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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.


Temporal dynamic topology metrics of the PCC node across different brain states (pre-task resting state, task state, and post-task resting state).(a) Properties of PCC topology metrics for nodal degree, clustering coefficient, and local efficiency. (b) The probability distribution functions for PCC nodal topology metrics differed significantly across different brain states (two-sample Kolmogorov–Smirnov test, p < 0.001). (c) Boxplots of the PCC topology metrics indicating that the metrics differ significantly across brain states (Wilcoxon rank sum test, p < 0.05).
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f4: Temporal dynamic topology metrics of the PCC node across different brain states (pre-task resting state, task state, and post-task resting state).(a) Properties of PCC topology metrics for nodal degree, clustering coefficient, and local efficiency. (b) The probability distribution functions for PCC nodal topology metrics differed significantly across different brain states (two-sample Kolmogorov–Smirnov test, p < 0.001). (c) Boxplots of the PCC topology metrics indicating that the metrics differ significantly across brain states (Wilcoxon rank sum test, p < 0.05).

Mentions: To investigate the properties of dynamic DMN nodal topology, we considered three metrics for data analysis (nodal degree, clustering coefficient, and local efficiency). The DMN can be viewed as being composed of sub-networks60, including the medial temporal sub-network associated with memory-related nodes (left and right parahippocampal gyri; LPHG and RPHG) and another sub-network associated with the PCC. Here, we primarily focused on analysing these two sub-networks during three different brain states (pre-task resting state network, task, post-task resting state network). Figures 4a and 5a show the average topology of each metric for the PCC and LPHG nodes across the three brain states. We calculated the statistical properties for the temporal evolution of the topology metrics. Figures 4b and 5b show that that the probability distribution functions for the PCC and LPHG topology metrics across all participants were significantly different from each other during the different brain states (p < 0.05, Kolmogorov–Smirnov test). Nodal degree, clustering coefficient, and local efficiency were all significantly altered across different brain states (Figs 4c and 5c, Wilcoxon rank sum test, p < 0.05). Other metrics of DMN nodal topology show similar patterns (see Supplementary Figs S2–S10). These results indicated that DMN nodal topology can become reorganized as brain states transition back and forth.


Dynamic Default Mode Network across Different Brain States
Temporal dynamic topology metrics of the PCC node across different brain states (pre-task resting state, task state, and post-task resting state).(a) Properties of PCC topology metrics for nodal degree, clustering coefficient, and local efficiency. (b) The probability distribution functions for PCC nodal topology metrics differed significantly across different brain states (two-sample Kolmogorov–Smirnov test, p < 0.001). (c) Boxplots of the PCC topology metrics indicating that the metrics differ significantly across brain states (Wilcoxon rank sum test, p < 0.05).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Temporal dynamic topology metrics of the PCC node across different brain states (pre-task resting state, task state, and post-task resting state).(a) Properties of PCC topology metrics for nodal degree, clustering coefficient, and local efficiency. (b) The probability distribution functions for PCC nodal topology metrics differed significantly across different brain states (two-sample Kolmogorov–Smirnov test, p < 0.001). (c) Boxplots of the PCC topology metrics indicating that the metrics differ significantly across brain states (Wilcoxon rank sum test, p < 0.05).
Mentions: To investigate the properties of dynamic DMN nodal topology, we considered three metrics for data analysis (nodal degree, clustering coefficient, and local efficiency). The DMN can be viewed as being composed of sub-networks60, including the medial temporal sub-network associated with memory-related nodes (left and right parahippocampal gyri; LPHG and RPHG) and another sub-network associated with the PCC. Here, we primarily focused on analysing these two sub-networks during three different brain states (pre-task resting state network, task, post-task resting state network). Figures 4a and 5a show the average topology of each metric for the PCC and LPHG nodes across the three brain states. We calculated the statistical properties for the temporal evolution of the topology metrics. Figures 4b and 5b show that that the probability distribution functions for the PCC and LPHG topology metrics across all participants were significantly different from each other during the different brain states (p < 0.05, Kolmogorov–Smirnov test). Nodal degree, clustering coefficient, and local efficiency were all significantly altered across different brain states (Figs 4c and 5c, Wilcoxon rank sum test, p < 0.05). Other metrics of DMN nodal topology show similar patterns (see Supplementary Figs S2–S10). These results indicated that DMN nodal topology can become reorganized as brain states transition back and forth.

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.