通过ComBat-Predict,可以将神经影像模型推广到新的地点
bioRxiv : the preprint server for biology
|September 5, 2025
概括
这项研究介绍了ComBat-Predict (CB-Predict),一种用于神经成像数据的新统一方法. CB-Predict有效地解决与地点相关的偏见,使不同数据集和新研究地点对大脑发育进行准确的分析.
科学领域:
- 神经成像和计算神经科学
- 生物统计和数据协调
- 神经退行性疾病研究
背景情况:
- 神经成像对于研究大脑衰老和阿尔茨海默氏症等疾病至关重要.
- 多部位研究对于大规模的大脑发育研究至关重要,但引入了特定部位的偏见.
- 现有的协调方法很难将其应用于新的未见的数据站点.
研究的目的:
- 开发一种新的协调方法,即ComBat-Predict (CB-Predict),可广泛应用于新站点.
- 在多个位置的神经成像数据集中减轻与位置相关的偏差.
- 改善神经成像模型的转化到新的临床和研究环境.
主要方法:
- 拟议的Combat-Predict (CB-Predict) 是Combat方法的扩展,用于场地效应的调整.
- 将CB-Predict应用于来自阿尔茨海默病神经成像计划 (ADNI) 和寿命大脑图谱联盟 (LBCC) 的数据.
- 评估了CB-Predict对有限数据和未知地点效应的新地点的概括能力.
主要成果:
- 在对新数据进行概括时,CB-Predict有效地减轻了ADNI皮质厚度的偏差.
- 这种方法在预测皮质厚度方面表现出很高的准确性.
- 从LBCC数据集中,CB-Predict成功地减少了百分点的与位置相关的差异.
结论:
- 提供一个强大的解决方案来协调多个站点的神经成像数据,包括新的,未见的站点.
- 该方法提高了神经影像研究的概括性和翻译潜力.
- 通过CB-Predict, 便于对大脑发育和神经退化进行更可靠的大规模研究.
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