Soma Daiki, Hirosawa Tetsu, Hasegawa Chiaki, An Kyung-Min, Kameya Masafumi, Hino Shoryoku, Yoshimura Yuko, Nobukawa Sou, Iwasaki Sumie, Tanaka Sanae, Yaoi Ken, Sano Masuhiko, Shiota Yuka, Naito Nobushige, Kikuchi Mitsuru
Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.
Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan.
Front Psychiatry. 2021 Dec 14;12:790234. doi: 10.3389/fpsyt.2021.790234. eCollection 2021.
Measuring whole brain networks is a promising approach to extract features of autism spectrum disorder (ASD), a brain disorder of widespread regions. Objectives of this study were to evaluate properties of resting-state functional brain networks in children with and without ASD and to evaluate their relation with social impairment severity. Magnetoencephalographic (MEG) data were recorded for 21 children with ASD (7 girls, 60-89 months old) and for 25 typically developing (TD) control children (10 girls, 60-91 months old) in a resting state while gazing at a fixation cross. After signal sources were localized onto the Desikan-Killiany brain atlas, statistical relations between localized activities were found and evaluated in terms of the phase lag index. After brain networks were constructed and after matching with intelligence using a coarsened exact matching algorithm, ASD and TD graph theoretical measures were compared. We measured autism symptoms severity using the Social Responsiveness Scale and investigated its relation with altered small-worldness using linear regression models. Children with ASD were found to have significantly lower small-worldness in the beta band ( = 0.007) than TD children had. Lower small-worldness in the beta band of children with ASD was associated with higher Social Responsiveness Scale total -scores ( = 0.047). Significant relations were also inferred for the Social Awareness ( = 0.008) and Social Cognition ( = 0.015) sub-scales. Results obtained using graph theory demonstrate a difference between children with and without ASD in MEG-derived resting-state functional brain networks, and the relation of that difference with social impairment. Combining graph theory and MEG might be a promising approach to establish a biological marker for ASD.
测量全脑网络是提取自闭症谱系障碍（ASD）特征的一种很有前景的方法，自闭症谱系障碍是一种涉及广泛脑区的脑部疾病。本研究的目的是评估患有和未患ASD的儿童静息态功能脑网络的特性，并评估它们与社交障碍严重程度的关系。对21名患有ASD的儿童（7名女孩，60 - 89个月大）和25名发育正常（TD）的对照儿童（10名女孩，60 - 91个月大）在静息状态下注视固定十字时记录脑磁图（MEG）数据。在将信号源定位到Desikan - Killiany脑图谱上之后，找到局部活动之间的统计关系，并根据相位滞后指数进行评估。在构建脑网络并使用粗化精确匹配算法与智力进行匹配之后，比较ASD和TD的图论指标。我们使用社交反应量表测量自闭症症状的严重程度，并使用线性回归模型研究其与小世界特性改变的关系。发现患有ASD的儿童在β波段的小世界特性（ = 0.007）显著低于TD儿童。患有ASD的儿童在β波段较低的小世界特性与较高的社交反应量表总分（ = 0.047）相关。社交意识（ = 0.008）和社交认知（ = 0.015）子量表也存在显著关系。使用图论获得的结果表明，患有和未患ASD的儿童在MEG衍生的静息态功能脑网络中存在差异，并且这种差异与社交障碍有关。结合图论和MEG可能是建立ASD生物学标志物的一种很有前景的方法。
