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Resting-State and Task Functional Magnetic Resonance Imaging Network Topology Metrics With no Threshold Selection to

Charly Hugo Alexandre Billaud1, Junhong Yu1

  • 1Psychology, School of Social Sciences, Nanyang Technological University, Singapore, Singapore.

Human Brain Mapping
|April 8, 2026
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Summary

Brain network topology measures link to cognition. Backbone strength (BS) from task-based fMRI best predicts cognitive performance, outperforming resting-state measures and traditional graph theory metrics.

Keywords:
cognitionfMRIgraph theoryminimum spanning treenetworkpersistent homologythreshold

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Network Science

Background:

  • Brain network organization is characterized by topology measures.
  • Graph theory metrics derived from functional magnetic resonance imaging (fMRI) correlate with cognitive performance.
  • Traditional methods using arbitrary connectivity thresholds can bias these metrics, prompting the development of alternatives like minimum spanning tree (MST) and persistent homology-based measures.

Purpose of the Study:

  • To compare the association between novel and traditional network topology measures with cognitive performance.
  • To investigate these associations using both resting-state and task-based fMRI data.
  • To identify which network topology measures are most robustly linked to cognitive abilities.

Main Methods:

  • Functional connectivity matrices were calculated from Human Connectome Project (Young Adult) fMRI data across various tasks (resting-state, working memory, gambling, language, motor, relational processing, social cognition, movie-watching).
  • Network topology metrics including global efficiency, clustering coefficient, diameter, leaf fraction (LF), backbone strength (BS), and cycle strength were computed.
  • Each metric was statistically associated with cognitive test scores.

Main Results:

  • Backbone strength (BS) significantly predicted general cognitive performance, including scores on progressive matrices, fluid and crystallized cognition, vocabulary, spatial orientation, and working memory (WM).
  • Diameter also significantly predicted WM performance.
  • While BS from WM tasks outperformed traditional graph theory metrics, MST LF was superior for resting-state data.
  • Associations between cognitive scores and topology measures were stronger for task-based fMRI, particularly the N-Back task, compared to resting-state fMRI.
  • BS derived from task-based fMRI showed the strongest relationship with cognition.

Conclusions:

  • Network topology measures, especially backbone strength derived from task-based fMRI, are strongly associated with cognitive performance.
  • Task-based fMRI provides more sensitive insights into the relationship between brain network organization and cognition than resting-state fMRI.
  • Novel network topology measures offer valuable alternatives to traditional graph theory metrics, particularly in specific contexts like resting-state analysis (MST LF).