A classification method to identify common neural markers across several fMRI datasets is proposed.

This method is applied to classify patients with autism spectrum disorders and controls.

The method classified autistic patients from controls with an accuracy ranging from 69% to 92.3%.

Discriminative activity patterns were found in several regions of the social brain.

Classifier outputs were correlated with social motivation.