1. Autism spectrum disorders detection based on multi-task transformer neural network.
- Author
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Gao, Le, Wang, Zhimin, Long, Yun, Zhang, Xin, Su, Hexing, Yu, Yong, and Hong, Jin
- Subjects
AUTISM spectrum disorders ,TRANSFORMER models ,FUNCTIONAL magnetic resonance imaging ,MACHINE learning ,CLINICAL neurosciences - Abstract
Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism. And it is difficult to effectively identify ASD patients with a single data source (single task). Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model. Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model. The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity. This work provides a new perspective and solution for ASD identification based on rs-fMRI data using multi-task learning. It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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