{"doi":"10.1176/appi.ajp.20230206","title":"Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial","abstract":null,"journal":"American Journal of Psychiatry","year":2024,"id":647171,"datarank":0.5806801516361837,"base_score":3.8712010109078907,"endowment":3.8712010109078907,"self_citation_contribution":0.5806801516361837,"citation_network_contribution":0.0,"self_endowment_contribution":0.5806801516361837,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":47,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1685945,"name":"Henricus G. Ruhe","orcid":null,"position":1,"is_corresponding":false},{"id":1685946,"name":"Henk-Jan M.M. Mutsaerts","orcid":null,"position":2,"is_corresponding":false},{"id":351021,"name":"Ivan I. Maximov","orcid":"0000-0001-6319-6774","position":3,"is_corresponding":false},{"id":1685947,"name":"Inge R. Groote","orcid":null,"position":4,"is_corresponding":false},{"id":694123,"name":"Atle Bjørnerud","orcid":"0000-0002-3486-3141","position":5,"is_corresponding":false},{"id":85654,"name":"Henk A. Marquering","orcid":"0000-0002-1414-6313","position":6,"is_corresponding":false},{"id":160917,"name":"Liesbeth Reneman","orcid":null,"position":7,"is_corresponding":false},{"id":1389856,"name":"Matthan W.A. Caan","orcid":"0000-0002-5162-8880","position":8,"is_corresponding":false},{"id":1389854,"name":"Maarten G. Poirot","orcid":"0000-0003-1937-7978","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial","abstract":"OBJECTIVE: Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment. METHODS: This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores. RESULTS: A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models. CONCLUSIONS: The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.","is_dataset_classified":null,"base_score":3.8501476017100584,"endowment":3.8501476017100584,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38321916","pmcid":null,"openalex_id":"https://openalex.org/W4391607669","authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"3U01MH092250-03S1","title":"Biosignatures of Treatment Remission in Major Depression"}],"total_grants":1,"fwci":14.759,"citation_percentile":0.99450824,"influential_citations":3,"citation_trend":[{"year":2024,"count":13},{"year":2025,"count":24},{"year":2026,"count":9}],"oa_status":"green","license":"other-oa","oa_locations":[{"url":"https://pure.amsterdamumc.nl/en/publications/b1a023e4-51d0-4c48-a3db-e53ecedc34de","host_type":"repository"},{"url":"https://repository.ubn.ru.nl/bitstream/handle/2066/305051/1/305051.pdf","host_type":"GREEN"},{"url":"https://pure.amsterdamumc.nl/en/publications/b1a023e4-51d0-4c48-a3db-e53ecedc34de","host_type":"repository"},{"url":"https://doi.org/10.1176/appi.ajp.20230206","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38321916","host_type":"repository"},{"url":"https://research.vumc.nl/en/publications/2a9f24b4-3d30-49f1-8fa2-44146751a10c","host_type":"repository"},{"url":"https://hdl.handle.net/2066/305051","host_type":"repository"},{"url":"https://hdl.handle.net/https://repository.ubn.ru.nl/handle/2066/305051","host_type":""},{"url":"https://www.scopus.com/pages/publications/85186531522","host_type":""},{"url":"https://repository.ubn.ru.nl//bitstream/handle/2066/305051/305051.pdf","host_type":""}],"fields_of_study":["Treatment of Major Depression","Functional Brain Connectivity Studies","Advanced MRI Techniques and Applications","Medicine","03 medical and health sciences","0302 clinical medicine","Adult","Humans","Female","Male","Sertraline","Major Depressive Disorder","Double-Blind Method","Antidepressive Agents","Magnetic Resonance Imaging"],"mesh_terms":["Adult","Antidepressive Agents","Depressive Disorder, Major","Major Depressive Disorder","Double-Blind Method","Female","Humans","Magnetic Resonance Imaging","Male","Sertraline"],"keywords":["Sertraline","Placebo","Major depressive disorder","Antidepressant","Clinical trial","Receiver operating characteristic","Internal medicine","Neuroimaging","Randomized controlled trial","Psychology","Medicine","Psychiatry","Pathology","Antidepressants","Machine Learning","Ssris","Adult","Male","220 Statistical Imaging Neuroscience","Psychiatry - Radboud University Medical Center","Magnetic Resonance Imaging","Antidepressive Agents","Psychiatry - Radboud University Medical Center - DCMN","Double-Blind Method","Humans","Female"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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