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Dynamic Default Mode Network across Different Brain States

View Article: PubMed Central - PubMed

ABSTRACT

The default mode network (DMN) is a complex dynamic network that is critical for understanding cognitive function. However, whether dynamic topological reconfiguration of the DMN occurs across different brain states, and whether this potential reorganization is associated with prior learning or experience is unclear. To better understand the temporally changing topology of the DMN, we investigated both nodal and global dynamic DMN-topology metrics across different brain states. We found that DMN topology changes over time and those different patterns are associated with different brain states. Further, the nodal and global topological organization can be rebuilt by different brain states. These results indicate that the post-task, resting-state topology of the brain network is dynamically altered as a function of immediately prior cognitive experience, and that these modulated networks are assembled in the subsequent state. Together, these findings suggest that the changing topology of the DMN may play an important role in characterizing brain states.

No MeSH data available.


Scatterplots of the association between Δ(task-pre) and Δ(post-pre) in the DMN.(a) DMN nodal topology metrics show significant correlation between Δ(task-pre) and Δ(post-pre) in the PCC and LPHG across brain states (p < 0.0001). (b) DMN global topology metrics show significant correlation between Δ(task-pre) and Δ(post-pre) across brain states (p < 0.0001).
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f8: Scatterplots of the association between Δ(task-pre) and Δ(post-pre) in the DMN.(a) DMN nodal topology metrics show significant correlation between Δ(task-pre) and Δ(post-pre) in the PCC and LPHG across brain states (p < 0.0001). (b) DMN global topology metrics show significant correlation between Δ(task-pre) and Δ(post-pre) across brain states (p < 0.0001).

Mentions: To explore the relationship between changes in topology metrics and different brain states, we used a robust regression approach. The change in network topology metrics Δ (task-pre) induced by task performance was associated with the change in network topology metrics Δ (post-pre). Our results show significant positive correlations between Δ (task-pre) and Δ (post-pre) in the PCC, LPHG nodal, and DMN global network topology metrics (see Fig. 8, p < 0.05). The other DMN nodal metrics showed similar results (Supplementary Fig. S12). Thus, the results indicate that task performance significantly modulates DMN topology structure.


Dynamic Default Mode Network across Different Brain States
Scatterplots of the association between Δ(task-pre) and Δ(post-pre) in the DMN.(a) DMN nodal topology metrics show significant correlation between Δ(task-pre) and Δ(post-pre) in the PCC and LPHG across brain states (p < 0.0001). (b) DMN global topology metrics show significant correlation between Δ(task-pre) and Δ(post-pre) across brain states (p < 0.0001).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5382672&req=5

f8: Scatterplots of the association between Δ(task-pre) and Δ(post-pre) in the DMN.(a) DMN nodal topology metrics show significant correlation between Δ(task-pre) and Δ(post-pre) in the PCC and LPHG across brain states (p < 0.0001). (b) DMN global topology metrics show significant correlation between Δ(task-pre) and Δ(post-pre) across brain states (p < 0.0001).
Mentions: To explore the relationship between changes in topology metrics and different brain states, we used a robust regression approach. The change in network topology metrics Δ (task-pre) induced by task performance was associated with the change in network topology metrics Δ (post-pre). Our results show significant positive correlations between Δ (task-pre) and Δ (post-pre) in the PCC, LPHG nodal, and DMN global network topology metrics (see Fig. 8, p < 0.05). The other DMN nodal metrics showed similar results (Supplementary Fig. S12). Thus, the results indicate that task performance significantly modulates DMN topology structure.

View Article: PubMed Central - PubMed

ABSTRACT

The default mode network (DMN) is a complex dynamic network that is critical for understanding cognitive function. However, whether dynamic topological reconfiguration of the DMN occurs across different brain states, and whether this potential reorganization is associated with prior learning or experience is unclear. To better understand the temporally changing topology of the DMN, we investigated both nodal and global dynamic DMN-topology metrics across different brain states. We found that DMN topology changes over time and those different patterns are associated with different brain states. Further, the nodal and global topological organization can be rebuilt by different brain states. These results indicate that the post-task, resting-state topology of the brain network is dynamically altered as a function of immediately prior cognitive experience, and that these modulated networks are assembled in the subsequent state. Together, these findings suggest that the changing topology of the DMN may play an important role in characterizing brain states.

No MeSH data available.