Differentiation of Schizophrenia by Combining the Spatial EEG Brain Network Patterns of Rest and Task P300

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
Fali LiPeng Xu

Abstract

The P300 is regarded as a psychosis endophenotype of schizophrenia and a putative biomarker of risk for schizophrenia. However, the brain activity (i.e., P300 amplitude) during tasks cannot always provide satisfying discrimination of patients with schizophrenia (SZs) from healthy controls (HCs). Spontaneous activity at rest indices the potential of the brain, such that if the task information can be efficiently processed, it provides a compensatory understanding of the cognitive deficits in SZs. In this paper, based on the resting and P300 task electroencephalogram (EEG) data sets, we constructed functional EEG networks and then extracted the inherent spatial pattern of network (SPN) features for both brain states. Finally, the combined SPN features of the rest and task networks were used to recognize SZs. The findings of this paper revealed that the combined SPN features could achieve the highest accuracy of 90.48%, with the sensitivity of 89.47%, and specificity of 91.30%. These findings consistently implied that the rest and task P300 EEGs could actually provide comprehensive information to reliably classify SZs from HCs, and the SPN is a promising tool for the clinical diagnosis of SZs.

Citations

Sep 15, 2020·Physical and Engineering Sciences in Medicine·Ahmad ShalbafArash Maghsoudi
Jan 31, 2020·Frontiers in Computational Neuroscience·Dennis Joe HarmahPeng Xu
Oct 31, 2020·Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine·Kuldeep SinghJyoteesh Malhotra
Dec 12, 2020·Schizophrenia Bulletin·Georg Northoff, Javier Gomez-Pilar
Apr 21, 2021·Artificial Intelligence in Medicine·Carla BarrosAna P Pinheiro
Oct 16, 2020·The International Journal of Neuroscience·Rutvi Prajapati, Isaac Arnold Emerson

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