Funded Projects

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.

Pain in muscles and surrounding connective tissue (myofascial pain) is a significant health concern affecting hundreds of millions of Americans. There is no objective way to identify and measure myofascial pain. This project will address this unmet challenge by developing a robust approach to identify imaging biomarker(s) that can distinguish different states of myofascial pain. The research will then examine the ability of identified biomarker(s) to predict patient responses to a myofascial pain treatment in a randomized controlled clinical trial.

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.

Sickle cell disease is an inherited blood disorder affecting about 100,000 Americans and more than 20 million people worldwide. It is caused by a mutation in the gene for beta-globin that results in the characteristic sickled shape of red blood cells, life-long severe pain, and shortened lifespan. Optimal treatment of  chronic pain from the condition targets psychological factors contributing to pain, such as pain-related anxiety, fear of movement, and depression. This project will interact with patients and their families to revise and test an existing mind–body and behavioral health treatment tool to target the unique needs and preferences of people managing chronic sickle cell disease pain.

More than half of children and adolescents diagnosed with a type of cancer called bone sarcoma experience pain that interferes with daily life. This project will adapt an evidence-based cognitive behavioral therapy mobile app for use with Black and Hispanic adolescents who disproportionately experience pain from this cancer, putting them at risk for opioid misuse. Once fully adapted, this therapy will be paired with a remotely delivered brain stimulation treatment (transcranial direct current stimulation). This research will also examine the impact of patient-reported conditions such as depression, anxiety, and sleep problems, as well as of various social determinants of health, on pain.

Clinician bias causes inequities in healthcare, and interventions are needed to mitigate and eradicate this bias. This project aims to develop and test the impact of two interventions on overcoming clinician implicit bias in the management of pain for children from ethnic minorities treated in the emergency department. The study will include pediatric patients from under-represented minority groups with pain from long-bone fractures or acute appendicitis who are cared for by racially and ethnically diverse caregivers. Researchers will use stakeholder-informed approaches to establish quality of care metrics and then use clinician audit and feedback as well as data from electronic health records to quantify evidence of bias.      

Both pain experience and treatment response are determined by a variety of factors, including race and ethnicity. Inequities in access to healthcare and pain treatment affect patients from minority populations, such as Hispanic/Latino populations of all age groups. This study will develop and test an online intervention—Web-based Tailored Intervention for Preparation of Parents and Children for Outpatient Surgery (L-WebTIPS)—tailored for Latino families of children having outpatient surgeries. The intervention aims to lower child and family anxiety before surgery as well as to reduce post-surgical pain by enhancing parent self-efficacy and behavioral pain coping strategies. After an exploratory phase to assess usability and acceptability of the intervention, the study will evaluate the impact of L-WebTIPS on child pre-surgery anxiety and post-surgery pain as well assess other child and parent outcomes.

Taxanes are among the most effective chemotherapeutic agents and are frequently used in the treatment of early stage and metastatic breast cancer. However, they are known to produce a pain condition known as Chemotherapy-Induced Peripheral Neuropathic Pain (CIPNP). CIPNP is one of the primary reasons a patient receives a limited dose of taxane. No diagnostic tool exists to identify patients that will develop CIPNP in response to taxane therapy. Biomarker signatures associated with taxane-induced neuropathic pain will be developed to: 1) identify patients at risk for developing debilitating taxane neuropathic pain before chemotherapy is initiated; and 2) to identify patients already on treatment who are at risk of developing neuropathic pain and need dosing adjustments to prevent CIPNP symptoms. This biomarker signature will be used to detect CIPNP-susceptible patients early and personalize their taxane therapy to minimize CIPNP while optimizing the therapeutic taxane dosing.

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.

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.

Up to 5 percent of adolescents suffer from high-impact chronic musculoskeletal (MSK) pain, and only about 50 percent with chronic MSK pain who present for treatment recover. Current treatments for chronic MSK pain are suboptimal and have been tied to the opioid crisis. Discovery of robust markers of the recovery versus persistence of pain and disability is essential to develop more resourceful and patient-specific treatment strategies, requiring measurements across multiple dimensions in the same patient cohort in combination with a suitable computational analysis pipeline. Preliminary data has implicated novel candidates for neuroimaging, immune, quantitative sensory, and psychological markers for discovery. In addition, a standardized specimen collection, processing, storage, and distribution system is in place, along with expertise in machine learning approaches to extract reliable and prognostic bio-signatures from a large and complex data set. This project will facilitate risk stratification and a resourceful selection of patients who are likely to respond to current multidisciplinary pain treatment approaches.

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.