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. © 2012 Springer-Verlag.
CITATION STYLE
Varoquaux, G., & Thirion, B. (2012). Statistical learning for resting-state fMRI: Successes and challenges. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7263 LNAI, pp. 172–177). https://doi.org/10.1007/978-3-642-34713-9_22
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