Analysis of synchronous clusters in the human connectome using the spectral block method

Karan K.H. Manjunatha, Paul Didier Kamdem Kuate, Joakim Vianney Ngamsa Tegnitsap, Feng Zhu, Yafang Dong, Wei Zhang, Pedro Antonio Valdes-Sosa, Mattia Frasca, Stefano Boccaletti and Ludovico Minati

Chaos, Solitons & Fractals, 2026, vol. 210, issue P2

Abstract: The spectral block method predicts the formation and sequence of synchronization clusters directly from a network’s Laplacian spectrum. In this work, the method is applied to two empirical proxies of human brain connectivity corresponding to the HCP-MMP1.0 atlas with 360 cortical regions: a structural connectivity (SC) matrix derived from diffusion tractography, and a functional connectivity (FC) matrix from resting-state fMRI correlations. The goal is to compare the hierarchical organization of the sequence of clusters across the two different types of connectivity that are indirect, coarse-grained empirical measurements of neural interactions. SC yields eight coarse-grained, hierarchical clusters with increasing bilateral symmetry, while FC yields 84 fine-grained clusters, with a moderate number of small-sized clusters (33 clusters of size 2), and many show perfect inter-hemispheric symmetry (52 clusters of sizes 2, 4, 6). For the alignment with the Yeo-7 resting-state networks, the SC cluster sequence progresses from selective alignment (e.g., Visual) to integrative coverage, whereas FC clusters align perfectly mostly with sensory-motor and limbic networks but do not selectively align with the Default or Frontoparietal networks. Binarizing the weighted matrices by retaining only the strongest links preserves the SC cluster hierarchy for thresholds of 1.5%–20% of connections, in terms of both cluster-size sequences and anatomical lobe distribution, when compared with the weighted SC matrix. In contrast, FC clusters become inconsistent under binarization when compared with the weighted FC matrix. Furthermore, almost none of the identified clusters satisfy the exact equitable partition condition but instead are quasi-equitable. Here, the clusters differ by only a few links or small weight discrepancies from satisfying the condition, which is likely a consequence of noise and the indirect nature of empirical connectomes. In summary, the spectral block method is a convenient approach for comparing the organizational principles of structural and functional connectomes as proxies of neural connectivity, without overinterpreting dynamical predictions from functional data.

Keywords: Cluster synchronization; Complex networks; Functional connectivity; Human connectome; Structural connectivity; Spectral blocks (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:210:y:2026:i:p2:s0960077926008064

DOI: 10.1016/j.chaos.2026.118665

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