Funded Projects

Chronic low back pain affects millions of people in the United States, resulting in high medical costs and lost productivity. New opioid-free treatments are needed to help people with chronic low back pain, but not all people respond equally to a given approach. IMPACT aims to use machine learning to analyze data such as physical activity, sleep, emotions, and pain levels and identify markers that can help predict how effective a mindfulness-based treatment approach (Mindfulness-Based Stress Reduction) can be for people with chronic low back pain.

There are currently no reliable tools to measure pain objectively, and health care providers must rely on patient’s subjective reports and observations of patient behavior to determine the level of pain a person experiences. This hampers both effective pain management and the development of new pain medications. This project will assess an imaging technology called Radiocaine that in animal studies has been able to identify the origin of pain as well as its intensity. The goal is to use Radiocaine in clinical trials for pain treatments, thereby enhancing treatment effectiveness and facilitating development of new treatments.

Chronic pancreatitis is a painful condition often caused by long-term alcohol use, and patients often require treatment with strong pain medications, including opioids. Therefore, alternative treatments for chronic pancreatitis are needed. This project will use machine learning approaches to create a prediction tool based on electroencephalography analyses, sensory tests, and psychological questionnaires that can help determine which patients with chronic pancreatitis will benefit most from a specific type of treatment called endoscopic therapy.

Pain in the muscles and surrounding connective tissue (myofascial pain) is a significant health concern affecting hundreds of millions of Americans.  Myofascial pain is primarily diagnosed by asking people about their amount of pain as well as through a physical examination. Both approaches are imprecise ways to diagnose the specific type of pain a patient is experiencing and what is causing it. This project aims to improve myofascial pain management and treatment by developing ways to measure changes to soft tissues (e.g., muscle, connective tissues, nerves, blood vessels) in people with myofascial pain compared with soft tissues in people who are not in pain. The project will develop an imaging biomarker that can distinguish healthy and diseased soft tissues that may contribute to myofascial pain syndrome. The project will then test the ability of these biomarkers to predict patient outcomes in a randomized controlled clinical trial.

Pain in the muscles and surrounding connective tissue (myofascial pain) is a significant and poorly understood health concern affecting hundreds of millions of Americans. There is a great need for tools to assess changes to myofascial tissues in individuals with chronic pain as well as to measure the effect of commonly used therapies. This project will use three imaging tools to look at differences between shoulder tissue in people with myofascial pain compared to those without pain. Using a machine learning approach, this research aims to develop a biomarker signature for myofascial pain, which will be evaluated in a randomized controlled clinical trial based on its ability to predict patient responses to myofascial pain treatments.

Shoulder pain occurs in many patients who are recovering from a stroke. In addition to impairments in the ability to move, persistent shoulder pain contributes to depression, and often reduces quality of life. Although the cause of post-stroke shoulder pain is complex and not completely understood, it is thought to arise in part to damage of muscles and surrounding connective tissues (myofascial tissues) in the shoulder. This project will use advanced medical imaging techniques to create biomarkers of that can reliably identify myofascial tissues. The research will then test the ability of these biomarkers to monitor, and ultimately predict treatment responses in patients with post-stroke shoulder pain in the context of a randomized controlled clinical trial.

Multidisciplinary chronic pain treatments show incomplete recovery at the population level because of significant heterogeneity on the individual level in the high impact chronic pain population. Subgroups of individuals either completely respond, do not change, or even worsen following pain management. Therefore, diagnostic biomarker signatures are needed to differentiate high impact chronic pain from low impact chronic pain. This study aims to develop prognostic biomarkers to predict the disease trajectory for individuals with musculoskeletal high-impact chronic pain. These biomarker signatures will integrate central nervous system (CNS), multi-?omic?, sensory, functional, psychosocial, and demographic domains into detection algorithms. Biomarker signatures from the proposed research are intended to facilitate risk and treatment stratification for clinical trial design and to facilitate treatment decisions in clinical practice for patients with musculoskeletal chronic pain.

Chronic pain is a major health burden associated with immense economic and social costs. Predictive biomarkers that can identify individuals at risk of developing severe and persistent pain, which is associated with worse disability and greater reliance on opioids, would promote aggressive, early intervention that could halt the transition to chronic pain. The applicant’s team uncovered evidence of a unique cortical biomarker signature that predicts pain susceptibility (severity and duration). This biomarker signature could be capable of predicting the severity of pain experienced by an individual minutes to months in the future, as well as the duration of pain (time to recovery). Analytical validation of this biomarker will be conducted in healthy participants using a standardized model of the transition to sustained myofascial temporomandibular pain. Specifically the biomarker signature will be tested for its ability to predict an individual’s pain sensitivity, pain severity, and pain duration and will perform initial clinical validation.

Debilitating neuropathic pain occurs in 40 percent to 70 percent of people who suffer from spinal cord injury (SCI). There are no distinguishing characteristics to identify who will develop neuropathic pain. The objective of this research is to develop a biomarker signature prognostic of SCI-induced neuropathic pain (NP). The aims of the project are to (1) identify autoantibodies in plasma samples from acute SCI patients to CNS autoantigens and determine the relationship between autoantibodies levels to the development of NP, (2) identify the autoantibody combination with maximal prognostic accuracy for the development of NP at six months after SCI, and (3) develop and optimize an assay to simultaneously measure several autoantibodies and independently validate the prognostic efficacy for NP using plasma samples collected prospectively. Establishing a panel will refine the prognostic value of these autoantibodies as biomarkers to detect who are vulnerable to NP and may be used to for development of nonaddictive pain therapeutics.

There is currently no recognized way of accurately predicting who will recover from post-traumatic headache (PTH) during the acute phase following concussion and who will go on to develop persistent post-traumatic headache (PPTH), a condition that is difficult to treat effectively. Clinical experience suggests that early treatment is most effective, before headache patterns become persistent, but treating all patients with PTH would expose some patients to unnecessary treatment. Clinicians lack the information needed to make informed treatment decisions. Therefore, the study goals are to develop a prognostic biomarker signature for PPTH using clinical data and structural and functional brain neuroimaging and to assess the predictive accuracy of an ensemble biomarker signature for the early identification of patients at high risk for PPTH. This study can be translated into clinical practice and integrated into PTH clinical trials for early identification of those individuals who are at high risk for PPTH.

Neuropathic corneal pain (NCP) causes patients to have severe discomfort and a compromised quality of life (QoL). The lack of signs observed by standard examination has resulted in misdiagnosis as dry eye disease (DED). An optical biopsy using laser in vivo confocal microscopy (IVCM) revealed that microneuromas (bulbs at the ends of severed nerves caused by buildup of molecular constituents) are present in NCP but not DED and may serve as a biomarker for NCP. The aims are to (1) use a database of more than 2,000 DED/NCP subjects and more than 500,000 IVCM images to confirm that the presence of microneuromas is an appropriate biomarker for NCP, (2) provide biological validation of microneuromas, (3) develop a validated artificial intelligence (AI) program for automated identification of microneuromas, and (4) establish the clinical utility of microneuromas observed by IVCM as a biomarker for NCP in a prospective, multicenter study.