Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach.

Journal: The journal of ECT
PMID: 32118692

Abstract

OBJECTIVE: To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach.

Authors

  • Akihiro Takamiya
    Department of Neuropsychiatry, Keio University School of Medicine.
  • Kuo-Ching Liang
    Department of Psychiatry, School of Medicine, Keio University, Tokyo 160-8582, Japan.
  • Shiro Nishikata
    From the Department of Neuropsychiatry, Keio University School of Medicine.
  • Ryosuke Tarumi
  • Kyosuke Sawada
    From the Department of Neuropsychiatry, Keio University School of Medicine.
  • Shunya Kurokawa
    From the Department of Neuropsychiatry, Keio University School of Medicine.
  • Jinichi Hirano
    Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
  • Bun Yamagata
    Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
  • Masaru Mimura
    Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
  • Taishiro Kishimoto
    Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Tokyo, Japan.

Keywords

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