Statistical Learning for Resting-State fMRI: Successes and Challenges
Résumé
In the absence of external stimuli, fluctuations in cerebral activity can be used to reveal intrinsic structures. Well-conditioned probabilistic models of this so-called resting-state activity are needed to support neuroscientific hypotheses. Exploring two specific descriptions of resting-state fMRI, namely spatial analysis and connectivity graphs, we discuss the progress brought by statistical learning techniques, but also the neuroscientific picture that they paint, and possible modeling pitfalls.
Domaines
Imagerie médicaleOrigine | Fichiers produits par l'(les) auteur(s) |
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