Analysis of cognitive performance in schizophrenia patients and healthy individuals with unsupervised clustering models.

Currently, assignment of cognitive test results to particular cognitive domains is guided by theoretical considerations and expert judgments which may vary. More objective means of classification may advance understanding of the relationships between test performance and the cognitive functions probed. We examined whether "atheoretical" analyses of cognitive test data can help identify potential hidden structures in cognitive performance. Novel data-mining methods which "let the data talk" without a priori theoretically bound constraints were used to analyze neuropsychological test results of 75 schizophrenia patients and 57 healthy individuals. The analyses were performed on the combined sample to maximize the "atheoretical" approach and allow it to reveal different structures of cognition in patients and controls. Analyses used unsupervised clustering methods, including hierarchical clustering, self-organizing maps (SOM), k-means and supermagnetic clustering (SPC). The model revealed two major clusters containing accuracy and reaction time measures respectively. The sensitivity (75% versus 52%) and specificity (95% versus 77% ) of these clusters for diagnosing schizophrenia differed. Downstream branching was influenced by stimulus domain. Predictions arising from this "atheoretical" model are supported by evidence from published studies. This preliminary study suggests that appropriate application of data-mining methods may contribute to investigation of cognitive functions.