Temporal stability of functional brain modules associated with human intelligence

Abstract

Individual differences in general cognitive ability (i.e., intelligence) have been linked to individual variations in the modular organization of functional brain networks.However, these analyses have been limited to static (time-averaged) connectivity, and have not yet addressed whether dynamic changes in the configuration of brain networks relate to general intelligence. Here, we used multiband functional MRI resting-state data (N = 281) and estimated subject-specific time-varying functional connectivity networks. Modularity optimization was applied to determine individual time-variant module partitions and to assess fluctuations in modularity across time.We show that higher intelligence, indexed by an established composite measure, the Wechsler Abbreviated Scale of Intelligence (WASI), is associated with higher temporal stability (lower temporal variability) of brain network modularity. Post-hoc analyses reveal that subjects with higher intelligence scores engage in fewer periods of extremely high modularitywhich are characterized by greater disconnection of task-positive from task-negative networks. Further, we show that brain regions of the dorsal attention network contribute most to the observed effect. In sum, our study suggests that investigating the temporal dynamics of functional brain network topology contributes to our understanding of the neural bases of general cognitive abilities.

Flexible Brain Transitions Between Hierarchical Network Segregation and Integration Predict Human Behavior

“…In principle, flexible transitions in brain network organization between segregated and integrated states support the efficient communication of brain network subsystems across local and global scales, which is essential for successful cognitive performance. Despite great progress in understanding the dynamic transition between segregation and integration at rest and during tasks 8,9,13,14 , how cognitive behaviors are associated with dynamic reconfigurations in large-scale brain functional organization is still not clear.…”

“…Complex brain networks display adaptive modular structures across time 13,15 that involve strong functional connectivity (FC) within modules and relatively weak connectivity between them. This modular feature allows for an integrated process within modules and segregation between them 16,17 .…”

Revealing the relevant spatiotemporal scale underlying whole-brain dynamics

“…Future studies could consider additional analyses using other metrics such as network measures or apply emerging techniques that specifically consider the change of spatial composition of functional networks over time (Geniesse et al 2019). As Arslan et al (2018) and Hilger et al (2020) demonstrated in their studies that network measures of integration and segregation are largely altered by the spatial scale, appropriate correction techniques should be used for such analyses across scales. In regard to the parcellation used, we used the Schaefer parcellation for our analyses across scales, which is ideal for cross-study comparisons at increasing spatial scales because it is offers fine-grained parcellations at various spatial scales in both MNI and surface space.…”

Computerized physical exercise improves the functional architecture of the brain in patients with Parkinson’s Disease: a network science resting-state EEG study

“…Changes of the CC (Figure 2) indicate a more robust functional clustering organization, further supporting our suggestion of a reparative brain network architecture. Our post-training network's robustness and segregation can provide neuroprotection in PwPD [16], but the efficacy of such a mechanism depends on the balance between integration and segregation, featuring SW network architecture [58], [60].…”