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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

The ICA components which correspond to different neural networks according to the goodness-of-fit analysis using the templates of Laird et al. AN = affective network; CAN = central attentional network; CEN = central executive network; DMN = default-mode network; SN = salience network.
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fig1: The ICA components which correspond to different neural networks according to the goodness-of-fit analysis using the templates of Laird et al. AN = affective network; CAN = central attentional network; CEN = central executive network; DMN = default-mode network; SN = salience network.

Mentions: Our aim was to examine whether we can identify altered interactions among networks that are related to depressive symptoms and cognitive dysfunctions in geriatric depression. To achieve this goal, using the results from Laird and colleagues [19] as templates, first we identified the components that were best matched to the default-mode network (DMN, IC1, corresponding to Laird et al.'s IC13), central executive network (CEN, IC4, and IC6 corresponding to Laird et al.'s IC15 and IC18, resp.), central attentional network (CAN, IC7 corresponding to Larid et al.'s IC7), salience network (SN, IC10, corresponding to Laird et al.'s IC4), and affective work (AN, IC12, and IC18, corresponding to Laid et al.'s IC2; the IC18 was also matched to Laird et al.'s IC1). Figure 1 shows the matched components between the ICs in our study with Larid et al.'s. The detailed coverage for each component is listed in Table 2 and Figure 2. More detailed coverage of each component is shown in axial views in supplementary sFigure 1 in Supplementary Material available online at http://dx.doi.org/10.1155/2015/343720.


Altered Synchronizations among Neural Networks in Geriatric Depression.

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

The ICA components which correspond to different neural networks according to the goodness-of-fit analysis using the templates of Laird et al. AN = affective network; CAN = central attentional network; CEN = central executive network; DMN = default-mode network; SN = salience network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: The ICA components which correspond to different neural networks according to the goodness-of-fit analysis using the templates of Laird et al. AN = affective network; CAN = central attentional network; CEN = central executive network; DMN = default-mode network; SN = salience network.
Mentions: Our aim was to examine whether we can identify altered interactions among networks that are related to depressive symptoms and cognitive dysfunctions in geriatric depression. To achieve this goal, using the results from Laird and colleagues [19] as templates, first we identified the components that were best matched to the default-mode network (DMN, IC1, corresponding to Laird et al.'s IC13), central executive network (CEN, IC4, and IC6 corresponding to Laird et al.'s IC15 and IC18, resp.), central attentional network (CAN, IC7 corresponding to Larid et al.'s IC7), salience network (SN, IC10, corresponding to Laird et al.'s IC4), and affective work (AN, IC12, and IC18, corresponding to Laid et al.'s IC2; the IC18 was also matched to Laird et al.'s IC1). Figure 1 shows the matched components between the ICs in our study with Larid et al.'s. The detailed coverage for each component is listed in Table 2 and Figure 2. More detailed coverage of each component is shown in axial views in supplementary sFigure 1 in Supplementary Material available online at http://dx.doi.org/10.1155/2015/343720.

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