How machine learning is shaping cognitive neuroimaging

Gaël Varoquaux 1, 2, * Bertrand Thirion 1, 2
* Auteur correspondant
1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
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.
Type de document :
Article dans une revue
GigaScience, BioMed Central, 2014, 3, pp.28. 〈10.1186/2047-217X-3-28〉
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Contributeur : Bertrand Thirion <>
Soumis le : vendredi 12 décembre 2014 - 19:42:38
Dernière modification le : jeudi 7 mars 2019 - 15:34:14
Document(s) archivé(s) le : vendredi 13 mars 2015 - 11:30:49


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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〉



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