Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors among disorders with commonly co-occurring features to examine this heterogeneity. Classification analyses were conducted with the University of California, Los Angeles Phenomics Study database using a support-vector classifier to differentiate disorders via whole brain task-based functional connectivity, predicting that model misclassifications would provide insight about brain connectivity characteristics shared across disorders.