School of Computer Engineering, Guangzhou Huali College, Guangzhou, 511325, China.
Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529000, China.
BMC Neurosci. 2024 Jun 13;25(1):27. doi: 10.1186/s12868-024-00870-3.
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.
自闭症谱系障碍（ASD）是一种神经发育障碍，导致患者在社交互动和沟通方面存在困难。基于静息态功能磁共振成像（rs-fMRI）数据识别 ASD 患者是一种很有前途的诊断工具，但由于自闭症的复杂和不明确的病因，这一任务极具挑战性。并且，仅使用单一数据源（单一任务）很难有效地识别 ASD 患者。因此，为了解决这一挑战，我们提出了一种基于 rs-fMRI 数据的用于 ASD 识别的新型多任务学习框架，该框架可以利用多个相关任务中的有用信息来提高模型的泛化性能。同时，我们采用注意力机制从每个 rs-fMRI 数据集提取与 ASD 相关的特征，从而增强模型的特征表示和可解释性。研究结果表明，我们的方法在准确性、敏感性和特异性方面均优于最先进的方法。这项工作为基于 rs-fMRI 数据的 ASD 识别提供了一种新的视角和解决方案。它还展示了机器学习在推进神经科学研究和临床实践方面的潜力和价值。
