Altered Synchronizations among Neural Networks in Geriatric Depression.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Wang, L; Chou, Y-H; Potter, GG; Steffens, DC

Published Date

  • January 2015

Published In

Volume / Issue

  • 2015 /

Start / End Page

  • 343720 -

PubMed ID

  • 26180795

Pubmed Central ID

  • 26180795

Electronic International Standard Serial Number (EISSN)

  • 2314-6141

International Standard Serial Number (ISSN)

  • 2314-6133

Digital Object Identifier (DOI)

  • 10.1155/2015/343720

Language

  • eng