The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications

Computer Science Review, Journal Year: 2025, Volume and Issue: 56, P. 100718 - 100718

Published: Jan. 6, 2025

Language: Английский

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mGNN-bw: Multi-scale Graph Neural Network Based on Biased Random Walk Path Aggregation for ASD Diagnosis

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2025, Volume and Issue: 33, P. 900 - 910

Published: Jan. 1, 2025

In recent years, computationally assisted diagnosis for classifying autism spectrum disorder (ASD) and typically developing (TD) individuals based on neuroimaging data, such as functional magnetic resonance imaging (fMRI), has garnered significant attention. Studies have shown that long-range connectivity patterns in ASD patients exhibit abnormalities, individual brain networks display considerable heterogeneity. However, current graph neural (GNNs) used research failed to adequately capture overlooked differences. To address these limitations, this study proposes a novel multi-scale network biased random walks (mGNN-bw). The model introduces co-optimization strategy between sub-models the main model, leveraging node pooling scores from guide walks, effectively capturing connectivity. By constructing high-order through path encoding aggregation, integrating them with low-order Pearson correlation, achieves robust feature representation. Experimental results publicly available ABIDE I dataset demonstrate superior performance of our approach, achieving accuracy rates 74.8% 73.2% using CC200 AAL atlases, respectively, outperforming existing methods. Additionally, identifies key ASD-associated regions, including frontal lobe, insula, cingulate, calcarine, supported by research. proposed method significantly contributes clinical ASD.

Language: Английский

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Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data

Neural Networks, Journal Year: 2025, Volume and Issue: 188, P. 107450 - 107450

Published: April 12, 2025

Language: Английский

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Enhancing Autism Spectrum Disorder identification in multi-site MRI imaging: A multi-head cross-attention and multi-context approach for addressing variability in un-harmonized data

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 157, P. 102998 - 102998

Published: Oct. 16, 2024

Language: Английский

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