Department of Rehabilitation Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan.
Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Brain Topogr. 2019 Sep;32(5):914-921. doi: 10.1007/s10548-019-00711-1. Epub 2019 Apr 21.
Multiscale entropy (MSE) model quantifies the complexity of brain functions by measuring the entropy across multiple time-scales. Although MSE model has been applied in children with Autism spectrum disorders (ASD) in previous studies, they were limited to distinguish children with ASD from those normally developed without corresponding severity level of their autistic features. Therefore, we aims to explore and to identify the MSE features and patterns in children with mild and severe ASD by using a high dense 64-channel array EEG system. This study is a cross-sectional study, where 36 children with ASD were recruited and classified into two groups: mild and severe ASD (18 children in each). Three calculated outcomes identified brain complexity of mild and severe ASD groups: averaged MSE values, MSE topographical cortical representation, and MSE curve plotting. Averaged MSE values of children with mild ASD were higher than averaged MSE value in children with severe ASD in right frontal (0.37 vs. 0.22, respectively, p = 0.022), right parietal (0.31 vs. 0.13, respectively, p = 0.017), left parietal (0.37 vs. 0.17, respectively, p = 0.018), and central cortical area (0.36 vs. 0.21, respectively, p = 0.026). In addition, children with mild ASD showed a clear and more increase in sample entropy values over increasing values of scale factors than children with severe ASD. Obtained data showed different brain complexity (MSE) features, values and topographical representations in children with mild ASD compared with those with severe ASD. As a result of this, MSE could serve as a sensitive method for identifying the severity level of ASD.
多尺度熵（MSE）模型通过测量多个时间尺度上的熵来量化大脑功能的复杂性。尽管在以前的研究中，MSE 模型已应用于自闭症谱系障碍（ASD）儿童，但它们仅限于区分 ASD 儿童和正常发育的儿童，而没有对应自闭症特征的严重程度水平。因此，我们旨在通过使用高密度 64 通道阵列 EEG 系统探索和识别轻度和重度 ASD 儿童的 MSE 特征和模式。这项研究是一项横断面研究，共招募了 36 名 ASD 儿童，并将其分为两组：轻度和重度 ASD（每组 18 名儿童）。三个计算结果确定了轻度和重度 ASD 组的大脑复杂性：平均 MSE 值、MSE 皮质表面代表和 MSE 曲线绘图。轻度 ASD 儿童的平均 MSE 值高于重度 ASD 儿童的平均 MSE 值，分别为右额（0.37 对 0.22，分别为 p = 0.022）、右顶（0.31 对 0.13，分别为 p = 0.017）、左顶（0.37 对 0.17，分别为 p = 0.018）和中央皮质区（0.36 对 0.21，分别为 p = 0.026）。此外，与重度 ASD 儿童相比，轻度 ASD 儿童的样本熵值随标度因子值的增加而增加，且增加更为明显。获得的数据显示，与重度 ASD 儿童相比，轻度 ASD 儿童的大脑复杂性（MSE）特征、值和表面代表存在差异。因此，MSE 可以作为一种敏感的方法来识别 ASD 的严重程度。
