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Graphic patterns of cortical functional connectivity of depressed patients on the basis of EEG measurements

Sun, Y, Hu, S, Chambers, J, Zhu, Y and Tong, S (2011) Graphic patterns of cortical functional connectivity of depressed patients on the basis of EEG measurements

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Abstract

Considerable evidences have shown a decrease of neu-ronal activity in the left frontal lobe of depressed patients, but the underlying cortical network is still unclear. The present study intends to investigate the conscious-state brain network patterns in depressed patients compared with control individuals. Cortical functional connectivity is quantified by the partial directed coherence (PDC) analysis of multichannel EEG signals from 12 depressed patients and 12 healthy volunteers. The corresponding PDC matrices are first converted into unweighted graphs by applying a threshold to obtain the topographic property in-degree (K in). A significantly larger K in in the left hemisphere is identified in depressed patients, while a symmetric pattern is found in the control group. Another two topographic measures, i.e., clustering coefficients (C) and characteristic path length (L), are obtained from the original weighted PDC digraphs. Compared with control individuals, significantly smaller C and L are revealed in the depression group, indicating a random network-like architecture due to affective disorder. This study thereby provides further support for the presence of a hemispheric asymmetry syndrome in the depressed patients. More importantly, we present evidence that depression is characterized by a loss of optimal small-world network characteristics in conscious state. © 2011 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Sun, YUNSPECIFIEDUNSPECIFIED
Hu, SUNSPECIFIEDUNSPECIFIED
Chambers, Jj.a.chambers@surrey.ac.ukUNSPECIFIED
Zhu, YUNSPECIFIEDUNSPECIFIED
Tong, SUNSPECIFIEDUNSPECIFIED
Date : 26 December 2011
Identification Number : https://doi.org/10.1109/IEMBS.2011.6090334
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:24
Last Modified : 17 May 2017 13:24
URI: http://epubs.surrey.ac.uk/id/eprint/839117

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