Abstract：
Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.
摘要：
最近对自闭症谱系障碍（ASD）的研究已经确定了由相似的共激活模式（CAP）主导的复发状态，并揭示了基于种子的大规模脑网络中的功能障碍与临床症状之间的关联。然而,ASD中即时全脑动力学异常的存在仍不确定.在这项研究中,我们采用无种子CAP分析来确定ASD中的一过性脑活动构型并研究动态异常.我们利用了大量的多站点静息状态fMRI数据集，该数据集包括354名ASD患者和446名健康对照（HC，来自HC组和2）。CAP是通过时间K均值聚类从所有HC受试者的亚组（HC组1）产生的，识别四个上限。这四个CAP表现出默认模式网络（DMN）的激活或抑制，并分为两对空间相对的CAP。HC组2和ASD的CAPs通过其与HC组1的空间相似性来识别。与HC2组相比，患有ASD的人在涉及腹侧注意网络的CAPs中花费的时间更多，而在与执行控制和背侧注意网络相关的CAPs中花费的时间更少。支持向量机分析表明，CAPs的异常动态特征在多位点分类中达到了74.87％的准确率。此外,我们使用全脑动力学来预测ASD的症状严重程度。我们的发现从单一的瞬时角度揭示了ASD的全脑动态功能异常，强调DMN在ASD异常动态功能活动中的重要性，并提出时间动态技术为时变神经过程提供了新的见解。
