轻度认知障碍的分类使用功能近红外光谱衍生生物标志物与卷积神经网络
1Department of Occupational Therapy, College of Medical Science, Soonchunhyang University, Asan, Republic of Korea.
Psychiatry investigation
|April 3, 2024
概括
功能近红外光谱学 (fNIRS) 与卷积神经网络 (CNNs) 结合,显示出早期轻度认知障碍 (MCI) 检测的前景. 这种先进的方法在识别MCI时优于传统的认知评估.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 轻度认知障碍 (MCI) 的早期检测至关重要,但由于敏感性低,传统的纸质神经心理评估具有挑战性.
- 生物标志物研究正在获得更敏感的MCI检测的引力.
研究的目的:
- 调查功能近红外光谱学 (fNIRS) 衍生数据的有效性,并通过卷积神经网络 (CNN) 进行分析,以识别MCI.
- 为了比较这种新的fNIRS-CNN方法的诊断性能与标准的认知查工具.
主要方法:
- 82名患有MCI的个体和148名健康对照 (HC) 接受了双背任务.
- 在任务期间,使用fNIRS记录了前额叶皮质 (PFC) 中的氧化血红蛋白 (HbO2) 变化.
- 一个CNN模型在fNIRS衍生的空间特征和HbO2斜率上受过训练,使用5倍交叉验证进行评估.
主要成果:
- 在MCI和HC组之间观察到平均HbO2值的显著差异.
- CNN模型在区分MCI方面取得了很高的准确性,超过89.57%.
- 使用基于HbO2斜率的CNN模型从左PFC20-60秒内获得了96.09%的最高精度.
结论:
- fNIRS衍生的空间特征与CNN相结合,代表了一种有希望的,敏感的方法,用于早期检测MCI.
- 这种fNIRS-CNN方法与韩国版本的蒙特利尔认知评估 (MoCA) 相比表现优越,表明其作为替代查工具的潜力.
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