Evidence map›Paper›PMID 40964020›Full record

ArticleResearch square2025

Turbulent-like Dynamics Predict Treatment Outcome in Major Depressive Disorder.

Henricus Ruhe, Maarten Poirot, Yonatan Sanz Perl, Matthan Caan, Henk Marquering, Gustavo Deco, Morten Kringelbach, Liesbeth Reneman

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Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Henricus RuheRadboudumc.ORCID 0000-0001-6072-0358
Maarten Poirot
Yonatan Sanz PerlUniversitat Pompeu Fabra.
Matthan Caan
Henk Marquering
Gustavo DecoUniversitat Pompeu Fabra.ORCID 0000-0002-8995-7583
Morten KringelbachUniversity of Oxford.ORCID 0000-0002-3908-6898
Liesbeth RenemanAmsterdam UMC - University of Amsterdam.

Funding

Establishing Moderators/Biosignatures of Antidepressant Response- Clinical CareU01MH092221 · NIMH · UT SOUTHWESTERN MEDICAL CENTER · PI TRIVEDI, MADHUKAR H. · 2010 to 2015
$9.7M
Biosignatures of Treatment Remission in Major DepressionU01MH092250 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MCGRATH, PATRICK J, PARSEY, RAMIN V. · 2010 to 2015
$8.9M
NIMH NIH HHS U01 MH092221NIMH NIH HHS U01 MH092250
6 · The paper itself

Abstract

introductionMajor depressive disorder is a prevalent and debilitating disease. Finding effective treatment is a lengthy trial-and-error process. Thus, identifying predictors of treatment outcome is key to reducing disease burden. Brain turbulence-like dynamic measures on transmission of information might be a potential methodological strategy for identifying informative predictors of antidepressant treatment response.

methodsWe analyzed data of the EMBARC study in which 296 adult outpatients were randomized to eight weeks of sertraline or placebo treatment. Resting state functional MRI scans were acquired at baseline and one week after treatment initiation. Turbulence-like dynamic measures were computed. Generalized linear models were used to predict week 8 response and symptom severity scores. Primary performance metrics were the area under the receiver operating characteristic (AUROC) and the root mean squared error (RMSE) respectively. Permutation testing was performed to test for significance against chance. Internal leave-one-out cross-validation was performed on the sertraline-treated patients (sample A) and external validation on the placebo arm (sample B) and on placebo-treated, non-responding patients who later switched to sertraline (sample C).

results226 patients were analyzed (age 37.9±13.4 years; 148 [65.5%] female). Sample A included 109 participants, sample B 121, and sample C 61. Internal cross-validation results were significantly better than chance at predicting response (AUROC=0.71, balanced accuracy=69.5%, p=0.008) and symptom severity (RMSE=4.7, p=0.025). External validation in sample B did not yield performance significantly better than chance, and in sample C only prediction of response did (AUROC=0.65, balanced accuracy=60.3%, p=0.038).

conclusionThe turbulence framework is a suitable paradigm for predicting sertraline response in major depressive disorder. Prediction specificity to sertraline treatment was limited. Non-responders to sertraline were characterized by increased long-distance and reduced short-distance information cascade flow.

Indexed as

Antidepressant Treatment ResponseMachine LearningMajor Depressive DisorderResting State Functional Magnetic Resonance ImagingTurbulence

Identifiers

PMID40964020
PMCPMC12440109

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.