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

TL;DR

This paper introduces a novel heterogeneous graph neural network that fuses functional and structural brain connectivity data to improve mild cognitive impairment diagnosis, addressing heterogeneity and data imbalance issues.

Contribution

It proposes a new HGNN-based method with hetero-meta-paths, a heterogeneous pooling strategy, and data augmentation for better dual-modal brain imaging analysis.

Findings

Achieved 93.3% classification accuracy on ADNI-3 dataset.

Outperformed existing algorithms in MCI diagnosis.

Effectively handled heterogeneity and sample imbalance.

Abstract

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%.