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Autism spectrum disorders detection based on multi-task transformer neural network
- Source :
- BMC Neuroscience, Vol 25, Iss 1, Pp 1-11 (2024)
- Publication Year :
- 2024
- Publisher :
- BMC, 2024.
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 14712202
- Volume :
- 25
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- BMC Neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.06372bbf459b4f53a512fdf6b0a34ab4
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12868-024-00870-3