Faculty Bibliography

Chronic pain is characterized by discrete pain episodes of unpredictable frequency and duration. This hinders the study of pain mechanisms and contributes to the use of pharmacological treatments associated with side effects, addiction and drug tolerance. Here, we show that a closed-loop brain-machine interface (BMI) can modulate sensory-affective experiences in real time in freely behaving rats by coupling neural codes for nociception directly with therapeutic cortical stimulation. The BMI decodes the onset of nociception via a state-space model on the basis of the analysis of online-sorted spikes recorded from the anterior cingulate cortex (which is critical for pain processing) and couples real-time pain detection with optogenetic activation of the prelimbic prefrontal cortex (which exerts top-down nociceptive regulation). In rats, the BMI effectively inhibited sensory and affective behaviours caused by acute mechanical or thermal pain, and by chronic inflammatory or neuropathic pain. The approach provides a blueprint for demand-based neuromodulation to treat sensory-affective disorders, and could be further leveraged for nociceptive control and to study pain mechanisms.

Pain is driven by sensation and emotion, and in turn, it motivates decisions and actions. To fully appreciate the multidimensional nature of pain, we formulate the study of pain within a closed-loop framework of sensory-motor prediction. In this closed-loop cycle, prediction plays an important role, as the interaction between prediction and actual sensory experience shapes pain perception and subsequently, action. In this Perspective, we describe the roles of two prominent computational theories-Bayesian inference and reinforcement learning-in modeling adaptive pain behaviors. We show that prediction serves as a common theme between these two theories, and that each of these theories can explain unique aspects of the pain perception-action cycle. We discuss how these computational theories and models can improve our mechanistic understandings of pain-centered processes such as anticipation, attention, placebo hypoalgesia, and pain chronification.

Pain is known to have sensory and affective components. The sensory pain component is encoded by neurons in the primary somatosensory cortex (S1), whereas the emotional or affective pain experience is in large part processed by neural activities in the anterior cingulate cortex (ACC). The timing of how a mechanical or thermal noxious stimulus triggers activation of peripheral pain fibers is well-known. However, the temporal processing of nociceptive inputs in the cortex remains little studied. Here, we took two approaches to examine how nociceptive inputs are processed by the S1 and ACC. We simultaneously recorded local field potentials in both regions, during the application of a brain-computer interface (BCI). First, we compared event related potentials in the S1 and ACC. Next, we used an algorithmic pain decoder enabled by machine-learning to detect the onset of pain which was used during the implementation of the BCI to automatically treat pain. We found that whereas mechanical pain triggered neural activity changes first in the S1, the S1 and ACC processed thermal pain with a reasonably similar time course. These results indicate that the temporal processing of nociceptive information in different regions of the cortex is likely important for the overall pain experience.

Background and Objectives: Perioperative ketamine has been shown to reduce opioid consumption and pain after surgery. Ketamine is most often given as an infusion, but an alternative is single-dose ketamine. Single-dose ketamine at up to 1 mg/kg has been shown to reduce symptoms of depression, and a wide range of dosages has been used for pain in the emergency department. However, limited data exists on the tolerability and efficacy of a single-dose of ketamine at 0.6 mg/kg for pain when administered immediately after surgery. We conducted a pilot study of single-dose ketamine in patients undergoing mastectomy with reconstruction, hypothesizing that a single-dose of ketamine is well tolerated and can relieve postoperative pain and improve mood and recovery. Methods: This is a randomized, single-blind, placebo-controlled, two-arm parallel, single-center study. Thirty adult women undergoing mastectomy with reconstruction for oncologic indication received a single-dose of ketamine (0.6mg/kg) or placebo after surgery in the post-anesthesia care unit (PACU). Patients were followed through postoperative day (POD) 7. The primary outcome was postoperative pain measured by the Brief Pain Inventory (BPI) pain subscale on POD 1 and 2. Secondary outcomes include effects on opioid use, PROMIS fatigue and sleep, mood, Quality of Recovery-15, and the Breast Cancer Pain Questionnaire. Results: Side effects were minor and not significantly different in frequency between groups. The ketamine group reported lower scores on the BPI pain severity subscale, especially at POD 7; however, the difference was not statistically significant. There were no statistically significant differences between ketamine and placebo groups for the secondary outcomes. Conclusion: A single-dose of ketamine at 0.6mg/kg administered postoperatively in the PACU is well tolerated in women undergoing mastectomy and may confer better pain control up to one week after surgery. Future studies with larger sample sizes are necessary to adequately characterize the effect of postoperative single-dose ketamine on pain control in this population.

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

INTRODUCTION/UNASSIGNED:Chronic pain negatively impacts a range of sensory and affective behaviors. Previous studies have shown that the presence of chronic pain not only causes hypersensitivity at the site of injury but may also be associated with pain-aversive experiences at anatomically unrelated sites. While animal studies have indicated that the cingulate and prefrontal cortices are involved in this generalized hyperalgesia, the mechanisms distinguishing increased sensitivity at the site of injury from a generalized site-nonspecific enhancement in the aversive response to nociceptive inputs are not well known. METHODS/UNASSIGNED: = 15) by analyzing behavioral and electroencephalographic (EEG) data. RESULTS/UNASSIGNED:As expected, participants with chronic pain endorsed enhanced pain with mechanical stimuli in both back and hand. We further analyzed electroencephalographic (EEG) recordings during these evoked pain episodes. Brain oscillations in theta and alpha bands in the medial orbitofrontal cortex (mOFC) were associated with localized hypersensitivity, while increased gamma oscillations in the anterior cingulate cortex (ACC) and increased theta oscillations in the dorsolateral prefrontal cortex (dlPFC) were associated with generalized hyperalgesia. DISCUSSION/UNASSIGNED:These findings indicate that chronic pain may disrupt multiple cortical circuits to impact nociceptive processing.

Pain associated with breast cancer is a prevalent problem that negatively affects quality of life. Breast cancer pain is not limited to the disease course itself but is also induced by current therapeutic strategies. This, combined with the increasing number of patients living with breast cancer, make pain management for breast cancer patients an increasingly important area of research. This narrative review presents a summary of pain associated with breast cancer, including pain related to the cancer disease process itself and pain associated with current therapeutic modalities including radiation, chemotherapy, immunotherapy, and surgery. Current pain management techniques, their limitations, and novel analgesic strategies are also discussed.

PURPOSE/UNASSIGNED:The COVID-19 pandemic led to a drastic expansion in utilizing telemedicine, circumventing some of the geographical barriers to accessing pain care. However, uncertainties around the impact of telemedicine across various sociodemographic groups still exist, prompting further exploration. Therefore, this study aimed to evaluate the impact of sociodemographic factors in telemedicine utilization during and after the COVID-19 pandemic. PATIENTS AND METHODS/UNASSIGNED:All outpatient non-procedural visits at the pain medicine division of a large academic institution in the epicenter of the pandemic (New York, USA), between March 2019 and October 2021, were retrospectively included. Sociodemographic data including gender, age, ethnicity/race, postal code, and type of health insurance, across three time periods associated with the COVID-19 pandemic - pre-lockdown (in-office visits only), lockdown (telemedicine visits only) and post-lockdown (offering both in-office and telemedicine visits) - were analyzed and compared. RESULTS/UNASSIGNED:In total, 12,615 unique patients - The majority being women (58%) - were seen during the whole study period. In the post-lockdown period, telemedicine was utilized by 42% of all patients. Follow-up visits, younger patients, white patients, patients residing further away from the hospital, and privately insured patients were more likely to utilize telemedicine post-lockdown (p <0.05). Older patients, minorities, Manhattan residents, and Medicare/Medicaid recipients, were more likely to use in-office visits post-lockdown (p <0.05). CONCLUSION/UNASSIGNED:We identified disparities in the utilization of telemedicine in Pain Medicine, which may be due to socioeconomic factors such as lack of access to reliable internet access, cost of devices, and technological know-how. This emphasizes the need for further studies to better understand the reasons for and barriers to telemedicine use. This could help inform policymaking to safeguard equitable access to telemedicine use for pain care.

Effective treatments for chronic pain remain limited. Conceptually, a closed-loop neural interface combining sensory signal detection with therapeutic delivery could produce timely and effective pain relief. Such systems are challenging to develop because of difficulties in accurate pain detection and ultrafast analgesic delivery. Pain has sensory and affective components, encoded in large part by neural activities in the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC), respectively. Meanwhile, studies show that stimulation of the prefrontal cortex (PFC) produces descending pain control. Here, we designed and tested a brain-machine interface (BMI) combining an automated pain detection arm, based on simultaneously recorded local field potential (LFP) signals from the S1 and ACC, with a treatment arm, based on optogenetic activation or electrical deep brain stimulation (DBS) of the PFC in freely behaving rats. Our multiregion neural interface accurately detected and treated acute evoked pain and chronic pain. This neural interface is activated rapidly, and its efficacy remained stable over time. Given the clinical feasibility of LFP recordings and DBS, our findings suggest that BMI is a promising approach for pain treatment.

Misfolding and aggregation of disease-specific proteins, resulting in the formation of filamentous cellular inclusions, is a hallmark of neurodegenerative disease with characteristic filament structures, or conformers, defining each proteinopathy. Here we show that a previously unsolved amyloid fibril composed of a 135 amino acid C-terminal fragment of TMEM106B is a common finding in distinct human neurodegenerative diseases, including cases characterized by abnormal aggregation of TDP-43, tau, or α-synuclein protein. A combination of cryoelectron microscopy and mass spectrometry was used to solve the structures of TMEM106B fibrils at a resolution of 2.7 Å from postmortem human brain tissue afflicted with frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP, n = 8), progressive supranuclear palsy (PSP, n = 2), or dementia with Lewy bodies (DLB, n = 1). The commonality of abundant amyloid fibrils composed of TMEM106B, a lysosomal/endosomal protein, to a broad range of debilitating human disorders indicates a shared fibrillization pathway that may initiate or accelerate neurodegeneration.