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. 2025 Aug 25:19:1525497.
doi: 10.3389/fnhum.2025.1525497. eCollection 2025.

Hubs, influencers, and communities of executive functions: a task-based fMRI graph analysis

Alexandra T Davis  1
Affiliations

Hubs, influencers, and communities of executive functions: a task-based fMRI graph analysis

Alexandra T Davis. Front Hum Neurosci. .

Abstract

Introduction: This study investigates four subdomains of executive functioning-initiation, cognitive inhibition, mental shifting, and working memory-using task-based functional magnetic resonance imaging (fMRI) data and graph analysis.

Methods: We used healthy adults' functional magnetic resonance imaging (fMRI) data to construct brain connectomes and network graphs for each task and analyzed global and node-level graph metrics.

Results: The bilateral precuneus and right medial prefrontal cortex emerged as pivotal hubs and influencers, emphasizing their crucial regulatory role in all four subdomains of executive function. Furthermore, distinct hubs and influencers were identified in cognitive inhibition and mental shifting tasks, elucidating unique network dynamics. Our results suggest a decentralized brain organization with critical hub regions pertinent to conditions such as stroke and traumatic brain injury.

Discussion: The precuneus and medial prefrontal cortex stand out as consistent, domain-general nodes in our findings, which show both unique and shared neural hubs across executive function subdomains. The presence of distinct hubs in cognitive inhibition and mental shifting tasks suggests flexible, task-specific network configurations. A decentralized yet structured brain network may also promote cognitive resilience.

Keywords: brain network; connectome; executive functioning; graph analysis; graph theory.

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Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Pipeline for data analysis. (A) Obtain raw archival data. (B) Functional connectivity matrices are constructed, and connections with less frequency were excluded. (C) Adjacency matrices were derived based on step (B). (D) Graphs were generated and plotted on connectome. (E) Graph theoretical analysis. Metrics such as degree centrality, clustering coefficient, and modularity are computed to characterize the network’s local and global properties. (F) Statistical analyses are applied to identify significant patterns or differences across tasks. (G) Results were plotted on Schaefer for visualizations of the findings.
Figure 2
Figure 2
Adjacency matrix for each task. (a) Initiation, (b) Inhibition, (c) Shifting, (d) 2-back.
Figure 3
Figure 3
Graphs plotted on connectome for each task. (a) Initiation, (b) Inhibition, (c) Shifting, (d) 2-back.
Figure 4
Figure 4
Graphs for each task. (a) Initiation, (b) Inhibition, (c) Shifting, (d) 2-back.
Figure 5
Figure 5
Hubs. (a) Initiation, (b) Inhibition, (c) Shifting, (d) 2-back.
Figure 6
Figure 6
Influencers. (a) Initiation, (b) Inhibition, (c) Shifting, (d) 2-back.
Figure 7
Figure 7
Hierarchical edge betweenness community. (a) Initiation, (b) Inhibition, (c) Shifting, (d) 2-back.
Figure 8
Figure 8
Main Louvain communities plotted on brain atlas. (a) Initiation, (b) Inhibition, (c) Shifting, (d) 2-back.
See this image and copyright information in PMC

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