在贝叶斯线性混合效应建模的家族前末叶退化中基于缩的个人化预测痴呆进展
Shubir Dutt1, Dana Leichter1, Yann Cobigo1
1Department of Neurology, Edward and Pearl Fein Memory and Aging Center, University of California, San Francisco, San Francisco, CA.
Annals of neurology
|January 29, 2026
概括
贝叶斯对大脑缩的建模预测了家族前叶退化 (f-FTLD) 突变载体中痴呆症转化. 这种方法可以通过更早地识别有风险的个体来改善临床试验设计.
科学领域:
- 神经成像是一种神经成像.
- 神经退行性疾病的神经退行性疾病
- 生物统计学 生物统计学
背景情况:
- 亲属前叶退行症 (f-FTLD) 发病率是可变的,使临床管理和试验招生复杂化.
- 个性化大脑缩图显示了预测f-FTLD中痴呆症转换的潜力.
- 预测痴呆症发病对于及时干预和高效的临床试验设计至关重要.
研究的目的:
- 评估贝叶斯线性混合效应 (BLME) 预测方法,用于识别加速的大脑体积损失.
- 评估BLME识别的缩集群在预测f-FTLD中痴呆症转化中的有用性.
- 将BLME的预测性能与其他神经成像方法进行比较.
主要方法:
- 使用了234个无症状或前进性f-FTLD突变载体 (C9orf72,GRN,MAPT) 的纵向MRI扫描,包括21个痴呆症转换器.
- 开发了BLME模型,以建立单个灰色物质轨迹,并确定加速体积损失的集群.
- 采用时间变化的考克斯比例危险模型和ROC曲线来测试预测准确性,并在24个月内区分转换器.
主要成果:
- 在不同基因亚型 (C9orf72,GRN,MAPT) 的f-FTLD突变载体中,BLME集群体积显著预测了痴呆症转化.
- BLME缩地图的预测性能与区域体积相当.
- 在24个月的时间内,BLME证明了痴呆症转换器与非转换器的优越歧视,由更大的AUC表示.
结论:
- 来自贝叶斯模型的个性化缩得分准确地预测了无症状f-FTLD突变载体中痴呆症的进展.
- 这种方法可以为临床试验设计相关的较短时间框架提供比其他结构成像方法更强大的实用性.
- 基于BLME的预测有望改善f-FTLD的管理和研究.
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