Guo Xiaonan, Wang Xueting, Zhou Rongjuan, Cui Dong, Liu Junfeng, Gao Le
School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China.
Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, 066004, China.
J Autism Dev Disord. 2024 Dec 11. doi: 10.1007/s10803-024-06661-3.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with altered brain activity. However, little is known about the integrated temporospatial variation of dynamic spontaneous brain activity in ASD. In the present study, resting-state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically-matched typically developmental controls (TC) children obtained from the Autism Brain Imaging Data Exchange database. Using the sliding-window approach, temporal, spatial, and temporospatial variability of dynamic amplitude of low-frequency fluctuation (tvALFF, svALFF, and tsvALFF) were calculated for each participant. Group-comparisons were further performed at global, network, and brain region levels to quantify differences between ASD and TC groups. The relationship between temporospatial dynamic amplitude of low-frequency fluctuation variation alterations and clinical symptoms of ASD was finally explored by a support vector regression model. Relative to TC, we found enhanced tvALFF in visual network (Vis), somatomotor network (SMT), and salience/ventral attention network (SVA) of ASD, and weakened tvALFF in dorsal attention network (DAN) of ASD. Besides, ASD showed decreased svALFF in Vis, SVA, and limbic network (Limbic), and increased svALFF in DAN and default mode network (DMN). Elevated tsvALFF was found in the Vis, SMT, and DMN of ASD. More importantly, the altered tsvALFF from the DMN can predict the symptom severity of ASD. These findings demonstrate altered temporospatial dynamics of the spontaneous brain activity in ASD and provide novel insights into the neural mechanism underlying ASD.
自闭症谱系障碍（ASD）是一种大脑活动改变的神经发育障碍。然而，对于ASD中动态自发脑活动的综合时空变化知之甚少。在本研究中，对从自闭症脑成像数据交换数据库中获取的105名ASD儿童和102名人口统计学匹配的典型发育对照（TC）儿童的静息态功能磁共振成像数据进行了分析。使用滑动窗口方法，计算了每个参与者低频波动动态幅度的时间、空间和时空变异性（tvALFF、svALFF和tsvALFF）。进一步在全局、网络和脑区水平进行组间比较，以量化ASD组和TC组之间的差异。最后通过支持向量回归模型探讨低频波动时空动态幅度变化与ASD临床症状之间的关系。相对于TC，我们发现ASD的视觉网络（Vis）、躯体运动网络（SMT）和突显/腹侧注意网络（SVA）中的tvALFF增强，而ASD的背侧注意网络（DAN）中的tvALFF减弱。此外，ASD在Vis、SVA和边缘网络（Limbic）中显示出svALFF降低，而在DAN和默认模式网络（DMN）中显示出svALFF增加。在ASD的Vis、SMT和DMN中发现tsvALFF升高。更重要的是，来自DMN的tsvALFF改变可以预测ASD的症状严重程度。这些发现表明ASD中自发脑活动的时空动态发生了改变，并为ASD潜在的神经机制提供了新的见解。
