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Comparative Evaluation of Assumption Lean Community Detection Methods for Human Connectome Networks.

Ayoushman Bhattacharya1, Nilanjan Chakraborty1,2, Xintian Wang3

  • 1Department of Statistics and Data Science, Washington University in St. Louis.

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PubMed
Summary
This summary is machine-generated.

We benchmarked brain network community detection methods, finding that threshold-free weighted approaches with a likelihood-based criterion reliably identify the number of brain communities (K). This method reveals distinct developmental brain architectures in adults and infants.

Keywords:
Brain NetworksCommunity detectionFunctional ConnectivityInfantsVariational BayesWeighted Stochastic Block Model

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Community detection in functional brain networks is crucial for understanding mesoscale organization.
  • Existing methods often rely on assumptions like assortative structure and arbitrary thresholding, complicating community count selection (K).
  • There is a need for assumption-lean, threshold-free methods for robust community detection in brain networks.

Purpose of the Study:

  • To systematically benchmark assumption-lean, threshold-free methods for community detection in weighted functional brain connectivity matrices.
  • To compare different strategies for selecting the number of communities (K), including post hoc indices and a novel likelihood-based criterion.
  • To assess the developmental trajectory of mesoscale brain architecture using these methods.

Main Methods:

  • Benchmarked Weighted Stochastic Block Model, Spectral Clustering, and K-means on synthetic and neuroimaging datasets (Human Connectome Project, Washington University 120, Baby Connectome Project).
  • Compared various post hoc indices (e.g., silhouette, modularity, NMI) against a likelihood-based criterion for selecting K.
  • Employed a consensus relabeling scheme (Hungarian matching with Hamming distance) to stabilize solutions.

Main Results:

  • In simulations, all methods recovered stable partitions, but post hoc indices failed under weak signals and nonassortative mixing.
  • The likelihood-based criterion identified a parsimonious K=11 in adults, aligning with known brain systems.
  • In infants, the same criterion suggested K≈15, revealing distinct developmental mesoscale architecture, including subdivisions in default mode and frontoparietal systems.

Conclusions:

  • Threshold-free weighted methods effectively mitigate assortative bias in brain network community detection.
  • The likelihood-based criterion offers a reproducible and reliable approach for selecting the number of communities (K).
  • This framework reveals significant developmental differences in the mesoscale architecture of the human brain.