Tools for assessing and mitigating confounding effects in Machine-Learning based studies of MRI data

Using Machine Learning (ML) techniques on neuroanatomical data obtained with magnetic resonance imaging (MRI) is becoming increasingly popular in the study of Psychiatric Disorders (PD). However, this kind of analyses can be affected by overfitting and thus be sensitive to biases in the dataset, producing hardly reproducible results. It is therefore important to identify and correct possible bias sources in the sample. We present two tools aimed at addressing this matter: a methodology to assess the confounding power of a variable in a specific classification task, and a cost function to use during classifier training on highly biased data.