自闭症谱系障碍学龄前儿童联合行动中的人际协调缺陷:来自fNIRS超扫描和机器学习的证据
Kaiyun Li1, Yue Yang2,3, Caiyan Zheng2,4
1School of Education and Psychology, University of Jinan, 250022, Jinan, China. sep_liky@ujn.edu.cn.
Journal of autism and developmental disorders
|January 24, 2026
概括
患有自闭症谱系障碍 (ASD) 的儿童在联合行动中表现出人际协调缺陷. 功能近红外光谱 (fNIRS) 超扫描揭示了自闭症患者的较低行为和神经同步,机器学习有效地识别了这些差异.
科学领域:
- 神经科学是一个神经科学.
- 发展心理学 发展心理学
- 人与计算机的交互
背景情况:
- 自闭症谱系障碍 (ASD) 是一种神经发育状况,其特点是社会互动和沟通挑战.
- 对于共同行动至关重要的人际协调,在ASD患者中经常受到损害,尽管经验证据仍在争论中.
研究的目的:
- 调查ASD儿童在合作和竞争任务中的人际协调缺陷.
- 利用功能近红外光谱 (fNIRS) 超扫描来检查二极交互期间的神经同步.
主要方法:
- 42名儿童 (21名有自闭症,21名没有) 在合作和竞争的意图下完成了双层块构建任务.
- fNIRS超扫描记录了大脑激活和人际神经同步 (INS).
- 机器学习算法 (随机森林,AdaBoost,XGBoost) 分析了INS的群体歧视.
主要成果:
- 患有自闭症的儿童在合作和竞争任务中表现出较低的行为准确性和同步性.
- 在合作期间,在特定大脑区域 (rTPJ-rIFG,rTPJ-rIPL,双边TPJ) 的自闭症儿童中观察到减少INS.
- 在竞赛期间,在自闭症儿童中观察到右下叶 (rIPL) 的较低激活.
结论:
- 该研究证实,自闭症儿童在联合行动期间存在人际协调缺陷,包括合作和竞争环境.
- 人际神经同步 (INS) 作为这些缺陷的有效神经指标.
- 机器学习为分析和验证ASD研究中的INS提供了一种有希望的方法.
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