No abstract

No 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-naive. 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 schizophrenics 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.

No abstract

Gamma-aminobutyric acid (GABA)A-receptors play a crucial role in the generation of electroencephalogram (EEG) oscillations and evoked potentials (ERPs). The present association study was designed to test whether EEG and ERPs are modulated by genetic variations of the human GABAA beta2 (GABRB2) and gamma2 (GABRG2) genes on chromosome 5q33. The genotypes of two nucleotide substitution polymorphisms of the GABRB2 and GABRG2 genes were assessed in 95 psychiatrically healthy subjects of German descent. Neurophysiological phenotyping was performed with four factorized EEG/ERP parameters: EEG activation, anterior and posterior EEG synchronization, and event-related activity (N100/ P200-complex). No genotypic association was found for the GABRB2 nucleotide exchange polymorphism with any electrophysiological parameter. A significant association was found between the genotype of the intronic GABRG2 G-->A nucleotide exchange and the event-related N100/P200 (ANOVA: F=3.81; df=2; P=0.026). A comparison of homozygous subjects carrying either the G/G or A/A genotype of the GABRG2 polymorphism consistently revealed an even stronger difference in the effect-size (ANOVA: F=11.13; df=1; P=0.002). Post hoc analysis of this association with current density analysis in three-dimensional neuroanatomic Talairach space-time showed a reduction in the event-related signal power after 120 ms in the right dorsolateral prefrontal cortex. Taking into account the risk of false-positive association findings attributable to multiple testing, our results encourage further replication studies to examine the phenotype-genotype relationship of GABRG2 gene variants and event-related prefrontal activity.