样本大小和共变分布对神经解剖学的规范建模的影响
Camille Elleaume1,2, Bruno Hebling Vieira1, Dorothea L Floris1
1Methods of Plasticity Research, Department of Psychology, University of Zürich, Zürich, Switzerland.
eLife
|February 23, 2026
概括
对大脑偏差的规范建模需要仔细选择参考样本. 在人口统计学上匹配的队列,即使是中等大小的队列,也会产生可靠的阿尔茨海默病 (AD) 分析,特别是在大规模的预训练中.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 生物统计学 生物统计学
背景情况:
- 神经成像中的规范模型评估个人的大脑偏差.
- 模型性能对参考样本的大小和组成敏感.
- 了解这些影响对于可靠的临床应用至关重要,特别是在阿尔茨海默病 (AD) 中.
研究的目的:
- 调查参考样本大小和人口组成对规范模型性能的影响.
- 评估适应性转移学习的有效性,以改善较小的参考队列的模型性能.
- 确定最佳策略,培养强大的规范模型,用于AD的神经成像研究.
主要方法:
- 在OASIS-3 (n=1032) 的健康对照 (HC) 的子样本 (5-600个人) 上训练有素的规范模型,年龄和性别分布各不相同.
- 通过在英国生物库 (n=42,747) 进行预训练并适应临床数据集,利用了自适应转移学习.
- 评估模型合适性,偏差估计,异常检测和HC测试集和AD队列上的分类准确性,在AIBL (n=463) 上进行外部验证.
主要成果:
- 随着参考样本大小的增加,模型性能持续改善.
- 人口统计对齐,特别是年龄对准,对于准确的偏差估计至关重要.
- 直接训练的模型需要~200个HC才能稳定适应;经过大规模预训练后,适应的模型与~50个HC取得了可比的性能.
结论:
- 强大的个体级大脑偏差建模可以通过中等大小的,人口统计学上匹配的参考队列来实现.
- 适应性转移学习在利用大型预训练模型时显著减少所需的参考样本大小.
- 这些发现支持在衰老和神经退行研究中更广泛地应用规范建模,强调样本特征的重要性.
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