1. Resting state alpha oscillatory activity is a valid and reliable marker of schizotypy
- Author
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Jelena Trajkovic, Vincenzo Romei, Francesca Ferri, Chiara Marzi, Francesco Di Gregorio, Stefano Diciotti, Trajkovic J., Di Gregorio F., Ferri F., Marzi C., Diciotti S., and Romei V.
- Subjects
Adult ,Male ,Psychosis ,Consciousness ,Schizotypy ,Rest ,Science ,Alpha (ethology) ,Cognitive neuroscience ,Electroencephalography ,Membrane Potential ,050105 experimental psychology ,Article ,Machine Learning ,Schizotypal Personality Disorder ,03 medical and health sciences ,0302 clinical medicine ,medicine ,0501 psychology and cognitive sciences ,Multidisciplinary ,medicine.diagnostic_test ,Resting state fMRI ,business.industry ,05 social sciences ,medicine.disease ,Schizotypal personality disorder ,Schizophrenia ,Medicine ,Female ,business ,Neuroscience ,030217 neurology & neurosurgery ,Human - Abstract
Schizophrenia is among the most debilitating neuropsychiatric disorders. However, clear neurophysiological markers that would identify at-risk individuals represent still an unknown. The aim of this study was to investigate possible alterations in the resting alpha oscillatory activity in normal population high on schizotypy trait, a physiological condition known to be severely altered in patients with schizophrenia. Direct comparison of resting-state EEG oscillatory activity between Low and High Schizotypy Group (LSG and HSG) has revealed a clear right hemisphere alteration in alpha activity of the HSG. Specifically, HSG shows a significant slowing down of right hemisphere posterior alpha frequency and an altered distribution of its amplitude, with a tendency towards a reduction in the right hemisphere in comparison to LSG. Furthermore, altered and reduced connectivity in the right fronto-parietal network within the alpha range was found in the HSG. Crucially, a trained pattern classifier based on these indices of alpha activity was able to successfully differentiate HSG from LSG on tested participants further confirming the specific importance of right hemispheric alpha activity and intrahemispheric functional connectivity. By combining alpha activity and connectivity measures with a machine learning predictive model optimized in a nested stratified cross-validation loop, current research offers a promising clinical tool able to identify individuals at-risk of developing psychosis (i.e., high schizotypy individuals).
- Published
- 2021