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Altered Synchronizations among Neural Networks in Geriatric Depression.

Wang L, Chou YH, Potter GG, Steffens DC - Biomed Res Int (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, University of Connecticut Health Center, 263 Farmington, CT 06119, USA ; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA ; Brain Imaging and Analysis Center, Duke University, Durham, NC, USA.

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.

No MeSH data available.


Related in: MedlinePlus

(a) Neural network pairs which revealed significant correlations in the control group. (b) The plots of time course of each network in a control subject (ID10) and a depression subject (ID37) to illustrate the interaction effect between each paired neural networks. The significance was tested using Monte-Carlo simulation.
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fig4: (a) Neural network pairs which revealed significant correlations in the control group. (b) The plots of time course of each network in a control subject (ID10) and a depression subject (ID37) to illustrate the interaction effect between each paired neural networks. The significance was tested using Monte-Carlo simulation.

Mentions: When comparing correlations among networks between the two groups (pairwise correlations), it was the IC12 that showed a significant group difference in the synchronizations between this network with several other networks (Table 3). Specifically, we found a positive correlation in the IC12 with IC6 (one of the CENs) in the control group; however, the correlation was significantly reduced (no significant correlation existed) in the depression group. We also found a significantly increased correlation (less negative) in the depression group relative to the control group in the IC12 with IC7 (CAN, mainly in the precuneus region) and the IC12 with IC10 (the salience network, SN) (Figure 4).


Altered Synchronizations among Neural Networks in Geriatric Depression.

Wang L, Chou YH, Potter GG, Steffens DC - Biomed Res Int (2015)

(a) Neural network pairs which revealed significant correlations in the control group. (b) The plots of time course of each network in a control subject (ID10) and a depression subject (ID37) to illustrate the interaction effect between each paired neural networks. The significance was tested using Monte-Carlo simulation.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4477114&req=5

fig4: (a) Neural network pairs which revealed significant correlations in the control group. (b) The plots of time course of each network in a control subject (ID10) and a depression subject (ID37) to illustrate the interaction effect between each paired neural networks. The significance was tested using Monte-Carlo simulation.
Mentions: When comparing correlations among networks between the two groups (pairwise correlations), it was the IC12 that showed a significant group difference in the synchronizations between this network with several other networks (Table 3). Specifically, we found a positive correlation in the IC12 with IC6 (one of the CENs) in the control group; however, the correlation was significantly reduced (no significant correlation existed) in the depression group. We also found a significantly increased correlation (less negative) in the depression group relative to the control group in the IC12 with IC7 (CAN, mainly in the precuneus region) and the IC12 with IC10 (the salience network, SN) (Figure 4).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, University of Connecticut Health Center, 263 Farmington, CT 06119, USA ; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA ; Brain Imaging and Analysis Center, Duke University, Durham, NC, USA.

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.

No MeSH data available.


Related in: MedlinePlus