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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 that their correlation or anticorrelation was correlated with the Stroop task performance in depression patients. (b) The correlation plots in the patient group (red) and control group (blue).
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fig5: (a) Neural network pairs that their correlation or anticorrelation was correlated with the Stroop task performance in depression patients. (b) The correlation plots in the patient group (red) and control group (blue).

Mentions: We did not find any significant correlation of the severity of depressive symptoms with any of the networks or interactions between any two networks. However, as shown in Figure 5, we found a negative correlation between the Stroop task performance and the interactions between the IC10 (SN) and IC6 (one of the CENs) and between IC10 (SN) and IC18 (one of the AN) in the depression group but not in the control group. In other words, in the depression group, those who had higher synchronization between the salience network and the central executive network and affective networks were the ones who had poor task performance on the Stroop task.


Altered Synchronizations among Neural Networks in Geriatric Depression.

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

(a) Neural network pairs that their correlation or anticorrelation was correlated with the Stroop task performance in depression patients. (b) The correlation plots in the patient group (red) and control group (blue).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: (a) Neural network pairs that their correlation or anticorrelation was correlated with the Stroop task performance in depression patients. (b) The correlation plots in the patient group (red) and control group (blue).
Mentions: We did not find any significant correlation of the severity of depressive symptoms with any of the networks or interactions between any two networks. However, as shown in Figure 5, we found a negative correlation between the Stroop task performance and the interactions between the IC10 (SN) and IC6 (one of the CENs) and between IC10 (SN) and IC18 (one of the AN) in the depression group but not in the control group. In other words, in the depression group, those who had higher synchronization between the salience network and the central executive network and affective networks were the ones who had poor task performance on the Stroop task.

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