根据纵向核磁共振成像 (MRI) 预测未来的大脑缩
Maryam Hadji1, Elaheh Moradi1, Jussi Tohka1
1A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio 70150, Finland.
medRxiv : the preprint server for health sciences
|November 24, 2025
概括
使用纵向MRI和风险因素预测未来的大脑缩,有望评估认知衰退风险,并帮助阿尔茨海默病研究中的临床试验选择.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 神经退行性疾病 神经退行性疾病
背景情况:
- 神经元损失和大脑缩是神经退行性疾病的标志,如阿尔茨海默病 (AD).
- 磁共振成像 (MRI) 对于检测大脑缩至关重要,对于AD研究至关重要.
- 精确测量大脑缩对于了解疾病进展和评估干预措施至关重要.
研究的目的:
- 使用机器学习预测大脑体积 (海马体,心室,总灰质) 的年度百分比变化.
- 为了比较基线与纵向MRI数据的预测能力,与风险因素相结合.
- 评估预测缩率对预测AD和相关痴呆症临床状况进展的有用性.
主要方法:
- 开发了一种弹性净线性回归模型,以预测未来每年大脑体积的变化.
- 评估了两个方法:基线 (单个时间点) 和纵向 (多个时间点).
- 仅用MRI模型与包含风险因素 (年龄,性别,APOE4,诊断) 的模型进行比较,这些模型在外部数据集上得到验证.
主要成果:
- 纵向MRI+风险因子模型实现了高预测准确度 (例如,海马0.62).
- 纵向模型始终优于基线模型;具有风险因素的模型优于仅MRI模型.
- 预测的缩率优于预测进展到轻度认知障碍和痴呆症的当前体积.
结论:
- 使用纵向数据和风险因素预测未来的缩是评估认知能力下降风险的宝贵工具.
- 这种方法可以帮助识别针对阿尔茨海默氏症疾病修饰疗法的临床试验中的个体.
- 预测的缩率提供了比静态体积测量更敏感的疾病进展标记.
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