How machine learning is shaping cognitive neuroimaging

GigaScience

Bertrand Thirion¹  Gael Varoquaux¹ 

[1] Parietal, INRIA, NeuroSpin, bat 145 CEA Saclay, 91191 Gif sur Yvette, France

关键词: Encoding;    Decoding;    fMRI;    Cognition;    Neuroimaging;    Machine learning;   

Others  :  1118575
DOI  :  10.1186/2047-217X-3-28

 received in 2014-07-18, accepted in 2014-10-23,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Varoquaux and Thirion; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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