TIBM: Brain connectivity

The study of brain anatomical and functional connectivity is one of the major issues in neuroscience, aiming at better understanding the organization and functioning of the brain, whether in healthy or diseased subjects. Anatomical connectivity is generally investigated using diffusion MRI sequences. A new method of tractography based on particle filtering and relying on the Q-ball model was developed to extract the main white matter fiber bundles 2-PROS13. Functional connectivity is usually investigated using functional MRI sequences (fMRI). A method was proposed for detecting functional networks using a multilevel brain parcellation into functionally homogeneous areas 7-KFTF11. Tools from graph theory have also been implemented for the study of consciousness in comatose patients AKSR11-2, 2-ADVR12. New work started on the analysis of dynamic functional connectivity, in particular on the interaction between brain resting state networks at the subject scale. These resting state networks are extracted from fMRI data using spatial independent component analysis. To cope with the high number of components produced for each subject, a method for the automatic selection of the components of interest has been developed 4-STRA15. In addition, approaches have been proposed to study the interactions of these networks using various Hidden Markov Models 5-STRA15, 4-STAF16.

PhD theses

• M. Sourty, Analyse de la dynamique temporelle et spatiale des réseaux cérébraux spontanés obtenus en imagerie par résonance magnétique fonctionnelle IRMf, september 2016
• J. Pontabry, Construction d'atlas en IRM de diffusion : application à l'étude de la maturation cérébrale, october 2013
• F. Renard, Création et utilisation d'atlas en IRM de diffusion. Application à l'étude des troubles de la conscience, september 2011
• S. Karkar, Parcellisation et analyse multi-niveaux de données IRM fonctionnelles - application à l'étude des réseaux de connectivité cérébrale, june 2011
• S. Faisan, Analyse et fusion markovienne de séquences en imagerie 3D+t. Application à l'analyse de séquences d'images IRM fonctionnelles cérébrales., december 2004

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