Skip to Main content Skip to Navigation
Journal articles

How machine learning is shaping cognitive neuroimaging

Gaël Varoquaux 1, 2, * Bertrand Thirion 1, 2
* Corresponding author
1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
Abstract : Functional brain images are rich and noisy data that can capture indirect signatures of neural activity underlying cognition in a given experimental setting. Can data mining leverage them to build models of cognition? Only if it is applied to well-posed questions, crafted to reveal cognitive mechanisms. Here we review how predictive models have been used on neuroimaging data to ask new questions, i.e., to uncover new aspects of cognitive organization. We also give a statistical learning perspective on these progresses and on the remaining gaping holes.
Document type :
Journal articles
Complete list of metadatas

Cited literature [67 references]  Display  Hide  Download
Contributor : Bertrand Thirion <>
Submitted on : Friday, December 12, 2014 - 7:42:38 PM
Last modification on : Monday, February 10, 2020 - 6:13:43 PM
Document(s) archivé(s) le : Friday, March 13, 2015 - 11:30:49 AM


Files produced by the author(s)




Gaël Varoquaux, Bertrand Thirion. How machine learning is shaping cognitive neuroimaging. GigaScience, BioMed Central, 2014, 3, pp.28. ⟨10.1186/2047-217X-3-28⟩. ⟨hal-01094737⟩



Record views


Files downloads