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Learning functional brain atlases modeling inter-subject variability

Abstract : Recent studies have shown that resting-state spontaneous brain activity unveils intrinsic cerebral functioning and complete information brought by prototype task study. From these signals, we will set up a functional atlas of the brain, along with an across-subject variability model. The novelty of our approach lies in the integration of neuroscientific priors and inter-individual variability in a probabilistic description of the rest activity. These models will be applied to large datasets. This variability, ignored until now, may lead to learning of fuzzy atlases, thus limited in term of resolution. This program yields both numerical and algorithmic challenges because of the data volume but also because of the complexity of modelisation.
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Submitted on : Monday, June 13, 2016 - 5:00:08 PM
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  • HAL Id : tel-01331308, version 1


Alexandre Abraham. Learning functional brain atlases modeling inter-subject variability. Machine Learning [cs.LG]. Université Paris-Saclay, 2015. English. ⟨NNT : 2015SACLS159⟩. ⟨tel-01331308⟩



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