Literature DB >> 22445216

Decreased cortical complexity in methamphetamine abusers.

Kyongsik Yun1, Hee-Kwon Park, Do-Hoon Kwon, Yang-Tae Kim, Sung-Nam Cho, Hyun-Jin Cho, Bradley S Peterson, Jaeseung Jeong.   

Abstract

This study aimed to investigate if methamphetamine (MA) abusers exhibit alterations in complexity of the electroencephalogram (EEG) and to determine if these possible alterations are associated with their abuse patterns. EEGs were recorded from 48 former MA-dependent males and 20 age- and sex-matched healthy subjects. Approximate Entropy (ApEn), an information-theoretical measure of irregularity, of the EEGs was estimated to quantify the degree of cortical complexity. The ApEn values in MA abusers were significantly lower than those of healthy subjects in most of the cortical regions, indicating decreased cortical complexity of MA abusers, which may be associated with impairment in specialization and integration of cortical activities owing to MA abuse. Moreover, ApEn values exhibited significant correlations with the clinical factors including abuse patterns, symptoms of psychoses, and their concurrent drinking and smoking habits. These findings provide insights into abnormal information processing in MA abusers and suggest that ApEn of EEG recordings may be used as a potential supplementary tool for quantitative diagnosis of MA abuse. This is the first investigation to assess the "severity-dependent dynamical complexity" of EEG patterns in former MA abusers and their associations with the subjects' abuse patterns and other clinical measures.
Copyright © 2011. Published by Elsevier Ireland Ltd.

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Year:  2012        PMID: 22445216     DOI: 10.1016/j.pscychresns.2011.07.009

Source DB:  PubMed          Journal:  Psychiatry Res        ISSN: 0165-1781            Impact factor:   3.222


  6 in total

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Review 4.  Electroencephalographic delta/alpha frequency activity differentiates psychotic disorders: a study of schizophrenia, bipolar disorder and methamphetamine-induced psychotic disorder.

Authors:  Fleur M Howells; Hendrik S Temmingh; Jennifer H Hsieh; Andrea V van Dijen; David S Baldwin; Dan J Stein
Journal:  Transl Psychiatry       Date:  2018-04-12       Impact factor: 6.222

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Authors:  Hassan Khajehpour; Bahador Makkiabadi; Hamed Ekhtiari; Sepideh Bakht; Alireza Noroozi; Fahimeh Mohagheghian
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6.  Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment.

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  6 in total

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