Community detection in the human connectome: Method types, differences and their impact on inference

Skylar J. Brooks, Victoria O. Jones, Haotian Wang, Chengyuan Deng, Staunton G.H. Golding, Jethro Lim, Jie Gao, Prodromos Daoutidis, Catherine Stamoulis

Research output: Contribution to journalArticlepeer-review

Abstract

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI from (Formula presented.) = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and (Formula presented.) = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

Original languageEnglish (US)
Article numbere26669
JournalHuman Brain Mapping
Volume45
Issue number5
DOIs
StatePublished - Apr 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

Keywords

  • community detection
  • data-driven synthetic graphs
  • fMRI
  • graph Ricci flow
  • human brain networks
  • stochastic block modeling

PubMed: MeSH publication types

  • Journal Article

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