Zhe Chen

Deciphering neuronal population codes for acute thermal pain

OBJECTIVEnPain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage. Current pain research mostly focuses on molecular and synaptic changes at the spinal and peripheral levels. However, a complete understanding of pain mechanisms requires the physiological study of the neocortex. Our goal is to apply a neural decoding approach to read out the onset of acute thermal pain signals, which can be used for brain-machine interface.nnnAPPROACHnWe used micro wire arrays to record ensemble neuronal activities from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) in freely behaving rats. We further investigated neural codes for acute thermal pain at both single-cell and population levels. To detect the onset of acute thermal pain signals, we developed a novel latent state-space framework to decipher the sorted or unsorted S1 and ACC ensemble spike activities, which reveal information about the onset of pain signals.nnnMAIN RESULTSnThe state space analysis allows us to uncover a latent state process that drives the observed ensemble spike activity, and to further detect the neuronal threshold for acute thermal pain on a single-trial basis. Our method achieved good detection performance in sensitivity and specificity. In addition, our results suggested that an optimal strategy for detecting the onset of acute thermal pain signals may be based on combined evidence from S1 and ACC population codes.nnnSIGNIFICANCEnOur study is the first to detect the onset of acute pain signals based on neuronal ensemble spike activity. It is important from a mechanistic viewpoint as it relates to the significance of S1 and ACC activities in the regulation of the acute pain onset.

Chronic pain induces generalized enhancement of aversion

A hallmark feature of chronic pain is its ability to impact other sensory and affective experiences. It is notably associated with hypersensitivity at the site of tissue injury. It is less clear, however, if chronic pain can also induce a generalized site-nonspecific enhancement in the aversive response to nociceptive inputs. Here, we showed that chronic pain in one limb in rats increased the aversive response to acute pain stimuli in the opposite limb, as assessed by conditioned place aversion. Interestingly, neural activities in the anterior cingulate cortex (ACC) correlated with noxious intensities, and optogenetic modulation of ACC neurons showed bidirectional control of the aversive response to acute pain. Chronic pain, however, altered acute pain intensity representation in the ACC to increase the aversive response to noxious stimuli at anatomically unrelated sites. Thus, chronic pain can disrupt cortical circuitry to enhance the aversive experience in a generalized anatomically nonspecific manner. DOI: http://dx.doi.org/10.7554/eLife.25302.001

Quickest detection for abrupt changes in neuronal ensemble spiking activity using model-based and model-free approaches

Many real-time brain-machine interface (BMI) applications require quickest detection of abrupt changes in observed neural signals in an online manner. In the presence of multi-neuronal recordings, we propose both model-based and model-free approaches to detect the change in neuronal ensemble spiking activity. The model-based approach is motivated from state space modeling and recursive Bayesian filtering. The model-free approach is motivated from the CUSUM algorithm that computes the cumulative log-likelihood statistics. In the application of detecting the onset of acute thermal pain signals, we validate these approaches using experimental population spike data recorded from freely behaving rats.

Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity

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 Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, 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 been 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 graphics 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. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

Scaling Up Cortical Control Inhibits Pain

SUMMARY 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.

Rate and Temporal Coding Mechanisms in the Anterior Cingulate Cortex for Pain Anticipation

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.

Local field potential decoding of the onset and intensity of acute pain in rats

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.

Methods for Assessment of Memory Reactivation

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

Ketamine reduces aversion in rodent pain models by suppressing hyperactivity of the anterior cingulate cortex

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.Ketamine is a short-acting analgesic that also has anti-depressant effects. Here the authors show in rat models of chronic pain that low-dose ketamine can induce an anti-aversive state that persists after the initial short term analgesia has ended.

Ensembles of change-point detectors: implications for real-time BMI applications

Brain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience experiments, many practical issues arise, such as trial-by-trial variability, and spike sorting noise or multi-unit activity. In this paper, we propose a new framework for change-point detection based on ensembles of independent detectors in the context of BMI application for detecting acute pain signals. Motivated from ensemble learning, our proposed “ensembles of change-point detectors” (ECPDs) integrate multiple decisions from independent detectors, which may be derived based on data recorded from different trials, data recorded from different brain regions, data of different modalities, or models derived from different learning methods. By integrating multiple sources of information, the ECPDs aim to improve detection accuracy (in terms of true positive and true negative rates) and achieve an optimal trade-off of sensitivity and specificity. We validate our method using computer simulations and experimental recordings from freely behaving rats. Our results have shown superior and robust performance of ECPDS in detecting the onset of acute pain signals based on neuronal population spike activity (or combined with local field potentials) recorded from single or multiple brain regions.