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Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.

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Title: Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.
Authors: Bruin, Willem Benjamin, Oltedal, Leif, Bartsch, Hauke, Abbott, Christopher, Argyelan, Miklos, Barbour, Tracy, Camprodon, Joan, Chowdhury, Samadrita, Espinoza, Randall, Mulders, Peter, Narr, Katherine, Oudega, Mardien, Rhebergen, Didi, ten Doesschate, Freek, Tendolkar, Indira, van Eijndhoven, Philip, van Exel, Eric, van Verseveld, Mike, Wade, Benjamin, van Waarde, Jeroen
Source: Psychological Medicine. Feb2024, Vol. 54 Issue 3, p495-506. 12p.
Subjects: Diagnosis of mental depression, Patient selection, Electroconvulsive therapy, Research funding, Clinical decision support systems, Disease remission, Treatment effectiveness, Magnetic resonance imaging, Descriptive statistics, Gray matter (Nerve tissue), Research, Neuroradiology, Comparative studies, Machine learning, Mental depression, Biomarkers
Abstract: Background: 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. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
Description
Abstract:Background: 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. [ABSTRACT FROM AUTHOR]
ISSN:00332917
DOI:10.1017/S0033291723002040