Faculty Bibliography

Pain is a salient and complex sensory experience with important affective and cognitive dimensions. The current definition of pain relies on subjective reports in both humans and experimental animals. Such definition lacks basic mechanistic insights and can lead to a high degree of variability. Research on biomarkers for pain has previously focused on genetic analysis. However, recent advances in human neuroimaging and research in animal models have begun to show the promise of a circuit-based neural signature for pain. At the treatment level, pharmacological therapy for pain remains limited. Neuromodulation has emerged as a specific form of treatment without the systemic side effects of pharmacotherapies. In this review, we will discuss some of the current neuromodulatory modalities for pain, research on newer targets, as well as emerging possibility for an integrated brain-computer interface approach for pain management.

Ca2+-sensing receptor (CaSR) represents a potential therapeutic target for inflammatory bowel diseases and strongly prefers aromatic amino acid ligands. We investigated the regulatory effects of dietary supplementation with aromatic amino acids - tryptophan, phenylalanine and tyrosine (TPT) - on the CaSR signalling pathway and intestinal inflammatory response. The in vivo study was conducted with weanling piglets using a 2 × 2 factorial arrangement in a randomised complete block design. Piglets were fed a basal diet or a basal diet supplemented with TPT and with or without inflammatory challenge. The in vitro study was performed in porcine intestinal epithelial cell line to investigate the effects of TPT on inflammatory response using NPS-2143 to inhibit CaSR. Dietary supplementation of TPT alleviated histopathological injury and decreased myeloperoxidase activity in intestine challenged with lipopolysaccharide. Dietary supplementation of TPT decreased serum concentration of pro-inflammatory cytokines (IL-1β, IL-6, IL-8, IL-12, granulocyte-macrophage colony-stimulating factor, TNF-α), as well as the mRNA abundances of pro-inflammatory cytokines in intestine but enhanced anti-inflammatory cytokines IL-4 and transforming growth factor-β mRNA levels compared with pigs fed control diet and infected by lipopolysaccharide. Supplementation of TPT increased CaSR and phospholipase Cβ2 protein levels, but decreased inhibitor of NF-κB kinase α/β and inhibitor of NF-κB (IκB) protein levels in the lipopolysaccharide-challenged piglets. When the CaSR signalling pathway was blocked by NPS-2143, supplementation of TPT decreased the CaSR protein level, but enhanced phosphorylated NF-κB and IκB levels in IPEC-J2 cells. To conclude, supplementation of aromatic amino acids alleviated intestinal inflammation as mediated through the CaSR signalling pathway.

OBJECTIVE:In the context of the current opioid epidemic, there has been a renewed interest in the use of ketamine as an analgesic agent. METHODS:We have reviewed ketamine analgesia. RESULTS:Ketamine is well-known as an antagonist for N-methyl-D-aspartate receptors. In addition, it can regulate the function of opioid receptors and sodium channels. Ketamine also increases signaling through α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors. These myriad of molecular and cellular mechanisms are responsible for a number of pharmacological functions including pain relief and mood regulation. Clinically, a number of studies have investigated the role of ketamine in the setting of acute and chronic pain, and there is evidence that ketamine can provide analgesia in a variety of pain syndromes. DISCUSSION/CONCLUSIONS:In this review, we have examined basic mechanisms of ketamine and its current clinical use as well as potential novel use in pain management.

WHAT WE ALREADY KNOW ABOUT THIS TOPIC/UNASSIGNED:WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Hypotension is a risk factor for adverse perioperative outcomes. Machine learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine learning methods can provide prediction for the risk of postinduction hypotension METHODS:: Data was extracted from the electronic health record of a single quaternary care center from November 2015 to May 2016 for patients over age 12 that underwent general anesthesia, without procedure exclusions. Multiple supervised machine learning classification techniques were attempted, with postinduction hypotension (mean arterial pressure less than 55 mmHg within 10 min of induction by any measurement) as primary outcome, and preoperative medications, medical comorbidities, induction medications, and intraoperative vital signs as features. Discrimination was assessed using cross-validated area under the receiver operating characteristic curve. The best performing model was tuned and final performance assessed using split-set validation. RESULTS:Out of 13,323 cases, 1,185 (8.9%) experienced postinduction hypotension. Area under the receiver operating characteristic curve using logistic regression was 0.71 (95% CI, 0.70 to 0.72), support vector machines was 0.63 (95% CI, 0.58 to 0.60), naive Bayes was 0.69 (95% CI, 0.67 to 0.69), k-nearest neighbor was 0.64 (95% CI, 0.63 to 0.65), linear discriminant analysis was 0.72 (95% CI, 0.71 to 0.73), random forest was 0.74 (95% CI, 0.73 to 0.75), neural nets 0.71 (95% CI, 0.69 to 0.71), and gradient boosting machine 0.76 (95% CI, 0.75 to 0.77). Test set area for the gradient boosting machine was 0.74 (95% CI, 0.72 to 0.77). CONCLUSIONS:The success of this technique in predicting postinduction hypotension demonstrates feasibility of machine learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.

Chronic pain is known to induce an amplified aversive reaction to peripheral nociceptive inputs. This enhanced affective response constitutes a key pathologic feature of chronic pain syndromes such as fibromyalgia. However, the neural mechanisms that underlie this important aspect of pain processing remain poorly understood, hindering the development of treatments. Here, we show that a single dose of ketamine can produce a persistent reduction in the aversive response to noxious stimuli in rodent chronic pain models, long after the termination of its anti-nociceptive effects. Furthermore, we demonstrated that this anti-aversive property is mediated by prolonged suppression of the hyperactivity of neurons in the anterior cingulate cortex (ACC), a brain region well known to regulate pain affect. Therefore, our results indicate that it is feasible to dissociate the affective from the sensory component of pain, and demonstrate the potential for low-dose ketamine to be an important therapy for chronic pain syndromes.

PURPOSE/OBJECTIVE:Obstructive sleep apnea (OSA) is a risk factor for complications with postoperative opioid use, and in those patients with known or suspected OSA, minimization of postoperative opioids is recommended. We hypothesize that despite these recommendations, surgical patients with known or suspected OSA are prescribed postoperative opioids at hospital discharge at similar doses to those without OSA. METHODS:This was a retrospective analysis of the electronic health records of surgical patients from 1 November 2016 to 30 April 2017 at a single academic institution. Patients with a known diagnosis of OSA or a STOP-Bang score ≥ 5 were compared with those without OSA for the amount of postoperative discharge opioid medication using multivariable linear regression. RESULTS:Of the 17,671 patients analyzed, 1,692 (9.6%) had known or suspected OSA with 1,450 (86%) of these patients discharged on opioid medications. Of the 15,979 patients without OSA, 12,273 (77%) were discharged on opioid medications. The total median [interquartile range (IQR)] oral morphine equivalents (OME) for all patients was 150 [0-338] mg and for patients with known or suspected OSA was 160 [0-450] mg, an unadjusted comparison showing an 18% difference in OME (95% confidence interval [CI], 3% to 35%; P = 0.02). The analysis, after adjusting for confounders, showed no significant difference in the amount of opioids prescribed to OSA or non-OSA patients (8% difference in total OME; 95% CI, -6% to 25%; P = 0.26). CONCLUSION/CONCLUSIONS:This study shows that surgical patients at risk for OSA or confirmed OSA are prescribed opioids at similar rates and doses upon discharge despite guidelines that recommend minimizing opioid use in OSA patients. These findings indicate a need to implement different strategies to reduce the prescription of opioids to patients with OSA.

Pain is a complex sensory and affective experience. Through its anticipation, animals can learn to avoid pain. Much is known about passive avoidance during a painful event; however, less is known about active pain avoidance. The anterior cingulate cortex (ACC) is a critical hub for affective pain processing. However, there is currently no mechanism that links ACC activities at the cellular level with behavioral anticipation or avoidance. Here we asked whether distinct populations of neurons in the ACC can encode information for pain anticipation. We used tetrodes to record from ACC neurons during a conditioning assay to train rats to avoid pain. We found that in rats that successfully avoid acute pain episodes, neurons that responded to pain shifted their firing rates to an earlier time, whereas neurons that responded to the anticipation of pain increased their firing rates prior to noxious stimulation. Furthermore, we found a selected group of neurons that shifted their firing from a pain-tuned response to an anticipatory response. Unsupervised learning analysis of ensemble spike activity indicates that temporal spiking patterns of ACC neurons can indeed predict the onset of pain avoidance. These results suggest rate and temporal coding schemes in the ACC for pain avoidance.

Pain is a complex sensory and affective experience. The current definition for pain relies on verbal reports in clinical settings and behavioral assays in animal models. These definitions can be subjective and do not take into consideration signals in the neural system. Local field potentials (LFPs) represent summed electrical currents from multiple neurons in a defined brain area. Although single neuronal spike activity has been shown to modulate the acute pain, it is not yet clear how ensemble activities in the form of LFPs can be used to decode the precise timing and intensity of pain. The anterior cingulate cortex (ACC) is known to play a role in the affective-aversive component of pain in human and animal studies. Few studies, however, have examined how neural activities in the ACC can be used to interpret or predict acute noxious inputs. Here, we recorded in vivo extracellular activity in the ACC from freely behaving rats after stimulus with non-noxious, low-intensity noxious, and high-intensity noxious stimuli, both in the absence and chronic pain. Using a supervised machine learning classifier with selected LFP features, we predicted the intensity and the onset of acute nociceptive signals with high degree of precision. These results suggest the potential to use LFPs to decode acute pain.

Acute pain evokes protective neural and behavioral responses. Chronic pain, however, disrupts normal nociceptive processing. The prefrontal cortex (PFC) is known to exert top-down regulation of sensory inputs; unfortunately, how individual PFC neurons respond to an acute pain signal is not well characterized. We found that neurons in the prelimbic region of the PFC increased firing rates of the neurons after noxious stimulations in free-moving rats. Chronic pain, however, suppressed both basal spontaneous and pain-evoked firing rates. Furthermore, we identified a linear correlation between basal and evoked firing rates of PFC neurons, whereby a decrease in basal firing leads to a nearly 2-fold reduction in pain-evoked response in chronic pain states. In contrast, enhancing basal PFC activity with low-frequency optogenetic stimulation scaled up prefrontal outputs to inhibit pain. These results demonstrate a cortical gain control system for nociceptive regulation and establish scaling up prefrontal outputs as an effective neuromodulation strategy to inhibit pain.

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen et al., 2017, J. Neural Eng.), we have developed a latent state space model, known as Poisson linear dynamical system (PLDS), for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced GPU (graphic processing unit) computing technology. We validate our algorithms using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications.