How can we obtain accurate information on the localization of cerebral regions involved in the coding of a stimulus category?

More precisely, we have tested 2 methods (for each subject, normalized in a same space):

  • Searchlight : for each voxel, we run an MVPA analysis in a sphere that is centered on the voxel. We thus obtain an accuracy map for each voxel tested. We can then test the value of this accuracy across all the subjects of our population.

  • MVPA analysis on the whole brain. For instance, in nilearn :

    • support vector classifier - svc, with penalty = tv-l1,
    • or SpaceNetClassifier with penalty=graph-net).

    Here we obtain a weight map for each subject. We can then test the value of these weights accross all the subjects of our population.

Which is the most rigorous method ? What biases do they each bring ? What differences in interpretations do we get from one approach or the other?

Neither of these is particularly great; I actually prefer ROI-based analyses: carry out the analyses in predefined (independent) regions, either chosen from the hypotheses (e.g., visual or motor regions) or from a full brain parcellation (e.g., Schaefer 2018

Very briefly, searchlights can identify focal areas well, but get hard to interpret when areas vary across people or are not focal, e.g. my paper (and associated blog posts

Weight maps can also be incredibly hard to interpret; perhaps even more so than searchlights. I listed some references in a reply on a previous thread NILEARN: Are SVM weights can be useful for group analysis?.

Hi, I agree, it’s a difficult question with no perfect answer…

Group-level searchlight can be a solution at the population level (cf our recent paper: with yet some other drawbacks (we need to perform registration of the data to the MNI or another template, with all the unperfections of the registration) but some potential advantages in detection power…