Discrimination of auditory verbal hallucination in schizophrenia based on EEG brain networks. (June 2023) - Wang, Jiuju; Dong, Wentian; Li, Yuqin; Wydell, Taeko N.; Quan, Wenxiang; Tian, Ju; Song, Yanping; Jiang, Lin; Li, Fali; Yi, Chanlin; Zhang, Yangsong; Yao, Dezhong; et al.
Highlights: P300 could discriminate schizophrenia from healthy controls, but failed to classify the two patient groups: AVH and non-AVHs. There were significant network differences between AVH and non-AVH schizophrenia in topological and property. The classification of AVH and non-AVH based on brain networks in Delta and Theta bands achieved the highest accuracy of 80.95%. Abstract: Auditory verbal hallucinations (AVH) are a core positive symptom of schizophrenia and are regarded as a consequence of the functional breakdown in the related sensory process. Yet, the potential mechanism of AVH is still lacking. In the present study, we explored the difference between AVHs (n = 23) and non-AVHs (n = 19) in schizophrenia and healthy controls (n = 29) by using multidimensional electroencephalograms data during an auditory oddball task. Compared to healthy controls, both AVH and non-AVH groups showed reduced P300 amplitudes. Additionally, the results from brain networks analysis revealed that AVH patients showed reduced left frontal to posterior parietal/temporal connectivity compared to non-AVH patients. Moreover, using the fused network properties of both delta and theta bands as features for in-depth learning made it possible to identify the AVH from non-AVH patients at an accuracy of 80.95%. The left frontal-parietal/temporal networks seen in the auditory oddball paradigm might be underlying biomarkers of AVH in schizophrenia. This study demonstrated for the first time the functional breakdown of the auditory processing pathway in the AVH patients, leading to a better understanding of the atypical brain network of the AVH patients.