Data-driven cluster selection for subcortical shape and cortical thickness predicts recovery from depressive symptoms

Benjamin S.C. Wade, Jing Sui, Stephanie Njau, Amber M. Leaver, Megha Vasvada, Boris A. Gutman, Paul M. Thompson, Randal Espinoza, Roger P. Woods, Christopher C. Abbott, Katherine L. Narr, Shantanu H. Joshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using structural MRI data from 42 ECT patients scanned prior to ECT treatment, we developed a random forest classifier based on data-driven shape cluster selection and cortical thickness features to predict remission. Right hemisphere hippocampal shape and right inferior temporal cortical thickness was most predictive of remission, with the predicted probability of recovery decreasing when these regions were thicker prior to treatment. Remission was predicted with an average 73% balanced accuracy. Classification of MRI data may help identify treatment-responsive patients and aid in clinical decision-making. Our results show promise for the development of personalized treatment strategies.

Original languageEnglish (US)
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Pages502-506
Number of pages5
ISBN (Electronic)9781509011711
DOIs
StatePublished - Jun 15 2017
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: Apr 18 2017Apr 21 2017

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
CountryAustralia
CityMelbourne
Period4/18/174/21/17

Keywords

  • Electroconvulsive therapy
  • Major depression
  • Random Forest
  • Shape analysis
  • Treatment response prediction

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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