向强大的神经解剖学规范模型:样本大小和共变量分布的影响
bioRxiv : the preprint server for biology
|September 5, 2025
概括
对于大脑健康的规范建模需要仔细的样本选择. 在阿尔茨海默病研究中,即使是中等规模的基准群体,对于准确的个体大脑偏差估计至关重要.
科学领域:
- 神经成像
- 计算神经科学
- 生物统计学
背景情况:
- 神经成像中的规范模型对于检测个体大脑偏差至关重要.
- 模型性能对参考样本的大小和人口特征敏感.
- 了解这些影响对于可靠的阿尔茨海默病 (AD) 研究至关重要.
研究的目的:
- 调查参考样本大小和共变量组成对AD规范模型性能的影响.
- 评估适应性转移学习提高模型准确性的有效性.
- 确定最佳的队列特征,以对个体水平的大脑偏差进行可靠的估计.
主要方法:
- 在不同规模 (5-600) 和人口统计数据的健康对照 (HCs) 中训练有素的规范模型.
- 在测试组和AD队列中评估模型合适性,偏差估计和临床读数.
- 通过对大规模数据进行预训练 (英国生物库) 并适应临床数据集,利用适应性转移学习.
- 在独立的外部样本 (AIBL) 中验证的结果.
主要成果:
- 随着基准样本大小的增加,模型的性能得到了持续的提高.
- 人口相匹配,特别是年龄相匹配,对于准确的偏差估计至关重要.
- 直接训练的模型稳定在200个HC左右; 适应的模型只用50个HC实现了类似的性能.
- 转移学习显著减少了可靠建模所需的样本大小.
结论:
- 通过适度大小,人口相匹配的队列,可以实现强大的个体级大脑偏差建模.
- 适应性转移学习提高了效率,使得使用较小的参考组可以进行可靠的估计.
- 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.
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