Funded Projects

Despite the significance of spine disorders, there are few reliable methods to determine appropriate patient care and evaluate intervention effectiveness. The research and tool development take the critical next step in the clinical translation of faster, quantitative magnetic resonance imaging (MR) of patients with lower back pain. The multidisciplinary Technology Research Site (Tech Site) of BACPAC will develop Phase IV (i.e., technology optimization) technologies and/or methods (TTMs) to leverage two key technical advancements: development of machine learning-based, faster MR acquisition methods and machine learning for image segmentation and extraction of objective disease related features from images. The team will develop, validate, and deploy end-to-end deep learning-based technologies (TTMs) for accelerated image reconstruction, tissue segmentation, and detection of spinal degeneration to facilitate automated, robust assessment of structure-function relationships between spine characteristics, neurocognitive pain response, and patient-reported outcomes.

There is an urgent need for new non-opioid therapeutic agents that treat the pain associated with Osteoarthritis (OA) ? a chronic, progressive disease that leads to pain in weightbearing joints, pain during movement, and pain at rest. This project will refine techniques for targeting several proteins expressed in sensory neurons associated with OA pain, with the goal of testing the potential of these proteins to serve as targets for development of effective, non-opioid painkillers.

 Quell Therapeutics uses the Optopatch platform for making all-optical electrophysiology measurements in neurons at a throughput sufficient for phenotypic screening. Using engineered optogenetic proteins, blue and red light can be used to stimulate and record neuronal activity, respectively. Custom microscopes enable electrophysiology recordings from 100’s of individual neurons in parallel with high sensitivity and temporal resolution, a capability currently not available with any other platform screening technology. Here, researchers combine the Optopatch platform with an in vitro model of chronic pain, where dorsal root ganglion (DRG) sensory neurons are bathed in a mixture of inflammatory mediators found in the joints of osteoarthritis patients. The neurons treated with the inflammatory mixture become hyperexcitable, mimicking the anticipated cellular pain response. Investigators calculate the functional phenotype of arthritis pain, which captures the difference in action potential shape and firing rate in response to diverse stimuli. The team will screen for small molecule compounds that reverse the pain phenotype while minimizing perturbation of neuronal behavior orthogonal to the pain phenotype, the in vitro “side effects.” The highest ranking compounds will be chemically optimized and their pharmacokinetic, drug metabolism, and in vivo efficacy will be characterized. The goal is to advance therapeutic discovery for pain, which may ultimately help relieve the US opioid crisis.

Fibromyalgia (FM) is a chronic pain condition characterized by widespread musculoskeletal pain, tenderness, and stiffness associated with fatigue and sleep disturbance. The investigators have recently completed a trial that demonstrated efficacy of active transcutaneous electrical nerve stimulation (TENS) compared with placebo TENS or no treatment in women with FM. While physical therapists are trained in using TENS, it is underused in clinical practice. This application proposes a pragmatic clinical trial of TENS for patients with FM to determine if the addition of TENS to physical therapy (PT) reduces pain, increases PT adherence, and helps achieve functional goals with less drug use. This study will address the critical need for strategies to implement effective nonpharmacologic treatments for FM. Successful completion of this trial will provide generalizable effectiveness data for referring providers, physical therapists, and insurers and will inform future pragmatic trials of nonpharmacologic treatments conducted in PT practices.

The University of Pittsburgh Low Back Pain: Biological, Biomechanical, and Behavioral Phenotypes (LB3P) Mechanistic Research Center (MRC) will to perform in-depth phenotyping of patients with chronic low back pain (cLBP), using a multimodal approach to characterize patients and provide insight into the phenotypes associated with experience of cLBP to direct targeted and improved treatments. The LB3P MRC will be formed of three Research Cores, three support cores, and one research project. This approach will leverage and integrate distinctive resources at the University of Pittsburgh laboratories to deliver quantified biomechanical, biological, and behavioral characteristics; functional assessments; and patient-reported outcomes, coupled with advanced data analytics using a novel Network Phenotyping Strategy (NPS). By eliminating isolated and disconnected approaches to treatment and focusing on personalized patient-centric approaches, this approach will yield improved outcomes and patient satisfaction.

The chronic low back pain (cLBP) subgroup with comorbid depression or anxiety disorders, known as high negative affect (NA), needs better non-opioid, comprehensive pain treatment options. Data shows that the combination of antidepressants (AD) and fear avoidance physical therapy is more efficacious at improving pain, function, depression, and anxiety in cLBP patients with high NA than each treatment alone or a control condition. Research also finds that an enhanced fear avoidance rehabilitation protocol (EFAR; fear avoidance-based physical therapy, pain education, and motivational messaging) further improves outcomes. To address the unmet needs of cLBP patients with high NA, this study will test in a randomized trial whether the combination of AD and EFAR is more effective than each treatment alone at relieving pain, improving function, combating depression, and preventing opioid misuse. This multimodal combination approach of pharmacotherapy and behavioral therapy is novel to the field and has the potential to shift current treatment paradigms.

Therapeutic virtual reality (VR) has emerged as a promising and evidence-based treatment modality for musculoskeletal pain, including chronic low back pain (cLBP). Users of VR wear a pair of goggles with a close-proximity stereoscopic screen that creates a sensation of being transported into lifelike, three-dimensional worlds. By stimulating the visual cortex while engaging other senses, VR modulates the user’s processing of nociceptive stimuli. Functional magnetic resonance imaging (fMRI) of the brain reveals that VR has similar effects on the sensory and insular cortex as opioids, and head-to-head trials show that VR achieves similar or greater analgesia as hydromorphone. Since there are few data regarding long-term efficacy and safety of VR in cLBP, this study will measure patient-reported outcomes, biometric outcomes, and opioid use in nonspecific cLBP patients under various experimental conditions using VR therapy.

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.

Physical therapy is the recommended treatment for patients with low back pain and is a cost-effective method for improving pain and reducing disability. However, only 7-13% of patients receive physical therapy services. Access is particularly limited in rural communities due to lack of provider availability, transportation, and missed work time. These factors have contributed to more low back pain-related disability and opioid use among rural populations. Physical therapy delivered through telemedicine may improve access by reducing patient-reported barriers. This randomized clinical trial will compare an innovative, patient-centered telemedicine version of physical therapy to a currently used psychologically based educational approach for rural patients with chronic low back pain. The research will match individual patients to a treatment approach based on their psychosocial risk of poor outcomes.

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.

Current diagnostics and treatments of chronic low back pain (cLBP) rely primarily on subjective metrics and do not target all the multidimensional biopsychosocial mechanisms. This multidisciplinary effort will develop and validate a digital health platform and provide meaningful data-driven metrics that enable an integrated approach to clinical evaluation and treatment of cLBP. This platform will facilitate the use of quantitative spinal motion metrics (function), patient-reported outcomes, and patient preference information to enable deep patient phenotyping and inform clinical decision making on personalized treatments in order to improve outcomes. This effort will involve software and hardware development to enable data collection, analysis, and visualization in clinical settings. The outcome of this project will be a digital health platform with data to support regulatory submission for clinical use. At the end of this effort, the researchers will have a validated tool for integration in clinical research studies supported by the BACPAC Consortium.

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.

The NIH Back Pain Consortium (BACPAC) Research Program brings together leading centers with expertise in studying and treating chronic low back pain to advance understanding of the mechanisms that underlie the condition and to identify novel treatment strategies. BACPAC is undertaking a multisite precision medicine clinical trial taking into account patient-specific information to understand which patients with chronic low back pain respond best to various nonopioid, evidence-based treatments. The trial seeks to enroll a racially, ethnically, and socioeconomically diverse patient population to ensure that the results are applicable to all Americans with chronic low back pain. This project aims to develop comprehensive recruitment and retention plans for study sites that can recruit from historically underrepresented populations in clinical research (e.g., Black and Hispanic populations) and to provide dedicated financial resources to engage patients from these populations using tailored, culturally appropriate strategies.