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      Abnormal Spontaneous Gamma Power Is Associated With Verbal Learning and Memory Dysfunction in Schizophrenia

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          Abstract

          Background

          Schizophrenia patients exhibit cognitive deficits across multiple domains, including verbal memory, working memory, and executive function, which substantially contribute to psychosocial disability. Gamma oscillations are associated with a wide range of cognitive operations, and are important for cortico-cortical transmission and the integration of information across neural networks. While previous reports have shown that schizophrenia patients have selective impairments in the ability to support gamma oscillations in response to 40-Hz auditory stimulation, it is unclear if patients show abnormalities in gamma power at rest, or whether resting-state activity in other frequency bands is associated with cognitive functioning in schizophrenia patients.

          Methods

          Resting-state electroencephalogram (EEG) was assessed over 3 min in 145 healthy comparison subjects and 157 schizophrenia patients. Single-word reading ability was measured via the reading subtest of the Wide Range Achievement Test-3 (WRAT). Auditory attention and working memory were evaluated using Letter-Number Span and Letter-Number Sequencing. Executive function was assessed via perseverative responses on the Wisconsin Card Sorting Test (WCST). Verbal learning performance was measured using the California Verbal Learning Test second edition (CVLT-II).

          Results

          Schizophrenia patients showed normal levels of delta-band power but abnormally elevated EEG power in theta, alpha, beta, and gamma bands. An exploratory correlation analysis showed a significant negative correlation of gamma-band power and verbal learning performance in schizophrenia patients.

          Conclusions

          Patients with schizophrenia have abnormal resting-state EEG power across multiple frequency bands; gamma-band abnormalities were selectively and negatively associated with impairments in verbal learning. Resting-state gamma-band EEG power may be useful for understanding the pathophysiology of cognitive dysfunction and developing novel therapeutics in schizophrenia patients.

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          Most cited references55

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

            This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
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              Abnormal neural oscillations and synchrony in schizophrenia.

              Converging evidence from electrophysiological, physiological and anatomical studies suggests that abnormalities in the synchronized oscillatory activity of neurons may have a central role in the pathophysiology of schizophrenia. Neural oscillations are a fundamental mechanism for the establishment of precise temporal relationships between neuronal responses that are in turn relevant for memory, perception and consciousness. In patients with schizophrenia, the synchronization of beta- and gamma-band activity is abnormal, suggesting a crucial role for dysfunctional oscillations in the generation of the cognitive deficits and other symptoms of the disorder. Dysfunctional oscillations may arise owing to anomalies in the brain's rhythm-generating networks of GABA (gamma-aminobutyric acid) interneurons and in cortico-cortical connections.
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                Author and article information

                Contributors
                Journal
                Front Psychiatry
                Front Psychiatry
                Front. Psychiatry
                Frontiers in Psychiatry
                Frontiers Media S.A.
                1664-0640
                31 August 2020
                2020
                : 11
                : 832
                Affiliations
                [1] 1Department of Psychiatry, University of California San Diego , La Jolla, CA, United States
                [2] 2Division of Law and Psychiatry, Center for Forensic Mental Health, Graduate School of Medical and Pharmaceutical Sciences, Chiba University , Chiba, Japan
                [3] 3Department of Psychiatry, Tokyo Metropolitan Matsuzawa Hospital , Tokyo, Japan
                [4] 4Swartz Center for Neural Computation, University of California San Diego , La Jolla, CA, United States
                [5] 5VISN-22 Mental Illness, Research, Education and Clinical Center (MIRECC), VA San Diego Healthcare System , San Diego, CA, United States
                Author notes

                Edited by: Błażej Misiak, Wroclaw Medical University, Poland

                Reviewed by: Bahar Güntekin, Istanbul Medipol University, Turkey; Antonio Ivano Triggiani, National Institute of Neurological Disorders and Stroke (NINDS), United States; Roberta Lizio, Institute of Research and Medical Care (IRCCS) SDN, Italy

                *Correspondence: Gregory A. Light, glight@ 123456health.ucsd.edu

                This article was submitted to Schizophrenia, a section of the journal Frontiers in Psychiatry

                Article
                10.3389/fpsyt.2020.00832
                7488980
                33110410
                6c46770d-7ae7-4830-9e02-bc7f8bc9e8c0
                Copyright © 2020 Tanaka-Koshiyama, Koshiyama, Miyakoshi, Joshi, Molina, Sprock, Braff and Light

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 07 May 2020
                : 31 July 2020
                Page count
                Figures: 4, Tables: 2, Equations: 2, References: 59, Pages: 9, Words: 4479
                Funding
                Funded by: Japan Society for the Promotion of Science 10.13039/501100001691
                Categories
                Psychiatry
                Original Research

                Clinical Psychology & Psychiatry
                spontaneous gamma oscillation,resting-state electroencephalography (eeg),schizophrenia,cognitive function,memory,verbal learning,translational biomarker

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