%0 Journal Article
%T Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines.
%A Varoquaux G
%A Raamana PR
%A Engemann DA
%A Hoyos-Idrobo A
%A Schwartz Y
%A Thirion B
%J Neuroimage
%V 145
%N 0
%D 01 2017 15
%M 27989847
%F 7.4
%R 10.1016/j.neuroimage.2016.10.038
%X Decoding, i.e. prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didactic overview of the relevant theoretical considerations. Practical aspects are highlighted with an extensive empirical study of the common decoders in within- and across-subject predictions, on multiple datasets -anatomical and functional MRI and MEG- and simulations. Theory and experiments outline that the popular "leave-one-out" strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred. Experiments outline the large error bars of cross-validation in neuroimaging settings: typical confidence intervals of 10%. Nested cross-validation can tune decoders' parameters while avoiding circularity bias. However we find that it can be favorable to use sane defaults, in particular for non-sparse decoders.