Using neuroimaging to predict relapse in stimulant dependence: A comparison of linear and machine learning models.

Journal: NeuroImage. Clinical
PMID: 30665102

Abstract

OBJECTIVE: Relapse rates are consistently high for stimulant user disorders. In order to obtain prognostic information about individuals in treatment, machine learning models have been applied to neuroimaging and clinical data. Yet few efforts have been made to test these models in independent samples or show that they can outperform linear models. In this exploratory study, we examine whether machine learning models relative to linear models provide greater predictive accuracy and less overfitting.

Authors

  • Joshua L Gowin
    Departments of Radiology and Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States. Electronic address: joshua.gowin@ucdenver.edu.
  • Monique Ernst
    Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, Bethesda, MD, United States.
  • Tali Ball
    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States.
  • April C May
    Department of Psychiatry, University of California San Diego, La Jolla, CA, United States.
  • Matthew E Sloan
    Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States.
  • Susan F Tapert
    Department of Psychiatry, University of California San Diego, La Jolla, CA, United States.
  • Martin P Paulus
    Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA.

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