# A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis

arXiv: https://arxiv.org/abs/2411.08424 · HTML: https://mlsanity.com/paper/2411.08424/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 40 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Built on by (most cited first, 3 of 3)

- [Zeus: Zero-shot LLM Instruction for Union Segmentation in Multimodal Medical Imaging](https://mlsanity.com/paper/2504.07336/) (2025)
- [Why Text Prevails: Vision May Undermine Multimodal Medical Decision Making](https://mlsanity.com/paper/2512.13747/) (2025)
- [Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS](https://mlsanity.com/paper/2606.18287/) (2026)

## Abstract (arXiv)

Brain connectivity alternations associated with brain disorders have been widely reported in resting-state functional imaging (rs-fMRI) and diffusion tensor imaging (DTI). While many dual-modal fusion methods based on graph neural networks (GNNs) have been proposed, they generally follow homogenous fusion ways ignoring rich heterogeneity of dual-modal information. To address this issue, we propose a novel method that integrates functional and structural connectivity based on heterogeneous graph neural networks (HGNNs) to better leverage the rich heterogeneity in dual-modal images. We firstly use blood oxygen level dependency and whiter matter structure information provided by rs-fMRI and DTI to establish homo-meta-path, capturing node relationships within the same modality. At the same time, we propose to establish hetero-meta-path based on structure-function coupling and brain community searching to capture relations among cross-modal nodes. Secondly, we further introduce a heterogeneous graph pooling strategy that automatically balances homo- and hetero-meta-path, effectively leveraging heterogeneous information and preventing feature confusion after pooling. Thirdly, based on the flexibility of heterogeneous graphs, we propose a heterogeneous graph data augmentation approach that can conveniently address the sample imbalance issue commonly seen in clinical diagnosis. We evaluate our method on ADNI-3 dataset for mild cognitive impairment (MCI) diagnosis. Experimental results indicate the proposed method is effective and superior to other algorithms, with a mean classification accuracy of 93.3%.