Faculty Bibliography

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.

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.

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.

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.

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.

Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to: (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria (RDoC) initiative provides a theoretical framework to understand health and illness as the product of multiple inter-related systems but does not provide a framework to characterize or statistically evaluate such complex relationships. Characterizing and statistically evaluating models that integrate multiple levels (e.g. synapses, genes, environmental factors) as they relate to outcomes that a free from prior diagnostic benchmarks represents a challenge requiring new computational tools that are capable to capture complex relationships and identify clinically relevant populations. In the current review, we will summarize machine learning methods that can achieve these goals.

Parametric maximum likelihood (ML) estimators of probability density functions (pdfs) are widely used today because they are efficient to compute and have several nice properties such as consistency, fast convergence rates, and asymptotic normality. However, data is often complex making parametrization of the pdf difficult, and nonparametric estimation is required. Popular nonparametric methods, such as kernel density estimation (KDE), produce consistent estimators but are not ML and have slower convergence rates than parametric ML estimators. Further, these nonparametric methods do not share the other desirable properties of parametric ML estimators. This paper introduces a nonparametric ML estimator that assumes that the square-root of the underlying pdf is band-limited (BL) and hence "smooth". The BLML estimator is computed and shown to be consistent. Although convergence rates are not theoretically derived, the BLML estimator exhibits faster convergence rates than state-of-the-art nonparametric methods in simulations. Further, algorithms to compute the BLML estimator with lesser computational complexity than that of KDE methods are presented. The efficacy of the BLML estimator is shown by applying it to (i) density tail estimation and (ii) density estimation of complex neuronal receptive fields where it outperforms state-of-the-art methods used in neuroscience.