Benedict Alter, MD, PhD
Systems Neuroscience of Pain and Pain Relief
Dr. Alter is Assistant Professor and Director of Translational Pain Research in the Division of Pain Medicine. His research explores how the brain modulates pain and how endogenous pain dampening systems can be systematically leveraged for novel pain therapies. Specific projects elucidate basic research questions on the systems neuroscience level, translational questions bringing basic knowledge towards novel treatments, and clinical questions aimed at understanding how pain modulatory systems change in chronic pain.
Examining temporal aspects of endogenous pain modulation to reduce pain and pain-related disability
Top-down modulation of pain, termed “endogenous pain modulation,” robustly inhibits or facilitates incoming nociceptive input at the first sensory synapse. However, the relevance of endogenous pain modulation in chronic musculoskeletal pain remains unclear, due to an incomplete characterization of pain modulation in patients. Endogenous pain modulation has historically been measured with conditioned pain modulation, reflecting body-wide spatial modulation, ignoring time-dependent changes in pain modulation. To address this gap, Dr. Alter studied “offset analgesia,” a previously established psychophysical paradigm reflecting temporal pain inhibition, and developed a novel paradigm, “onset hyperalgesia,” reflecting an opposing pain facilitation. He found that both offset analgesia and onset hyperalgesia, measured at nonpainful body sites and therefore reflecting central pain modulation, were altered in knee osteoarthritis. Dr. Alter also found that at a given pain intensity, patients with deficits in offset analgesia walked considerably less than those with robust offset analgesia, linking temporal pain inhibition to physical function. This finding suggests that pain modulatory systems may be targeted with the therapeutic goal of improving physical function, a conceptually novel approach which may reduce the functional impact of chronic musculoskeletal pain. Ongoing work examines the dynamic interplay between brain mechanisms of temporal pain modulation and pain-limited physical function using neuroimaging, sensory testing, comprehensive chronic pain assessment, and physical function testing (NIH R01AR086796).
Identifying the neural mechanisms of chronic musculoskeletal pain in patients using noninvasive brain imaging and brain stimulation
Brain circuits contribute to animal models of chronic musculoskeletal pain, but identification of these mechanisms in patients has been difficult due to limitations in current approaches. Experimental designs rely on applied noxious stimuli, fundamentally different from pain associated with joint loading. Brain regions implicated with fMRI are indirectly correlated with musculoskeletal pain, since the MRI environment does not allow movement or weight-bearing across painful joints. Analysis approaches often do not allow participant-level variation in hemodynamic responses and restrain temporal dynamics of pain variables. During Dr. Alter's training and subsequently in his lab, he addressed limitations in designs, techniques, and analysis to better understand brain mechanisms of chronic musculoskeletal pain. Ongoing work leverages naturalistic experimental designs combined with ambulatory brain imaging (functional near-infrared spectroscopy, fNIRS), including evoking weight-bearing pain with walking or stair climbing to identify the cortical correlates of knee osteoarthritis (NIH K23NS123429, R01AR086796). Funded by competitive Pitt CTSI grants, Dr. Pulkit Grover (CMU) and Dr. Alter applied novel pulsed transcranial electrical stimulation in patients with fibromyalgia to characterize pain inhibitory pathways in motor cortex, and allowing for experimental manipulation of brain circuits. In collaboration with Dr. Irina Strigo (UCSF) and Dr. Ted Huppert (Stony Brook University), they are applying fMRI and fNIRS approaches to rigorously identify brain systems involved in pain and pain prediction with implications for pain perception.
Advancing chronic pain clinical trials and chronic pain clinical care through feasible pain phenotyping
Recently, endophenotypes have been identified in chronic pain conditions. However, most pain phenotyping has involved complicated protocols that are difficult to incorporate in clinical trials or clinical care. Using informatic techniques and the Patient Outcomes Repository for Treatment (PORT) at the University of Pittsburgh University of Pittsburgh and in collaboration with Dr. Ajay Wasan, Dr. Alter identified ways to phenotype patients that are clinically relevant. Focusing on the pain body map, they found subgroups of patients using hierarchical clustering that impacted pain outcomes and were associated with pain diagnosis. They have also adapted sensory testing protocols to bedside approaches, successfully implementing these in neuropathic pain. Both approaches hold promise in identifying endophenotypes that impact patient outcomes. For example, they found that algorithmic body map clustering identified some patients likely to have fibromyalgia who lacked a clinical diagnosis on chart review. Ongoing work validates these findings with prospective data collection. Additionally, they are applying bedside sensory testing protocols to determine whether nociplastic phenotypes can be identified early in clinical care, potentially impacting future treatment outcomes.
Developing remote digital interventions to improvement pain management access and outcomes
Despite the evidence supporting multidisciplinary pain management, many patients are unable to access coordinated treatments, such as psychological treatments for pain or interventional care. Dr. Alter's team improved patient access by identifying best use cases for telehealth, including pre-procedural evaluations in anticipation of interventions such as lumbar epidural steroid injections. Using a learning health care model, Dr. Alter found that procedural outcomes were excellent even when patients were initially evaluated remotely using telehealth. He has also been involved in digital behavioral health intervention development and assessment, using patient-reported outcomes to track patient-centered outcomes. Ongoing projects seek to understand biopsychosocial factors that optimize digital interventions, as well as the remote measurement of pain and pain-relevant, physiologic measures using wearables.