Systematic misestimation of machine learning performance in neuroimaging studies of depression.

ABSTRACT: We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls based on neuroimaging data. Drawing upon structural MRI data from a balanced sample of N = 1868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset whi

SUBMITTER: Flint C 

PROVIDER: S-EPMC8209109 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature