NDLI: A study on validity of cortical alpha connectivity for schizophrenia

Content Provider	IEEE Xplore Digital Library
Author	Hong Peng Bin Hu Lanlan Li Ratcliffe, M. Jingwei Zhai Qinglin Zhao Qiuxia Shi Yunpeng Li Quanying Liu
Copyright Year	2013
Description	Author affiliation: Birmingham City Univ., Birmingham, UK (Ratcliffe, M.) \|\| Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China (Hong Peng; Bin Hu; Lanlan Li; Jingwei Zhai; Qinglin Zhao; Qiuxia Shi; Yunpeng Li; Quanying Liu)
Abstract	Abnormalities in schizophrenia are thought to be associated with functional disconnections between different brain regions. Most previous studies on schizophrenia have considered high-band connectivity in preference to the Alpha band, as there has been some uncertainty correlating the latter to the condition. In this paper we attempt to clarify this correlation using an Electroencephalogram (EEG) analysis of the Alpha band from schizophrenic patients. Global, regional Omega and dimensional complexity and local Omega complexity differentials (LCD) of single channel are calculated using 16 channels of resting EEG data from 31 adult patients with schizophrenia and 31 age/sex matched control subjects. It was found that, compared to the controls, anterior alpha Omega and dimensional complexity are higher in schizophrenia patients (p<;0.05) with the single channel LCD also increasing at FP1, FP2, F7 and F8 electrodes. Furthermore, higher left hemisphere dimensional complexity and LCD at T3 point was also found. The results suggest there is lower connectivity in the pre-frontal and left temporal regions with respect to the alpha band in schizophrenia patients.
Starting Page	3286
Ending Page	3290
File Size	450310
Page Count	5
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610243
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Complexity theory Electroencephalography Educational institutions Brain Electrodes Information processing Psychiatry
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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