L’Imagerie par résonance magnétique (IRM) est un outil fondamental pour l’exploration in vivo du développement du cerveau chez le fœtus, le bébé et l’enfant. Elle fournit plusieurs paramètres quantitatifs qui reflètent les changements des propriétés tissulaires au cours du développement en fonction de différents processus de maturation. Cependant, l’évaluation fiable de la maturation de la substance blanche est encore une question ouverte: d'une part, aucun de ces paramètres ne peut décrire toute la complexité des changements sous-jacents; d'autre part, aucun d'eux n’est spécifique d’un processus de développement ou d’une propriété tissulaire particulière. L’implémentation d’approches multiparamétriques combinant les informations complémentaires issues des différents paramètres IRM devrait permettre d’améliorer notre compréhension du développement du cerveau. Dans ce travail de thèse, je présente deux exemples de telles approches et montre leur pertinence pour l'étude de la maturation des faisceaux de substance blanche. La première approche fournit une mesure globale de la maturation basée sur la distance de Mahalanobis calculée à partir des différents paramètres IRM (temps de relaxation T1 et T2, diffusivités longitudinale et transverse du tenseur de diffusion DTI) chez des nourrissons (âgés de 3 à 21 semaines) et des adultes. Cette approche offre une meilleure description de l’asynchronisme de maturation à travers les différents faisceaux que les approches uniparamétriques. De plus, elle permet d'estimer les délais relatifs de maturation entre faisceaux. La seconde approche vise à quantifier la myélinisation des tissus cérébraux, en calculant la fraction de molécules d’eau liées à la myéline (MWF) en chaque voxel des images. Cette approche est basée sur un modèle tissulaire avec trois composantes ayant des caractéristiques de relaxation spécifiques, lesquelles ont été pré-calibrées sur trois jeunes adultes sains. Elle permet le calcul rapide des cartes MWF chez les nourrissons et semble bien révéler la progression de la myélinisation à l’échelle cérébrale. La robustesse de cette approche a également été étudiée en simulations. Une autre question cruciale pour l'étude du développement de la substance blanche est l'identification des faisceaux dans le cerveau des enfants. Dans ce travail de thèse, je décris également la création d'un atlas préliminaire de connectivité structurelle chez des enfants âgés de 17 à 81 mois, permettant l'extraction automatique des faisceaux à partir des données de tractographie. Cette approche a démontré sa pertinence pour l'évaluation régionale de la maturation de la substance blanche normale chez l’enfant. Pour finir, j’envisage dans la dernière partie du manuscrit les applications potentielles des différentes méthodes précédemment décrites pour l’étude fine des réseaux de substance blanche dans le cadre de deux exemples spécifiques de pathologies : les épilepsies focales et la leucodystrophie métachromatique. / Magnetic Resonance Imaging (MRI) is a fundamental tool for in vivo investigation of brain development in newborns, infants and children. It provides several quantitative parameters that reflect changes in tissue properties during development depending on different undergoing maturational processes. However, reliable evaluation of the white matter maturation is still an open question: on one side, none of these parameters can describe the whole complexity of the undergoing changes; on the other side, neither of them is specific to any particular developmental process or tissue property. Developing multiparametric approaches combining complementary information from different MRI parameters is expected to improve our understanding of brain development. In this PhD work, I present two examples of such approaches and demonstrate their relevancy for investigation of maturation across different white matter bundles. The first approach provides a global measure of maturation based on the Mahalanobis distance calculated from different MRI parameters (relaxation times T1 and T2, longitudinal and transverse diffusivities from Diffusion Tensor Imaging, DTI) in infants (3-21 weeks) and adults. This approach provides a better description of the asynchronous maturation across the bundles than univariate approaches. Furthermore, it allows estimating the relative maturational delays between the bundles. The second approach aims at quantifying myelination of brain tissues by calculating Myelin Water Fraction (MWF) in each image voxel. This approach is based on a 3-component tissue model, with each model component having specific relaxation characteristics that were pre-calibrated in three healthy adult subjects. This approach allows fast computing of the MWF maps from infant data and could reveal progression of the brain myelination. The robustness of this approach was further investigated using computer simulations. Another important issue for studying white matter development in children is bundles identification. In the last part of this work I also describe creation of a preliminary atlas of white matter structural connectivity in children aged 17-81 months. This atlas allows automatic extraction of the bundles from tractography datasets. This approach demonstrated its relevance for evaluation of regional maturation of normal white matter in children. Finally, in the last part of the manuscript I describe potential future applications of the previously developed methods to investigation of the white matter in cases of two specific pathologies: focal epilepsy and metachromatic leukodystrophy.

Women exhibit greater habituation to painful stimuli than men. The neural mechanism underlying this sex difference is unknown. However, pain habituation has been associated with pain-evoked activity of the subgenual anterior cingulate cortex (sgACC), implicating a connection between the sgACC and the descending pain antinociceptive system. Therefore, the thesis hypothesis was that women have stronger connectivity than men between the sgACC and the descending antinociceptive system. Healthy subjects provided informed consent. 3T MRI images included anatomical diffusion-weighted imaging for structural connectivity analyses (SC) with probabilistic tractography and resting-state functional images for functional connectivity (FC) analyses. Women had stronger sgACC FC with nodes of the descending pain modulation system (raphe, PAG) and the medial thalamus. In contrast, men had stronger sgACC FC with nodes of the salience/attention network (anterior insula, TPJ) and stronger sgACC SC with the hypothalamus. These findings implicate a mechanism for pain habituation and its associated sex differences.

Women exhibit greater habituation to painful stimuli than men. The neural mechanism underlying this sex difference is unknown. However, pain habituation has been associated with pain-evoked activity of the subgenual anterior cingulate cortex (sgACC), implicating a connection between the sgACC and the descending pain antinociceptive system. Therefore, the thesis hypothesis was that women have stronger connectivity than men between the sgACC and the descending antinociceptive system. Healthy subjects provided informed consent. 3T MRI images included anatomical diffusion-weighted imaging for structural connectivity analyses (SC) with probabilistic tractography and resting-state functional images for functional connectivity (FC) analyses. Women had stronger sgACC FC with nodes of the descending pain modulation system (raphe, PAG) and the medial thalamus. In contrast, men had stronger sgACC FC with nodes of the salience/attention network (anterior insula, TPJ) and stronger sgACC SC with the hypothalamus. These findings implicate a mechanism for pain habituation and its associated sex differences.

Adaptability and flexibility are some of the most important human characteristics. Learning based on new experiences enables adaptation by changing the structural connectivity of the brain through plasticity mechanisms. But the human brain can also adapt to new tasks and situations in a matter of milliseconds by dynamic coordination of functional activation. To understand how this flexibility can be achieved in the computations performed by neural networks, we have to understand how the relatively fixed structural backbone interacts with the functional dynamics. In this thesis, I will analyze these interactions between the structural network connectivity and functional activations and their dynamic interactions on different levels of abstraction and spatial and temporal scales. One of the big questions in neuroscience is how functional interactions in the brain can adapt instantly to different tasks while the brain structure remains almost static. To improve our knowledge of the neural mechanisms involved, I will first analyze how dynamics in functional brain activations can be simulated based on the structural brain connectivity obtained with diffusion tensor imaging. In particular, I will show that a dynamic model of functional connectivity in the human cortex is more predictive of empirically measured functional connectivity than a stationary model of functional dynamics. More specifically, the simulations of a coupled oscillator model predict 54\% of the variance in the empirically measured EEG functional connectivity. Hypotheses of temporal coding have been proposed for the computational role of these dynamic oscillatory interactions on fast timescales. These oscillatory interactions play a role in the dynamic coordination between brain areas as well as between cortical columns or individual cells. Here I will extend neural network models, which learn unsupervised from statistics of natural stimuli, with phase variables that allow temporal coding in distributed representations. The analysis shows that synchronization of these phase variables provides a useful mechanism for binding of activated neurons, contextual coding, and figure ground segregation. Importantly, these results could also provide new insights for improvements of deep learning methods for machine learning tasks. The dynamic coordination in neural networks has also large influences on behavior and cognition. In a behavioral experiment, we analyzed multisensory integration between a native and an augmented sense. The participants were blindfolded and had to estimate their rotation angle based on their native vestibular input and the augmented information. Our results show that subjects alternate in the use between these modalities, indicating that subjects dynamically coordinate the information transfer of the involved brain regions. Dynamic coordination is also highly relevant for the consolidation and retrieval of associative memories. In this regard, I investigated the beneficial effects of sleep for memory consolidation in an electroencephalography (EEG) study. Importantly, the results demonstrate that sleep leads to reduced event-related theta and gamma power in the cortical EEG during the retrieval of associative memories, which could indicate the consolidation of information from hippocampal to neocortical networks. This highlights that cognitive flexibility comprises both dynamic organization on fast timescales and structural changes on slow timescales. Overall, the computational and empirical experiments demonstrate how the brain evolved to a system that can flexibly adapt to any situation in a matter of milliseconds. This flexibility in information processing is enabled by an effective interplay between the structure of the neural network, the functional activations, and the dynamic interactions on fast time scales.