Department of Electronics and Communication Engineering, Dr B R Ambedkar National Institute of Technology , Jalandhar, India.
Neurol Res. 2020 Oct;42(10):869-878. doi: 10.1080/01616412.2020.1788844. Epub 2020 Jul 4.
Preliminary evidence has documented functional connectivity during the cognitive task in Autism Spectrum Disorder (ASD). However, evidence of effective neural connectivity with respect to information flow between different brain regions during complex tasks is missing. The present paper aims to provide insights into the cognition-based neural dynamics reflecting information exchange in brain network under cognitive load in ASD.
Twenty-two individuals with ASD (8-18 years) and 18 Typically Developing (TD; 6-17 years) individuals participated in the cognitive task of differentiating risky from neutral stimuli. The Conditional Entropy (CE) technique is applied upon task-activated Electroencephalogram (EEG) to measure the causal influence of the activity of brain's one Region of interest (ROI) over another.
A higher CE in frontal ROI and left hemisphere reflected atypical brain complexity in ASD. The absence of causal effect, poor Coupling Strength (CS; measured using CE) and hemisphere lateralization is responsible for lower cognition in ASD. However, the persistent information exchange during the task reflects the existence of certain alternative paths when other direct paths remained disconnected due to cognitive impairment. The Support Vector Machine (SVM) classifier showed that CE can identify the atypical information exchange with an accuracy of 96.89% and area under curve = 0.987.
The statistical results reflect a significant change in the information flow between different ROIs in ASD. A correlation of CS and behavioral domain suggests that the cognitive decline could be predicted from the connectivity patterns. Thus, CS could be a potential biomarker to identify cognitive status at a higher discrimination rate in ASD.
初步证据记录了自闭症谱系障碍（ASD）认知任务中的功能连接。然而，在复杂任务中，关于不同大脑区域之间信息流动的有效神经连接的证据尚缺乏。本文旨在提供有关基于认知的神经动力学的见解，这些动力学反映了认知负荷下 ASD 中大脑网络中的信息交换。
22 名 ASD（8-18 岁）和 18 名典型发育（TD；6-17 岁）个体参与了区分风险与中性刺激的认知任务。条件熵（CE）技术应用于任务激活的脑电图（EEG），以测量大脑一个感兴趣区域（ROI）的活动对另一个 ROI 的因果影响。
额 ROI 和左半球的 CE 较高反映了 ASD 中大脑复杂性的异常。因果效应缺失、耦合强度（CE 测量）低且大脑半球侧化，导致 ASD 的认知能力降低。然而，任务期间持续的信息交换反映了在由于认知障碍而导致其他直接路径断开时，存在某些替代路径。支持向量机（SVM）分类器表明，CE 可以以 96.89%的准确率和曲线下面积=0.987 来识别异常的信息交换。
统计结果反映了 ASD 中不同 ROI 之间信息流的显著变化。CS 与行为领域的相关性表明，认知能力下降可以从连接模式中预测出来。因此，CS 可以成为识别 ASD 中更高区分率的认知状态的潜在生物标志物。
