Individual Study / Redlich_JAMA Psychiatry_2016

Prediction of individual response to Electroconvulsive Therapy via machine learning on structural Magnetic Resonance Imaging data (The GEMRIC Munster, Germany center)

Prediction of individual response to Electroconvulsive Therapy via machine learning on structural Magnetic Resonance Imaging data (The GEMRIC Munster, Germany center)

Initiatives -
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
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Members

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Investigators Contacts
  • Dr. Ronny Redlich
    University of Muenster

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
  • 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/