Stability of Network Communities as a Function of Task Complexity

Abstract

The analysis of the community architecture in functional brain networks has revealed important relations between specific behavioral patterns and characteristic features of the associated functional organization. Numerous studies assessed changes in functional communities during different states of awareness, learning, information processing, and various behavioral patterns. The robustness of detected communities within a network has been an often-discussed topic in complex systems research. However, our knowledge regarding the inter-subject stability of functional communities in the human brain while performing different tasks is still lacking. In this study, we examined the variability of functional communities in weighted undirected graphs based on fMRI recordings of healthy subjects across three conditions, the resting state, syllable production as a simple vocal motor task, and meaningful speech production representing a complex behavioral pattern with cognitive involvement. Based on the constructed empirical networks, we simulated a large cohort of artificial graphs and performed a leave-one-out stability analysis to assess the sensitivity of communities in the group-averaged networks with respect to perturbations in the averaging cohort. We found that the stability of partitions derived from group-averaged networks depended on task complexity. The determined community architecture in mean networks reflected within-behavior network stability and between-behavior flexibility of the human functional connectome. The sensitivity of functional communities increased from rest to syllable production to speaking, which suggests that the approximation quality of the community structure in the average network to reflect individual per-subject partitions depends on task complexity.

Task‐specificity in focal dystonia is shaped by aberrant diversity of a functional network kernel

“…Individual regional averages of residual time-series were computed using 212 regions of interest (ROIs), comprising 142 cortical, 36 subcortical and 34 cerebellar areas, based on the maximum probability maps and macrolabel atlas 14, 16 . The pair-wise interaction of ROIs was estimated using normalized mutual information coefficients 6, 17, 18 . Group-specific mean networks were constructed by averaging weights of edges present in at least 50% of subject networks 14, 19 within each group, while controlling for connectedness of the mean graph.…”

“…13,15 The pairwise interaction of regions of interest was estimated using normalized mutual information coefficients. 6,16,17 Group-specific mean networks were constructed by averaging weights of edges present in at least 50% of subject networks 13,18 within each group while controlling for connectedness of the mean graph. To assess the architecture of the constructed functional networks, a community detection analysis was performed following the strategy described earlier 17 (see details of network construction in the Supplementary Methods).…”

Fronto‐Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue

“…Studies on the modular organization or community structure of brain networks would provide additional information about (1) subnetworks consisting of brain regions dedicated to common cognitive functions, (2) the relationships and communication between such subnetworks, and (3) the topological roles of individual nodes within subnetworks, all of which would be useful for further understanding the neural processes associated with mental fatigue. In fact, modular organization can be altered depending on learning (Bassett et al, ), pathological states (Alexander‐Bloch et al, ; Han et al, ; Sun et al, ), age (Meunier, Achard, Morcom, & Bullmore, ), individual cognitive capacity (Stevens, Tappon, Garg, & Fair, ), awareness (Godwin, Barry, & Marois, ), and task complexity (Fuertinger & Simonyan, ).…”

Large-scale network interactions supporting item-context memory formation

“…However, fMRI studies of the network-basis of memory have mainly used targeted analyses that focus either exclusively on the hippocampal formation and its connectivity [8–10] or only consider connectivity in relation to specific components of task-related signal [6, 11, 12]. In other domains, such as language, broader interactions have been explored, thus providing information on how the structure of large-scale brain networks vary with different cognitive task demands [13–15]. Although some studies have focused on network interactions supporting episodic memory retrieval [16–18], such interactions during memory formation have rarely been explored.…”