一个人口条件变异自编码器用于fMRI分布采样和消除混杂
bioRxiv : the preprint server for biology
|May 27, 2024
概括
这项研究介绍了DemoVAE,这是一种生成合成fMRI数据并消除年龄和性别等人口统计学混的模型. 这提高了脑成像分析的可靠性,减少了人口因素的偏差.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 来自fMRI的功能连接 (FC) 用于预测各种神经和精神疾病.
- 人口因素 (年龄,性别,种族) 可以混fMRI数据,影响预测的准确性.
- 数据的有限可访问性阻碍了广泛使用有价值的fMRI数据集.
结论:
- 通过DemoVAE,可以生成高质量的,人口统计学条件下的合成fMRI数据.
- 该模型有效地从fMRI数据中消除了混的人口学影响.
- 基于FC的预测任务在很大程度上受到人口混的影响,这凸显了人口无偏分析的重要性.
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