Prediction of individual response to Electroconvulsive Therapy via machine learning on structural Magnetic Resonance Imaging data (The GEMRIC Munster, Germany center)
Initiatives
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Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified. In this study, the researchers investigated whether certain factors identified by structural magnetic resonance imaging (MRI) techniques are able to predict ECT response.
- Start Year
- 2010
- End Year
- 2015
Visit Redlich_JAMA Psychiatry_2016
| Investigators | Contacts |
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Design
- Study design
- Patients' cohort
Marker Paper
Redlich R, Opel N, Grotegerd D, et al. Prediction of individual response to Electroconvulsive Therapy via machine learning on structural Magnetic Resonance Imaging data. JAMA Psychiatry. 2016;73(6):557-64. doi:10.1001/jamapsychiatry.2016.0316
PUBMED 27145449
Recruitment
- Sources of Recruitment
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- Individuals
Number of participants
- Number of participants
- 28
- Number of participants with biosamples
Access
Availability of data and biosamples
| Data | |
| Biosamples | |
| Other |
Availability of access information
On the study website : https://mmiv.no/gemric/