Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex machine learning models. A potential solution is to increase sample size by pooling scans from several datasets. In this work, we combine 12,207 MRI scans from 15 studies and show that simple pooling is often ill-advised due to introducing various types of biases in the training data. First, we systematically define these biases. Second, we detect bias by experimentally showing that scans can be correctly assigned to their respective dataset with 73.3% accuracy. Finally, we propose to tell causal from confounding factors by quantifying the extent of confounding and causality in a single dataset using causal inference. We achieve this by finding the simplest graphical model in terms of Kolmogorov complexity. As Kolmogorov complexity is not directly computable, we employ the minimum description length to approximate it. We empirically show that our approach is able to estimate plausible causal relationships from real neuroimaging data.

本文提出一种通过利用来自多个数据集的成像数据来增加样本数量的方法，分析了通过简单汇集这些数据可能引入的偏差类型，提出通过量化因果推断中的混淆度和因果程度来区分因果和混淆因素的方法，并在实验中表明这种方法能有效地估计从真实脑成像数据中得出的合理因果关系。

运用因果推断量化神经影像数据集中潜在混淆偏差