USING PREFRONTAL CORTICAL ACTIVITY AS A BIOLOGICAL MARKER TO PREDICT RELAPSE RISK IN PRESCRIPTION OPIOID DEPENDENT PATIENTS: A STUDY USING FUNCTIONAL NEAR-INFRARED SPECTROSCOP
Open Access
- Author:
- Huhn, Andrew Stephen
- Graduate Program:
- Neuroscience
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 17, 2016
- Committee Members:
- Scott C Bunce, Dissertation Advisor/Co-Advisor
- Keywords:
- Opioid dependence
prefrontal cortex
functional near-infrared spectroscopy
risky decision making - Abstract:
- ABSTRACT The incidence of prescription opioid dependence has reached epidemic proportions in the United States. For addicted individuals and their families, the behavioral manifestations and long-term health consequences associated with opioid dependence are both baffling and disconcerting. As in all addictions, treatment of prescription opioid dependence is extremely challenging due to high relapse rates. Identifying sensitive and specific markers of relapse risk is critical in (1) characterizing risk factors that should be targets of treatment, and (2) categorizing high risk individuals that may require further treatment for opioid dependence. Self-report measures have not been reliable in predicting treatment outcome, and the entire field lacks a gold standard in regard to assessment of relapse risk in treatment seeking individuals. Identifying biological markers of relapse risk is critical in recognizing high risk individuals that may require extended residential treatment for opioid dependence. To address this issue, we conducted a study at the Caron Treatment Center in Wernersville, PA which sought to establish a predictive model for relapse risk using prefrontal cortical activity. Furthermore, our study sought to delineate the course of reregulation of biological and psychological factors relevant to prescription opioid dependence. The study sample consisted of 76 patients and 40 age/gender matched healthy controls. Participants in the study provided self-report data about their affective state and personality traits, gave salivary cortisol samples, and also performed a risky decision-making and drug cue paradigm adapted for functional near-infrared spectroscopy (fNIRS). We collected repeated measures on a subset of 22 patients that stayed in residential treatment beyond 28 days. We also collected information regarding relapse for 90 days post-discharge from residential treatment. Opioid dependent patients exhibited dysregulation of a triad of relapse risk factors when compared to controls. These factors include increased risky decision-making, anhedonia, and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis. We found that patients that relapse to opioids display differential neural activity in the prefrontal cortex compared to patients that remain abstinent. More specifically, neuroimaging results showed that, during a risky decision-making task, neural activity in the ventromedial and dorsolateral prefrontal cortices are reliable biomarkers of relapse risk. As such, we were able to accurately predict relapse to opioids in the first 90 days post-residential treatment. The results from this study improve our understanding of biological indicators of relapse risk in opioid dependent patients. Importantly, our model identifies prefrontal cortex measurements that predict treatment outcome in the first three months post-discharge from residential treatment. Moreover, these data characterize symptomatology during the post-withdrawal state in a way that is both relevant and beneficial to the clinical and scientific communities. Identifying those individuals at heightened relapse risk is extremely important, both in guiding clinicians in determining next level of care, and convincing patients that additional care is warranted. Continued focus on clinically translatable research should seek to develop biotechnologies aimed at improving treatment outcome.