Subtyping of psychiatric patients by cluster analysis of QEEG.

We have previously reported successful classification of patients with a variety of psychiatric disorders, using multiple discriminant functions based upon selected neurometric QEEG variables. In independent replications, these functions accurately separate patients with different DSM-III-R diagnoses from one another and from normals. This capability demonstrates that distinctive and replicable patterns of neurometric abnormalities are correlated with the clinical symptom clusters upon which DSM-III-R diagnostic criteria are based. However, patients with the same clinical diagnoses often respond very differently to the same treatments. Similar symptoms may arise from different pathophysiology. This study explored the 'natural structure' of a population of psychiatric patients in 8 diagnostic categories, using uninformed cluster analysis based upon the same set of neurometric variables found useful in separating each of these categories from normal. This preliminary numerical taxonomic approach reveals that groups of patients in each of these DSM-III-R categories contain subtypes with markedly different pathophysiology; further, patients in different DSM-III-R categories were aggregated together within each cluster, displaying similar pathophysiological profiles. Objective classification based on such physiological measurements may add information useful to improve treatment outcomes.