Multi-subject joint parcellation detection estimation in functional MRI

Abstract : fMRI experiments are usually conducted over a population of interest for investigating brain activity across different regions stimuli and objects. Multi-subject analysis proceeds in two steps, intra-subject analysis is performed sequentially on each individual and then group-level analysis is addressed to report significant results at the population level. This paper considers an existing Joint Parcellation Detection Estimation (JPDE) model which performs joint hemodynamic parcellation, brain dynamics estimation and evoked activity detection. The hierarchy of the JPDE model is extended for multi-subject analysis in order to perform group-level parcellation. Then, the corresponding underlying dynamics is estimated in each parcel while the detection and estimation steps are iterated over each individual. Validation on synthetic and real fMRI data shows its robustness in inferring the group-level parcellation and the corresponding hemodynamic profiles.
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Communication dans un congrès
13th IEEE International Symposium on Biomedical Imaging, Apr 2016, Prague, Czech Republic. IEEE, 13th IEEE International Symposium on Biomedical Imaging, pp.74-77, 2016, 〈10.1109/ISBI.2016.7493214〉
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Mohanad Albughdadi, Lotfi Chaari, Florence Forbes, Jean-Yves Tourneret, Philippe Ciuciu. Multi-subject joint parcellation detection estimation in functional MRI. 13th IEEE International Symposium on Biomedical Imaging, Apr 2016, Prague, Czech Republic. IEEE, 13th IEEE International Symposium on Biomedical Imaging, pp.74-77, 2016, 〈10.1109/ISBI.2016.7493214〉. 〈hal-01261982〉

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