Differences in atypical resting-state effective connectivity distinguish autism from schizophrenia

Autism and schizophrenia share overlapping genetic etiology, common changes in brain structure and common cognitive deficits. A number of studies using resting state fMRI have shown that machine learning algorithms can distinguish between healthy controls and individuals diagnosed with either autism spectrum disorder or schizophrenia. However, it has not yet been determined whether machine learning algorithms can be used to distinguish between the two disorders. Using a linear support vector machine, we identify features that are most diagnostic for each disorder and successfully use them to classify an independent cohort of subjects. We find both common and divergent connectivity differences largely in the default mode network as well as in salience, and motor networks. Using divergent connectivity differences, we are able to distinguish autistic subjects from those with schizophrenia. Understanding the common and divergent connectivity changes associated with these disorders may provide a framework for understanding their shared cognitive deficits. •Common and divergent diagnostic features for autism and schizophrenia are identified.•SVM classifiers successfully identify patients from training-naive patient groups.•Diagnostic connectivity changes cluster in the default mode and salience networks.•Shared diagnostic features predict deficits in communication skills in autism.•Unique features distinguish autism and schizophrenic patients.