Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse☆
Introduction
Relapse is a vexing problem in addictive disorders and, typically, only 40–60% of individuals with addictive disorders are able to maintain abstinence for more than a year after initiating treatment (Hunt et al., 1971, McLellan et al., 2000). Since numerous studies have suggested that treatment can lower relapse rates (Baker et al., 2001, Irvin et al., 1999, Kosten and O’Connor, 2003, Lancaster et al., 2006, Schmitz et al., 2001), identifying treatment-seeking patients at greatest risk of relapse could help clinicians to appropriate more resources to those individuals to more effectively reduce relapse rates. Previous studies have shown that demographic (e.g., lower socioeconomic status; Mclellan et al., 1994), social (e.g., lack of family support; National Institute of Drug Abuse, 1999), and neuroimaging measures (Janes et al., 2010, Paulus et al., 2005; e.g., failure to show differential activation during risky and safe decisions; Gowin et al., 2014a), can indicate relapse likelihood. More recent investigations have used machine learning techniques to predict individual outcomes (Connor et al., 2007, Weinstein et al., 2009). To date, few such studies have used brain imaging measures and have focused on making individually specific predictions. There is some indication that the combination of imaging and sophisticated analytic approaches may provide sufficient prediction accuracy that would allow one to develop prognostic tests of relapse. Such tests could aid a clinician in providing a patient-specific risk assessment that could be used to objectively communicate risk to the patient or change the course of treatment to reduce risk status.
One proposed marker of substance use disorders (SUDs), including methamphetamine dependence (MD; May et al., 2013, Schouw et al., 2013, Stewart et al., 2014), is altered neural response of the limbic reward system (Koob, 2013, Volkow and Fowler, 2000). There are two prominent hypotheses on how the response changes: individuals with SUDs may have hyper- or hypo-activation in response to rewarding stimuli, reflecting either enhanced incentive salience or reward deficiency, respectively. The incentive salience hypothesis derives from evidence that repeated pairing of a cue with a rewarding substance leads to enhanced dopaminergic responding, and drug-craving, when shown the cue (Berridge, 2012). The reward deficiency hypothesis derives from evidence that individuals with SUDs have impaired function of the dopamine reward system, and thus have lower response to rewards such as food, and may use substances to enhance dopamine signaling (Blum et al., 2012). A recent review suggests that the presence of drug cues may modulate reward circuitry activation, where drug cues enhance reward circuitry activation relative to controls, but natural rewards produce lower levels of activity (Leyton and Vezina, 2013, Limbrick-Oldfield et al., 2013). Corroborating this, several studies using monetary or food rewards have shown that individuals with SUDs relative to controls show decreased activation in the striatum, amygdala and insula when viewing or receiving rewards (Ihssen et al., 2011, Jia et al., 2011, Konova et al., 2012, Peters et al., 2011). The ability to stimulate reward circuitry through natural rewards may diminish the desire to stimulate it through substance use, potentially reducing the risk of relapse. It remains unclear whether processing of non-drug rewards during early abstinence can distinguish between individuals who will relapse or remain abstinent.
In a previous study, we examined early-abstinent MD during the decision phase of a risk-taking task and showed that a lack of differentiation between safe and risky options distinguished individuals who would relapse (Gowin et al., 2014a). That study attempted to identify processing differences between individuals who relapse versus abstain (i.e., disrupted risky decision-making). Here, we use data from the same sample to focus on a different question: can neuroimaging be developed as a practical prediction tool to identify individuals at risk of relapse? Improving diagnosis of SUDs is a critical issue to the field (Volkow and Baler, 2013). To address this question, we use a novel statistical model to determine how well neuroimaging can be used to predict clinical outcomes in combination with clinical, demographic and behavioral measures. We wanted to address a problem in neuroimaging prediction models identified by Whelan and Garavan (2013); they showed that the failure of neuroimaging studies to use out-of-sample data disposes them to inflate prediction estimates. We reduced the risk of inflated estimates by using random forest (Breiman, 2001), a robust model that employs a training and testing set. We hypothesized that those individuals who have the greatest difficulty in differentially processing levels of reward, i.e., the neural activation difference to small versus large rewards, might be at greatest risk for relapse. Moreover, we aimed to examine whether a machine learning approach, i.e., random forest, using neural activation during reward could be used to develop a robust test to assess relapse risk of individual participants. Support for the hypotheses and evidence for a robust test would integrate reward-processing dysfunctions with a practically useful tool that would make a significant contribution to addiction medicine.
Section snippets
Sample
Sixty-eight (fifteen female) MD individuals were recruited through 28-day inpatient drug treatment programs at the Veterans Affairs San Diego Healthcare System and Scripps Green Hospital (La Jolla, CA). Both treatment programs employ 12-step models, daily education and exercise, and require participants to attend Narcotics Anonymous meetings. All participants completed the 28-day program and consented to participate in a clinical interview, a brain scan, and a follow-up phone interview one year
Group characteristics
There were no significant differences between the group that relapsed and the group that remained abstinent in demographics or drug use history (p > 0.05; Table 1). Mean number of days to relapse among MD who relapsed was 175.1 (SEM = 29.3). A chi-squared analysis showed that the relapse group (21%) had a higher prevalence of current marijuana dependence relative to the abstinent group (4%), but the small number of participants meeting this criteria (N = 6) precludes a subgroup analysis. MD who
Discussion
We examined whether brain activation during reward-processing can accurately predict which methamphetamine-dependent individuals will relapse in the year following treatment. Our results suggest that the degree to which the striatum differentially processes large, risky versus small, safe rewards is a robust predictor of relapse. In particular, those individuals who show brain activation that fails to differentiate reward magnitudes relapsed sooner. The present results complement our previous
Role of funding sources
This work was supported by grants from the National Institute on Drug Abuse (R01-DA016663, P20-DA027834, R01-DA027797, and R01-DA018307 as well as a VA Merit Grant to Martin Paulus). Sponsors played no role in the design, conduct of the study, collection, management, analysis, and interpretation of the data; or with preparation, review, or approval of the manuscript.
Contributors
Authors MW, SFT and MPP designed and implemented the study. Authors JLG, TMB and MPP analyzed the data. All authors contributed to and have approved the final manuscript.
Conflict of interest
All authors declare that they have no conflicts of interest.
Acknowledgments
Martin Paulus had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. We would like to thank Dr. F. Berger, T. Flagan, H. Donovan, D. Leland, M. Mortezaei and B. Friedrich for assistance and support during data acquisition. We would like to thank Drs. Jennifer Stewart and Anna Konova for critical reviews of the manuscript.
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Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org/10.1016/j.drugalcdep.2015.04.018.