Children and adolescents with concussion display aberrant functional connectivity in some of the major neurocognitive networks. This includes the Default Mode Network, Central Executive Network and Salience Network. Using resting state fMRI, the purpose of this thesis was to explore the functional connectivity of cognition-related networks in youth experiencing concussion. With a prospective cohort study, the functional connectivity (defined as the temporal coherence between spatially separated brain regions) of children and adolescents ages 10-18 years old was evaluated in relation to a number of demographic and injury-specific factors including recovery length, age at the time of injury, symptom severity, and neurocognitive performance. The results showed two general trends: (1) a reduction in connectivity (i.e., hypoconnectivity) between the regions of the Default Mode Network, and (2) an increase in connectivity (i.e., hyperconnectivity) between additional sensory-related regions like the cerebellum and hippocampus. The Default Mode Network, which processes self-referential information, has a long-protracted development across childhood through adulthood. Given that the participants in this cohort exhibited reduced functional connectivity within the Default Mode Network and between the Default Mode Network and other neurocognitive networks suggests that this is an area of vulnerability in youth in the event of concussion. Increased connectivity between the Central Executive Network and Salience Network, and between cognitive- and sensory-related regions such as the hippocampus and cerebellum might be interpreted as a compensatory mechanism to supplement deficits of the Default Mode Network. This thesis sheds light on important concussion-related regions for future research to investigate further and delves into the possible neural mechanisms contributing to the cognitive, sensory, mood, and sleep disturbances in children and adolescents with concussion. / Dissertation / Doctor of Philosophy (PhD) / Your brain at rest is not resting. In fact, your many brain regions are continuously communicating even during rest to maintain important communication between them. This communication between brain regions is termed functional connectivity. When you receive a blow to the head, face, neck, or another part of your body that senses a biomechanical force to your brain, the functional connectivity (i.e., communication lines) between your brain regions may be altered. A blow of this nature is considered a concussion, also known as a mild traumatic brain injury. With disruptions to the typical functional connectivity between your brain regions following a concussion, you may experience difficulty in managing cognitive tasks, emotions, and body coordination. Among those most vulnerable to the effects of concussion are children and adolescents whose brains have yet to develop fully. The goal of this thesis was to evaluate the functional connectivity between brain regions of children and adolescents to determine how brain communication might be disrupted following concussion. These evaluations were done using functional magnetic resonance imaging (fMRI) of the brains of children and adolescents ages 10-18 years old. It was discovered that the functional connectivity of the frontal lobe is related severity of post-concussion symptoms such that individuals with worse symptoms had reduced functional connectivity in the frontal lobe compared to individuals who reported less severe symptoms. Further, children and adolescents with longer recovery periods have a different level of functional connectivity in the temporal lobe compared to youth with relatively shorter recovery periods. This might suggest that both of these regions could provide prognostic value in determining who might have worse symptoms or a longer recovery time following injury. In comparison to children and adolescents who have not had a concussion, children and adolescents experiencing a concussion are more likely to have abnormal functional connectivity between the hippocampus and cerebellum, which are particularly involved in processing sensory information and navigation. This was interpreted to mean that the brain responded to the concussion by increasing the communication between regions that might help a child with a concussion coordinate their bodies so that they can move from place to place. This was additionally supported by a further investigation which showed that children and adolescents have reduced communication between areas of the brain that might allow them to process information about the self (e.g., memories, sensations, relationships with others, etc.). Overall, the results demonstrated that following a concussion, children and adolescents may have a deficit in the functioning of the frontal lobe in a specific region that allows them to process cognitive and sensory information. This might explain why concussion leads to poor memory, body coordination, sensitivity to light and sounds, and even difficulty sleeping. Their brains might then compensate for the disruption by increasing alternate pathways of communication. Together these findings open gateways for future researchers to look more deeply at the specific regions affected by concussion in youth. It draws attention to the many neurocognitive, emotional, and somatic symptoms a child with a concussion exhibits and their symptoms’ underlying neurological processes.

Le cerveau humain est un réseau très complexe. Le fonctionnement cérébral ne résulte donc pas de l'activation de régions cérébrales isolées mais au contraire met en jeu des réseaux distribués dans le cerveau (Bassett and Sporns, 2017; McIntosh, 2000). Par conséquent, l'analyse de la connectivité cérébrale à partir des données de neuroimagerie occupe aujourd'hui une place centrale dans la compréhension des fonctions cognitives (Sporns, 2010). Grâce à son excellente résolution spatiale, l'IRMf est devenue l'une des méthodes non invasives les plus couramment utilisées pour étudier cette connectivité. Cependant, l'IRMf a une faible résolution temporelle ce qui rend très difficile le suivi de la dynamique des réseaux cérébraux. Un défi considérable en neuroscience cognitive est donc l'identification et le suivi des réseaux cérébraux sur des durées courtes (Hutchison et al., 2013), généralement <1s pour une tâche de dénomination d'images, par exemple. Jusqu'à présent, peu d'études ont abordé cette question qui nécessite l'utilisation de techniques ayant une résolution temporelle très élevée (de l'ordre de la ms), ce qui est le cas pour la magnéto- ou l'électro-encéphalographie (MEG ou EEG). Cependant, l'interprétation des mesures de connectivité à partir d'enregistrements effectués au niveau des électrodes (scalp) n'est pas simple, car ces enregistrements ont une faible résolution spatiale et leur précision est altérée par les effets de conduction par le volume (Schoffelen and Gross, 2009). Ainsi, au cours des dernières années, l'analyse de la connectivité fonctionnelle au niveau des sources corticales reconstruites à partir des signaux du scalp a fait l'objet d'un intérêt croissant. L'avantage de cette méthode est d'améliorer la résolution spatiale, tout en conservant l'excellente résolution temporelle de l'EEG ou de la MEG (Hassan et al., 2014; Hassan and Wendling, 2018; Schoffelen and Gross, 2009). Cependant, l'aspect dynamique n'a pas été suffisamment exploité par cette méthode. Le premier objectif de cette thèse est de montrer comment l'approche « EEG connectivité source » permet de suivre la dynamique spatio-temporelle des réseaux cérébraux impliqués soit dans une tache cognitive, soit à l'état de repos. Par ailleurs, les études récentes ont montré que les désordres neurologiques sont le plus souvent associés à des anomalies dans la connectivité cérébrale qui entraînent des altérations dans des réseaux cérébraux «large-échelle» impliquant des régions distantes (Fornito and Bullmore, 2014). C'est particulièrement le cas pour l'épilepsie et les maladies neurodégénératives (Alzheimer, Parkinson) qui constituent, selon l'OMS, un enjeu majeur de santé publique. Dans ce contexte, la demande clinique est très forte pour de nouvelles méthodes capables d'identifier des réseaux pathologiques, méthodes simples à mettre en œuvre et surtout non invasives. Ceci est le deuxième objectif de cette thèse. / The human brain is a very complex network. Cerebral function therefore does not imply activation of isolated brain regions but instead involves distributed networks in the brain (Bassett and Sporns, 2017, McIntosh, 2000). Therefore, the analysis of the brain connectivity from neuroimaging data has an important role to understand cognitive functions (Sporns, 2010). Thanks to its excellent spatial resolution, fMRI has become one of the most common non-invasive methods used to study this connectivity. However, fMRI has a low temporal resolution which makes it very difficult to monitor the dynamics of brain networks. A considerable challenge in cognitive neuroscience is therefore the identification and monitoring of brain networks over short time durations(Hutchison et al., 2013), usually <1s for a picture naming task, for example. So far, few studies have addressed this issue which requires the use of techniques with a very high temporal resolution (of the order of the ms), which is the case for magneto- or electro-encephalography (MEG or EEG). However, the interpretation of connectivity measurements from recordings made at the level of the electrodes (scalp) is not simple because these recordings have low spatial resolution and their accuracy is impaired by volume conduction effects (Schoffelen and Gross, 2009). Thus, during recent years, the analysis of functional connectivity at the level of cortical sources reconstructed from scalp signals has been of increasing interest. The advantage of this method is to improve the spatial resolution, while maintaining the excellent resolution of EEG or MEG (Hassan et al., 2014; Hassan and Wendling, 2018; Schoffelen and Gross, 2009). However, the dynamic aspect has not been sufficiently exploited by this method. The first objective of this thesis is to show how the EEG connectivity approach source "makes it possible to follow the spatio-temporal dynamics of the cerebral networks involved either in a cognitive task or at rest. Moreover, recent studies have shown that neurological disorders are most often associated with abnormalities in cerebral connectivity that result in alterations in wide-scale brain networks involving remote regions (Fornito and Bullmore, 2014). This is particularly the case for epilepsy and neurodegenerative diseases (Alzheimer's, Parkinson's) which constitute, according to WHO, a major issue of public health.In this context, the need is high for new methods capable of identifying Pathological networks, from easy to use and non-invasive techniques. This is the second objective of this thesis.

The mammalian brain is, with its numerous neural elements and structured complex connectivity, one of the most complex systems in nature. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex networks. Here, we try to shed some light on the relationship between structural and functional connectivities by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the cortical areas by a subnetwork of interacting excitable neurons (multilevel model) and by a neural mass model (population model). With weak couplings, the multilevel model displays biologically plausible dynamics and the synchronization patterns reveal a hierarchical cluster organization in the network structure. We can identify a group of brain areas involved in multifunctional tasks by comparing the dynamical clusters to the topological communities of the network. With strong couplings of multilevel model and by using neural mass model, the dynamics are characterized by well-defined oscillations. The synchronization patterns are mainly determined by the node intensity (total input strengths of a node); the detailed network topology is of secondary importance. The biologically improved multilevel model exhibits similar dynamical patterns in the two regimes. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks. / Das Gehirn von Säugetieren stellt mit seinen zahlreichen, hochgradig vernetzten Neuronen ein natürliches Netzwerk von immenser Komplexität dar. In der jüngsten Vergangenheit sind die großflächige kortikale Konnektivitäten, sowohl unter strukturellen wie auch funktionalen Gesichtspunkten, in den Fokus der Forschung getreten. Die Verwendung von komplexe Netzwerke spielt hierbei eine entscheidende Rolle. In der vorliegenden Dissertation versuchen wir, das Verhältnis von struktureller und funktionaler Konnektivität durch Untersuchung der Synchronisationsdynamik anhand eines realistischen Modells der Konnektivität im Kortex einer Katze näher zu beleuchten. Wir modellieren die Kortexareale durch ein Subnetzwerk interagierender, erregbarer Neuronen (multilevel model) und durch ein Modell von Neuronenensembles (population model). Bei schwacher Kopplung zeigt das multilevel model eine biologisch plausible Dynamik und die Synchronisationsmuster lassen eine hierarchische Organisation der Netzwerkstruktur erkennen. Indem wir die dynamischen Cluster mit den topologischen Einheiten des Netzwerks vergleichen, sind wir in der Lage die Hirnareale, die an der Bewältigung komplexer Aufgaben beteiligt sind, zu identifizieren. Bei starker Kopplung im multilevel model und unter Verwendung des Ensemblemodells weist die Dynamik klare Oszillationen auf. Die Synchronisationsmuster werden hauptsächlich durch die Eingangsstärke an den einzelnen Knoten bestimmt, während die genaue Netzwerktopologie zweitrangig ist. Eine Erweiterung des Modells auf andere biologisch relevante Faktoren bestätigt die vorherigen Ergebnisse. Die Untersuchung der Synchronisation in einem multilevel model des Kortex ermöglicht daher tiefere Einblicke in die Zusammenhänge zwischen Netzwerktopologie und funktionaler Organisation in komplexen Hirn-Netzwerken.

This thesis provides a novel dynamic large-scale network perspective on brain activity of human sleep based on the analysis of unique human neuroimaging data. Specifically, I provide new information based on integrating spatial and temporal aspects of brain activity both in the transitions between and during wakefulness and various stages of non-rapid-eye movement (NREM) sleep. This is achieved through investigations of inter-regional interactions, functional connectivity (FC), between activity timecourses throughout the brain. Overall, the presented findings provide new important whole-brain insights for our current understanding of sleep, and potentially also of sleep disorders and consciousness in general. In Chapter 2 I present a robust global increase in similarity between the structural connectivity (SC) and the FC in slow-wave sleep (SWS) in almost all of the participants of two independent fMRI datasets. This could point to a decreased state repertoire and more rigid brain dynamics during SWS. Chapter 2 further identifies the changes in FC strengths between wakefulness and individual stages of NREM sleep across the whole-brain fMRI network. I report connectivity in posterior parts of the brain as particularly strong during wakefulness, while connections between temporal and frontal cortices are increased in strength during N1 and N2 sleep. SWS is characterised by a global drop in FC. In Chapter 3 I take advantage of rare MEG recordings of NREM sleep to show, for the first time, the feasibility of constructing source-space FC networks of sleep using power envelope correlations. The increased temporal information of MEG signals allows me to identify the specific frequencies underlying the FC differences identified in Chapter 2 with fMRI. The beta band (16 – 30 Hz) thus stands out as important for the strong posterior connectivity of wakefulness, while a range of frequency bands from delta (0.25 – 4 Hz) to sigma (13 – 16 Hz) all appear to contribute to N2-specific FC increases. Consistent with the fMRI results, slow-wave sleep shows the lowest level of FC. Interestingly, however, the MEG signals suggest a fronto-temporal network of high connectivity in the alpha band, possibly reflecting memory processes. In Chapter 4 I expand the within-frequency FC analysis of Chapter 3 to explore potential cross-frequency interactions in the MEG FC networks. It is shown that N2 sleep involves an abundance of frequency cross-talk, while SWS includes very little. A multi-layer network approach shows that the gamma band (30 – 48 Hz) is particularly integrated in wakefulness. Chapter 5 addresses the identified MEG FC findings from the perspective of traditional spectral sleep staging. By correlating temporal changes in spectral power at the sensor level to fluctuations in average FC, a specific type of transient events is found to underlie the strong N2-specific coupling in static FC values. Lastly, in Chapter 6 I make the leap out of the constraints of traditional low-resolution sleep staging, and extract dynamic states of FC from fMRI timecourses in a completely unsupervised fashion. This provides a novel representation of whole-brain states of sleep and the dynamics governing them. I argue that data-driven approaches like this are necessary to fully characterise the spatiotemporal principles underlying wakefulness and sleep in the human brain.

Le cerveau humain est un réseau «large-échelle» formé de régions corticales distribuées et fonctionnellement interconnectées. Le traitement de l'information par le cerveau est un processus dynamique mettant en jeu une réorganisation rapide des réseaux cérébraux fonctionnels, sur une échelle de temps très courte (inférieure à la seconde). Dans le champ des neurosciences cognitives, deux grandes questions restent ouvertes concernant ces réseaux. D'une part, est-il possible de suivre leur dynamique spatio-temporelle avec une résolution temporelle nettement supérieure à celle de l'IRM fonctionnelle? D'autre part, est-il possible de mettre en évidence des différences significatives dans ces réseaux lorsque le cerveau traite des stimuli (visuels, par exemple) ayant des caractéristiques différentes. Ces deux questions ont guidé les développements méthodologiques élaborés dans cette thèse. En effet, de nouvelles méthodes basées sur l'électroencéphalographie sont proposées. Ces méthodes permettent, d'une part de suivre la reconfiguration dynamique des réseaux cérébraux fonctionnels à une échelle de temps inférieure à la seconde. Elles permettent, d'autre part, de comparer deux réseaux cérébraux activés dans des conditions spécifiques. Nous proposons donc un nouvel algorithme bénéficiant de l'excellente résolution temporelle de l'EEG afin de suivre la reconfiguration rapide des réseaux fonctionnels cérébraux à l'échelle de la milliseconde. L'objectif principal de cet algorithme est de segmenter les réseaux cérébraux en un ensemble d' «états de connectivité fonctionnelle» à l'aide d'une approche de type « clustering ». L'algorithme est basé sur celui des K-means et a été appliqué sur les graphes de connectivité obtenus à partir de l'estimation des valeurs de connectivité fonctionnelle entre les régions d'intérêt considérées. La seconde question abordée dans ce travail relève de la mesure de similarité entre graphes. Ainsi, afin de comparer des réseaux de connectivité fonctionnelle, nous avons développé un algorithme (SimNet) capable de quantifier la similarité entre deux réseaux dont les nœuds sont définis spatialement. Cet algorithme met en correspondance les deux graphes en « déformant » le premier pour le rendre identique au second sur une contrainte de coût minimal associée à la déformation (insertion, suppression, substitution de nœuds et d’arêtes). Il procède selon deux étapes, la première consistant à calculer une distance sur les nœuds et la seconde une distance sur les arrêtes. Cet algorithme fournit un indice de similarité normalisé: 0 pour aucune similarité et 1 pour deux réseaux identiques. Il a été évalué sur des graphes simulés puis comparé à des algorithmes existants. Il montre de meilleures performances pour détecter la variation spatiale entre les graphes. Il a également été appliqué sur des données réelles afin de comparer différents réseaux cérébraux. Les résultats ont montré des performances élevées pour comparer deux réseaux cérébraux réels obtenus à partir l'EEG à haute résolution spatiale, au cours d'une tâche cognitive consistant à nommer des éléments de deux catégories différentes (objets vs animaux). / The human brain is a "large-scale" network consisting of distributed and functionally interconnected regions. The information processing in the brain is a dynamic process that involves a fast reorganization of functional brain networks in a very short time scale (less than one second). In the field of cognitive neuroscience, two big questions remain about these networks. Firstly, is it possible to follow the spatiotemporal dynamics of the brain networks with a temporal resolution significantly higher than the functional MRI? Secondly, is it possible to detect a significant difference between these networks when the brain processes stimuli (visual, for example) with different characteristics? These two questions are the main motivations of this thesis. Indeed, we proposed new methods based on dense electroencephalography. These methods allow: i) to follow the dynamic reconfiguration of brain functional networks at millisecond time scale and ii) to compare two activated brain networks under specific conditions. We propose a new algorithm benefiting from the excellent temporal resolution of EEG to track the fast reconfiguration of the functional brain networks at millisecond time scale. The main objective of this algorithm is to segment the brain networks into a set of "functional connectivity states" using a network-clustering approach. The algorithm is based on K-means and was applied on the connectivity graphs obtained by estimation the functional connectivity values between the considered regions of interest. The second challenge addressed in this work falls within the measure of similarity between graphs. Thus, to compare functional connectivity networks, we developed an algorithm (SimNet) that able to quantify the similarity between two networks whose node coordinates is known. This algorithm maps one graph to the other using different operations (insertion, deletion, substitution of nodes and edges). The algorithm is based on two main parts, the first one is based on calculating the nodes distance and the second one is to calculate the edges distance. This algorithm provides a normalized similarity index: 0 for no similarity and 1 for two identical networks. SimNet was evaluated with simulated graphs and was compared with previously-published graph similarity algorithms. It shows high performance to detect the similarity variation between graphs involving a shifting of the location of nodes. It was also applied on real data to compare different brain networks. Results showed high performance in the comparison of real brain networks obtained from dense EEG during a cognitive task consisting in naming items of two different categories (objects vs. animals).

No description available.

Causal inference may help us to understand the underlying mechanisms and the risk factors of diseases. In Genetics, it is crucial to understand how the connectivity among variables is influenced by genetic and environmental factors. Family data have proven to be useful in elucidating genetic and environmental influences, however, few existing approaches are able of addressing structure learning of probabilistic graphical models (PGMs) and family data analysis jointly. We propose methodologies for learning, from observational Gaussian family data, the most likely PGM and its decomposition into genetic and environmental components. They were evaluated by a simulation study and applied to the Genetic Analysis Workshop 13 simulated data, which mimic the real Framingham Heart Study data, and to the metabolic syndrome phenotypes from the Baependi Heart Study. In neuroscience, one challenge consists in identifying interactions between functional brain networks (FBNs) - graphs. We propose a method to identify Granger causality among FBNs. We show the statistical power of the proposed method by simulations and its usefulness by two applications: the identification of Granger causality between the FBNs of two musicians playing a violin duo, and the identification of a differential connectivity from the right to the left brain hemispheres of autistic subjects. / Inferência causal pode nos ajudar a compreender melhor as relações de dependência direta entre variáveis e, assim, a identificar fatores de riscos de doenças. Em Genética, a análise de dados agrupados em famílias permite investigar influências genéticas e ambientais nas relações entre as variáveis. Neste trabalho, nós propomos métodos para aprender, a partir de dados Gaussianos agrupados em famílias, o mais provável modelo gráfico probabilístico (dirigido ou não dirigido) e também sua decomposição em dois componentes: genético e ambiental. Os métodos foram avaliados por simulações e aplicados tanto aos dados simulados do Genetic Analysis Workshop 13, que imitam características dos dados do Framingham Heart Study, como aos dados da síndrome metabólica do estudo Corações de Baependi. Em Neurociência, um desafio consiste em identificar interações entre redes funcionais cerebrais - grafos. Nós propomos um método que identifica causalidade de Granger entre grafos e, por meio de simulações, mostramos que o método tem alto poder estatístico. Além disso, mostramos sua utilidade por meio de duas aplicações: 1) identificação de causalidade de Granger entre as redes cerebrais de dois músicos enquanto tocam um dueto de violino e 2) identificação de conectividade diferencial do hemisfério cerebral direito para o esquerdo em indivíduos autistas.

Functional magnetic resonance imaging (fMRI) has emerged as the method of choice to non-invasively investigate brain function in humans. Though brain is known to act as a nonlinear system, here has not been much effort to explore the applicability of nonlinear analysis techniques to fMRI data. Also, recent trends have suggested that functional localization as a model of brain function is incomplete and efforts are being made to develop models based on networks of regions to understand brain function. Therefore this thesis attempts to introduce the twin concepts of nonlinear dynamics and network analysis into a broad spectrum of fMRI data analysis techniques. First, we characterized the nonlinear univariate dynamics of fMRI noise using the concept of embedding to explain the origin of tissue-specific differences of baseline activity in the brain. The embedding concept was extended to the multivariate case to study nonlinear functional connectivity in the distributed motor network during resting state and continuous motor task. The results showed that the nonlinear method may be more sensitive to the desired gray matter signal. Subsequently, the scope of connectivity was extended to include directional interactions using Granger causality. An integrated approach was developed to alleviate the confounding effect of the spatial variability of the hemodynamic response and graph theory was employed to characterize the network topology. This methodology proved effective in characterizing the dynamics of cortical networks during motor fatigue. The nonlinear extension of Granger causality showed that it was more robust in the presence of confounds such as baseline drifts. Finally, we utilized the integration of the spatial correlation function to study connectivity in local brain networks. We showed that our method is robust and can reveal interesting information including the default mode network during resting state. Application of this technique to anesthesia data showed dose dependent suppression of local connectivity in the default mode network, particularly in the frontal areas. Given the body of evidence emerging from our studies, nonlinear and network characterization of fMRI data seems to provide novel insights into brain function.

Objet de nombreuses spéculations religieuses ou philosophiques, le rêve reste encore l'une des grandes terra incognita de la cognition humaine.Une des questions récurrentes sur le rêve porte sur la grande variabilité de fréquence de rappel de rêve. En effet, alors que certaines personnes se souviennent de leurs rêves quotidiennement (« Rêveurs »), d'autres ne s'en souviennent que très rarement (« Non-rêveurs »). Le principal objectif de notre travail de thèse a été de caractériser les corrélats cérébraux et comportementaux de cette variabilité interindividuelle, en comparant entre ces deux groupes la structure du sommeil (Étude 1), mais aussi l'activité cérébrale pendant les minutes qui suivent le réveil (Étude 2). Nous avons entre autres montré que les « Rêveurs » faisaient preuve d'une plus grande connectivité fonctionnelle au sein du réseau par défaut et de régions impliquées dans des processus mnésiques dans les minutes suivant l'éveil, ce qui pourrait faciliter chez ces personnes le rappel et/ou la consolidation du rêve. Cette étude nous a également permis, grâce à l'analyse des nombreuses réponses obtenues au questionnaire de recrutement, de mesurer les habitudes de sommeil et de rêve chez un échantillon large d'étudiants de l'Université de Lyon 1 (Étude 3).Dans une quatrième étude comportementale, nous nous sommes intéressés au lien existant entre la vie éveillée et le contenu du rêve. Nos résultats ont permis de mieux caractériser les facteurs influençant la probabilité d'incorporation des évènements de la vie éveillée dans le rêve, et ont mis en évidence l'importance du rêve dans des processus de régulation émotionnelle.Finalement, en parallèle de ces travaux, nous nous sommes attachés au développement d'un logiciel gratuit de visualisation et d'analyse de tracés de polysomnographie, dont l'objectif est de fournir une interface intuitive et portable aux étudiants et chercheurs travaillant sur le sommeil / Since the dawn of time, humans have sought to understand the nature and meaning of their dreams. However, despite millennia of philosophical speculation and more than a century of scientific exploration, several questions regarding dreams remain pending.One question that constitutes the core problematic of this thesis relates to why there are such individual differences in the frequency of dream recall, or in other words, why some people remember up to several dreams per morning (High-recallers, HR) while some hardly ever recall one (Low-recallers, LR). To characterize the cerebral and behavioral correlates of this variability, we compared the sleep microstructure (Study 1), as well as the brain functional connectivity in the minutes following awakeningfrom sleep, a period marked by sleep inertia (Study 2). Among other results, we have shown that just after awakening, HR demonstrated a greater functional connectivity within regions involved in memory processes (default mode network). We proposed that this reflect a differential neurophysiological profile, which could facilitate in HRthe retrieval of dream content upon awakening. Second, the numerous answers to the recruitment questionnaire of this study allowed us to conduct an epidemiological survey to characterize the sleep and dream habits of a large sample of French college students from Lyon 1 University (Study 3). In another study, we focused on the relationships between waking-life and dream content (Study 4). Our results enhanced and refined our comprehension of the factors influencing the likelihood of incorporation of waking-life elements into dreams, and provided support for the hypothesis of an active role of dreaming in emotional regulation.Lastly, we designed a free and open-source software dedicated to the visualization and analysis of polysomnographic recordings (Study 5), which aims at providing an intuitive and portable graphical interface to students and researchers working on sleep

Abstract The purpose of the present work was to investigate whether children with non-lesional temporal lobe epilepsy (TLE) have deficits in neuropsychological performance and whether the possible deficits can be investigated using functional magnetic resonance imaging (fMRI). In this population-based study, 21 children aged 8-15 with non-lesional TLE and a normal intelligence quotient were evaluated and compared with 21 healthy, age- and gender-matched controls. Neuropsychological assessments, clinical examinations, electroencephalography (EEG) and structural and functional MRI were performed on all the subjects. Three fMRI methods were used: resting-state regional homogeneity, resting-state functional connectivity and task-induced blood oxygenation level-dependent activation. The patients with non-lesional TLE showed good neuropsychological performance on average, although the girls were found to have significant problems in several neuropsychological tests. The deficits were not restricted to elements of performance involving the classical temporal lobe memory system but were also found in tests requiring frontal and parietal lobe functioning. Early onset of epilepsy and duration of epilepsy had significant negative effects on neuropsychological performance. All the fMRI methods detected significant functional differences between the TLE patients and the healthy controls, not only in the temporal lobes but also in broad networks extending to the frontal, parietal and thalamic areas. These differences seemed to differ markedly in location between the TLE patients depending on the interictal EEG findings. Neuropsychological performance results were supported by the fMRI findings, implying that TLE should be regarded as a widespread disruption of the brain networks and not just malfunction of a single region in the brain within these networks. This needs to be taken into consideration when evaluating learning abilities among TLE patients even at an early stage in epilepsy. / Tiivistelmä Tutkimuksen tarkoituksena oli selvittää onko lapsilla, jotka sairastavat tuntemattomasta syystä aiheutuvaa ohimolohkoepilepsiaa, neuropsykologisia ongelmia ja aiheuttavatko mahdolliset ongelmat aivojen toiminnallisessa magneettikuvauksessa nähtäviä muutoksia. Tähän väestöpohjaiseen tutkimukseen otettiin 21 tuntemattomasta syystä ohimolohkoepilepsiaa sairastavaa normaaliälyistä 8-15-vuotiaista lasta ja verrattiin heitä 21 terveeseen, ikä- ja sukupuolivakioituun kontrollihenkilöön. Kaikille tutkimukseen osallistuneille tehtiin neuropsykologinen tutkimus, kliininen tutkimus, aivosähkökäyrä sekä rakenteellinen ja toiminnallinen aivojen magneettikuvaus. Toiminnallisessa magneettikuvauksessa käytettiin veren happipitoisuudesta riippuvaista (engl. blood oxygenation level-dependent) kontrastia kuvantamaan levossa aivojen paikallista homogeniteettia (engl. regional homogeneity) ja toiminnallista kytkennällisyyttä (engl. functional connectivity) sekä kognitiivisten tehtävien herättämiä aktivaatio-vasteita. Tuntemattomasta syystä ohimolohkoepilepsiaa sairastavien lasten neuropsykologinen suoriutuminen oli keskimäärin hyvää, vaikkakin tytöillä oli nähtävillä tilastollisesti merkitseviä ongelmia useissa eri testeissä. Ongelmat eivät rajoittuneet pelkästään klassisiin ohimolohkoalueen muistitoimintoihin, vaan niitä havaittiin myös otsa- ja päälakilohkojen toimintoja edellyttävissä testeissä. Varhainen sairastumisikä ja epilepsian kesto heikensivät suoriutumista tilastollisesti merkitsevästi osatesteissä, joissa tarvittiin näönvaraisen hahmottamisen taitoja, psykomotorista nopeutta ja työmuistia. Ohimolohkoepilepsiaa sairastavien ja terveiden kontrollien aivoissa löydettiin toiminnallisia eroja kaikilla toiminnallisen magneettikuvauksen menetelmillä. Eroja ei todettu ainoastaan ohimolohkoissa, vaan niitä löytyi myös otsa- ja päälakilohkoon sekä tyvitumakealueelle ylettyvissä laaja-alaisissa hermoverkostoissa. Epilepsiapotilailla erojen paikantuminen riippui kohtaustenvälisestä aivosähkökäyrälöydöksestä. Neuropsykologisen suoriutumisen tulokset tukevat toiminnallisen magneettikuvauksen löydöksiä kuvastaen temporaaliepilepsian olevan laaja-alainen hermoverkostojen häiriö eikä pelkästään tietyn aivoalueen toiminnan häiriö. Tämä tulee huomioida arvioitaessa ohimolohkoepilepsiaa sairastavien lasten oppimiskykyä jo epilepsian alkuvaiheessa.