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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]
Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.
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  Data: <searchLink fieldCode="AR" term="%22Bruin%2C+Willem+Benjamin%22">Bruin, Willem Benjamin</searchLink><br /><searchLink fieldCode="AR" term="%22Oltedal%2C+Leif%22">Oltedal, Leif</searchLink><br /><searchLink fieldCode="AR" term="%22Bartsch%2C+Hauke%22">Bartsch, Hauke</searchLink><br /><searchLink fieldCode="AR" term="%22Abbott%2C+Christopher%22">Abbott, Christopher</searchLink><br /><searchLink fieldCode="AR" term="%22Argyelan%2C+Miklos%22">Argyelan, Miklos</searchLink><br /><searchLink fieldCode="AR" term="%22Barbour%2C+Tracy%22">Barbour, Tracy</searchLink><br /><searchLink fieldCode="AR" term="%22Camprodon%2C+Joan%22">Camprodon, Joan</searchLink><br /><searchLink fieldCode="AR" term="%22Chowdhury%2C+Samadrita%22">Chowdhury, Samadrita</searchLink><br /><searchLink fieldCode="AR" term="%22Espinoza%2C+Randall%22">Espinoza, Randall</searchLink><br /><searchLink fieldCode="AR" term="%22Mulders%2C+Peter%22">Mulders, Peter</searchLink><br /><searchLink fieldCode="AR" term="%22Narr%2C+Katherine%22">Narr, Katherine</searchLink><br /><searchLink fieldCode="AR" term="%22Oudega%2C+Mardien%22">Oudega, Mardien</searchLink><br /><searchLink fieldCode="AR" term="%22Rhebergen%2C+Didi%22">Rhebergen, Didi</searchLink><br /><searchLink fieldCode="AR" term="%22ten+Doesschate%2C+Freek%22">ten Doesschate, Freek</searchLink><br /><searchLink fieldCode="AR" term="%22Tendolkar%2C+Indira%22">Tendolkar, Indira</searchLink><br /><searchLink fieldCode="AR" term="%22van+Eijndhoven%2C+Philip%22">van Eijndhoven, Philip</searchLink><br /><searchLink fieldCode="AR" term="%22van+Exel%2C+Eric%22">van Exel, Eric</searchLink><br /><searchLink fieldCode="AR" term="%22van+Verseveld%2C+Mike%22">van Verseveld, Mike</searchLink><br /><searchLink fieldCode="AR" term="%22Wade%2C+Benjamin%22">Wade, Benjamin</searchLink><br /><searchLink fieldCode="AR" term="%22van+Waarde%2C+Jeroen%22">van Waarde, Jeroen</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Psychological+Medicine%22">Psychological Medicine</searchLink>. Feb2024, Vol. 54 Issue 3, p495-506. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Diagnosis+of+mental+depression%22">Diagnosis of mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+selection%22">Patient selection</searchLink><br /><searchLink fieldCode="DE" term="%22Electroconvulsive+therapy%22">Electroconvulsive therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+decision+support+systems%22">Clinical decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+remission%22">Disease remission</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Gray+matter+%28Nerve+tissue%29%22">Gray matter (Nerve tissue)</searchLink><br /><searchLink fieldCode="DE" term="%22Research%22">Research</searchLink><br /><searchLink fieldCode="DE" term="%22Neuroradiology%22">Neuroradiology</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1017/S0033291723002040
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      – Code: eng
        Text: English
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      – SubjectFull: Diagnosis of mental depression
        Type: general
      – SubjectFull: Patient selection
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      – SubjectFull: Electroconvulsive therapy
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      – SubjectFull: Research funding
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      – SubjectFull: Disease remission
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      – SubjectFull: Treatment effectiveness
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      – SubjectFull: Magnetic resonance imaging
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Machine learning
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      – SubjectFull: Mental depression
        Type: general
      – SubjectFull: Biomarkers
        Type: general
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      – TitleFull: Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.
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