Organizations: School of Medicine, University of Dundee, UK · College of Medicine and Biological Information Engineering, Northeastern University, China · School of Airspace Science and Engineering, Shandong University, China · College of Medical Informatics, Chongqing Medical University, China · School of Science and Engineering, University of Dundee, UK · Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK · Chinese Institute for Brain Research, China · Department of Clinical Neurosciences, University of Cambridge, UK
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 & tables
Fig. 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.
Fig. 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.
Fig. 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.
Fig. 4: The coupling-guided clustering loss LCC . 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.
Method
HCPYA
NKI-Rockland
UKB
MAE ↓
RMSE ↓
MAE ↓
RMSE ↓
MAE ↓
RMSE ↓
GCN [ 29 ]
4.46 (0.62)
6.02 (1.26)
16.20 (1.49)
21.67 (2.78)
5.97 (0.23)
7.15 (0.29)
GIN [ 60 ]
4.17 (0.60)
5.16 (0.55)
17.70 (2.44)
23.40 (3.43)
6.17 (0.67)
7.39 (0.78)
GraphSage [ 20 ]
4.15 (0.43)
5.50 (0.83)
15.96 (2.08)
18.48 (1.31)
6.06 (0.51)
8.58 (2.87)
M-GCN [ 15 ]
3.45 (0.34)
4.36 (0.52)
17.36 (0.49)
20.16 (0.56)
7.59 (0.57)
12.55 (0.47)
MME-GCN [ 32 ]
6.02 (1.38)
7.14 (1.32)
14.41 (0.34)
19.44 (0.52)
6.64 (0.65)
7.93 (0.72)
Table 1: Comparison results on HCPYA, NKI-Rockland and UKB for brain age prediction based on FC and SC.
Method
HCPYA (MMSE)
UKB (Fluid Intelligence Score)
MAE ↓
RMSE ↓
MAE ↓
RMSE ↓
GCN [ 29 ]
3.73 (0.57)
5.18 (1.43)
3.17 (0.26)
3.69 (0.23)
GIN [ 60 ]
3.52 (0.43)
4.09 (0.30)
3.60 (0.30)
4.01 (0.43)
GraphSage [ 20 ]
3.65 (0.68)
5.18 (1.43)
3.14 (0.43)
3.66 (0.40)
M-GCN [ 15 ]
1.48 (0.11)
1.95 (0.21)
3.23 (1.43)
5.30 (3.98)
MME-GCN [ 32 ]
6.12 (0.33)
6.61 (0.32)
2.72 (0.06)
3.17 (0.09)
Table 2: Comparison results on HCPYA and UKB for MMSE and fluid intelligence score prediction based on FC and SC.
Method
ACC ↑
AUC ↑
Sen ↑
Spec ↑
GCN [ 29 ]
0.68 (0.06)
0.60 (0.14)
0.50 (0.15)
0.74 (0.16)
GIN [ 60 ]
0.64 (0.04)
0.71 (0.03)
0.61 (0.16)
0.68 (0.15)
GraphSAGE [ 20 ]
0.68 (0.07)
0.62 (0.14)
0.67 (0.15)
0.69 (0.15)
M-GCN [ 15 ]
0.73 (0.07)
0.70 (0.06)
0.61 (0.23)
0.74 (0.22)
MME-GCN [ 32 ]
0.74 (0.07)
0.72 (0.10)
0.66 (0.14)
0.79 (0.08)
Joint-GCN [ 31 ]
0.75 (0.08)
0.70 (0.08)
0.61 (0.20)
0.79 (0.21)
Table 3: Comparison results on ADNI for disease classification based on FC and SC.
Fig. 5: Cognitive score prediction performance of the proposed method with different pooling ratios, number of hierarchies, and regularization weight of LCC on (a) HCPYA and (b) ADNI.
HCPYA: MMSE prediction
Method
MAE ↓
RMSE ↓
PCC ↑
w/o PMPool
2.02 (0.33)
2.32 (0.41)
0.12 (0.04)
w/o AHCM
2.63 (0.31)
3.35 (0.41)
0.09 (0.03)
w/o CgC-Loss
1.17 (0.18)
1.57 (0.44)
0.16 (0.04)
HiM-SFC(ours)
1.01 (0.24)
1.25 (0.25)
0.25 (0.09)
ADNI: disease classification
Table 4: Ablation study on PMPool, AHCM, and CgC-Loss on HCPYA (MMSE prediction) and ADNI (disease classification).
HCPYA: MMSE prediction
Method
MAE ↓
RMSE ↓
PCC ↑
GIN
1.11 (0.04)
1.33 (0.17)
0.18 (0.06)
GraphSAGE
1.05 (0.02)
1.27 (0.15)
0.20 (0.12)
GCN
1.01 (0.24)
1.25 (0.25)
0.25 (0.09)
ADNI: disease classification
Method
ACC ↑
Sen ↑
Spec ↑
Table 5: Results of MMSE score prediction on HCPYA and disease classification on ADNI using different GNN encoders.
Fig. 6: Multiscale coupling strength learned by the proposed model. Regional-level maps show global coupling strength, whereas modular-level maps show local coupling strength, for (a) brain-age prediction in UKB and (b) disease classification in ADNI; (c) Overlapping of the top-50% coupling pattern between brain age and disease prediction tasks at regional and modular levels, mapped to canonical seven modules (Visual (VN), Somatomotor (SMN), Dorsal Attention (DAN), Ventral Attention (VAN), Limbic (LN), Frontoparietal (FPN), and Default Mode (DMN) networks).
HCPYA: MMSE prediction
Method
MAE ↓
RMSE ↓
PCC ↑
softmax
1.17 (0.03)
1.33 (0.16)
0.18 (0.04)
entmax
1.01 (0.24)
1.25 (0.25)
0.25 (0.09)
ADNI: disease classification
Method
ACC ↑
Sen ↑
Spec ↑
softmax
0.76 (0.17)
0.66 (0.12)
0.77 (0.14)
Table 6: Results of MMSE score prediction on HCPYA and disease classification on ADNI using entmax and softmax.
Fig. 7: Visualization of age-group (40–50 vs. 60–70 years) differences in learned coupling strength in UKB at the regional (Schaefer 500 ROIs) and modular levels (a), the overlap percentage between regional and modular coupling-difference patterns across top-k% ROIs (b), and their projection onto the canonical seven modules (Visual (VN), Somatomotor (SMN), Dorsal Attention (DAN), Ventral Attention (VAN), Limbic (LN), Frontoparietal (FPN), and Default Mode (DMN) networks) (c).
Fig. 8: Visualization of subgroup differences (HC vs. dementia) in learned coupling strength in ADNI at the regional (Schaefer 500 ROIs) and modular levels (a), the overlap percentage between regional and modular coupling-difference patterns across top-k% ROIs (b), and their projection onto the canonical seven modules (Visual (VN), Somatomotor (SMN), Dorsal Attention (DAN), Ventral Attention (VAN), Limbic (LN), Frontoparietal (FPN), and Default Mode (DMN) networks) (c).
Structural connectivity (SC) and functional connectivity (FC) provide complementary information on interactions between brain regions and are widely used in neuroimaging studies of neuropsychiatric disorders. Generative modelling can alleviate the scarcity of large-scale paired SC-FC data, but existing approaches typically use pairwise graphs that capture only dyadic interactions and often generate SC and FC independently, limiting preservation of higher-order structure-function relationships. We propose a Multimodal Hypergraph Flow Matching (MHG-FM) framework for joint SC-FC connectivity generation and cross-modal translation. MHG-FM constructs modality-specific hypergraphs, learns higher-order representations with Hypergraph Neural Network (HGNN) encoders, and performs bidirectional cross-modal fusion using Dual Cross-Attention (DCA). A variational autoencoder maps the fused representations to a compact latent space, where conditional flow matching enables connectivity synthesis and multimodal translation via latent transport. Experiments on the Human Connectome Project Young Adult (HCP-YA) dataset show that MHG-FM outperforms several state-of-the-art baselines in reconstruction quality, topology preservation, distributional similarity, and SC-FC coupling, while achieving approximately 8x faster sampling than a matched diffusion backbone.
Chyong Yi Poh, Hwa Hui Tew, Junn Yong Loo +4
School of Information Technology, Monash University Malaysia · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Understanding how brain structure and function interact is key to explaining intelligence yet modeling them jointly is challenging as the structural and functional connectome capture complementary aspects of organization. We introduced Multi-scale Adaptive Graph Network (MAGNet), a Transformer-style graph neural network framework that adaptively learns structure-function interactions. MAGNet leverages source-based morphometry from structural MRI to extract inter-regional morphological features and fuses them with functional network connectivity from resting-state fMRI. A hybrid graph integrates direct and indirect pathways, while local-global attention refines connectivity importance and a joint loss simultaneously enforces cross-modal coherence and optimizes the prediction objective end-to-end. On the ABCD dataset, MAGNet outperformed relevant baselines, demonstrating effective multimodal integration for advancing our understanding of cognitive function.
Badhan Mazumder, Sir-Lord Wiafe, Aline Kotoski +2
Department of Computer Science, Georgia State University · Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University · Neuroscience Institute, Georgia State University
Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or fingerprints, that can identify individuals across repeated sessions and tasks. Existing methods mostly rely on edge-level features that are sensitive to noise, difficult to interpret, and limited in their ability to generalize across tasks and datasets. Methods: We propose a multiscale community-based functional connectome fingerprinting framework that characterizes each individual by the mesoscale structure of their functional networks. We introduce a signed multilayer community detection framework that incorporates both correlated and anti-correlated brain activity to identify subject-specific community structures across tasks and sessions. Graph-theoretic metrics are then computed from the resulting joint community structures to derive low-dimensional community-level fingerprint representations. Results: The proposed framework is evaluated on 810 healthy control subjects from the Human Connectome Project (HCP). The results show that community-based fingerprints provide a reliable and interpretable substrate for individualized brain characterization across sessions and tasks. Conclusion: Mesoscale community structure provides meaningful and discriminative subject-specific fingerprints. Significance: The proposed framework offers a promising foundation for precision neuroimaging and personalized neuroscience applications.
Sema Athamnah, Selin Aviyente
Department of Biomedical Engineering, Michigan State University, USA. · Department of Electrical and Computer Engineering, Michigan State University, USA.