在患有自闭症谱系障碍的学龄前儿童中,皮层血液动力学反应和情感面部处理的深度学习模型:fNIRS研究
Liping Qi1, Jing-Wen Ni2, Guijun Dong3
1School of Control Science and Engineering, Dalian University of Technology, Dalian, China.
Frontiers in psychiatry
|February 2, 2026
概括
患有自闭症谱系障碍 (ASD) 的学龄前儿童在观看情感面孔时表现出不同的大脑活动. 机器学习模型从他们的大脑信号中准确地识别了情绪.
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
- 生物医学工程 生物医学工程
背景情况:
- 自闭症谱系障碍 (ASD) 是一种影响社会互动和沟通的神经发育状况.
- 在患有自闭症的人群中,情感面部处理通常是不典型的.
- 了解这些差异对于早期干预和支持至关重要.
研究的目的:
- 在情感面部处理过程中调查ASD学龄前儿童的皮质血液动力学反应.
- 开发机器学习 (ML) 模型,使用功能近红外光谱 (fNIRS) 数据进行情绪识别.
- 为了比较对动态与静态情绪刺激的反应.
主要方法:
- 53名患有自闭症的学龄前儿童 (3-7岁) 参与了这项研究.
- 全脑fNIRS在接触动态和静态面部刺激 (愤怒,快乐) 和中性花朵时记录了大脑活动.
- 一个卷积神经网络长期短期记忆 (CNN-LSTM) 模型被用于情绪识别.
主要成果:
- fNIRS揭示了双边背侧额前皮层 (DLPFC) 和前极对动态刺激的高度激活.
- 愤怒的表情触发了DLPFC,腹侧前额皮层和视觉区域的显著神经反应.
- 该CNN-LSTM模型在分类动态愤怒和快乐情绪方面取得了86.2%的准确性.
结论:
- 患有自闭症的儿童在动态情感面部处理过程中表现出明显的皮质血液动力学模式.
- 在CNN-LSTM模型中,有望客观地评估ASD幼儿的情绪识别能力.
- 这项研究突出了神经成像和ML的潜力,以了解与ASD相关的挑战.
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