Learning Dynamic Bayesian Networks From fMRI Time Series Under Conditions of Chronic Pain and Opioid Addiction
Abstract
There is a prevalence of comorbid chronic pain and opioid addiction, (Rosenblum et al, 2003; Clark et al, 2008), presenting a serious healthcare challenge. Independently, chronic pain and opioid addiction are difficult to treat, and the comorbidity only increases the complexity. Patients with a substance use disorder (SUD) and co-occurring physical pain are more likely to misuse opioids than SUD patients without pain (Potter et al., 2008). Chronic pain is positively associated with substance use disorder severity, psychiatric disorders, psychological distress, medical comorbidities (Rosenblum et al, 2003), generally physical health problems, medical care utilization (Rosenblum et al, 2003; Trafton et al, 2004) and psychosocial factors (Jamison et al, 2000; Rosenblum et al, 2003; Potter et al, 2004; Trafton et al, 2004). Data were collected during pain induction in 18 opioid-addicted participants who displayed chronic low back pain and 18 age- and sex-matched healthy controls. Identification of a plausible model included employing augmented naïve Bayes classification within Bayesian Networks. Model performance involved study (target) group sensitivity analysis, mutual information and statistical tests of edge parameter differences based on regional (node) alterations.
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About the Presenter
Larry R. Price, Ph.D., PStat is Director of the Methodology, Measurement and Statistical Analysis (MMSA) at Texas State University, a university-wide role that involves conceptualizing and writing the analytic segments of large-scale competitive grant proposals for funding agencies such as the Department of Education/Institute of Education Sciences, National Science Foundation, National Institutes of Health and the Department of Defense in collaboration with interdisciplinary research teams.
Dr. Price is also Professor of Psychometrics & Statistics with faculty appointments in the College of Education and Department of Mathematics (by courtesy). Prior to arriving at Texas State University, he served as a Psychometrician and Statistician for the Emory University School Medicine, Departments of Psychiatry & Behavioral Sciences and Psychology from 1996 to 1999. Between 1999 and 2002, Dr. Price was employed at The Psychological Corporation in San Antonio as a Psychometrician/Statistician where his work focused on improving the psychometric properties of the Wechsler Scales of Intelligence Memory (e.g., WISC-III, WISC-IV, WMS-III, and WPPSI-III), and Achievement (WIAT-II) and other psychological measures such as the Beck Depression Inventory (BDI) and Clinical Evaluation of Language Fundamentals (CELF-IV). Dr. Price is a Fellow of the American Psychological Association, Division 5 – Evaluation, Measurement & Statistics, and Accredited Professional Statistician of the American Statistical Association. He has presented, published in and/or reviews for publications such as Structural Equation Modeling, Institute of Electrical and Electronics Engineers, Journal of Educational & Behavioral Statistics, Psychological Assessment, Neuroimage, Human Brain Mapping, Elementary School Journal, Journal of Experimental Education, and The Journal of Clinical and Experimental Neuropsychology and the Oxford Handbook of Quantitative Methods. He is currently writing a textbook tilted Psychometric Methods: Theory into Practice to be published in 2015 by Guilford Press.