Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data | Zendy

Ronny Redlich | Zendy; Nils Opel | Zendy; Dominik Grotegerd | Zendy; Katharina Dohm | Zendy; Dario Zaremba | Zendy; Christian Bürger | Zendy; Sandra Münker | Zendy; Lisa Mühlmann | Zendy; Patricia Wahl | Zendy; Walter Heindel | Zendy; Volker Arolt | Zendy; Judith Alferink | Zendy; Peter Zwanzger | Zendy; Maxim Zavorotnyy | Zendy; Harald Kugel | Zendy; Udo Dannlowski | Zendy

Open Access

Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data

Author(s) -

Ronny Redlich,

Nils Opel,

Dominik Grotegerd,

Katharina Dohm,

Dario Zaremba,

Christian Bürger,

Sandra Münker,

Lisa Mühlmann,

Patricia Wahl,

Walter Heindel,

Volker Arolt,

Judith Alferink,

Peter Zwanzger,

Maxim Zavorotnyy,

Harald Kugel,

Udo Dannlowski

Publication year - 2016

Publication title -

jama psychiatry

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 7.531

H-Index - 365

eISSN - 2168-6238

pISSN - 2168-622X

DOI - 10.1001/jamapsychiatry.2016.0316

Subject(s) - electroconvulsive therapy , magnetic resonance imaging , major depressive disorder , depression (economics) , psychology , functional magnetic resonance imaging , voxel , medicine , nuclear medicine , psychiatry , radiology , mood , cognition , macroeconomics , economics

Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified.

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