Automated classification of fMRI during cognitive control identifies more severely disorganized subjects with schizophrenia.

The establishment of a neurobiologically based nosological system is one of the ultimate goals of modern biological psychiatry research. Developments in neuroimaging and statistical/machine learning have provided useful basic tools for these efforts. Recent studies have demonstrated the utility of fMRI as input data for the classification of schizophrenia, but none, to date, has used fMRI of cognitive control for this purpose. In this study, we evaluated the accuracy of an unbiased classification method on fMRI data from a large cohort of subjects with first episode schizophrenia and a cohort of age matched healthy control subjects while they completed the AX version of the Continuous Performance Task (AX-CPT). We compared these results to classifications based on AX-CPT behavioral data. Classification accuracy for DSM-IV defined schizophrenia using fMRI data was modest and comparable to classifications conducted with behavioral data. Interestingly fMRI classifications did however identify a distinct subgroup of patients with greater behavioral disorganization, whereas behavioral data classifications did not. These results suggest that fMRI-based classification could be a useful tool in defining a neurobiologically distinct subgroup within the clinically defined syndrome of schizophrenia, reflecting alterations in discrete neural circuits. Independent validation of classification-based phenotypes using other biological data such as genetics would provide a strong test of this hypothesis.