Multi-Resolution Spatio-Temporal Fusion Graph Convolutional Network for Attention Deficit Hyperactivity Disorder Classification

SONG Xiaoying; HAO Chunyu; CHAI Li

doi:10.11999/JEIT240872

Volume 47 Issue 6

Jun. 2025

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SONG Xiaoying, HAO Chunyu, CHAI Li. Multi-Resolution Spatio-Temporal Fusion Graph Convolutional Network for Attention Deficit Hyperactivity Disorder Classification[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1927-1936. doi: 10.11999/JEIT240872

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SONG Xiaoying, HAO Chunyu, CHAI Li. Multi-Resolution Spatio-Temporal Fusion Graph Convolutional Network for Attention Deficit Hyperactivity Disorder Classification[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1927-1936. doi: 10.11999/JEIT240872

Citation:

SONG Xiaoying, HAO Chunyu, CHAI Li. Multi-Resolution Spatio-Temporal Fusion Graph Convolutional Network for Attention Deficit Hyperactivity Disorder Classification[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1927-1936. doi: 10.11999/JEIT240872

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Multi-Resolution Spatio-Temporal Fusion Graph Convolutional Network for Attention Deficit Hyperactivity Disorder Classification

doi: 10.11999/JEIT240872 cstr: 32379.14.JEIT240872

1.
School of Electronic Information, Wuhan University of Science and Technology, Wuhan 430081, China
2.
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Funds: The National Natural Science Foundation of China (62176192, 62173259, U2441244), The Natural Science Foundation of Zhejiang Province (LZ24F030006)

Received Date: 2024-10-15
Rev Recd Date: 2025-05-07

Available Online: 2025-05-22

Publish Date: 2025-06-30

Abstract

Abstract

Objective Predicting neurodevelopmental disorders remains a central challenge in neuroscience and artificial intelligence. Attention Deficit Hyperactivity Disorder (ADHD), a representative complex brain disorder, presents diagnostic difficulties due to its increasing prevalence, clinical heterogeneity, and reliance on subjective criteria, which impede early and accurate detection. Developing objective, data-driven classification models is therefore of significant clinical relevance. Existing graph convolutional network-based approaches for functional brain network analysis are constrained by several limitations. Most adopt single-resolution brain parcellation schemes, reducing their capacity to capture complementary features from multi-resolution functional Magnetic Resonance Imaging (fMRI) data. Moreover, the lack of effective cross-scale feature fusion restricts the integration of essential features across resolutions, hampering the modeling of hierarchical dependencies among brain regions. To address these limitations, this study proposes a Multi-resolution Spatio-Temporal Fusion Graph Convolutional Network (MSTF-GCN), which integrates spatiotemporal features across multiple fMRI resolutions. The proposed method substantially improves the accuracy and robustness of functional brain network classification for ADHD. Methods The MSTF-GCN improves learning performance through two main components: (1) construction of multi-resolution, multi-channel networks, and (2) comprehensive fusion of temporal and spatial information. Multiple brain atlases at different resolutions are employed to parcellate the brain and generate functional connectivity networks. Spatial features are extracted from these networks, and optimal nodal features are selected using Support Vector Machine-Recursive Feature Elimination (SVM-RFE). To preserve global temporal characteristics and capture hierarchical signal variations, both the original time series and their differential signals are processed using a temporal convolutional network. This structure enables the extraction of complex temporal features and inter-subject temporal correlations. Spatial features from different resolutions are then fused with temporal correlations to form population graphs, which are adaptively integrated via a multi-channel graph convolutional network. Non-imaging data are also integrated to produce effective multi-channel, multi-modal spatiotemporal fusion features. The final classification is performed using a fully connected layer. Results and Discussions The proposed MSTF-GCN model is evaluated for ADHD classification using two independent sites from the ADHD-200 dataset: Peking and NI. The model consistently outperforms existing methods, achieving classification accuracies of 75.92% at the Peking site and 82.95% at the NI site (Table 2, Table 3). Ablation studies confirm the contributions of two key components: (1) The multi-atlas, multi-resolution feature extraction strategy significantly enhances classification accuracy (Table 4), supporting the utility of complementary cross-scale topological information; (2) The multimodal fusion strategy, which incorporates non-imaging variables (gender and age), yields notable performance improvements (Table 5). Furthermore, t-SNE visualization and inter-class distance analysis (Fig. 6) show that MSTF-GCN generates a feature space with clearer class separation, reflecting the effectiveness of its multi-channel spatiotemporal fusion design. Overall, the MSTF-GCN model achieves superior performance compared with state-of-the-art methods and demonstrates strong robustness across sites, offering a promising tool for auxiliary diagnosis of brain disorders. Conclusions This study proposes a novel multi-channel graph embedding framework that integrates spatial topological and temporal features derived from multi-resolution fMRI data, leading to marked improvements in classification performance. Experimental results show that the MSTF-GCN method exceeds current state-of-the-art algorithms, with accuracy gains of 3.92% and 8.98% on the Peking and NI sites, respectively. These findings confirm its strong performance and cross-site robustness in ADHD classification. Future work will focus on constructing more expressive hypergraph neural networks to capture higher-order relationships within functional brain networks.

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References(26)

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