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Dynamic Default Mode Network across Different Brain States

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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.


Dynamic DMN network topology metrics for different sliding windows.(a–c) The PCC, LPHG, and DMN whole-network dynamic topology metrics for each sliding window length (60 s, 75 s, and 90 s). Error bars indicate SEM.
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f9: Dynamic DMN network topology metrics for different sliding windows.(a–c) The PCC, LPHG, and DMN whole-network dynamic topology metrics for each sliding window length (60 s, 75 s, and 90 s). Error bars indicate SEM.

Mentions: To investigate how the length of the sliding window affects the analysis of DMN topology, we further examined our data using different sliding-window lengths. Previous studies have suggested that longer sliding windows would not be able to adequately characterize dynamic FC properties. Thus, here we focused on shorter sliding-window lengths (60 s, 75 s and 90 s). We consistently observed that DMN nodal and global topology metrics differed significantly depending on task state, regardless of the window length (Fig. 9, Wilcoxon rank sum test, p < 0.05). We found similar results for the other DMN regions (Supplementary Fig. S13).


Dynamic Default Mode Network across Different Brain States
Dynamic DMN network topology metrics for different sliding windows.(a–c) The PCC, LPHG, and DMN whole-network dynamic topology metrics for each sliding window length (60 s, 75 s, and 90 s). Error bars indicate SEM.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f9: Dynamic DMN network topology metrics for different sliding windows.(a–c) The PCC, LPHG, and DMN whole-network dynamic topology metrics for each sliding window length (60 s, 75 s, and 90 s). Error bars indicate SEM.
Mentions: To investigate how the length of the sliding window affects the analysis of DMN topology, we further examined our data using different sliding-window lengths. Previous studies have suggested that longer sliding windows would not be able to adequately characterize dynamic FC properties. Thus, here we focused on shorter sliding-window lengths (60 s, 75 s and 90 s). We consistently observed that DMN nodal and global topology metrics differed significantly depending on task state, regardless of the window length (Fig. 9, Wilcoxon rank sum test, p < 0.05). We found similar results for the other DMN regions (Supplementary Fig. S13).

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.