Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

S Gallo; A El-Gazzar; P Zhutovsky; RM Thomas; N Javaheripour; M Li; L Bartova; D Bathula; U Dannlowski; C Davey; T Frodl; I Gotlib; S Grimm; D Grotegerd; T Hahn; PJ Hamilton; BJ Harrison; A Jansen; T Kircher; B Meyer; I Nenadić; S Olbrich; E Paul; L Pezawas; MD Sacchet; P Sämann; G Wagner; H Walter; M Walter; G van Wingen

Journal article

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

S Gallo, A El-Gazzar, P Zhutovsky, RM Thomas, N Javaheripour, M Li, L Bartova, D Bathula, U Dannlowski, C Davey, T Frodl, I Gotlib, S Grimm, D Grotegerd, T Hahn, PJ Hamilton, BJ Harrison, A Jansen, T Kircher, B Meyer Show all

Molecular Psychiatry | Published : 2023

DOI: 10.1038/s41380-023-01977-5

Abstract

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functiona..

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University of Melbourne Researchers

Chris Davey Author

Ben Harrison Author

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Grants

Awarded by Philips

Funding Acknowledgements

This work was supported by the Netherlands Organization for Scientific Research (NWO; 628.011.023); Philips Research; ZonMW (Vidi; 016.156.318). The access of the UKbioBank data was granted under the application number 30091. Data collection was supported by Swedish Research Council; ALF grant from Region OEstergoetland; the Phyllis and Jerome Lyle Rappaport Foundation, Ad Astra Chandaria Foundation, BIAL Foundation, Brain and Behavior Research Foundation, Anonymous donors, and the Center for Depression, Anxiety, and Stress Research at McLean Hospital; The German Research Foundation (DFG, grant FOR2107 DA1151/5-1 and DA1151/5-2 to UD; SFB-TRR58, Projects C09 and Z02 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster (grant Dan3/012/17 to UD); European Commission (grant number H2020-634541); German Research Foundation (GR 4510/2-1); Australian National Health and Medical Research Council of Australia (NHMRC) Project Grants 1064643 (principal investigator, BJH) and 1024570 (principal investigator, CGD); Austrian Science Fund (FWF, grant nr. KLI 597-827, KLI-148-B00, F3514-B1); Science Foundation Ireland (SFI); The German Research Foundation (DFG WA1539/4-1). This work also acknowledges the DIRECT consortium for providing the Rest-Meta-MDD dataset.