Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion.

Journal: Biological psychiatry. Cognitive neuroscience and neuroimaging
PMID: 29529409

Abstract

BACKGROUND: Successfully treating illicit drug use has become paramount, yet elusive. Devising specialized treatment interventions could increase positive outcomes, but it is necessary to identify risk factors of poor long-term outcomes to develop specialized, efficacious treatments. We investigated whether functional network connectivity (FNC) measures were predictive of substance abuse treatment completion using machine learning pattern classification of functional magnetic resonance imaging data.

Authors

  • Vaughn R Steele
    Intramural Research Program, Neuroimaging Research Branch, National Institute of Drug Abuse, National Institutes of Health, Baltimore, Maryland. Electronic address: vaughn.r.steele@gmail.com.
  • J Michael Maurer
    Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Department of Psychology, University of New Mexico, Albuquerque, New Mexico.
  • Mohammad R Arbabshirani
    Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Institute for Advanced Application, Geisinger Health System, Danville, Pennsylvania.
  • Eric D Claus
    Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.
  • Brandi C Fink
    Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico.
  • Vikram Rao
    Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.
  • Vince D Calhoun
    Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico; Department of Neurosciences, University of New Mexico, Albuquerque, New Mexico.
  • Kent A Kiehl
    Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Department of Psychology, University of New Mexico, Albuquerque, New Mexico; Department of Neurosciences, University of New Mexico, Albuquerque, New Mexico.

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