Evaluation of spontaneous dense array gamma oscillatory activity and minor physical anomalies as a composite...

Minor physical anomalies (MPAs) and gamma oscillatory activity have been proposed as associated endophenotypes in schizophrenia. Combining these endophenotypes to create a composite endophenotype may help identify those at risk for schizophrenia better. The present study aims to investigate MPAs and gamma oscillatory activity in schizophrenia patients, their unaffected first degree relatives and healthy controls and appreciate whether they can be used together as a composite endophenotype.

This was a cross sectional family study conducted at a tertiary care mental health setup. Ninety participants including schizophrenia patients, their first degree relatives and controls (thirty each) were assessed for MPAs on the Extended Waldrop Scale. All participants underwent an awake, resting 192-channel EEG recording. Spectral power and coherence in 30-100Hz gamma bands were estimated using Welch's averaged periodogram method. One-way ANOVA, chi square test were used for comparing socio-demographic-clinical variables. MANOVA supplemented by one-way ANOVAs (post hoc Tukey HSD) were done for comparison of spectral measures. Pearson's correlation, step-by-step linear discriminant functional and intra-familial correlation analysis were subsequently performed.

An endophenotype pattern of finding was found for MPAs in the craniofacial region, the total number of MPAs, spectral power in right temporal region on all bands and in the right parietal region in 50-70Hz and 70-100Hz gamma bands. The three groups were most accurately classified when MPA total score, right temporal 30-50Hz gamma power and right occipital 'intra hemispheric' 50-70Hz gamma coherence were considered together than when considered independently. Significant intra familial correlation was seen for MPA total score and right temporal gamma 30-50Hz power.

Composite evaluation of two developmentally linked markers i.e. MPAs and gamma spectral measures may prove useful in categorizing schizophrenia and identifying at-risk individuals.