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dc.contributor.authorBruin, Willem Benjamin
dc.contributor.authorOltedal, Leif
dc.contributor.authorBartsch, Hauke
dc.contributor.authorAbbott, Christopher
dc.contributor.authorArgyelan, Miklos
dc.contributor.authorBarbour, Tracy
dc.contributor.authorCamprodon, Joan
dc.contributor.authorChowdhury, Samadrita
dc.contributor.authorEspinoza, Randall
dc.contributor.authorMulders, Peter
dc.contributor.authorNarr, Katherine
dc.contributor.authorOudega, Mardien
dc.contributor.authorRhebergen, Didi
dc.contributor.authorTen Doesschate, Freek
dc.contributor.authorTendolkar, Indira
dc.contributor.authorVan Eijndhoven, Philip
dc.contributor.authorVan Exel, Eric
dc.contributor.authorVan Verseveld, Mike
dc.contributor.authorWade, Benjamin
dc.contributor.authorVan Waarde, Jeroen
dc.contributor.authorZhutovsky, Paul
dc.contributor.authorDols, Annemiek
dc.contributor.authorVan Wingen, Guido
dc.description.abstractBackground Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. Methods Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. Results Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82–0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70–0.73 AUC). Conclusions These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.en_US
dc.publisherCambridge University Pressen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleDevelopment and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: A multicenter machine learning analysisen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.source.journalPsychological Medicineen_US
dc.identifier.citationPsychological Medicine. 2023en_US

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