A systematic evaluation of machine learning–based biomarkers for major depressive disorder (10 January 2024)

Summary

This study in JAMA Psychiatry aimed to assess whether multivariate machine learning approaches can identify the neural signature of major depressive disorder in individual patients. The study was conducted as a case-control neuroimaging study that included 1801 patients with depression and healthy controls. The results showed that the best machine learning algorithm only achieved a diagnostic classification accuracy of 62% across major neuroimaging modalities. The authors concluded that although multivariate neuroimaging markers increase predictive power compared with univariate analyses, no depression biomarker could be uncovered that is able to identify individual patients.