Identification of Infants at High-Risk for Autism Spectrum Disorder Using Multiparameter Multiscale White Matter Connectivity Networks

Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive
impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention
are important for improving the life quality of autistic patients. However, in the current practice,
diagnosis often has to be delayed until the behavioral symptoms become evident during
childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying
high-risk ASD infants at as early as six months after birth. This is based on the observation that
ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already
started to appear within 24 months after birth. In particular, we propose a novel multikernel support
vector machine classification framework by using the connectivity features gathered from WM connectivity
networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion
statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework
achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve
(AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best singleparameter
single-scale network. The improvement in accuracy is mainly due to the complementary
information provided by multiparameter multiscale networks. In addition, our framework also provides
the potential imaging connectomic markers and an objective means for early ASD diagnosis.

自闭症谱系障碍（Autism spectrum disorder, ASD）是一类会引发终身认知障碍，以及社交、沟通与行为障碍的广泛性残疾。早期诊断与医学干预对改善自闭症患者的生活质量至关重要，但在当前临床实践中，诊断往往要延迟至儿童期出现明显行为症状时才可开展。本研究验证了利用机器学习技术，在婴儿出生后仅6个月时即可识别高风险自闭症谱系障碍患儿的可行性。该结论基于如下观察：自闭症谱系障碍相关的白质（WM, white matter）束异常与全脑连接异常，在出生后24个月内便已显现。具体而言，本研究提出了一种新颖的多核支持向量机分类框架，该框架使用从白质连接网络中提取的连接特征，而这些白质连接网络是通过多尺度感兴趣区（ROIs, regions of interest）以及多种扩散统计量，包括各向异性分数、平均扩散率与平均纤维长度生成的。相较于最佳单参数单尺度网络所取得的70%准确率与70%的AUC（受试者工作特征曲线下面积），本研究提出的框架实现了76%的准确率与0.80的受试者工作特征曲线下面积（AUC）。准确率的提升主要得益于多参数多尺度网络所提供的互补信息。此外，该框架还可为早期自闭症谱系障碍诊断提供潜在的影像连接组学标志物与客观手段。