TY - JOUR
T1 - Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion
AU - Steele, Vaughn R.
AU - Maurer, J. Michael
AU - Arbabshirani, Mohammad R.
AU - Claus, Eric D.
AU - Fink, Brandi C.
AU - Rao, Vikram
AU - Calhoun, Vince D.
AU - Kiehl, Kent A.
N1 - Funding Information:
This work was supported by National Institute on Drug Abuse Grant Nos. 1 R01 DA020870 and R01DA026964 (to KAK); National Institute of Child Health and Human Development Grant No. 1R01HD082257 (to KAK); the Intramural Research Program of the National Institute on Drug Abuse, National Institutes of Health (Baltimore, Maryland) (to VRS); National Center for Advancing Translational Science Grant Nos. KL2 TR000089 and UL1 TR000041 (to BCF); National Institute on Alcohol Abuse and Alcoholism and National Institute on Drug Abuse Grant Nos. R21AA020594, R21AA021201, and R21DA037546 (to EDC); and National Institutes of Health Grants Nos. P20GM103472, R01DA040487, and R01EB020407 (to VDC).
Publisher Copyright:
© 2017 Society of Biological Psychiatry
PY - 2018/2
Y1 - 2018/2
N2 - 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. Methods: Treatment-seeking stimulant- or heroin-dependent incarcerated participants (n = 139; 89 women) volunteered for a 12-week substance abuse treatment program. Participants performed a response inhibition Go/NoGo functional magnetic resonance imaging task prior to onset of the substance abuse treatment. We tested whether FNC related to the anterior cingulate cortex would be predictive of those who would or would not complete a 12-week substance abuse treatment program. Results: Machine learning pattern classification models using FNC between networks incorporating the anterior cingulate cortex, striatum, and insula predicted which individuals would (sensitivity: 81.31%) or would not (specificity: 78.13%) complete substance abuse treatment. FNC analyses predicted treatment completion above and beyond other clinical assessment measures, including age, sex, IQ, years of substance use, psychopathy, anxiety and depressive symptomatology, and motivation for change. Conclusions: Aberrant neural network connections predicted substance abuse treatment outcomes, which could illuminate new targets for developing interventions designed to reduce or eliminate substance use while facilitating long-term outcomes. This work represents the first application of machine-learning models of FNC analyses of functional magnetic resonance imaging data to predict which substance abusers would or would not complete treatment.
AB - 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. Methods: Treatment-seeking stimulant- or heroin-dependent incarcerated participants (n = 139; 89 women) volunteered for a 12-week substance abuse treatment program. Participants performed a response inhibition Go/NoGo functional magnetic resonance imaging task prior to onset of the substance abuse treatment. We tested whether FNC related to the anterior cingulate cortex would be predictive of those who would or would not complete a 12-week substance abuse treatment program. Results: Machine learning pattern classification models using FNC between networks incorporating the anterior cingulate cortex, striatum, and insula predicted which individuals would (sensitivity: 81.31%) or would not (specificity: 78.13%) complete substance abuse treatment. FNC analyses predicted treatment completion above and beyond other clinical assessment measures, including age, sex, IQ, years of substance use, psychopathy, anxiety and depressive symptomatology, and motivation for change. Conclusions: Aberrant neural network connections predicted substance abuse treatment outcomes, which could illuminate new targets for developing interventions designed to reduce or eliminate substance use while facilitating long-term outcomes. This work represents the first application of machine-learning models of FNC analyses of functional magnetic resonance imaging data to predict which substance abusers would or would not complete treatment.
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U2 - 10.1016/j.bpsc.2017.07.003
DO - 10.1016/j.bpsc.2017.07.003
M3 - Article
C2 - 29529409
AN - SCOPUS:85029232850
VL - 3
SP - 141
EP - 149
JO - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
JF - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
SN - 2451-9022
IS - 2
ER -
