Frontal dysfunction in schizophrenia – a new electrophysiological classifier for research and clinical applications

Abstract

We determined whether schizophrenic patients can be reliably classified with electrophysiological tools. We developed a fully computerized classifier based on 5 minutes of EEG recording during an acoustical choice reaction time task (AMDP-module IV). We included factorized variables from the frequency domain and evoked potentials (N100/P200-complex) from central and frontal electrodes, which were preprocessed in a sample of 150 normal subjects prior to classification. We applied discriminant analyses to the electrophysiological data from depressive, schizophrenic and schizotypal subjects, most of them being unmedicated or drug-naïve. The classifier was developed on a training set (33 schizophrenics, 49 normals) and tested on an independent sample (32 schizophrenics, 49 normals). A simple three-variable classifier was found to classify schizophrencis and normals in 77% of those tested correctly. Diagnostic specificity of the classifier proved to be low as the inclusion of depressive patients (n=60) significantly decreased classification power. It was demonstrated that atypical but not typical neuroleptic drugs may influence the classification results. Correctly classified schizophrenics showed significantly more negative symptoms and slower reaction times than those schizophrenics who were misclassified as normals. In contrast, these misclassified schizophrenics showed a non-significant trend for more positive symptoms and shorter reaction times. As the correctly classified schizophrenics showed increased frontally pronounced delta-activity and decreased signal power of the N100/P200 amplitude, it was concluded that these schizophrenics show dysfunction of the frontal lobe. It is proposed that this new classifier can be useful for clinical and research applications when subtyping of schizophrenics with detection of frontal dysfunction as the aim.