Altered Synchronizations among Neural Networks in Geriatric Depression.

Content Provider	Europe PMC
Author	Wang, Lihong Chou, Ying-Hui Potter, Guy G. Steffens, David C.
Copyright Year	2015
Abstract	Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.
ISSN	23146133
Journal	Biomed Research International
Volume Number	2015
PubMed Central reference number	PMC4477114
PubMed reference number	26180795
e-ISSN	23146141
DOI	10.1155/2015/343720
Language	English
Publisher	Hindawi
Publisher Date	2015-06-09
Access Restriction	Open
Rights License	This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2015 Lihong Wang et al.
Content Type	Text
Resource Type	Article
Subject	Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology