Faculty Bibliography

As pain consists of both sensory and affective components, its management by pharmaceutical agents remains difficult. Alternative forms of neuromodulation, such as electrical stimulation, have been studied in recent years as potential pain treatment options. Although electrical stimulation of the brain has shown promise, more research into stimulation frequency and targets is required to support its clinical applications. Here, we studied the effect that stimulation frequency has on pain modulation in the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC) in acute pain models in rats. We found that low-frequency stimulation in the prelimbic region of the PFC (PL-PFC) provides reduction of sensory and affective pain components. Meanwhile, high-frequency stimulation of the ACC, a region involved in processing pain affect, reduces pain aversive behaviors. Our results demonstrate that frequency-dependent neuromodulation of the PFC or ACC has the potential for pain modulation.

Chronic pain alters cortical and subcortical plasticity, causing enhanced sensory and affective responses to peripheral nociceptive inputs. Previous studies have shown that ketamine had the potential to inhibit abnormally amplified affective responses of single neurons by suppressing hyperactivity in the anterior cingulate cortex (ACC). However, the mechanism of this enduring effect has yet to be understood at the network level. In this study, we recorded local field potentials from the ACC of freely moving rats. Animals were injected with complete Freund's adjuvant (CFA) to induce persistent inflammatory pain. Mechanical stimulations were administered to the hind paw before and after CFA administration. We found a significant increase in the high-gamma band (60-100 Hz) power in response to evoked pain after CFA treatment. Ketamine, however, reduced the high-gamma band power in response to evoked pain in CFA-treated rats. In addition, ketamine had a sustained effect on the high-gamma band power lasting up to five days after a single dose administration. These results demonstrate that ketamine has the potential to alter maladaptive neural responses in the ACC induced by chronic pain.

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Pain is an integrated sensory and affective experience. Cortical mechanisms of sensory and affective integration, however, remain poorly defined. Here, we investigate the projection from the primary somatosensory cortex (S1), which encodes the sensory pain information, to the anterior cingulate cortex (ACC), a key area for processing pain affect, in freely behaving rats. By using a combination of optogenetics, in vivo electrophysiology, and machine learning analysis, we find that a subset of neurons in the ACC receives S1 inputs, and activation of the S1 axon terminals increases the response to noxious stimuli in ACC neurons. Chronic pain enhances this cortico-cortical connection, as manifested by an increased number of ACC neurons that respond to S1 inputs and the magnified contribution of these neurons to the nociceptive response in the ACC. Furthermore, modulation of this S1→ACC projection regulates aversive responses to pain. Our results thus define a cortical circuit that plays a potentially important role in integrating sensory and affective pain signals.

PURPOSE:To develop criteria to interpret mitochondrial transfer RNA (mt-tRNA) variants based on unique characteristics of mitochondrial genetics and conserved structural/functional properties of tRNA. METHODS:We developed rules on a set of established pathogenic/benign variants by examining heteroplasmy correlations with phenotype, tissue distribution, family members, and among unrelated families from published literature. We validated these deduced rules using our new cases and applied them to classify novel variants. RESULTS:Evaluation of previously reported pathogenic variants found that 80.6% had sufficient evidence to support phenotypic correlation with heteroplasmy levels among and within families. The remaining variants were downgraded due to the lack of similar evidence. Application of the verified criteria resulted in rescoring 80.8% of reported variants of uncertain significance (VUS) to benign and likely benign. Among 97 novel variants, none met pathogenic criteria. A large proportion of novel variants (84.5%) remained as VUS, while only 10.3% were likely pathogenic. Detection of these novel variants in additional individuals would facilitate their classification. CONCLUSION:Proper interpretation of mt-tRNA variants is crucial for accurate clinical diagnosis and genetic counseling. Correlations with tissue distribution, heteroplasmy levels, predicted perturbations to tRNA structure, and phenotypes provide important evidence for determining the clinical significance of mt-tRNA variants.

PURPOSE/OBJECTIVE:Biomarkers for disease specific survival (DSS) in early stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction. EXPERIMENTAL DESIGN/METHODS:The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM). A receiver operating characteristic (ROC) curve was generated based on vote aggregation of individual image sequences, an optimized cutoff was selected, and the computational model was tested on a third independent population of 51 patients from Geisinger Health Systems (GHS). RESULTS:Area under the curve (AUC) in the YSM patients was 0.905 (p<0.0001). AUC in the GHS patients was 0.880 (p<0.0001). Using the cutoff selected in the YSM cohort, the computational model predicted DSS in the GHS cohort based on Kaplan-Meier (KM) analysis (p<0.0001). CONCLUSIONS:The novel method presented is applicable to digital images, obviating the need for sample shipment and manipulation and representing a practical advance over current genetic and IHC-based methods.

OBJECTIVE:The primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) are two most important brain regions encoding the sensory-discriminative and affective-emotional aspects of pain, respectively. However, the functional connectivity of these two areas during cortical pain processing remains unclear. Developing methods to dissect the functional connectivity and directed information flow between cortical pain circuits can reveal insight into neural mechanisms of pain perception. APPROACH/METHODS:We recorded multichannel local field potentials (LFPs) from the S1 and ACC from freely behaving rats under various conditions of pain stimulus (thermal vs. mechanical) and pain state (naive vs. chronic pain). We applied Granger causality (GC) analysis to the LFP recordings and inferred frequency-dependent GC statistics and directed information flow. MAIN RESULTS/RESULTS:We found increased information flow during noxious pain stimulus presentation in both S1-->ACC and ACC-->S1 directions, especially at theta and gamma frequency bands. Similar results were found between thermal and mechanical pain stimuli. The chronic pain state shares common observations, but with elevated GC statistics especially in the gamma band. Furthermore, time-varying GC analysis revealed negative correlation between direction-specific and frequency-dependent GC and animal's paw withdrawal latency. In addition, we used computer simulations to investigate the impact of model mismatch, noise, missing variables, and common input on the conditional GC estimate. We also compared the GC results with the transfer entropy (TE) estimates. SIGNIFICANCE/CONCLUSIONS:Our results reveal functional connectivity and directed information flow between the S1 and ACC during various pain conditions. The time-varying GC analysis support the cortico-cortical information loop consistent with the computational predictive coding paradigm.

Pain is known to disrupt sleep patterns, and disturbances in sleep can further worsen pain symptoms. Sleep spindles occur during slow wave sleep and have established effects on sensory and affective processing in mammals. A number of chronic neuropsychiatric conditions, meanwhile, are known to alter sleep spindle density. The effect of persistent pain on sleep spindle waves, however, remains unknown, and studies of sleep spindles are challenging due to long period of monitoring and data analysis. Utilizing automated sleep spindle detection algorithms built on deep learning, we can monitor the effect of pain states on sleep spindle activity. In this study, we show that in a chronic pain model in rodents, there is a significant decrease in sleep spindle activity compared to controls. Meanwhile, methods to restore sleep spindles are associated with decreased pain symptoms. These results suggest that sleep spindle density correlates with chronic pain and may be both a potential biomarker for chronic pain and a target for neuromodulaton therapy.

OBJECTIVES/OBJECTIVE:To compare postoperative outcomes in patients prescribed long-acting opioids vs opioid-naïve patients who underwent elective noncardiac surgeries. DESIGN/METHODS:Retrospective cohort study. SETTING/METHODS:Single urban academic institution. METHODS AND SUBJECTS/METHODS:We retrospectively compared postoperative outcomes in long-acting opioid users vs opioid-naïve patients who underwent elective noncardiac surgeries. Inpatient and ambulatory surgery cohorts were separately analyzed. Preoperative medication lists were queried for the presence of long-acting opioids or absence of opioids. Multivariable logistic regression was performed to analyze the impact of long-acting opioid use on readmission rate, respiratory failure, and adverse cardiac events. Multivariable zero-truncated negative binomial regression was used to examine length of stay. RESULTS:After exclusions, there were 93,644 adult patients in the study population, 23,605 of whom underwent inpatient surgeries and 70,039 of whom underwent ambulatory surgeries. After adjusting for potential confounders and inpatient surgeries, preoperative long-acting opioid use was associated with increased risk of prolonged length of stay (incidence rate ratio = 1.1, 99% confidence interval [CI] = 1.0-1.2, P < 0.01) but not readmission. For ambulatory surgeries, preoperative long-acting opioid use was associated with increased risk of all-cause as well as pain-related readmission (odds ratio [OR] = 2.1, 99% CI = 1.5-2.9, P < 0.001; OR = 2.0, 99% CI = 0.85-4.2, P = 0.02, respectively). There were no significant differences for respiratory failure or adverse cardiac events. CONCLUSIONS:The use of preoperative long-acting opioids was associated with prolonged length of stay for inpatient surgeries and increased risk of all-cause and pain-related readmission for ambulatory surgeries. Timely interventions for patients on preoperative long-acting opioids may be needed to improve these outcomes.

Acute pain has an evolutionary role for the detection of and response to physical harm. In some cases, however, acute pain can impair function and lead to other morbidities. Chronic pain, meanwhile, can present as a psychopathological condition that significantly interferes with daily living. Most basic and translational pain research has focused on the molecular and cellular mechanisms in the spinal and peripheral nervous systems. In contrast, the brain plays a key role in the affective manifestation and cognitive control of pain. In particular, several cortical regions, such as the somatosensory cortex, prefrontal cortex, insular, and anterior cingulate cortex, are well-known to be activated by acute pain signals, and neurons in these regions have been demonstrated to undergo changes in response to chronic pain. Furthermore, these cortical regions can project to a number of forebrain and limbic structures to exert powerful top-down control of not only sensory pain transmission but also affective pain expression, and such cortical regulatory mechanisms are particularly relevant in chronic pain states. Newer techniques have emerged that allow detailed studies of central pain circuits in animal models, as well as how such circuits are modified by the presence of chronic pain and other predisposing psychosomatic factors. These mechanistic approaches can complement imaging in human studies. At the therapeutic level, a number of pharmacological and non-pharmacological interventions have recently been shown to engage these top-down control systems to provide analgesia. In this review, we will discuss how pain signals reach important cortical regions, and how these regions in turn project to sub-cortical areas of the brain to exert profound modulation of the pain experience. In addition, we will discuss the clinical relevance of such top-down pain regulation mechanisms.