Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence—Preliminary Data

Author:

Gjini Klevest1,Bowyer Susan M.23,Wang Frank4,Boutros Nash N.5ORCID

Affiliation:

1. Department of Neurology, University of Wisconsin–Madison, Madison, WI, USA

2. Department of Neurology, Henry Ford Hospital, Detroit, MI, USA

3. Wayne State University, Detroit, MI, USA

4. University of California, Berkeley, Berkeley, CA, USA

5. Department of Psychiatry, Wayne State University, Detroit, MI, USA

Abstract

This study investigated the magneto- and electroencephalography (MEG and EEG, respectively) resting state to identify the deviations closely associated with the deficit syndrome (DS) in schizophrenia patients. Ten subjects in each group (control, DS, and nondeficit schizophrenia [NDS]) were included. Subjects underwent MEG-EEG recordings during a resting state condition. MEG coherence source imaging (CSI) in source space and spectral analysis in sensor space were performed. Significant differences were found between the 2 patient groups: (1) MEG and EEG spectral analysis showed significantly higher power at low frequencies (delta band) at sensor space in DS compared with NDS patients; (2) source analysis revealed larger power in the DS compared with NDS group at low frequencies in the frontal region; (3) NDS patients showed significantly higher MEG signal relative power in beta bands in sensor space compared with DS patients; (4) both DS and NDS patients showed higher EEG absolute power at higher beta band compared to controls; and (5) patients with DS were found to have a significantly higher MEG CSI than controls in the beta frequency band. These data support the observation of increased power in the low-frequency EEG/MEG rhythms associated with the DS. Increased power in the beta rhythms was more associated with the NDS.

Funder

henry ford health system

wayne state university

Saint Luke’s Marion Bloch Neuroscience Institute

Publisher

SAGE Publications

Subject

Neurology (clinical),Neurology,General Medicine

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3. Electroencephalograph (EEG) signal analysis for the Detection of Schizophrenia using Empirical Wavelet Transform;2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T);2022-03-01

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