Dual Graph Strategy with Diffusion Tensor Imaging for Autism Spectrum Disorder Diagnosis

Diffusion Tensor Imaging (DTI) is a special magnetic resonance imaging (MRI) technique. Most of the existing research on DTI data primarily focuses either on Structural Connectivity (SC) networks derived from DTI or on DTI-derived metrics like Fractional Anisotropy, Mean Diffusivity, $\lambda _{1}$, $\lambda _{2}$, and $\lambda _{3}$. This may lead to the neglect of potential complementary information provided by different graphs, thereby preventing the improvement of classification performance. In this study, we propose a graph neural network framework based on a dual graph strategy using DTI data for the diagnosis of ASD.

Specifically, we have done the following: 1) To address the challenges of small datasets and class imbalance, we employed data augmentation techniques (including replication of minority class samples and the mixup method) to enhance data diversity and representativeness. 2) We combined a threshold-based real physical connectivity adjacency matrix with a local microstructure adjacency matrix learned from node features to mitigate the limitations of relying on single structural information. 3) We designed a Multi-Layer Pooling Fusion (MLPF) method to capture multi-layered and richer feature representations.

Our proposed method was evaluated on 198 subjects and the experimental results showed that our proposed method outperformed multiple existing methods in five-fold cross-validation, achieving 75.24% accuracy and 73.12% AUC.

DTI is crucial for analyzing connectivity abnormalities in ASD. Our proposed method enables more efficient, objective, and reliable diagnosis of ASD.

This work provides a valuable reference framework for utilizing DTI data in research on neurological disorders.