Funded Projects

Profound sleep disturbances during abstinence have long been suspected of perpetuating vulnerability to relapse of people who misuse or are addicted to opioids. An animal model has shown that long-term sleep deficiency results in a persistent state of physiological dysregulation that is expected to modify the biology of abstinence and increase relapse potential. This study seeks to discover how persistent sleep restriction during withdrawal from opioid use increases vulnerability to relapse in the animal model by testing whether persistent sleep restriction during abstinence from opioid use is sufficient to increase opioid drug seeking. The functional outcome measure will be the degree of mitigation of opioid seeking. These studies will provide a basis for novel translational approaches to target mechanisms that are demonstrated to cause increased vulnerability to relapse.

Thoracic (chest) surgeries often cause both short-term (acute) and long-term (chronic) pain and long-term opioid use. Unique genetic and clinical risk factors affect individual responses to surgical pain and pain medications. Current trial-and-error approaches to managing post-surgical pain and opioid prescribing are not ideal. This project will develop predictive software within a medical device that takes into account an individual’s genetic and clinical information to predict the likelihood of chronic pain following thoracic surgery. 

Chronic pain affects millions of Americans and remains poorly understood and challenging to manage. Researchers do not fully understand brain processes involved in chronic pain, which can vary considerably from person to person. This project will analyze brain function using magnetic resonance imaging (MRI) in individuals with and without chronic pain. The research will also directly determine the degree of pain-related brain imaging changes by using a large database of brain imaging data.

Prescriptions for narcotic pain relief after surgery result in unintended prolonged opioid use for hundreds of thousands of Americans. Nonpharmacological pain care is effective and recommended by guidelines for perioperative pain while offering a more favorable risk-benefit ratio. However, nonpharmacological pain care is rarely used as first or second-line therapy after surgery. Patient and clinician decision support interventions are effective in encouraging patient-centered and guideline-concordant care, but these strategies have not been tested pragmatically as a bundle in everyday postoperative pain care. The NOHARM trial will first confirm the feasibility of patient-facing and clinician-facing decision support components of an EHR-embedded evidence-based bundle. The investigators will test the bundle in a stepped-wedge cluster randomized trial. They will test a sustainable system strategy that could change the paradigm of perioperative pain management toward nonpharmacological options in a manner that preserves patient function, honors patient values, and maintains availability of opioids as a last resort.

Chronic pain is a persistent source of disability and reduced quality of life for aging adults. Chronic pain-related outcomes are disproportionately worse for aging African Americans, who report greater pain severity and worse pain-related disability compared to White peers. A significant pain risk factor for African Americans is chronic stress (including family-related stress), which is worsened by structural inequities that affect this population. Although many African Americans identify family support as critical for pain self-management, this influence has not been studied thoroughly. This project will study how pain conditions develop and persist for aging African Americans by analyzing existing data from African American participants in two large aging studies: Midlife in the U.S. (721 participants) and the Health and Retirement Study (2,698 participants). The research aims to determine how family emotional climate affects pain risk, taking into account structural factors like discrimination, socioeconomic disparity, and the influence of various neighborhood settings.

Substance use disorders (SUDs) can have devastating consequences for people with serious mental illness (SMI). SUDs can increase morbidity and mortality and are associated with higher healthcare and social costs, homelessness, and incarceration. Unfortunately, despite the availability of effective treatments, most individuals with co-occurring SMI and SUD (COD) never receive SUD treatment. We propose to evaluate system, provider, and patient-level facilitators and barriers and develop an implementation strategy and toolkit to promote the use of medication-assisted treatment (MAT) for people with COD. Aims 1–3 seek to assess organizational capacity (at the system and provider level); organizational readiness (at the provider level); and perceived needs, attitudes, and preferences (at the patient level) to identify barriers and facilitators. In Aim 4, we will use findings from Aims 1–3 to guide development of the implementation strategy and toolkit, using stakeholder input and a systematic process for strategy development.

High rates of relapse and overdose deaths pose significant challenges to the treatment of Opioid Use Disorder (OUD). Anti-opioid immunotherapies (i.e., vaccines and monoclonal antibodies) have great potential to reduce long-term opioid use and overdose, with minimal risk of side effects, when used in conjunction with pharmacological treatments and/or behavioral therapies. The ability of an anti-opioid vaccine to induce antibodies that render an opioid less effective, or less rewarding, and protect from accidental overdose could provide an important therapeutic option for patients undergoing treatment for OUD. The goal of this collaborative study is to design, develop, and evaluate vaccines for use in the treatment of opioid use disorder

Social big data analysis of publicly available user data on social media platforms is a promising approach for attaining organic observations of behavior that can monitor and predict real-world public health problems, such as HIV incidence. In preliminary research, our team identified and collected tweets suggesting HIV risk behaviors (e.g., drug use, high-risk sexual behaviors), modeled them alongside CDC statistics on HIV diagnoses, and found a significant positive relationship between HIV-related tweets and county-level HIV cases. We propose to create a single automated platform that collects social media data, identifies and labels tweets that suggest HIV-related behaviors, and predicts regional HIV incidence. We will interview staff and participants at local and regional HIV organizations to understand ethical issues associated with mining people’s data. The software developed from this application will be shared with HIV researchers and health care workers to combat the spread of HIV.

Chronic low back pain is difficult to diagnose and treat effectively in part, because of the interplay of biophysical and psychosocial influences that complicate the relationship between impairment, disability, and pain. Psychological factors such as fear of movement and catastrophyzing can lead to compensatory movement patterns that affect movement biomechanics and paraspinal structure and function, driving further impairment, disrupting the balance between passive and active spine stabilizers, and reinforcing the patient?s perceived disability status. This study will support research to determine how psychological factors, spinal pathology, and perception of pain severity and disability status influence compensatory movement strategies, how movement biomechanics, psychological factors, and pain mechanisms relate to paraspinal muscle quality, and their relative changes during treatment. The supplement will provide training opportunities for skills in clinical pain management research.

Despite the significance of spine disorders, there are few reliable methods to determine appropriate patient care and evaluate intervention effectiveness. The Back Pain Consortium Research Program
(BACPAC) is developing machine learning-based methods to obtain disease-related features from biological images. This project supports a scientist from a group underrepresented in biomedicine to expand ongoing research to improve ways to interpret medical data about spine disorders and associated pain.

Chronic overlapping pain conditions represent up to half of all chronic pain cases and can be more debilitating than other forms of chronic pain. These conditions include but are not limited to the following: temporomandibular disorders, fibromyalgia, irritable bowel syndrome, vulvodynia, interstitial cystitis/painful bladder syndrome, painful endometriosis, chronic tension type headache, migraine headache, chronic low back pain, and chronic fatigue syndrome. Common neurobiological mechanisms have been suspected to account for the overlap between these conditions, but until recently it has been difficult to efficiently classify each condition within individual patients. A digital classification tool for clinicians has been developed for this purpose, but access to the tool remains limited. Here we propose converting this chronic overlapping pain conditions classification tool into a common web-based application format.

Knee osteoarthritis is one of the leading causes of chronic pain and disability worldwide, affecting more than 30% of older adults. Rates of this condition have more than doubled in the past 70 years and continue to grow sharply, given increases in life expectancy and body mass index among the U.S. population. This project supports a scientist from a group underrepresented in biomedicine to expand ongoing clinical research comparing various non-medication-based treatments for knee osteoarthritis.

This project will examine the association between end plate pathology and chronic low back pain (cLBP) and improve patient selection by developing and translating new imaging tools, technologies, and/or methods (iTTM) that provide accurate, noninvasive measures of end plate pathologies. A search for clinically relevant biomarkers of end plate pathology will focus on novel imaging measures of end plate bone marrow lesion (BML) severity with IDEAL MRI and cartilage endplate (CEP) fibrosis/damage with UTE MRI, assess interactions with paraspinal muscles, and identify metrics that associate with pain, disability, and degeneration. The research will refine imaging and post-processing methodologies by leveraging and expanding existing cross-sectional cohorts and then deploy and validate the new end plate iTTM to other BACPAC sites to test the most promising metrics’ clinical utility. These studies will provide validated iTTM that are useful for addressing the end plates pathology’s role in cLBP, identifying sub-phenotypes, discovering pain mechanisms, uncovering treatment targets, and selecting patients.