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).
Highlights
Cortico-thalamic, and thalamo-cortical connections[24,25]
To fill this gap in the literature, in the present study we first established the association between sub-clinical schizotypy and specific indices of rest EEG alpha oscillatory activity and connectivity using both non-directional and directional indices
Our results clearly demonstrate that the altered patterns of resting-state alpha activity observed in schizophrenia patients can be tracked already before the onset of the psychosis
Summary
Cortico-thalamic, and thalamo-cortical connections[24,25]. It is well known that large bursts of alpha (7–13 Hz) band activity dominate the human electroencephalogram (EEG) during periods of rest[26]. While there is enough evidence available indicating that abnormal rest EEG alpha rhythm characterizes already the onset of schizophrenia, its potential role as an early marker of a predisposition toward schizophrenia in non-clinical populations is still poorly investigated[20,38]. To fill this gap in the literature, in the present study we first established the association between sub-clinical schizotypy and specific indices of rest EEG alpha oscillatory activity (i.e., individual alpha peak frequency, IAF) and connectivity using both non-directional (i.e., weighted phase lag index, wPLI) and directional (i.e., time lag index, TLI) indices. We trained and tested a pattern classifier to create a predictive model able to assess the presence of high schizotypal traits based on the alpha resting state activity of an individual
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