OBJECTIVES To assess the possibility of distinguishing autism spectrum disorder (ASD) based on the characteristic of spontaneous hemodynamic fluctuations and to explore the location of abnormality in the brain. METHODS Using the sample entropy (SampEn) of functional near-infrared spectroscopy (fNIRS) from bilateral inferior frontal gyrus (IFG) and temporal cortex (TC) on 25 children with ASD and 22 typical development (TD) children, the pattern of mind-wandering was assessed. With the SampEn as feature variables, a machine learning classifier was applied to mark ASD and locate the abnormal area in the brain. RESULTS The SampEn was generally lower for ASD than TD, indicating the fNIRS series from ASD was unstable, had low fluctuation, and high self-similarity. The classification between ASD and TD could reach 97.6% in accuracy. CONCLUSIONS The SampEn of fNIRS could accurately distinguish ASD. The abnormality in terms of the SampEn occurs more frequently in IFG than TC, and more frequently in the left than in the right hemisphere. SIGNIFICANCE The results of this study may help to understand the cortical mechanism of ASD and provide a fNIRS-based diagnosis for ASD.

中文翻译：

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基于自发功能近红外光谱信号样本熵的自闭症谱系障碍分类

目的 评估基于自发性血流动力学波动特征区分自闭症谱系障碍（ASD）的可能性，并探讨异常在大脑中的位置。方法 使用来自双侧额下回 (IFG) 和颞叶皮层 (TC) 的功能性近红外光谱 (fNIRS) 的样本熵 (SampEn)，对 25 名 ASD 儿童和 22 名典型发育 (TD) 儿童的思维模式进行分析。流浪被评估。以 SampEn 作为特征变量，应用机器学习分类器对 ASD 进行标记并定位大脑中的异常区域。结果 ASD 的 SampEn 普遍低于 TD，表明 ASD 的 fNIRS 系列不稳定，波动小，自相似性高。ASD和TD之间的分类准确率可以达到97.6%。结论 fNIRS 的 SampEn 可以准确区分 ASD。SampEn 异常在 IFG 中比在 TC 中更频繁地发生，并且在左半球比在右半球更频繁地发生。意义 本研究的结果可能有助于了解 ASD 的皮层机制，并为 ASD 提供基于 fNIRS 的诊断。