Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 100049.
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA 30303.
J Neurosci Methods. 2021 Sep 1;361:109271. doi: 10.1016/j.jneumeth.2021.109271. Epub 2021 Jun 24.
Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is comprised of a constellation of behavioral symptoms. Non-invasive brain imaging techniques, such as magnetic resonance imaging (MRI), provide a valuable objective measurement of the brain. Many efforts have been devoted to developing imaging-based diagnostic tools for ASD based on machine learning (ML) technologies. In this survey, we review recent advances that utilize machine learning approaches to classify individuals with and without ASD. First, we provide a brief overview of neuroimaging-based ASD classification studies, including the analysis of publications and general classification pipeline. Next, representative studies are highlighted and discussed in detail regarding different imaging modalities, methods and sample sizes. Finally, we highlight several common challenges and provide recommendations on future directions. In summary, identifying discriminative biomarkers for ASD diagnosis is challenging, and further establishing more comprehensive datasets and dissecting the individual and group heterogeneity will be critical to achieve better ADS diagnosis performance. Machine learning methods will continue to be developed and are poised to help advance the field in this regard.
自闭症谱系障碍（ASD）是一种神经发育障碍，发病于儿童早期，具有高度异质性。由于发病机制仍不清楚，ASD 的诊断包括一系列行为症状。非侵入性脑成像技术，如磁共振成像（MRI），为大脑提供了有价值的客观测量。许多人致力于开发基于机器学习（ML）技术的 ASD 成像诊断工具。在本综述中，我们回顾了利用机器学习方法对 ASD 患者和非 ASD 患者进行分类的最新进展。首先，我们简要概述了基于神经影像学的 ASD 分类研究，包括出版物分析和一般分类流程。接下来，重点介绍了具有代表性的研究，并详细讨论了不同的成像方式、方法和样本量。最后，我们强调了几个常见的挑战，并就未来的方向提出了建议。总之，识别 ASD 诊断的有区分性生物标志物具有挑战性，进一步建立更全面的数据集并剖析个体和群体异质性对于实现更好的 ASD 诊断性能至关重要。机器学习方法将继续得到开发，并有望在这方面推动该领域的发展。
