Hierarchical Multiscale Structure-Function Coupling for Brain Connectome Integration

TL;DR

This paper introduces a hierarchical multiscale framework that integrates structural and functional brain connectomes by jointly learning modular organization and coupling, improving prediction and interpretability.

Contribution

The proposed framework combines novel modules for multiscale community detection and hierarchical coupling, advancing connectome integration methods.

Findings

Outperforms state-of-the-art models in brain age prediction

Achieves higher accuracy in cognitive and disease classification tasks

Provides interpretable visualizations of structure-function relationships

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.