A novel method for extracting hierarchical subnetworks based on a multi-subject spectral clustering approach

Brain-network has an intrinsic hierarchical structure, which, however, cannot be uncovered using the current methods exclusively for modularity analysis. A recent study has investigated hierarchical structure of brain-network using a hierarchical-clustering approach, which, nevertheless, has the following issues: (i) it relies on applying somewhat arbitrary thresholds to cross-coefficients (different thresholds likely yield distinct clustering outcomes); (ii) individual-level clustering results at early steps are likely to introduce biases at later stages, compromising the final clustering. We propose a method, Network Hierarchical Clustering (NetHiClus), based on a multi-subject spectral-clustering approach, which can robustly identify functional sub-networks at hierarchical-level, without thresholding the cross-coefficients.