A presença de redes cerebrais funcionais ativadas durante o repouso é bem conhecida e verificada por diferentes técnicas de imagens, como as Imagens por Ressonância Magnética funcionais (IRMf) baseadas no contraste dependente do nível de oxigenação do sangue (BOLD, Blood Oxygenation Level Dependent). Entretanto, apesar de ser atualmente o método não invasivo convencional para tais estudos, o contraste BOLD é sensível a diferentes parâmetros hemodinâmicos (fluxo sanguíneo cerebral, CBF; volume sanguíneo cerebral e extração de oxigênio), cuja relação não é completamente conhecida em diversas patologias. Por outro lado, o método de Marcação dos Spins Arteriais (ASL) é uma técnica de IRM não invasiva que fornece mapas quantitativos de CBF e pode ser usada para avaliar as redes de repouso. Portanto, o objetivo do presente estudo foi investigar a viabilidade de usar sequências de ASL (pulsada e pseudocontínua), disponíveis para o uso na rotina clínica, para o estudo da conectividade funcional do cérebro em estado de repouso. Imagens de ASL e BOLD, de 23 indivíduos jovens e saudáveis, foram adquiridas em um equipamento de 3T. Após o pré-processamento usual das imagens e cálculos dos mapas de perfusão, CBF e pseudo-BOLD (pBOLD), a partir das imagens de ASL, as redes cerebrais de repouso foram obtidas pela Análise de Componentes Independentes (ICA) e pelo método baseado em semente. Utilizando ICA, a análise em grupo conjunta de pBOLD e BOLD identificou cinco redes: rede de modo padrão (DMN), visual, auditiva, saliência e motora. Quando analisados separadamente, os dados de pBOLD mostraram apenas as redes DMN e visual, enquanto os dados de BOLD mostraram também as redes auditiva, saliência, motora, atentiva e frontoparietais direita e esquerda. Para ambas as análises, comparações entre as redes de pBOLD e BOLD apresentaram similaridades de moderadas a altas. Entretanto, nenhuma rede foi observada utilizando os dados de perfusão e CBF. Já as análises baseadas em sementes mostraram correlações significativas, para as séries temporais de pBOLD e CBF, entre regiões que constituem algumas redes de repouso conhecidas (DMN, visual, sensorial-motora, atentiva e frontoparietal). Os valores obtidos para a força das conectividades nas redes de pBOLD e CBF se correlacionaram com aqueles obtidos nas redes de BOLD. As diferenças no desempenho de ASL e BOLD devem-se a uma combinação de fatores, como relação sinal ruído e resolução temporal. Além disso, a natureza dos sinais não é a mesma. O sinal BOLD é influenciado por diferentes parâmetros fisiológicos e é proveniente principalmente de grandes veias; enquanto o sinal de ASL é proveniente da rede de capilares, fornecendo especificidade espacial mais alta para a atividade neuronal, além de permitir a quantificação do CBF, que está relacionado mais diretamente ao metabolismo cerebral. Portanto, o presente estudo mostrou ser possível investigar a conectividade funcional do cérebro no estado de repouso com uma sequência comercial, apesar das limitações técnicas da ASL. Além disso, as séries temporais de CBF e BOLD refletem diferentes aspectos do cérebro em repouso, fornecendo informações complementares dos seus processos fisiológicos / The presence of functional brain networks activated during resting state is well known and has been verified by different imaging techniques, such as the functional Magnetic Resonance Imaging (fMRI) based on the Blood Oxygenation Level-Dependent (BOLD) contrast. Although BOLD-fMRI is currently the conventional non invasive method for such studies, BOLD contrast is sensitive to different hemodynamic parameters (Cerebral Blood Flow, CBF; cerebral blood volume and oxygen extraction fraction), whose relationship is not fully understood in several pathologies. In contrast, the Arterial Spin Labeling (ASL) MRI technique is a non invasive tool for CBF quantification and can be used to investigate resting-state networks. Therefore, the goal of the present study was to investigate the feasibility of using ASL sequences (pulsed and pseudocontinuous), available for clinical routine use, for the study of functional connectivity of the brain at rest. ASL and BOLD images of 23 healthy young subjects were acquired in a 3T machine. After the usual image pre-processing and quantification of perfusion, CBF and pseudo-BOLD (pBOLD) maps, from ASL images, resting-state brain networks were obtained by Independent Component Analysis (ICA) and a seed-based method. Five networks were identified in a joint analysis of pBOLD and BOLD: Default Mode Network (DMN), visual, auditory, salience, and motor. When analyzed separately, pBOLD showed only the DMN and visual networks, while BOLD also showed auditory, salience, motor, attentive, right and left frontoparietal networks. For both analyses, comparisons between pBOLD and BOLD networks showed from moderate to high similarities. However, no network was obtained from perfusion and CBF time series. Seed-based analysis showed significant correlations, for pBOLD e CBF time series, between regions that integrate some known networks (DMN, visual, sensorial-motor, attentive and frontoparietal). Functional connectivity strength obtained from pBOLD and CBF networks correlated with the ones from BOLD data. Differences in performance with ASL and BOLD are due to a combination of factors, such as SNR and temporal resolution. Moreover, the nature of the signals is not the same. BOLD signal is influenced by different physiologic parameters and comes mainly from large veins; while ASL signal comes from small capillaries, providing higher spatial specificity regarding neural activity, in addition to allow the quantification of CBF, which is closer related to the cerebral metabolism. In conclusion, the present study showed the feasibility of investigating functional connectivity of the brain at rest using a commercial ASL sequence, even with its technical limitations. Moreover, CBF and BOLD time series reflect different aspects of the resting-state brain and provide complementary information on its physiological processes

Les objectifs de cette thèse étaient de caractériser les corrélats neuronaux fonctionnels et structurels de l'abolition de la conscience observés pendant le coma et d'identifier les signatures neuronales précoces de la récupération neurologique à partir de cet état. Pour atteindre ce but, nous avons étudié des patients cérébrolésés, recrutés au stade aigu du coma, à l'aide de l'IRM fonctionnelle au repos et IRM structurale. Nos résultats indiquent une réorganisation topologique globale du cerveau des patients, reflétée par une dédifférenciation et une réduction de la résilience des réseaux fonctionnels au repos d'ordre élevé. Ces anomalies sont accompagnées d'une perte de connexions fronto-pariétales à longue distance. Au niveau régional, nous avons observé un schéma complexe de diminution et d'augmentation de la densité de connexion fonctionnelle entre le cortex postéromédial et le cortex préfrontal médial : régions précédemment décrites pour avoir un rôle critique dans la conscience. De manière intéressante, ces modifications de densité de connexion étaient significativement liées à la récupération des patients trois mois après le coma. Enfin, l'analyse multimodale a permis de démontrer une association significative entre la connectivité fonctionnelle et l'intégrité structurelle cérébrales antéro-postérieure, fournissant des informations importantes sur le lien structure/fonction au décours de ces troubles acquis de la conscience. / The aim of the present thesis was to characterize the functional and structural neural correlates of acute consciousness abolition induced by severe brain injury and identify early neural signatures of long-term neurological recovery. To do so, we studied brain-injured patients, recruited in the acute stage of coma, using resting-state functional and structural MRI. Our findings indicated a global topological brain reorganization in coma patients, reflected in dedifferentiated and less resilient high-order resting-state functional networks, paralleled with a loss of long-range fronto-parietal connections. On a regional level, we found a complex pattern of voxel-wise decrease and increase in functional connection density between the posteromedial cortex and the medial prefrontal cortex, regions previously described to have a critical role in conscious processing. These connection density patterns seemed to permit outcome prediction in patients, assessed three months post-coma. Furthermore, the multi-modal MRI analysis demonstrated a significant association between antero-posterior functional connectivity and structural integrity, providing further insights into the pathological underpinning of conscious processing.

Extracting structure from initially incomprehensible streams of events is fundamental to a range of human abilities: from navigating in a new environment to learning a language. These skills rely on our ability to extract spatial and temporal regularities, often with minimal explicit feedback, that is known as statistical learning. Despite the importance of statistical learning for making perceptual decisions, we know surprisingly little about the brain circuits and how they change when learning temporal regularities. In my thesis, I combine behavioural measurements, Diffusion Tensor Imaging (DTI) and resting-state fMRI (rs-fMRI) to investigate the structural and functional circuits that are involved in statistical learning of temporal structures. In particular, I compare structural connectivity as measured by DTI and functional connectivity as measured by rs-fMRI before vs. after training to investigate learning-dependent changes in human brain pathways. Further, I combine the two imaging modalities using graph theory and regression analyses to identify key predictors of individual learning performance. Using a prediction task in the context of sequence learning without explicit feedback, I demonstrate that individuals adapt to the environment’s statistics as they change over time from simple repetition to probabilistic combinations. Importantly, I show that learning of temporal structures relates to decision strategy that varies among individuals between two prototypical distributions: matching the exact sequence statistics or selecting the most probable outcome in a given context (i.e. maximising). Further, combining DTI and rs-fMRI, I show that learning-dependent plasticity in dissociable cortico-striatal circuits relates to decision strategy. In particular, matching relates to connectivity between visual cortex, hippocampus and caudate, while maximisation relates to connectivity between frontal and motor cortices and striatum. These findings have potential translational applications, as alternate brain routes may be re-trained to support learning ability when specific pathways (e.g. memory-related circuits) are compromised by age or disease.

Esta tese apresenta um novo método para analisar dados de ressonância magnética funcional (FMRI) durante o estado de repouso denominado Análise de Componentes Esparsos Locais (LSCA). A LSCA é uma especialização da Análise de Componentes Esparsos (SCA) que leva em consideração a informação espacial dos dados para reconstruir a informação temporal de fontes bem localizadas, ou seja, fontes que representam a atividade de regiões corticais conectadas. Este estudo contém dados de simulação e dados reais. Os dados simulados foram preparados para avaliar a LSCA em diferentes cenários. Em um primeiro cenário, a LSCA é comparada com a Análise de Componentes Principais (PCA) em relação a capacidade de detectar fontes locais sob ruído branco e gaussiano. Em seguida, a LSCA é comparada com o algoritmo de Maximização da Expectativa (EM) no quesito detecção de fontes dinâmicas locais. Os dados reais foram coletados para fins comparativos e ilustrativos. Imagens de FMRI de onze voluntários sadios foram adquiridas utilizando um equipamento de ressonância magnética de 3T durante um protocolo de estado de repouso. As imagens foram pré-processadas e analisadas por dois métodos: a LSCA e a Análise de Componentes Independentes (ICA). Os componentes identificados pela LSCA foram comparados com componentes comumente reportados na literatura utilizando a ICA. Além da comparação direta com a ICA, a LSCA foi aplicada com o propósito único de caracterizar a dinâmica das redes de estado de repouso. Resultados simulados mostram que a LSCA é apropriada para identificar fontes esparsas locais. Em dados de FMRI no estado de repouso, a LSCA é capaz de identificar as mesmas fontes que são identificadas pela ICA, permitindo uma análise mais detalhada das relações entre regiões dentro de e entre componentes e sugerindo que muitos componentes identificados pela ICA em FMRI durante o estado de repouso representam um conjunto de componentes esparsos locais. Utilizando a LSCA, grande parte das fontes identificadas pela ICA podem ser decompostas em um conjunto de fontes esparsas locais que não são necessariamente independentes entre si. Além disso, as fontes identificadas pela LSCA aproximam muito melhor o sinal temporal observado nas regiões representadas por seus componentes do que as fontes identificadas pela ICA. Finalmente, uma análise mais elaborada utilizando a LSCA permite estimar também relações dinâmicas entre os componentes previamente identificados. Assim, a LSCA permite identificar relações clássicas bem como relações causais entre componentes do estado de repouso. As principais implicações desse resultado são que diferentes premissas permitem decomposições aproximadamente equivalentes, entretanto, critérios menos restritivos tais como esparsidade e localização permitem construir modelos mais compactos e biologicamente mais plausíveis. / This thesis presents Local Sparse Component Analysis (LSCA), a new method for analyzing resting state functional magnetic resonance imaging (fMRI) datasets. LSCA, a extension of Sparse Component Analysis (SCA), takes into account data spatial information to reconstruct temporal sources representing connected regions of significant activity. This study contains simulation data and real data. The simulated data were prepared to evaluate the LSCA in different scenarios. In the first scenario, the LSCA is compared with Principal Component Analysis (PCA) for detecting local sources under Gaussian white noise. Then, LSCA is compared with the expectation maximization algorithm (EM) for detecting the dynamics of local sources. Real data were collected for comparative and illustrative purposes. FMRI images from eleven healthy volunteers were acquired using a 3T MRI scanner during a resting state protocol. Images were preprocessed and analyzed using LSCA and Independent Components Analysis (ICA). LSCA components were compared with commonly reported ICA components. In addition, LSCA was applied for characterizing the dynamics of resting state networks. Simulated results have shown that LSCA is suitable for identifying local sparse sources.For real resting state FMRI data, LSCA is able to identify the same sources that are identified using ICA, allowing detailed functional connectivity analysis of the identified regions within and between components. This suggests that ICA resting state networks can be further decomposed into local sparse components that are not necessarily independent from each other. Moreover, LSCA sources better represent local FMRI signal oscillations than ISCA sources. Finally, brain connectivity analysis shows that LSCA can identify both instantaneous and causal relationships between resting state components. The main implication of this study is that independence and sparsity are equivalent assumptions in resting state FMRI. However, less restrictive criteria such as sparsity and source localization allow building much more compact and biologically plausible brain connectivity models.

The analysis of Human brain activity in magnetoencephalography (MEG) can be generally conducted in two ways: either by focusing on the average response evoked by a stimulus repeated over time, more commonly known as an ''event-related field'' (ERF), or by decomposing the signal into functionally relevant oscillatory or frequency bands (such as alpha, beta or gamma). However, the major part of brain activity is arrhythmic and these approaches fail in describing its complexity, particularly in resting-state. As an alternative, the analysis of the 1/f-type power spectrum observed in the very low frequencies, a hallmark of scale-free dynamics, can overcome these issues. Yet it remains unclear whether this scale-free property is functionally relevant and whether its fluctuations matter for behavior. To address this question, our first concern was to establish a visual learning paradigm that would entail functional plasticity during an MEG session. In order to optimize the training effects, we developed new audiovisual (AV) stimuli (an acoustic texture paired with a colored visual motion) that induced multisensory integration and indeed improved learning compared to visual training solely (V) or accompanied with acoustic noise (AVn). This led us to investigate the neural correlates of these three types of training using first a classical method such as the ERF analysis. After source reconstruction on each individual cortical surface using MNE-dSPM, the network involved in the task was identified at the group-level. The selective plasticity observed in the human motion area (hMT+) correlated across all individuals with the behavioral improvement and was supported by a larger network in AV comprising multisensory areas. On the basis of these findings, we further explored the links between the behavior and scale-free properties of these same source-reconstructed MEG signals. Although most studies restricted their analysis to the global measure of self-similarity (i.e. long-range fluctuations), we also considered local fluctuations (i.e. multifractality) by using the Wavelet Leader Based Multifractal Formalism (WLBMF). We found intertwined modulations of self-similarity and multifractality in the same cortical regions as those revealed by the ERF analysis. Most astonishing, the degree of multifractality observed in each individual converged during the training towards a single attractor that reflected the asymptotic behavioral performance in hMT+. Finally, these findings and their associated methodological issues are compared with the ones that came out from the ERF analysis.

A presença de redes cerebrais funcionais ativadas durante o repouso é bem conhecida e verificada por diferentes técnicas de imagens, como as Imagens por Ressonância Magnética funcionais (IRMf) baseadas no contraste dependente do nível de oxigenação do sangue (BOLD, Blood Oxygenation Level Dependent). Entretanto, apesar de ser atualmente o método não invasivo convencional para tais estudos, o contraste BOLD é sensível a diferentes parâmetros hemodinâmicos (fluxo sanguíneo cerebral, CBF; volume sanguíneo cerebral e extração de oxigênio), cuja relação não é completamente conhecida em diversas patologias. Por outro lado, o método de Marcação dos Spins Arteriais (ASL) é uma técnica de IRM não invasiva que fornece mapas quantitativos de CBF e pode ser usada para avaliar as redes de repouso. Portanto, o objetivo do presente estudo foi investigar a viabilidade de usar sequências de ASL (pulsada e pseudocontínua), disponíveis para o uso na rotina clínica, para o estudo da conectividade funcional do cérebro em estado de repouso. Imagens de ASL e BOLD, de 23 indivíduos jovens e saudáveis, foram adquiridas em um equipamento de 3T. Após o pré-processamento usual das imagens e cálculos dos mapas de perfusão, CBF e pseudo-BOLD (pBOLD), a partir das imagens de ASL, as redes cerebrais de repouso foram obtidas pela Análise de Componentes Independentes (ICA) e pelo método baseado em semente. Utilizando ICA, a análise em grupo conjunta de pBOLD e BOLD identificou cinco redes: rede de modo padrão (DMN), visual, auditiva, saliência e motora. Quando analisados separadamente, os dados de pBOLD mostraram apenas as redes DMN e visual, enquanto os dados de BOLD mostraram também as redes auditiva, saliência, motora, atentiva e frontoparietais direita e esquerda. Para ambas as análises, comparações entre as redes de pBOLD e BOLD apresentaram similaridades de moderadas a altas. Entretanto, nenhuma rede foi observada utilizando os dados de perfusão e CBF. Já as análises baseadas em sementes mostraram correlações significativas, para as séries temporais de pBOLD e CBF, entre regiões que constituem algumas redes de repouso conhecidas (DMN, visual, sensorial-motora, atentiva e frontoparietal). Os valores obtidos para a força das conectividades nas redes de pBOLD e CBF se correlacionaram com aqueles obtidos nas redes de BOLD. As diferenças no desempenho de ASL e BOLD devem-se a uma combinação de fatores, como relação sinal ruído e resolução temporal. Além disso, a natureza dos sinais não é a mesma. O sinal BOLD é influenciado por diferentes parâmetros fisiológicos e é proveniente principalmente de grandes veias; enquanto o sinal de ASL é proveniente da rede de capilares, fornecendo especificidade espacial mais alta para a atividade neuronal, além de permitir a quantificação do CBF, que está relacionado mais diretamente ao metabolismo cerebral. Portanto, o presente estudo mostrou ser possível investigar a conectividade funcional do cérebro no estado de repouso com uma sequência comercial, apesar das limitações técnicas da ASL. Além disso, as séries temporais de CBF e BOLD refletem diferentes aspectos do cérebro em repouso, fornecendo informações complementares dos seus processos fisiológicos / The presence of functional brain networks activated during resting state is well known and has been verified by different imaging techniques, such as the functional Magnetic Resonance Imaging (fMRI) based on the Blood Oxygenation Level-Dependent (BOLD) contrast. Although BOLD-fMRI is currently the conventional non invasive method for such studies, BOLD contrast is sensitive to different hemodynamic parameters (Cerebral Blood Flow, CBF; cerebral blood volume and oxygen extraction fraction), whose relationship is not fully understood in several pathologies. In contrast, the Arterial Spin Labeling (ASL) MRI technique is a non invasive tool for CBF quantification and can be used to investigate resting-state networks. Therefore, the goal of the present study was to investigate the feasibility of using ASL sequences (pulsed and pseudocontinuous), available for clinical routine use, for the study of functional connectivity of the brain at rest. ASL and BOLD images of 23 healthy young subjects were acquired in a 3T machine. After the usual image pre-processing and quantification of perfusion, CBF and pseudo-BOLD (pBOLD) maps, from ASL images, resting-state brain networks were obtained by Independent Component Analysis (ICA) and a seed-based method. Five networks were identified in a joint analysis of pBOLD and BOLD: Default Mode Network (DMN), visual, auditory, salience, and motor. When analyzed separately, pBOLD showed only the DMN and visual networks, while BOLD also showed auditory, salience, motor, attentive, right and left frontoparietal networks. For both analyses, comparisons between pBOLD and BOLD networks showed from moderate to high similarities. However, no network was obtained from perfusion and CBF time series. Seed-based analysis showed significant correlations, for pBOLD e CBF time series, between regions that integrate some known networks (DMN, visual, sensorial-motor, attentive and frontoparietal). Functional connectivity strength obtained from pBOLD and CBF networks correlated with the ones from BOLD data. Differences in performance with ASL and BOLD are due to a combination of factors, such as SNR and temporal resolution. Moreover, the nature of the signals is not the same. BOLD signal is influenced by different physiologic parameters and comes mainly from large veins; while ASL signal comes from small capillaries, providing higher spatial specificity regarding neural activity, in addition to allow the quantification of CBF, which is closer related to the cerebral metabolism. In conclusion, the present study showed the feasibility of investigating functional connectivity of the brain at rest using a commercial ASL sequence, even with its technical limitations. Moreover, CBF and BOLD time series reflect different aspects of the resting-state brain and provide complementary information on its physiological processes

Esta tese apresenta um novo método para analisar dados de ressonância magnética funcional (FMRI) durante o estado de repouso denominado Análise de Componentes Esparsos Locais (LSCA). A LSCA é uma especialização da Análise de Componentes Esparsos (SCA) que leva em consideração a informação espacial dos dados para reconstruir a informação temporal de fontes bem localizadas, ou seja, fontes que representam a atividade de regiões corticais conectadas. Este estudo contém dados de simulação e dados reais. Os dados simulados foram preparados para avaliar a LSCA em diferentes cenários. Em um primeiro cenário, a LSCA é comparada com a Análise de Componentes Principais (PCA) em relação a capacidade de detectar fontes locais sob ruído branco e gaussiano. Em seguida, a LSCA é comparada com o algoritmo de Maximização da Expectativa (EM) no quesito detecção de fontes dinâmicas locais. Os dados reais foram coletados para fins comparativos e ilustrativos. Imagens de FMRI de onze voluntários sadios foram adquiridas utilizando um equipamento de ressonância magnética de 3T durante um protocolo de estado de repouso. As imagens foram pré-processadas e analisadas por dois métodos: a LSCA e a Análise de Componentes Independentes (ICA). Os componentes identificados pela LSCA foram comparados com componentes comumente reportados na literatura utilizando a ICA. Além da comparação direta com a ICA, a LSCA foi aplicada com o propósito único de caracterizar a dinâmica das redes de estado de repouso. Resultados simulados mostram que a LSCA é apropriada para identificar fontes esparsas locais. Em dados de FMRI no estado de repouso, a LSCA é capaz de identificar as mesmas fontes que são identificadas pela ICA, permitindo uma análise mais detalhada das relações entre regiões dentro de e entre componentes e sugerindo que muitos componentes identificados pela ICA em FMRI durante o estado de repouso representam um conjunto de componentes esparsos locais. Utilizando a LSCA, grande parte das fontes identificadas pela ICA podem ser decompostas em um conjunto de fontes esparsas locais que não são necessariamente independentes entre si. Além disso, as fontes identificadas pela LSCA aproximam muito melhor o sinal temporal observado nas regiões representadas por seus componentes do que as fontes identificadas pela ICA. Finalmente, uma análise mais elaborada utilizando a LSCA permite estimar também relações dinâmicas entre os componentes previamente identificados. Assim, a LSCA permite identificar relações clássicas bem como relações causais entre componentes do estado de repouso. As principais implicações desse resultado são que diferentes premissas permitem decomposições aproximadamente equivalentes, entretanto, critérios menos restritivos tais como esparsidade e localização permitem construir modelos mais compactos e biologicamente mais plausíveis. / This thesis presents Local Sparse Component Analysis (LSCA), a new method for analyzing resting state functional magnetic resonance imaging (fMRI) datasets. LSCA, a extension of Sparse Component Analysis (SCA), takes into account data spatial information to reconstruct temporal sources representing connected regions of significant activity. This study contains simulation data and real data. The simulated data were prepared to evaluate the LSCA in different scenarios. In the first scenario, the LSCA is compared with Principal Component Analysis (PCA) for detecting local sources under Gaussian white noise. Then, LSCA is compared with the expectation maximization algorithm (EM) for detecting the dynamics of local sources. Real data were collected for comparative and illustrative purposes. FMRI images from eleven healthy volunteers were acquired using a 3T MRI scanner during a resting state protocol. Images were preprocessed and analyzed using LSCA and Independent Components Analysis (ICA). LSCA components were compared with commonly reported ICA components. In addition, LSCA was applied for characterizing the dynamics of resting state networks. Simulated results have shown that LSCA is suitable for identifying local sparse sources.For real resting state FMRI data, LSCA is able to identify the same sources that are identified using ICA, allowing detailed functional connectivity analysis of the identified regions within and between components. This suggests that ICA resting state networks can be further decomposed into local sparse components that are not necessarily independent from each other. Moreover, LSCA sources better represent local FMRI signal oscillations than ISCA sources. Finally, brain connectivity analysis shows that LSCA can identify both instantaneous and causal relationships between resting state components. The main implication of this study is that independence and sparsity are equivalent assumptions in resting state FMRI. However, less restrictive criteria such as sparsity and source localization allow building much more compact and biologically plausible brain connectivity models.

Les aires cérébrales entretiennent des relations fonctionnelles, formant ainsi des réseaux qui peuvent être altérés dans diverses pathologies. L'étude de ces réseaux de connectivité fonctionnelle pourrait potentiellement aider au diagnostic d'un individu et au traitement de sa pathologie. À travers quatre études, nous avons montré que l'analyse en composantes indépendantes spatiale est une méthode suffisamment sensible, reproductible et spécifique pour mettre en évidence, à l’échelle individuelle et au repos, des réseaux sains et pathologiques fiables. Ainsi, le réseau pathologique sous-tendant les hallucinations acoustico-verbales permet de définir les aires cérébrales à traiter par stimulation magnétique transcrânienne. Parmi les réseaux sains, ceux qui impliquent le cortex cingulaire postérieur et le précunéus semblent profondément altérés dans les troubles de la conscience, et peuvent servir d'outil diagnostic pour distinguer le locked-in syndrome de l'état végétatif. Il est désormais possible de cartographier, à l'échelle individuelle, les relations fonctionnelles entre les aires cérébrales. L’étude à venir de la dynamique et du niveau d’activité des réseaux de connectivité fonctionnelle nous renseignera davantage sur leurs fonctions et leur implication dans la pathologie. / Brain areas are functionally connected in networks, even at rest. Since such connectivity networks could be impaired in several pathologies, they could potentially serve for diagnosis and treatment. Based on four studies, spatial independent component analysis has shown sufficient sensitivity, reproducibility and specificity to produce reliable healthy as well as pathological networks at the individual level. Therefore, the network underlying auditory hallucination could define the brain areas to treat by transcranial magnetic stimulation. Among the common resting-state networks, the ones involving the posterior cingular cortex and the precuneus seem deeply altered in disorders of consciousness, and so could be used as a diagnostic tool to distinguish the locked-in syndrome from the vegetative state. We can now map, at the individual level, the functional relationships between brain areas. Further studies on the dynamic and on the level of activity of the functional connectivity networks might provide relevant information about their functions and their involvement in the pathology.

Abstract Frontotemporal lobar degeneration (FTLD) is the second most common neurodegenerative disease leading to early-onset dementia with an estimated worldwide prevalence of 10 to 30 cases per 100000 individuals in the age group of 46 to 65 years. Behavioral variant frontotemporal dementia (bvFTD) is the most common FTLD subtype. It is characterized by a progressive deterioration of behavior and personality as well as executive dysfunction. In Finland, almost 50 % of familial FTLD cases are attributable to the C9ORF72 mutation. bvFTD is associated with a characteristic pattern of brain atrophy detectable in structural MRI. However, these changes are typically not visible in the early stages of the disease. Resting-state functional MRI (RS-fMRI) is being increasingly used to evaluate changes in functional connectivity within neuronal networks in the brain but only a few RS-fMRI investigations of bvFTD patients have been published with inconsistent results. The object of this thesis was to investigate functional connectivity changes detected in the salience (SLN) and default mode networks (DMN) in bvFTD. Another aim was to clarify the role of other cognitive resting-state networks in this disease. A cohort of 26 bvFTD patients was studied, with 8 of these patients carrying the C9ORF72 expansion. Connectivity changes were detected in multiple clinically relevant cognitive networks. Decreased functional connectivity was observed in the SLN, which is associated with guiding of behavior. Increased activity was present in the DMN and the dorsal attention network (DAN). In C9ORF72 associated bvFTD, there was an abnormal linkage detected between the DMN and the thalamus. Currently, fMRI is generally used as a research tool and in a group setting. Different study methods have been used in the literature and also in the studies of this thesis, the analysis procedures differed to some extent. The variety of analysis methods may explain the heterogeneity in fMRI findings in bvFTD patients. There is a need for standardization of the fMRI methodology, larger study groups and also in the future the methodology should be improved so that single patient analysis would provide results to allow a confident diagnosis of this disease. / Tiivistelmä Otsa-ohimolohkorappeuma (Frontotemporal lobar degeneration, FTLD) on toiseksi yleisin etenevä dementiaan johtava sairaus, joka ilmaantuu usein jo työikäisenä. Otsalohkodementia on otsa-ohimolohkorappeuman yleisin alamuoto, jonka oireisto painottuu persoonan ja käyttäytymisen muutoksiin sekä toiminnan ohjauksen ongelmiin. Suomessa C9ORF72-toistojaksomutaatio selittää lähes 50 % perinnöllisistä otsa-ohimolohkorappeumista. Aivojen rakenteellisella magneettikuvauksella (MK) voidaan havaita rakenteellisia muutoksia, jotka ilmaantuvat kuitenkin vasta taudin edettyä vaikeampaan vaiheeseen. Aivojen lepotilan toiminnallinen magneettikuvaus (TMK) mahdollistaa aivojen hermoverkkojen toiminnan eli konnektiviteetin kartoituksen. Aiemmin TMK:a on tutkittu esim. Alzheimerin taudissa. Otsalohkodementiassa TMK:sta on julkaistu ainoastaan yksittäisiä tutkimuksia ja tulokset ovat olleet osin ristiriitaisia. Väitöskirjatutkimuksen tarkoituksena on ollut selvittää valve-lepotilan hermoverkossa ja olennaisen tunnistavassa hermoverkossa tapahtuvia muutoksia otsalohkodementiaa sairastavilla potilailla. Toisena tavoitteena on ollut tutkia muissa kognitiivisissa hermoverkoissa tapahtuvia muutoksia. Otsalohkodementiaa sairastaville potilaille (n= 26) sekä ikä- ja sukupuolivakioiduille kontrolleille on tehty kliininen tutkimus ja rakenteellinen sekä toiminnallinen aivojen magneettikuvaus. Kahdeksalla potilaalla todettiin C9ORF72-toistojaksomutaatio. Useiden kognitiivisten hermoverkkojen toiminnassa havaittiin muutoksia, jotka korreloivat potilaiden kliinisiin oireisiin. Alentunutta konnektiviteettia todettiin olennaisen tunnistavassa hermoverkossa, joka osallistuu käyttäytymisen säätelyyn. Lisääntynyttä konnektiviteettia esiintyi valve-lepotilan hermoverkossa ja tarkkaavaisuus hermoverkossa. Potilailla, joilla on C9ORF72-mutaatio, havaittiin epänormaali yhteys valve-lepotilan hermoverkon ja talamuksen välillä. TMK:ta käytetään tällä hetkellä lähinnä tutkimustyökaluna. Analyysityökaluissa on ollut vaihtelevuutta eri julkaisuissa ja osin myös tämän väitöskirjan osatöissä. Julkaistut TMK-löydökset otsalohkodementiassa ovat osin ristiriitaisia ja se saattaa selittyä erilaisilla analyysimenetelmillä. Metodologiaa tulisi standardisoida ja lisäksi tarvitaan suurempia potilasryhmiä ja menetelmien kehittämistä, jotta TMK:n käyttö yksilötason kliinisessä diagnostiikassa olisi jatkossa mahdollista.

Près de 30% des patients ayant survécus à un AVC, développent une dépression (DPAVC) dont le retentissement sur la qualité de vie peut être majeur. Sa physiopathologie est encore méconnue et les critères diagnostiques ne sont pas clairement définis. Notre objectif est d'identifier des facteurs précoces neuropsychologiques et de neuroimagerie prédictifs d'une dépression 3 mois après l’AVC.Cinquante-cinq patients présentant un premier AVC, sans antécédent neurologique ou psychiatrique ont été inclus. Dix jours après l’AVC, la sévérité des symptômes dépressifs et anxieux a été évaluée d’une part, par les échelles standard d’Hamilton et d’autre part, en vie quotidienne durant 7 jours, par la méthode d’échantillonnage des expériences (ESM). Au même temps, un examen d’IRM multimodale a été réalisé (IRM fonctionnelle de repos, DTI et 3D T1) afin d'évaluer les modifications anatomo-fonctionnelles de l’organisation cérébrale. Trois mois après l’AVC, une mesure standard de la sévérité des symptômes dépressifs et anxieux est à nouveau effectuée. A partir de ces données nous avons exploré la relation existant entre 1/ la sévérité des symptômes dépressifs et les données IRM 2/ la sévérité des symptômes dépressifs et les données ESM 3/ la sévérité des symptômes dépressifs mesurée par ESM et les modifications anatomo-fonctionnelles cérébrales. Nous avons mis en évidence une modification de la connectivité fonctionnelle entre les régions postérieures du réseau en "default mode", de la même façon que dans les dépressions majeure et vasculaire ; et entre le cortex temporal moyen et ce réseau. A la phase aigue de l’AVC, 2 profils symptomatologiques se distinguent : le premier est caractérisé par une grande fatigue et une forte anhédonie, le deuxième est définit par de la tristesse, une forte anxiété, des pensées négatives et une forte réactivité émotionnelle. Ce dernier est associé au risque de DPAVC à 3 mois. Enfin, nous avons montré que les modifications fonctionnelles du DMN prédictives de l’AVC étaient associées à la réactivité émotionnelle, alors que le volume de substance grise du cervelet était corrélé à la fréquence des pensées positives et négatives.En conclusion, la physiopathologie de la DPAVC présenterait des similitudes avec celle de la dépression majeure et de la dépression vasculaire, mais aussi des différences comme l’engagement du cortex temporal moyen au sein du réseau en « default mode ». De plus, cette étude suggère qu'à côté de l'implication de la lésion cérébro-vasculaire, des critères de vulnérabilité psychobiologiques antérieurs à l’AVC influenceraient la survenue d’une dépression. / 30% of stroke survivors will experience Post-Stroke Depression (PSD) that is associated to a poor quality of life. PSD is still under-diagnosed due to the absence of clear diagnostic criteria and its pathophysiology remains unknown. The aim of this study was to identify early imaging and psychiatric risk factors of depression 3 months after stroke. Patients with a first ischemic stroke, without any neurologic and psychiatric history were included. Daily-life symptoms were evaluated using ESM 10 days after stroke. Brain MRI acquisition was performed at 10 days after stroke including DWI, FLAIR/T2, resting state fMRI and anatomical sequences. We explored the association between 1/ the severity of depressive symptoms and MRI data 2/ the severity of depressive symptoms and ESM data 3/ the severity of depressive symptoms measured by ESM and MRI data.Results revealed a modification of the functional connectivity between posterior structures of the DMN (Default Mode Network) and between the middle temporal cortex and the DMN. In the acute phase, depressed patients presented either high fatigue and anhedonia or another profile including high anxiety, negative thoughts and emotional reactivity which is associated to the risk of depression 3 months after stroke. Moreover, we demonstrated that functional connectivity modifications within the DMN and the cerebellum grey matter were respectively associated to emotional reactivity and the frequency of positive and negative thoughts.In conclusion, modifications of the DMN were implicated in the physiopathology of PSD in the same way that major or vascular depression, with a specificity represented by the new contribution of the middle temporal cortex within the DMN. Furthermore, this study suggests that more than a stroke lesion, anterior psychobiological vulnerabilities of an individual patient could mediate PSD occurrence.