Lingyu Xu1, Qianling Hua2, Jie Yu3, Jun Li4. 1. Department of Computer Engineering and Science, Shanghai University, Shanghai, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China. 2. Department of Computer Engineering and Science, Shanghai University, Shanghai, China. 3. Department of Computer Engineering and Science, Shanghai University, Shanghai, China. Electronic address: jieyu@shu.edu.cn. 4. South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China; Key Lab for Behavioral Economic Science & Technology, South China Normal University, Guangzhou, China. Electronic address: jun.li@coer-scnu.org.
Abstract
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.
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.