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自闭症谱系障碍和发育协调障碍之间的运动特征差异及其神经机制

Christiana Butera^1,2, Jonathan Delafield-Butt^3,4, Szu-Ching Lu^3,4

¹USC Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA. cbutera@usc.edu.

Journal of autism and developmental disorders

|December 7, 2023

概括

机器学习有效地区分了患有自闭症谱系障碍 (ASD) 和发育协调障碍 (DCD) 的儿童使用基于平板电脑的运动任务. 这些发现突出了与小脑功能相关的独特神经运动模式,用于早期识别.

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
计算精神病学是一种计算精神病学.

背景情况:

自闭症谱系障碍 (ASD) 和发育协调障碍 (DCD) 共享重叠的运动挑战,使差异诊断复杂化.
准确的区分对于有针对性的干预和理解神经发育途径至关重要.

研究的目的:

用智能平板电脑彩色任务区分患有ASD,DCD和典型发育 (TD) 同龄儿童.
为了确定与这些群体之间的运动差异相关的神经相关物.

主要方法:

利用标准化的运动评估和视频分析,对5分钟的平板电脑彩色游戏进行了分析.
从平板电脑任务中使用机器学习分析对运动动力学.
在动作制作任务期间获得的功能磁共振成像 (fMRI) 数据.

主要成果:

标准化的运动评估未能区分ASD和DCD组.
机器学习实现了良好的预测准确性:TD与ASD (76%),TD与DCD (78%),ASD与DCD (71%).
关键的动力学标记与小脑活动有显著的相关性.

结论:

基于平板电脑的运动任务的计算分析显示了区分ASD和DCD的希望.
在ASD和DCD中,独特的神经运动模式与小脑功能有关.
这些发现表明使用计算技术进行早期识别的潜在途径.

关键词:

自闭症谱系障碍自闭症谱系障碍发育协调障碍是一种发展协调障碍.早期检测早期检测机器学习是机器学习.智能平板电脑是一个智能平板电脑.功能磁力共振成像 (fMRI) 是一种

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