Hierarchical Multiscale Structure-Function Coupling for Brain Connectome Integration

Abstract

Integrating structural and functional connectomes remains challenging because their relationship is non-linear and organized over nested modular hierarchies. We propose a hierarchical multiscale structure-function coupling framework for connectome integration that jointly learns individualized modular organization and hierarchical coupling across structural connectivity (SC) and functional connectivity (FC). The framework includes: (i) Prototype-based Modular Pooling (PMPool), which learns modality-specific multiscale communities by selecting prototypical ROIs and optimizing a differentiable modularity-inspired objective; (ii) an Attention-based Hierarchical Coupling Module (AHCM) that models both within-hierarchy and cross-hierarchy SC-FC interactions to produce enriched hierarchical coupling representations; and (iii) a Coupling-guided Clustering loss (CgC-Loss) that regularizes SC and FC community assignments with coupling signals, allowing cross-modal interactions to shape community alignment across hierarchies. We evaluate the model's performance across four cohorts for predicting brain age, cognitive score, and disease classification. Our model consistently outperforms baselines and other state-of-the-art approaches across three tasks. Ablation and sensitivity analyses verify the contributions of key components. Finally, the visualizations of learned coupling reveal interpretable differences, suggesting that the framework captures biologically meaningful structure-function relationships.

Figures (8)

• Figure 1: Framework of HiM-SFC. Paired SC and FC are encoded with a GNN and coarsened by (a) prototype-based modular pooling (PMPool) to obtain hierarchical community representations. (b) The attention-based hierarchical coupling module (AHCM) then models within- and cross-hierarchy structure–function interactions to produce coupling-enhanced features, while (c) a coupling-guided clustering loss (CgC-Loss) encourages cross-modal consistency of community assignments. The resulting hierarchical features are fused for the downstream prediction task.
• Figure 2: PMPool includes prototypical ROI selection and community assignment for pooling the connectome into a coarsened network. A modularity-inspired pooling objective encourages PMPool to capture the intrinsic modular structure of each subject’s connectome.
• Figure 3: Architecture of AHCM. Within-hierarchy coupling is computed via bidirectional attention, while cross-hierarchy coupling is captured by two cross-attention branches. These hierarchical coupling signals are aggregated to obtain enriched hierarchical multiscale representations.
• Figure 4: The coupling-guided clustering loss $L_{CC}$. ROI-wise coupling weights are computed from the hierarchical coupling matrix via row and column aggregation and normalization, and used to weight a symmetric KL divergence between SC and FC assignment matrices, promoting cross-modal consistency driven by SFC.
• Figure 5: Cognitive score prediction performance of the proposed method with different pooling ratios, number of hierarchies, and regularization weight of $L_{CC}$ on (a) HCPYA and (b) ADNI.