A Systematic Evaluation of Machine Learning-based Biomarkers for Major Depressive Disorder across Modalities, 2023, Winter el al

Preprint

A Systematic Evaluation of Machine Learning-based Biomarkers for Major Depressive Disorder across Modalities

Nils R. Winter, Julian Blanke, Ramona Leenings, Jan Ernsting, Lukas Fisch, Kelvin Sarink, Carlotta Barkhau, Katharina Thiel, Kira Flinkenflügel, Alexandra Winter, Janik Goltermann, Susanne Meinert, Katharina Dohm, Jonathan Repple, Marius Gruber, Elisabeth J. Leehr, Nils Opel, Dominik Grotegerd, Ronny Redlich, Robert Nitsch, Jochen Bauer, Walter Heindel, Joachim Groß, View ORCID ProfileTill F. M. Andlauer, Andreas J. Forstner, View ORCID ProfileMarkus M. Nöthen, Marcella Rietschel, Stefan G. Hofmann, Julia-Katharina Pfarr, Lea Teutenberg, Paula Usemann, Florian Thomas-Odenthal, Adrian Wroblewski, Katharina Brosch, Frederike Stein, Andreas Jansen, Hamidreza Jamalabadi, Nina Alexander, Benjamin Straube, Igor Nenadić, Tilo Kircher, Udo Dannlowski, Tim Hahn

Abstract

Background:

Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.

Methods:

Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.

https://www.medrxiv.org/content/10.1101/2023.02.27.23286311v1

 

Having, yet again, not found biomarkers, I was lead to believe this means they accept that it's not a real thing.

Surely there will be consistency in applying standards.

Oh, Shirley, you so funny.