响马读paper

一个要求成员每月至少读一篇文献并打卡的学术交流社群

2024, JAMA Psychiatry. DOI: 10.1001/jamapsychiatry.2023.5083
A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder
Nils R. Winter, Julian Blanke, Ramona Leenings, Jan Ernsting, Lukas Fisch, Kelvin Sarink, Carlotta Barkhau, Daniel Emden, 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, Benjamin Risse, Till F. M. Andlauer, Andreas J. Forstner, Markus 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:
ImportanceBiological 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), no informative biomarkers have been identified.ObjectiveTo evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD.Design, Setting, and ParticipantsThis study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023.ExposurePatients with MDD and healthy controls.Main Outcome and MeasureDiagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression.ResultsOf 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups.Conclusion and RelevanceDespite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker—even under extensive ML optimization in a large sample of diagnosed patients—could be identified.
2024-01-31 21:42:00
#paper doi:10.1001/jamapsychiatry 2023.5083 A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive 本文使用了四百万个机器学习模型,基于结构、功能、扩散磁共振成像探索了区分抑郁症患者和正常人的生物标记物。研究发现,通过多次优化,也很难找到可靠的生物标记物,说明了寻找抑郁症个体生物标记物的困难性,进一步阐明了对抑郁症进行分亚型的意义。
TOP