在主观认知衰退中多层次动态大脑功能网络的功能障碍
Mianxin Liu1,2, Qi Huang3, Lin Huang4
1Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China.
Brain communications
|February 2, 2024
概括
一种新的深度学习方法利用大脑网络的变化来识别早期阿尔茨海默病的症状. 这种方法可以检测大脑功能的微妙变化,为主观认知衰退提供潜在的生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 主观认知衰退 (SCD) 可能是阿尔茨海默病 (AD) 的前身,但其神经基础尚不清楚.
- 确定可靠的SCD生物标志物对于早期AD检测和干预至关重要.
- 目前用于大脑网络分析的深度学习方法在捕捉动态功能变化方面存在局限性.
研究的目的:
- 开发和验证一种新的深度学习方法,用于识别SCD使用多尺度动态大脑功能网络.
- 将新方法的性能与传统的深度学习方法进行比较.
- 研究与SCD相关的异常动态功能网络状态的神经特征.
主要方法:
- 开发一种深度学习模型,分析多尺度动态大脑功能网络.
- 追溯内部数据集 (112个SCD,64个控制) 用于模型开发和验证.
- 未来的外部数据集 (26个SCD,12个控制) 用于独立的模型测试.
- 使用网络和可变性指标量化异常动态功能网络状态的量化.
主要成果:
- 这种新型深度学习方法在内部验证中实现了0.807 ± 0.046的AUC,在外部测试中达到0.707 (P = 0.007).
- 该方法比传统的静态和动态网络方法表现出更好的性能.
- 异常状态显示局部连接强度下降,变性增加,模块化变化,网络水平效率降低.
- 减少异常状态的时间和更高的过渡变性与更好的认知功能相关.
结论:
- 通过新型深度学习方法检测到的多层次大脑动态功能网络中的缺陷代表了SCD背后的可靠的神经改变.
- 开发的深度学习方法显示,作为早期检测AD相关认知变化的生物标志物具有前途.
- 这些发现强调了动态和多尺度网络分析对于理解神经系统疾病的重要性.
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