a global crisis that exacerbates sociopolitico-economic burdens in South Africa. MA use is associated with a myriad of neural abnormalities of structure and function, with associated neurocognitive deficits, particularly executive function (EF). Working memory (WM) training has been identified as a potential adjunct to treatment of substance use disorder (SUD) to improve EF in the hope of reducing relapse rates. Neuroimaging suggests MA alters intrinsic resting state functional connectivity (rsFC), and this may contribute to neuropsychological deficits observed in methamphetamine use disorder (MUD). Methods: This nested study analysed data described in Brooks et al. (2016), in which WM training was used as an adjunct to inpatient treatment of MUD. Healthy controls (HC, N = 25) were compared to two MUD groups, one receiving treatment as usual (TAU, N = 17), and one receiving additional cognitive training (CT, N = 24) in the form of a modified version of the ‘N-back' task (C-Ya). This task was also used to assess WMA in the neural scanner, using conditions of 0-back and 1-back across groups. The current research explored these data in a novel manner through examining rsFC. Hypotheses: It was predicted that: 1) HC and MUD participants would differ on measures of WMA, but WMA would improve in MA groups at follow-up compared to baseline and this would be augmented in the CT group; 2) rsFC networks of neural regions supporting WM would be predictive of ability to perform well and improve on WM tasks; and 3) MA groups would display heightened rsFC activity within and between resting state neural networks of the default mode network (DMN) and canonical cognitive control networks (CCNs). Results: Significant differences were observed between HC and MA groups in race and level of education, but not on WMA as tested in the scanner. The CT group, who completed WMA 3-back conditions, demonstrated significant improvement on this task post- intervention. Exploratory regression models showed the WM rsFC network did not demonstrate significant relationships with any clinical, demographic, or WM variables when controlling for multiple comparisons. Heightened connectivity within and between the DMN and CCNs was observed in the MUD compared to the HC group, which provided support for hypothesis 3. Exploratory multivariate regression models demonstrated race, age, education, duration of drug use, and an interaction of group and abstinence may impact rsFC in these networks. Post-hoc analyses identified pairwise network combinations affected by these variables. Conclusions: Despite limitations of this small study, it offers tentative preliminary insights into the largely unexplored field of rsFC in MA populations. This study supports limited research demonstrating hyperconnectivity within and between CCNs and DMN of MA users. This study also offers support for recent research suggesting that easier conditions of the Nback task may not reliably test all aspects of WM function. Exploratory analyses of covariates potentially affecting rsFC provide a platform for directions of future research.

No description available.

This thesis is concerned with convex optimization problems over matrix polynomials that are constrained to be positive semidefinite on the unit circle. Problems of this form appear in signal processing and can often be solved as semidefinite programs (SDPs). Interior-point solvers for these SDPs scale poorly, and this thesis aims to design first-order methods that are more efficient. We propose methods based on a generalized proximal operator defined in terms of a Bregman divergence. Empirical results on three applications in signal processing demonstrate that the proposed methods scale much better than interior-point solvers. As an example, for sparse estimation of spectral density matrices, Douglas--Rachford splitting with the generalized proximal operator is about 1000 times faster and scales to much larger problems. The ability to solve larger problems allows us to perform functional connectivity analysis of the brain by constructing a sparse estimate of the inverse spectral density matrix.

Neuroscientists are increasingly overwhelmed by new recordings of the nervous system. These recordings are significantly increasing in size due to new electrophysiological techniques, such as multi-electrode arrays. These techniques can simultaneously record the electrical activity (or spike trains) from thousands of neurons. These new datasets are larger than the traditional datasets recorded from single electrodes where fewer than ten spike trains are usually recorded. Consequently, new tools are now required to effectively analyse these new datasets. This thesis describes how techniques from the field of Visual Analytics can be applied to detect specific patterns in spike train data. These techniques are realised in a software tool called Neurigma. Neurigma is a collection of visual representations of spike train data that are unified to provide a coordinated representation of the data. The visual representations within Neurigma include: an interactive raster plot, an improved correlation grid, a novel representation called the correlation plot (which includes a novel coupling estimation algorithm), and a novel network diagram. These views provide insight into spike train data, and particularly, they identify correlated patterns, called functional connectivity. Within this thesis Neurigma is used to analyse synthetically generated datasets and experimental recordings. Three main findings are presented. First, propagating spiral patterns are identified within recordings from the neonatal mouse retina. Second, functional connectivity is identified within the cat visual cortex. Finally, the functional connectivity of a large synthetic dataset, of 1000 spike trains, is accurately classified into direct, indirect and common input coupling.

Human infants have been shown to have an innate capacity to acquire their mother tongue. In recent decades, the advent of the functional magnetic resonance imaging (fMRI) technique has made it feasible to explore the neural basis underlying language acquisition and processing in children, even in newborn infants (for reviews, see Kuhl & Rivera-Gaxiola, 2008; Kuhl, 2010) . Spontaneous low-frequency (< 0.1 Hz) fluctuations (LFFs) in the resting brain have been shown to be physiologically meaningful in the seminal study (Biswal et al., 1995) . Compared to task-based fMRI, resting-state fMRI (rs-fMRI) has some unique advantages in neuroimaging research, especially in obtaining data from pediatric and clinical populations. Moreover, it enables us to characterize the functional organization of the brain in a systematic manner in the absence of explicit tasks. Among brain systems, the language network has been well investigated by analyzing LFFs in the resting brain. This thesis attempts to investigate the functional connectivity within the language network in typically developing preschool children and the covariation of this connectivity with children’s language development by using the rs-fMRI technique. The first study (see Chapter 2.1; Xiao et al., 2016a) revealed connectivity differences in language-related regions between 5-year-olds and adults, and demonstrated distinct correlation patterns between functional connections within the language network and sentence comprehension performance in children. The results showed a left fronto-temporal connection for processing syntactically more complex sentences, suggesting that this connection is already in place at age 5 when it is needed for complex sentence comprehension, even though the whole functional network is still immature. In the second study (see Chapter 2.2; Xiao et al., 2016b), sentence comprehension performance and rs-fMRI data were obtained from a cohort of children at age 5 and a one-year follow-up. This study examined the changes in functional connectivity in the developing brain and their relation to the development of language abilities. The findings showed that the development of intrinsic functional connectivity in preschool children over the course of one year is clearly observable and individual differences in this development are related to the advancement in sentence comprehension ability with age. In summary, the present thesis provides new insights into the relationship between intrinsic functional connectivity in the brain and language processing, as well as between the changes in intrinsic functional connectivity and concurrent language development in preschool children. Moreover, it allows for a better understanding of the neural mechanisms underlying language processing and the advancement of language abilities in the developing brain.

Résumé : L’interaction entre la douleur et le système moteur est bien connue en clinique et en réadaptation. Il est sans surprise que la douleur est un phénomène considérablement invalidant, affectant la qualité de vie de ceux et celles qui en souffrent. Toutefois, les bases neurophysiologiques qui sous-tendent cette interaction demeurent, encore aujourd’hui, mal comprises. Le but de la présente étude était de mieux comprendre les mécanismes corticaux impliqués dans l’interaction entre la douleur et le système moteur. Pour ce faire, une douleur expérimentale a été induite à l’aide d’une crème à base de capsaïcine au niveau de l’avant-bras gauche des participants. L'effet de la douleur sur la force des projections corticospinales ainsi que sur l’activité cérébrale a été mesuré à l’aide de la stimulation magnétique transcrânienne (TMS) et de l’électroencéphalographie (EEG), respectivement. L’analyse des données EEG a permis de révéler qu'en présence de douleur aiguë, il y a une augmentation de l’activité cérébrale au niveau du cuneus central (fréquence têta), du cortex dorsolatéral préfrontal gauche (fréquence alpha) ainsi que du cuneus gauche et de l'insula droite (toutes deux fréquence bêta), lorsque comparée à la condition initiale (sans douleur). Également, les analyses démontrent une augmentation de l'activité du cortex moteur primaire droit en présence de douleur, mais seulement chez les participants qui présentaient simultanément une diminution de leur force de projections corticales (mesurée avec la TMS t=4,45, p<0,05). Ces participants ont également montré une plus grande connectivité entre M1 et le cuneus que les participants dont la douleur n’a pas affecté la force des projections corticospinales (t=3,58, p<0,05). Ces résultats suggèrent qu’une douleur expérimentale induit, chez certains individus, une altération au niveau des forces de projections corticomotrices. Les connexions entre M1 et le cuneus seraient possiblement impliquées dans la survenue de ces changements corticomoteurs. / Abstract : The interaction between pain and the motor system is well-known in clinic. For instance, it is well documented that pain significantly complicates the rehabilitation of the patients. The aim of the present study was to better understand the cortical mechanisms underlying the interaction between pain and the motor system. Nineteen healthy adults participated in the study. The effect of pain (induced with a capsaicin cream) on brain activity and on the corticomotor system was assessed with electroencephalography (EEG) and transcranial magnetic stimulation (TMS), respectively. For EEG, 15 non-overlapping, 2-seconds artifacts were randomly selected for each participant. Intracranial source current density and functional connectivity was determined using sLORETA software. When participants experienced experimentally-induced inflammatory pain, their resting state brain activity increased significantly in the central cuneus (theta frequency), left dorsolateral prefrontal cortex (alpha frequency), and both left cuneus and right insula (beta frequency; all ts >3.66; all ps<0.01). A pain-evoked increase in the right primary motor cortex (M1) activity was also observed (beta frequency), but only among participants who showed a simultaneous reduction in the strength of the corticospinal projections (quantified using the recruitment curves obtained with TMS; t=4.45, p<0.05). These participants further showed greater beta motor-cuneus connectivity than participants for whom pain did not affect M1 somatotopy (t=3.58, p<0.05). These results suggest that pain-evoked increases in M1 beta power are intimately tied to alterations in corticospinal system. Moreover, we provide evidence that beta motor-cuneus connectivity is related to the corticomotor alterations induced by pain.

L'IRMf de repos qui repose sur l'étude des fluctuations du signal BOLD chez un sujet au repos, pourrait permettre d'explorer les mécanismes du handicap dans la Sclérose En Plaques (SEP). En utilisant l'IRMf de repos, ce travail a eu pour objectif de caractériser la réorganisation fonctionnelle des patients atteints de SEP et ses liens avec leur handicap.Nous avons d'abord réalisé une étude combinant IRMf de repos et d'activation au stade précoce de la SEP et montré l'existence d'une corrélation entre la plasticité fonctionnelle du système moteur des patients au repos et durant la tâche. Nous avons ensuite montré l'existence d'une augmentation diffuse du niveau de connectivité fonctionnelle des patients présentant une SEP débutante, corrélée à leurs performances. Dans la 3ème partie, nous avons objectivé l'existence d'un déclin dynamique de la topologie fonctionnelle corrélée à la progression du handicap grâce à une étude longitudinale utilisant la théorie des graphes. Enfin, nous avons démontré que le gain fonctionnel obtenu par la rééducation chez les patients SEP était corrélé à une augmentation de connectivité fonctionnelle du réseau cérébral par défaut et central exécutif et de densité de substance grise dans le cortex frontal.Nos travaux montrent l'existence d'une réorganisation cérébrale fonctionnelle complexe et dynamique dans la SEP qui pourrait correspondre à des phénomènes compensatoires, dont le déclin avec l'évolution de la maladie participe à la progression du handicap. Ils démontrent l'intérêt de l'IRMf de repos pour la compréhension des substrats anatomo-fonctionnels du handicap dans la SEP et comme potentiel instrument futur d'évaluation thérapeutique. / Resting-state fMRI (rs-fMRI) may provide important clue concerning disability in multiple sclerosis (MS) by exploring the spontaneous BOLD fluctuations at rest in the whole brain. The aim of this work is to depict the functional reorganization of resting-state networks in MS patients and to assess its potential relationships with disability.In the first part, we performed an fMRI protocol combining a rs-fMRI and task-associated fMRI during a motor task, in a group of early MS patients. This study evidenced a direct association between reorganization of connectivity at rest and during activation in the motor system of patients. In the second rs-fMRI study, we evidenced an increased of the global level of connectivity in most of the rs-networks, strongly associated with the level of disability of patients. In the third part, we evidenced in a 2-year longitudinal study using graph theoretical approach that MS patients exhibited a dynamical alteration of functional brain topology that significantly correlated with disability progression. In the last part, we evidenced that the transient clinical improvement following physical rehabilitation in MS patients is associated with reversible plasticity mechanisms located in the default mode network, the central executive network and in the left fronto-orbital cortex. These works evidence that MS patients exhibit a complex and dynamical functional reorganization of rs-networks, significantly associated with disability progression. This PhD thesis confirms that rs-fMRI is a relevant biomarker of pathophysiology leading to disability in MS and represents a promising tool for therapeutic assessment of MS patients in the future.