在阿尔茨海默病连续体中使用可解释的AI进行多变全脑神经退行性-认知-临床严重性映射
Taslim Murad1, Hui-Yuan Miao1, Deepa S Thakuri1,2
1Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
medRxiv : the preprint server for health sciences
|July 17, 2025
概括
研究人员绘制了大脑变化,认知和阿尔茨海默病 (AD) 严重程度之间的复杂联系. 可解释的人工智能确定了与AD频谱的认知衰退相关的关键大脑区域.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 阿尔茨海默病 (AD) 的特点是神经退行和认知障碍,但多变量关系尚未完全理解.
- 了解这些复杂的联系对于开发有效的AD诊断和治疗策略至关重要.
研究的目的:
- 绘制整个大脑神经退行性 (WBN) 标记物,全球认知和阿尔茨海默病连续体内的临床严重性之间的多变量关系.
- 开发和验证可解释的人工智能 (AI) 方法来分析这些复杂的关系.
主要方法:
- 在半模拟数据上开发和验证可解释的AI方法.
- 将表现最好的AI方法应用于大规模实验数据 (N=1,756) 来自AD连续的个体.
- 确定了与认知功能和临床严重程度相关的区域WBN标志物.
主要成果:
- 可解释的人工智能方法从区域WBN标记器准确预测了认知.
- 确定了特定的大脑区域,分层和同时与AD的认知衰退相关.
- 证明了这些神经成像特征与临床严重程度之间的相关性,表明临床相关性.
结论:
- 这项研究使用可解释的AI创新地绘制了AD连续体中的多变量区域WBN-认知-临床严重性关系.
- 显著提升了对AD相关神经生物学途径的理解,并确定了涉及认知衰退的关键大脑区域.
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