Publications

Filter by type:

Sort by year:

Dynamic Neuroscience: Statistics, Modeling, and Control

Zhe Chen, Sridevi S. Sarma
Book Springer | 2018 | ISBN: 978-3-319-71976-4

Presents innovative methodological and algorithmic development in statistics, modeling, control, and signal processing for neural data analysis

This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers.

Advanced State Space Methods for Neural and Clinical Data

Zhe Chen
Book Cambridge University Press | 2015 | ISBN: 9781107079199

A book on state space analysis on dynamical systems, with applications on neuroscience and medicine

This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-the-art research topics are detailed, including topics in state space analyses, maximum likelihood methods, variational Bayes, sequential Monte Carlo, Markov chain Monte Carlo, nonparametric Bayesian, and deep learning methods. Details are provided on practical applications in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate motor cortex, or the human EEG, MEG or fMRI, or physiological measurements of heartbeats or blood pressures. With real-world case studies of neuroscience experiments and clinical data sets, and written by expert authors from across the field, this is an ideal resource for anyone working in neuroscience and physiological data analysis.

Engineering Approaches to Study Cardiovascular Physiology: Modeling, Estimation, and Signal Processing

Zhe Chen, Riccardo Barbieri
Book Frontiers Media SA | 2013 | ISBN: 978-2-88919-079-9

A research topic on cardiovascular signal processing

With cardiovascular diseases being one of the main causes of death in the world, quantitative modeling, assessment and monitoring of the cardiovascular control system plays a critical role in bringing important breakthroughs to cardiovascular care. Quantification of cardiovascular physiology and its control dynamics from physiological recordings and by use of mathematical models and algorithms has been proved to be of important value in understanding the causes of cardiovascular diseases and assisting the prognostic or diagnostic process. Nowadays, development of new recording technologies (e.g., electrophysiology, imaging, ultrasound, etc) has enabled us to improve and expand acquisition of a wide spectrum of physiological measures related to cardiovascular control. An emerging challenge is to process and interpret such increasing amount of information by using state-of-the-art approaches in systems modeling, estimation and control, and signal processing, which would lead to further insightful scientific findings. In particular, multi-disciplinary engineering-empowered approaches of studying cardiovascular systems would greatly deepen our understanding of cardiovascular functions (e.g., heart rate variability, baroreflex sensitivity) and autonomic control, as it would also improve the knowledge about heart pathology, cardiovascular rehabilitation and therapy. Meanwhile, developing cardiovascular biomedical devices or heart-machine interface for either clinical monitoring or rehabilitation purpose is of greater and greater interest for both scientific advancement and potential medical benefits.

Correlative Learning: A Basis for Brain and Adaptive Systems

Zhe Chen, Simon Haykin, Joe J. Eggermont, Suzanna Becker
Book John Wiley & Sons | 2007 | ISBN: 978-0-470-04488-9

A research monograph on correlation-based learning in brain and machine

Correlative Learning: A Basis for Brain and Adaptive Systems provides a bridge between three disciplines: computational neuroscience, neural networks, and signal processing. First, the authors lay down the preliminary neuroscience background for engineers. The book also presents an overview of the role of correlation in the human brain as well as in the adaptive signal processing world; unifies many well-established synaptic adaptations (learning) rules within the correlation-based learning framework, focusing on a particular correlative learning paradigm, ALOPEX; and presents case studies that illustrate how to use different computational tools and ALOPEX to help readers understand certain brain functions or fit specific engineering applications.

  • Focuses on advanced and state-of-the-art state space methods.
  • Details the practical applications for neural and clinical data.
  • Includes real-world case studies of neuroscience experiments and clinical data sets.

On different facets of regularization theory

Zhe Chen, Simon Haykin
Journal PaperNeural Computation, Volume 14, Issue 12, 2002, Pages 2791-2846

Abstract

This review provides a comprehensive understanding of regularization theory from different perspectives, emphasizing smoothness and simplicity principles. Using the tools of operator theory and Fourier analysis, it is shown that the solution of the classical Tikhonov regularization problem can be derived from the regularized functional defined by a linear differential (integral) operator in the spatial (Fourier) domain. State-of-the-art research relevant to the regularization theory is reviewed, covering Occam's razor, minimum length description, Bayesian theory, pruning algorithms, informational (entropy) theory, statistical learning theory, and equivalent regularization. The universal principle of regularization in terms of Kolmogorov complexity is discussed. Finally, some prospective studies on regularization theory and beyond are suggested.

Bayesian sequential state estimation for MIMO wireless communications

Simon Haykin, Kris Huber, Zhe Chen
Journal PaperProceedings of the IEEE, Volume 92, Issue 3, 2002, Pages 439-454

Abstract

This paper explores the use of particle filters, rooted in Bayesian estimation, as a device for tracking statistical variations in the channel matrix of a narrowband multiple-input, multiple-output (MIMO) wireless channel. The motivation is to permit the receiver to acquire channel state information through a semiblind strategy and thereby improve the receiver performance of the wireless communication system. To that end, the paper compares the particle filter as well as an improved version of the particle filter using gradient information, to the conventional Kalman filter and mixture Kalman filter with two metrics in mind: receiver performance curves and computational complexity. The comparisons, also including differential phase modulation, are carried out using real-life recorded MIMO wireless data.

Stochastic correlative learning algorithms

Simon Haykin*, Zhe Chen*, Suzanna Becker
Journal PaperIEEE Transactions on Signal Processing, Volume 52, Issue 8, 2004, Pages 2200-2209

Abstract

This paper addresses stochastic correlative learning as the basis for a broadly defined class of statistical learning algorithms known collectively as the algorithm of pattern extraction (ALOPEX) family. Starting with the neurobiologically motivated Hebb's rule, the two conventional forms of the ALOPEX algorithm are derived, followed by a modified variant designed to improve the convergence speed. We next describe two more elaborate versions of the ALOPEX algorithm, which incorporate particle filtering that exemplifies a form of Monte Carlo simulation, to exchange computational complexity for an improved convergence and tracking behavior. In support of the different forms of the ALOPEX algorithm developed herein, we present three different experiments using synthetic and real-life data on binocular fusion of stereo images, on-line prediction, and system identification.

The cocktail party problem

Simon Haykin*, Zhe Chen*
Journal PaperNeural Computation, Volume 17, Issue 9, 2005, Pages 1875-1902

Abstract

This review presents an overview of a challenging problem in auditory perception, the cocktail party phenomenon, the delineation of which goes back to a classic paper by Cherry in 1953. In this review, we address the following issues: (1) human auditory scene analysis, which is a general process carried out by the auditory system of a human listener; (2) insight into auditory perception, which is derived from Marr's vision theory; (3) computational auditory scene analysis, which focuses on specific approaches aimed at solving the machine cocktail party problem; (4) active audition, the proposal for which is motivated by analogy with active vision, and (5) discussion of brain theory and independent component analysis, on the one hand, and correlative neural firing, on the other.

Stochastic correlative firing for figure-ground segregation

Zhe Chen
Journal PaperBiological Cybernetics, Volume 92, Issue 3, 2005, Pages 192-198

Abstract

Segregation of sensory inputs into separate objects is a central aspect of perception and arises in all sensory modalities. The figure-ground segregation problem requires identifying an object of interest in a complex scene, in many cases given binaural auditory or binocular visual observations. The computations required for visual and auditory figure-ground segregation share many common features and can be cast within a unified framework. Sensory perception can be viewed as a problem of optimizing information transmission. Here we suggest a stochastic correlative firing mechanism and an associative learning rule for figure-ground segregation in several classic sensory perception tasks, including the cocktail party problem in binaural hearing, binocular fusion of stereo images, and Gestalt grouping in motion perception.

A novel model-based hearing compensation design using a gradient-free optimization method

Zhe Chen, Suzanna Becker, Jeff Bondy, Ian Bruce, Simon Haykin
Journal PaperNeural Computation, Volume 17, Issue 12, 2005, Pages 2648-2671

Abstract

We propose a novel model-based hearing compensation strategy and gradient-free optimization procedure for a learning-based hearing aid design. Motivated by physiological data and normal and impaired auditory nerve models, a hearing compensation strategy is cast as a neural coding problem, and a Neurocompensator is designed to compensate for the hearing loss and enhance the speech. With the goal of learning the Neurocompensator parameters, we use a gradient-free optimization procedure, an improved version of the ALOPEX that we have developed, to learn the unknown parameters of the Neurocompensator. We present our methodology, learning procedure, and experimental results in detail; discussion is also given regarding the unsupervised learning and optimization methods.

Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents

Christos Papadelis, Zhe Chen, C. Kourtidou-Papadeli, P.D. Bamidis, I. Chouvarda, A. Bekiaris, N. Maglaveras
Journal PaperClinical Neurophysiology, Volume 118, Issue 9, 2007, Pages 1906-1922

Abstract

OBJECTIVE: The objective of this study is th development and evaluation of efficient neurophysiological signal statistics, which may assess the driver's alertness level and serve as potential indicators of sleepiness in the design of an on-board countermeasure system. METHODS: Multichannel EEG, EOG, EMG, and ECG were recorded from sleep-deprived subjects exposed to real field driving conditions. A number of severe driving errors occurred during the experiments. The analysis was performed in two main dimensions: the macroscopic analysis that estimates the on-going temporal evolution of physiological measurements during the driving task, and the microscopic event analysis that focuses on the physiological measurements' alterations just before, during, and after the driving errors. Two independent neurophysiologists visually interpreted the measurements. The EEG data were analyzed by using both linear and non-linear analysis tools. RESULTS: We observed the occurrence of brief paroxysmal bursts of alpha activity and an increased synchrony among EEG channels before the driving errors. The alpha relative band ratio (RBR) significantly increased, and the Cross Approximate Entropy that quantifies the synchrony among channels also significantly decreased before the driving errors. Quantitative EEG analysis revealed significant variations of RBR by driving time in the frequency bands of delta, alpha, beta, and gamma. Most of the estimated EEG statistics, such as the Shannon Entropy, Kullback-Leibler Entropy, Coherence, and Cross-Approximate Entropy, were significantly affected by driving time. We also observed an alteration of eyes blinking duration by increased driving time and a significant increase of eye blinks' number and duration before driving errors. CONCLUSIONS: EEG and EOG are promising neurophysiological indicators of driver sleepiness and have the potential of monitoring sleepiness in occupational settings incorporated in a sleepiness countermeasure device. SIGNIFICANCE: The occurrence of brief paroxysmal bursts of alpha activity before severe driving errors is described in detail for the first time. Clear evidence is presented that eye-blinking statistics are sensitive to the driver's sleepiness and should be considered in the design of an efficient and driver-friendly sleepiness detection countermeasure device.

Statistical modelling and analysis of laser-evoked potentials of electrocorticogram recordings from awake humans

Zhe Chen, S. Ohara, J. Cao, F. Vialatte, F.A. Lenz, A. Cichocki
Journal PaperComputational Intelligence and Neuroscience, Volume 2007, Article ID 10479

Abstract

This article is devoted to statistical modeling and analysis of electrocorticogram (ECoG) signals induced by painful cutaneous laser stimuli, which were recorded from implanted electrodes in awake humans. Specifically, with statistical tools of factor analysis and independent component analysis, the pain-induced laser-evoked potentials (LEPs) were extracted and investigated under different controlled conditions. With the help of wavelet analysis, quantitative and qualitative analyses were conducted regarding the LEPs' attributes of power, amplitude, and latency, in both averaging and single-trial experiments. Statistical hypothesis tests were also applied in various experimental setups. Experimental results reported herein also confirm previous findings in the neurophysiology literature. In addition, single-trial analysis has also revealed many new observations that might be interesting to the neuroscientists or clinical neurophysiologists. These promising results show convincing validation that advanced signal processing and statistical analysis may open new avenues for future studies of such ECoG or other relevant biomedical recordings.

An empirical EEG analysis in brain death diagnosis for adults

Zhe Chen, Jianting Cao, Y. Cao, Y. Zhang, F. Gu, G. Zhu, Z. Hong, B. Wang, A. Cichocki
Journal PaperCognitive Neurodynamics, Volume 2, Issue 3, 2008, Pages 257-271

Abstract

Electroencephalogram (EEG) is often used in the confirmatory test for brain death diagnosis in clinical practice. Because EEG recording and monitoring is relatively safe for the patients in deep coma, it is believed to be valuable for either reducing the risk of brain death diagnosis (while comparing other tests such as the apnea) or preventing mistaken diagnosis. The objective of this paper is to study several statistical methods for quantitative EEG analysis in order to help bedside or ambulatory monitoring or diagnosis. We apply signal processing and quantitative statistical analysis for the EEG recordings of 32 adult patients. For EEG signal processing, independent component analysis (ICA) was applied to separate the independent source components, followed by Fourier and time-frequency analysis. For quantitative EEG analysis, we apply several statistical complexity measures to the EEG signals and evaluate the differences between two groups of patients: the subjects in deep coma, and the subjects who were categorized as brain death. We report statistically significant differences of quantitative statistics with real-life EEG recordings in such a clinical study, and we also present interpretation and discussions on the preliminary experimental results.

Discrete- and continuous-time probabilistic models and algorithms for inferring neuronal UP and DOWN states

Zhe Chen, Riccardo Barbieri, Emery N. Brown
Journal PaperNeural Computation, Volume 21, Issue 7, 2009, Pages 1797-1862

Abstract

UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.

Assessment of autonomic control and respiratory sinus arrhythmia using point process models of human heart beat dynamics

Zhe Chen, Emery N. Brown, Riccardo Barbieri
Journal PaperIEEE Transactions on Biomedical Engineering, Volume 56, Issue 7, 2009, Pages 1791-1802

Abstract

Tracking the autonomic control and respiratory sinus arrhythmia (RSA) from electrocardiogram and respiratory measurements is an important problem in cardiovascular control. We propose a point process adaptive filter algorithm based on an inverse Gaussian model to track heart beat intervals that incorporates respiratory measurements as a covariate and provides an analytic form for computing a dynamic estimate of RSA gain. We use Kolmogorov-Smirnov tests and autocorrelation function analyses to assess model goodness-of-fit. We illustrate the properties of the new dynamic estimate of RSA in the analysis of simulated heart beat data and actual heart beat data recorded from subjects in a four-state postural study of heart beat dynamics: control, sympathetic blockade, parasympathetic blockade, and combined sympathetic and parasympathetic blockade. In addition to giving an accurate description of the heart beat data, our adaptive filter algorithm confirms established findings pointing at a vagally mediated RSA, and it provides a new dynamic RSA estimate that can be used to track cardiovascular control between and within a broad range of postural, pharmacological and age conditions. Our paradigm suggests a possible framework for designing a device for ambulatory monitoring and assessment of autonomic control in both laboratory research and clinical practice.

Characterizing nonlinear heartbeat dynamics within a point process framework

Zhe Chen, Emery N. Brown, Riccardo Barbieri
Journal PaperIEEE Transactions on Biomedical Engineering, Volume 57, Issue 6, 2010, Pages 1335-1347

Abstract

Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings.

Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data

Zhe Chen, David Putrino, S. Ghosh, Riccardo Barbieri, Emery N. Brown
Journal PaperIEEE Transactions on Neural Systems and Rehabilitation Engineering, Volume 19, Issue 2, 2011, Pages 121-135

Abstract

The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l(2) or l(1) regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods.

Dynamic assessment of baroreflex control of heart rate during induction of propofol anesthesia using a point process method

Zhe Chen, Patrick L. Purdon, Grace Harrell, E. T. Pierce, J. Walsh, Emery N. Brown, Riccardo Barbieri
Journal Paper Annals of Biomedical Engineering, Volume 39, Issue 1, 2011, Pages 260-276

Abstract

In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R-R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the RR-->BP feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care.

A hierarchical Bayesian approach for learning spatio-temporal decomposition of multichannel EEG

Wei Wu, Zhe Chen, Shangkai Gao, Emery N. Brown
Journal Paper Neuroimage, Volume 56, Issue 4, 2011, Pages 1929-1945

Abstract

Multichannel electroencephalography (EEG) offers a non-invasive tool to explore spatio-temporal dynamics of brain activity. With EEG recordings consisting of multiple trials, traditional signal processing approaches that ignore inter-trial variability in the data may fail to accurately estimate the underlying spatio-temporal brain patterns. Moreover, precise characterization of such inter-trial variability per se can be of high scientific value in establishing the relationship between brain activity and behavior. In this paper, a statistical modeling framework is introduced for learning spatio-temporal decompositions of multiple-trial EEG data recorded under two contrasting experimental conditions. By modeling the variance of source signals as random variables varying across trials, the proposed two-stage hierarchical Bayesian model is able to capture inter-trial amplitude variability in the data in a sparse way where a parsimonious representation of the data can be obtained. A variational Bayesian (VB) algorithm is developed for statistical inference of the hierarchical model. The efficacy of the proposed modeling framework is validated with the analysis of both synthetic and real EEG data. In the simulation study we show that even at low signal-to-noise ratios our approach is able to recover with high precision the underlying spatio-temporal patterns and the dynamics of source amplitude across trials; on two brain-computer interface (BCI) data sets we show that our VB algorithm can extract physiologically meaningful spatio-temporal patterns and make more accurate predictions than other two widely used algorithms: the common spatial patterns (CSP) algorithm and the Infomax algorithm for independent component analysis (ICA). The results demonstrate that our statistical modeling framework can serve as a powerful tool for extracting brain patterns, characterizing trial-to-trial brain dynamics, and decoding brain states by exploiting useful structures in the data.

Motor cortical networks for skilled movements have dynamic properties that are related to accurate reaching

David Putrino*, Zhe Chen*, S. Ghosh, Emery N. Brown
Journal Paper Neural Plasticity, Volume 2011, Article ID 413543

Abstract

Neurons in the Primary Motor Cortex (MI) are known to form functional ensembles with one another in order to produce voluntary movement. Neural network changes during skill learning are thought to be involved in improved fluency and accuracy of motor tasks. Unforced errors during skilled tasks provide an avenue to study network connections related to motor learning. In order to investigate network activity in MI, microwires were implanted in the MI of cats trained to perform a reaching task. Spike trains from eight groups of simultaneously recorded cells (95 neurons in total) were acquired. A point process generalized linear model (GLM) was developed to assess simultaneously recorded cells for functional connectivity during reaching attempts where unforced errors or no errors were made. Whilst the same groups of neurons were often functionally connected regardless of trial success, functional connectivity between neurons was significantly different at fine time scales when the outcome of task performance changed. Furthermore, connections were shown to be significantly more robust across multiple latencies during successful trials of task performance. The results of this study indicate that reach-related neurons in MI form dynamic spiking dependencies whose temporal features are highly sensitive to unforced movement errors.

Uncovering spatial topology represented by rat hippocampal population neuronal codes

Zhe Chen, Fabian Kloosterman, Emery N. Brown, Matthew A. Wilson
Journal Paper Journal of Computational Neuroscience, Volume 33, Issue 2, 2012, Pages 227-255

Abstract

Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden "spatial topology" represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a variational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep.

Point process time-frequency analysis of dynamic breathing patterns during meditation practice

Sandun Kodituwakku, Sara W. Lazar, Premananda Indic, Zhe Chen, Emery N. Brown, Riccardo Barbieri
Journal Paper Medical and Biological Engineering and Computing, Volume 50, 2012, Pages 261-275

Abstract

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heart beats. We propose a robust algorithm for quantifying instantaneous RSA as applied to heart beat intervals and respiratory recordings under dynamic breathing patterns. The blood volume pressure derived heart beat series (pulse intervals, PIs) are modeled as an inverse gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PIs and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated via a frequency domain transfer function evaluated at instantaneous respiratory frequency where high coherence between respiration and PIs is observed. The model is statistically validated using Kolmogorov-Smirnov (KS) goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. The presented analysis confirms the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states, reporting statistically significant increase in RSA gain as measured by our paradigm.

A unified point process probabilistic framework to assess heartbeat dynamics and autonomic cardiovascular control

Zhe Chen, Patrick Purdon, Emery N. Brown, Riccardo Barbieri
Journal Paper Frontiers in Physiology, Volume 3, Article 4

Abstract

In recent years, time-varying inhomogeneous point process models have been introduced for assessment of instantaneous heartbeat dynamics as well as specific cardiovascular control mechanisms and hemodynamics. Assessment of the model's statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (baroreflex) sensitivity (BRS), are derived within a parametric framework and instantaneously updated with adaptive and local maximum likelihood estimation algorithms. Inclusion of second-order non-linearities, with subsequent bispectral quantification in the frequency domain, further allows for definition of instantaneous metrics of non-linearity. We here present a comprehensive review of the devised methods as applied to experimental recordings from healthy subjects during propofol anesthesia. Collective results reveal interesting dynamic trends across the different pharmacological interventions operated within each anesthesia session, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, non-invasive assessment in clinical practice. We also discuss the limitations and other alternative modeling strategies of our point process approach.

Mapping of visual receptive fields by tomographic reconstruction

Gordon Pipa*, Zhe Chen*, S. Neuenschwander, B. Lima, Emery N. Brown
Journal Paper Neural Computation, Volume 24, Issue 10, 2012, Pages 2543-2578

Abstract

The moving bar experiment is a classic paradigm for characterizing the receptive field (RF) properties of neurons in primary visual cortex (V1). Current approaches for analyzing neural spiking activity recorded from these experiments do not take into account the point-process nature of these data and the circular geometry of the stimulus presentation. We present a novel analysis approach to mapping V1 receptive fields that combines point-process generalized linear models (PPGLM) with tomographic reconstruction computed by filtered-back projection. We use the method to map the RF sizes and orientations of 251 V1 neurons recorded from two macaque monkeys during a moving bar experiment. Our cross-validated goodness-of-fit analyses show that the PPGLM provides a more accurate characterization of spike train data than analyses based on rate functions computed by the methods of spike-triggered averages or first-order Wiener-Volterra kernel. Our analysis leads to a new definition of RF size as the spatial area over which the spiking activity is significantly greater than baseline activity. Our approach yields larger RF sizes and sharper orientation tuning estimates. The tomographic reconstruction paradigm further suggests an efficient approach to choosing the number of directions and the number of trials per direction in designing moving bar experiments. Our results demonstrate that standard tomographic principles for image reconstruction can be adapted to characterize V1 RFs and that two fundamental properties, size and orientation, may be substantially different from what is currently reported.

State space model

Zhe Chen, Emery N. Brown
Journal Paper Scholarpedia, Volume 8, Issue 3, Pages 30868

Abstract

An overview of Bayesian methods for neural spike train analysis

Zhe Chen
Journal Paper Computational Intelligence in Neuroscience, Volume 2013, Article ID 251905

Abstract

Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

Bayesian decoding of unsorted spikes in the rat hippocampus

Fabian Kloosterman, Stuart Layton, Zhe Chen, Matthew A. Wilson
Journal Paper Journal of Neurophysiology, Volume 111, Issue 1, 2014, Pages 217-227

Abstract

A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces.

Neural representation of spatial topology in the rodent hippocampus

Zhe Chen, Stephen N. Gomperts, Jun Yamamoto, Matthew A. Wilson
Journal Paper Neural Computation, Volume 26, Issue 1, 2014, Pages 1-39

Abstract

Pyramidal cells in the rodent hippocampus often exhibit clear spatial tuning in navigation. Although it has been long suggested that pyramidal cell activity may underlie a topological code rather than a topographic code, it remains unclear whether an abstract spatial topology can be encoded in the ensemble spiking activity of hippocampal place cells. Using a statistical approach developed previously, we investigate this question and related issues in greater detail. We recorded ensembles of hippocampal neurons as rodents freely foraged in one- and two-dimensional spatial environments and used a "decode-to-uncover" strategy to examine the temporally structured patterns embedded in the ensemble spiking activity in the absence of observed spatial correlates during periods of rodent navigation or awake immobility. Specifically, the spatial environment was represented by a finite discrete state space. Trajectories across spatial locations ("states") were associated with consistent hippocampal ensemble spiking patterns, which were characterized by a state transition matrix. From this state transition matrix, we inferred a topology graph that defined the connectivity in the state space. In both one- and two-dimensional environments, the extracted behavior patterns from the rodent hippocampal population codes were compared against randomly shuffled spike data. In contrast to a topographic code, our results support the efficiency of topological coding in the presence of sparse sample size and fuzzy space mapping. This computational approach allows us to quantify the variability of ensemble spiking activity, examine hippocampal population codes during off-line states, and quantify the topological complexity of the environment.

State-dependent architecture of thalamic reticular subnetwork

Michael M. Halassa, Zhe Chen, Ralf D. Wimmer, P. Brunetti, S. Zhao, F. Wang, Emery N. Brown, Matthew A. Wilson
Journal Paper Cell, Volume 158, Issue 4, 2014, Pages 808-821

Abstract

Behavioral state is known to influence interactions between thalamus and cortex, which are important for sensation, action, and cognition. The thalamic reticular nucleus (TRN) is hypothesized to regulate thalamo-cortical interactions, but the underlying functional architecture of this process and its state dependence are unknown. By combining the first TRN ensemble recording with psychophysics and connectivity-based optogenetic tagging, we found reticular circuits to be composed of distinct subnetworks. While activity of limbic-projecting TRN neurons positively correlates with arousal, sensory-projecting neurons participate in spindles and show elevated synchrony by slow waves during sleep. Sensory-projecting neurons are suppressed by attentional states, demonstrating that their gating of thalamo-cortical interactions is matched to behavioral state. Bidirectional manipulation of attentional performance was achieved through subnetwork-specific optogenetic stimulation. Together, our findings provide evidence for differential inhibition of thalamic nuclei across brain states, where the TRN separately controls external sensory and internal limbic processing facilitating normal cognitive function. .

Probabilistic common spatial patterns for multichannel EEG analysis

Wei Wu, Zhe Chen, Xiaorong Gao, Yuanqing Li, Emery N. Brown, Shangkai Gao
Journal Paper IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 37, Issue 3, 2015, Pages 639-653

Abstract

Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms. The proposed framework enables us to resolve the overfitting issue of CSP in a principled manner. We derive statistical inference algorithms that can alleviate the issue of local optima. In particular, an efficient algorithm based on eigendecomposition is developed for maximum a posteriori (MAP) estimation in the case of isotropic noise. For more general cases, a variational algorithm is developed for group-wise sparse Bayesian learning for the P-CSP model and for automatically determining the model size. The two proposed algorithms are validated on a simulated data set. Their practical efficacy is also demonstrated by successful applications to single-trial classifications of three motor imagery EEG data sets and by the spatio-temporal pattern analysis of one EEG data set recorded in a Stroop color naming task.

Estimating latent attentional states based on simultaneous binary and continuous behavioral measures

Zhe Chen
Journal Paper Computational Intelligence in Neuroscience, Volume 2015, Article ID 493769

Abstract

Cognition is a complex and dynamic process. It is an essential goal to estimate latent attentional states based on behavioral measures in many sequences of behavioral tasks. Here, we propose a probabilistic modeling and inference framework for estimating the attentional state using simultaneous binary and continuous behavioral measures. The proposed model extends the standard hidden Markov model (HMM) by explicitly modeling the state duration distribution, which yields a special example of the hidden semi-Markov model (HSMM). We validate our methods using computer simulations and experimental data. In computer simulations, we systematically investigate the impacts of model mismatch and the latency distribution. For the experimental data collected from a rodent visual detection task, we validate the results with predictive log-likelihood. Our work is useful for many behavioral neuroscience experiments, where the common goal is to infer the discrete (binary or multinomial) state sequences from multiple behavioral measures.

Thalamic circuit mechanisms link sensory processing in sleep and attention

Zhe Chen, Ralf D. Wimmer, Matthew A. Wilson, Michael M. Halassa
Journal Paper Frontiers in Neural Circuits, Volume 9, 2016, Pages 83

Abstract

The correlation between sleep integrity and attentional performance is normally interpreted as poor sleep causing impaired attention. Here, we provide an alternative explanation for this correlation: common thalamic circuits regulate sensory processing across sleep and attention, and their disruption may lead to correlated dysfunction. Using multi-electrode recordings in mice, we find that rate and rhythmicity of thalamic reticular nucleus (TRN) neurons are predictive of their functional organization in sleep and suggestive of their participation in sensory processing across states. Surprisingly, TRN neurons associated with spindles in sleep are also associated with alpha oscillations during attention. As such, we propose that common thalamic circuit principles regulate sensory processing in a state-invariant manner and that in certain disorders, targeting these circuits may be a more viable therapeutic strategy than considering individual states in isolation.

Bayesian Machine Learning: EEG/MEG signal processing measurements

Wei Wu, Srikantan S. Nagarajan, Zhe Chen
Journal Paper IEEE Signal Processing Magazine, Volume 33, Issue 1, 2016, Pages 15-36

Abstract

Electroencephalography (EEG) and magnetoencephalography (MEG) are the most common noninvasive brain-imaging techniques for monitoring electrical brain activity and inferring brain function. The central goal of EEG/MEG analysis is to extract informative brain spatiotemporal?spectral patterns or to infer functional connectivity between different brain areas, which is directly useful for neuroscience or clinical investigations. Due to its potentially complex nature [such as nonstationarity, high dimensionality, subject variability, and low signal-to-noise ratio (SNR)], EEG/MEG signal processing poses some great challenges for researchers. These challenges can be addressed in a principled manner via Bayesian machine learning (BML). BML is an emerging field that integrates Bayesian statistics, variational methods, and machine-learning techniques to solve various problems from regression, prediction, outlier detection, feature extraction, and classification. BML has recently gained increasing attention and widespread successes in signal processing and big-data analytics, such as in source reconstruction, compressed sensing, and information fusion. To review recent advances and to foster new research ideas, we provide a tutorial on several important emerging BML research topics in EEG/MEG signal processing and present representative examples in EEG/MEG applications.

A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation

Scott Linderman, Matthew J. Johnson, Matthew A. Wilson, Zhe Chen,
Journal Paper Journal of Neuroscience Methods, Volume 236, 2016, Pages 36-47

Abstract

BACKGROUND: Rodent hippocampal population codes represent important spatial information about the environment during navigation. Computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. NEW METHOD: We extend our previous work and propose a novel Bayesian nonparametric approach to infer rat hippocampal population codes during spatial navigation. To tackle the model selection problem, we leverage a Bayesian nonparametric model. Specifically, we apply a hierarchical Dirichlet process-hidden Markov model (HDP-HMM) using two Bayesian inference methods, one based on Markov chain Monte Carlo (MCMC) and the other based on variational Bayes (VB). RESULTS: The effectiveness of our Bayesian approaches is demonstrated on recordings from a freely behaving rat navigating in an open field environment. COMPARISON WITH EXISTING METHODS: The HDP-HMM outperforms the finite-state HMM in both simulated and experimental data. For HPD-HMM, the MCMC-based inference with Hamiltonian Monte Carlo (HMC) hyperparameter sampling is flexible and efficient, and outperforms VB and MCMC approaches with hyperparameters set by empirical Bayes. CONCLUSION: The Bayesian nonparametric HDP-HMM method can efficiently perform model selection and identify model parameters, which can used for modeling latent-state neuronal population dynamics.

A novel nonparametric approach for neural encoding and decoding models of multimodal receptive fields

Rahul Agarwal, Zhe Chen, Fabian Kloosterman, Matthew A. Wilson, Sridevi V. Sarma
Journal Paper Neural Computation, Volume 28, Issue 7, 2016, Pages 1356-1387

Abstract

Pyramidal neurons recorded from the rat hippocampus and entorhinal cortex, such as place and grid cells, have diverse receptive fields, which are either unimodal or multimodal. Spiking activity from these cells encodes information about the spatial position of a freely foraging rat. At fine timescales, a neuron's spike activity also depends significantly on its own spike history. However, due to limitations of current parametric modeling approaches, it remains a challenge to estimate complex, multimodal neuronal receptive fields while incorporating spike history dependence. Furthermore, efforts to decode the rat's trajectory in one- or two-dimensional space from hippocampal ensemble spiking activity have mainly focused on spike history-independent neuronal encoding models. In this letter, we address these two important issues by extending a recently introduced nonparametric neural encoding framework that allows modeling both complex spatial receptive fields and spike history dependencies. Using this extended nonparametric approach, we develop novel algorithms for decoding a rat's trajectory based on recordings of hippocampal place cells and entorhinal grid cells. Results show that both encoding and decoding models derived from our new method performed significantly better than state-of-the-art encoding and decoding models on 6 minutes of test data. In addition, our model's performance remains invariant to the apparent modality of the neuron's receptive field.

Uncovering representations of sleep-associated hippocampal ensemble spike activity

Zhe Chen, Andres D. Grosmark, Hector Penagos, Matthew A. Wilson
Journal Paper Scientific Reports, Volume 6, 2016, Pages 32193

Abstract

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.

Novel nonparametric maximum likelihood estimator for probability density functions

Rahul Agarwal, Zhe Chen, Sridevi V. Sarma
Journal Paper IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 39, Issue 7, 2017, Pages 1294-1308

Abstract

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.

A primer on neural signal processing

Zhe Chen
Journal Paper IEEE Circuits and Systems Magazine, Volume 17, Issue 1, 2017, Pages 33-50

Abstract

The role of neural signal processing has become increasingly important in the field of neuroscience with the increase of complexity and scale in neural recordings. In neuroscience, neural signal processing is aimed to extract information from neural signals for the purpose of understanding how the brain represents and transmits information through neuronal ensembles. In neural engineering, neural signal processing is aimed to read out neural signals to send neurofeedback to the brain or computer devices that assist or facilitate brain-machine communications. Here we provide a short review of neural signal processing on important principles and state-of-the-art research. Through representative examples, we illustrate how statistical signal processing can be applied to many diverse neuroscience applications.

Deciphering neuronal population codes for acute thermal pain

Zhe Chen, Qiaosheng Zhang, A.P.S. Tong, T.R. Manders, Jing Wang
Journal Paper Journal of Neural Engineering, Volume 14, Issue 3, 2017, Pages 036023

Abstract

Objective. Pain 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. Approach. We 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. Main results. The 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. Significance. Our 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 to noxious inputs

Qiaosheng Zhang, T.R. Mandes, A.P.S. Tong, R. Yang, A. Garg, E. Martinez, A. Goyal, J. Dale, H. Zhou, L. Urien, A. Sideris, G. Yang, Zhe Chen, Jing Wang
Journal Paper eLife, Volume 6, 2017, Pages e25302

Abstract

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.

Deciphering neural codes of memory during sleep

Zhe Chen, Matthew A. Wilson
Journal Paper Trends in Neurosciences, Volume 40, Issue 5, 2017, Pages 260-275

Abstract

Memories of experiences are stored in the cerebral cortex. Sleep is critical for the consolidation of hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms in memory consolidation and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches to the analysis of sleep-associated neural codes (SANCs). We focus on two analysis paradigms for sleep-associated memory and propose a new unsupervised learning framework ('memory first, meaning later') for unbiased assessment of SANCs.

Data science in the RDoC era: relevance of machine learning to the study of stress pathology, recovery and resilience

Issac R. Galatzer-Levy, Kelly V. Ruggles, Zhe Chen
Journal Paper Chronic Stress, Volume 1, 2018, Pages 1-14

Abstract

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 initiative provides a theoretical framework to understand health and illness as the product of multiple interrelated 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, and envir- onmental factors) as they relate to outcomes that are free from prior diagnostic benchmarks represent 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.

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

Sile Hu, Qiaosheng Zhang, Jing Wang, Zhe Chen
Journal Paper Journal of Neurophysiology, Volume 149, Issue 4, 2018, Pages 1394-1410

Abstract

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.

Methods for assessment of memory reactivation

Shizhao Liu, Andres D. Grosmark, Zhe Chen
Journal Paper Neural Computation, Volume 30, Issue 8, 2018, Pages 2175-2209

Abstract

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 spiking activity during off-line states. Using population-decoding methods, we propose a new statistical metric known as 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 such that it can detect statistical dependency beyond linearity in the memory traces and is invariant to latent state labeling. 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.

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

Zhengdong Xiao, Sile Hu, Qiaosheng Zhang, Xiang Tian, Yaowu Chen, Jing Wang, Zhe Chen
Journal Paper Journal of Computational Neuroscience, Volume , 2018, Pages

Abstract

Brain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience applications, 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 unsupervised 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, or data recorded from different brain regions, or data of different modalities. By integrating more information, the ECPDs are aimed to improve the detection accuracy (in terms of true positive and true negative rates) and to 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 from single or multiple brain regions.

Scaling up cortical control inhibits pain

Jahrane Dale, Haocheng Zhou, Qiaosheng Zhang, Erik Martinez, Sile Hu, Kevin Liu, Louise Urien, Zhe Chen, Jing Wang
Journal Paper Cell Reports, Volume 23, 2018, Pages 1301-1313

Abstract

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

Louise Urien, Zhengdong Xiao, Jahrane Dale, Elizabeth P. Bauer, Zhe Chen, Jing Wang
Journal Paper Scientific Reports, Volume 8, 2018, 8298

Abstract

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 thermal pain in rats

Qiaosheng Zhang, Zhengdong Xiao, Conan Huang, Sile Hu, Prathamesh Kulkarni, Erik Martinez, Ai Phuong Tong, Arpan Garg, Haocheng Zhou, Zhe Chen*, Jing Wang*
Journal Paper Scientific Reports, Volume 8, 2018, 8299

Abstract

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.

Ketamine reduces hyperactivity of the anterior cingulate cortex to provide enduring relief of chronic pain

Haocheng Zhou, Qiaosheng Zhang, Erik Maritinez, Sile Hu, Kevin Liu, Jahrane Dale, D. Huang, Guang Yang, Zhe Chen, Jing Wang
Journal Paper Nature Communications, Volume 9, 2018, Pages 3751

Abstract

A core pathologic feature of chronic pain is enhanced affective response to normal nociceptive inputs. This abnormality in pain affect is driven by disrupted aversive processing, due to maladaptive plasticity within the cerebral cortex. We show that a single dose of ketamine can reduce hyperactivity of neurons in the anterior cingulate cortex to selectively inhibit pain aversion over prolonged duration, and can thus be a novel treatment for chronic pain.

Real-time read out of large-scale unsorted neural ensemble place codes

Sile Hu, David Ciliberti, Andres D. Grosmark, Frederic Michon, Daoyun Ji, Hector Panagos, György Buzsáki, Matthew A. Wilson, Fabian Kloosterman, Zhe Chen
Journal Paper Cell Reports, Volume 25, 2018, Pages 2635-2642

Abstract

Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a GPU-powered population-decoding system for ultrafast reconstruction of spatial positions from rodent’s unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core CPU implementation, our approach achieves a ~20-50 fold speedup in eight tested rat hippocampal, cortical and thalamic ensemble recordings, with real-time decoding speed (~fraction of millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time less than 15 ms), our approach enables assessment of the statistical significance of online-decoded ‘memory replay’ candidates during quiet wakefulness or sleep. This open-source software toolkit supports the decoding of spatial correlates or content-triggered experimental manipulation in closed-loop neuroscience experiments.

Dynamics of motor cortical activity during naturalistic feeding behavior

Shizhao Liu, Jose Iriate-Diaz, Nicholas G. Hatsopoulos, Callum F. Ross, Kazutaka Takahashi, Zhe Chen
Journal Paper Journal of Neural Engineering, Volume , 2018, Pages

Abstract

The orofacial primary motor cortex (MIo) plays a critical role in controlling tongue and jaw movements during oral motor functions, such as chewing, swallowing and speech. However, the neural mechanisms of MIo during naturalistic feeding are still poorly understood. To investigate the neural dynamics and variability of MIo neuronal activity during naturalistic feed- ing, we used chronically implanted micro-electrode arrays to simultaneously recorded ensembles of neuronal activity in MIo of two monkeys (Macaca mulatta) while eating various types of food. Rhythmic neuronal firing patterns and oscillatory dynamics are evident in single-unit activity. At the population level, we used a latent variable model and Bayesian inference method to (i) unfold the neural dynamics of rhythmic chewing, and (ii) to quantify the neural variability at multiple timescales (complete feeding sequences, chewing sequence stages, chewing gape cycle phases) within and between food types. Overall, our novel population analyses reveal latent structures of neuronal population codes of MIo and unfold the mapping between MIo ensemble spike activity and high-dimensional kinematic measures.

Multimodal neural responses differ between evoked and spontaneous pain in rats

Zhengdong Xiao, Erik Martinez, Parathamesh Kulkarni, Qiaosheng Zhang, Qianning Hou, David Rosenberg, Haocheng Zhao, Jing Wang, Zhe Chen
Journal Paper Scientific Reports, Volume , 2018, Pages

Abstract

A deep learning approach for real-time detection of sleep spindles

Parathamesh Kulkarni*, Zhengdong Xiao*, Eric J. Robinson, Apoorva Sagarwa Jami, Jianping Zhang, Haocheng Zhou, Simon Henin, Anli A. Liu, Ricardo S. Osorio, Jing Wang, Zhe Chen
Journal Paper Journal of Neural Engineering, Volume , 2018, Pages

Abstract

An image segmentation approach based on wavelet and fractal features extraction

Zhe Chen, Tianjing Feng
Journal PaperChinese Journal of Image & Graphics (A), Volume 4, Issue 12, 1999, Pages 1072-1077

Abstract

Research development and prospects on wavelet neural networks

Zhe Chen, Tianjing Feng
Journal PaperJournal of Ocean University of Qingdao, Volume 29, Issue 4, 1999, Pages 663-668

Abstract

A fuzzy adaptive algorithm for learning parameters of BP networks

Tianjing Feng, Zhe Chen, Fanfan Gu
Journal PaperJournal of Ocean University of Qingdao, Volume 30, Issue 1, 2000, Pages 137-141

Abstract

A BP algorithm-based wavelet neural network

Zhe Chen, Tianjing Feng, Gang Chen
Journal PaperJournal of Ocean University of Qingdao, Volume 31, Issue 1, 2000, Pages 122-128

Abstract

A review: the research advances on combination of wavelet analysis and neural networks

Zhe Chen, Tianjing Feng
Journal PaperChinese Journal of Electronics, Volume 22, Issue 3, 2000, Pages 496-504

Abstract

A study on internal decision pattern of MLP networks

Tianjing Feng, Zhe Chen, Jianshe Xiong
Journal PaperJournal of Data Acquisition and Processing, Volume 15, Issue 4, 2001, Pages 408-412

Abstract

Wavelet neural networks for time series analysis and state space reconstruction

Zhe Chen, Tianjing Feng, Qin Sun
Journal PaperChinese Journal of Computer Research and Development, Volume 38, Issue 5, 2002, Pages 591-596

Abstract

Texture segmentation based on wavelet decomposition and Kohonen network for remotely sensed images

Zhe Chen, Tianjin Feng, Z. Houkes
Conference Papers Proceedings of IEEE Conference on Systems, Man and Cybernetics, 1999, Pages 816-821

Abstract

The application of wavelet neural network for time series prediction and system modeling based on multiresolution learning

Zhe Chen, Tianjin Feng, Q.C. Meng
Conference Papers Proceedings of IEEE Conference on Systems, Man and Cybernetics, 1999, Pages 425-430

Abstract

Genetic algorithms encoding study and a sufficient convergence condition of GAs

Q.C. Meng, Tianjin Feng, Zhe Chen
Conference Papers Proceedings of IEEE Conference on Systems, Man and Cybernetics, 1999, Pages 649-652

Abstract

Genetic algorithms encoding study and a sufficient convergence condition of GAs

Q.C. Meng, Tianjing Feng, Zhe Chen
Conference Papers Proceedings of Chinese Intelligent Automation Conference, 1999, Pages 1-11

Abstract

Mexican hat wavelet and its application in edge detection

Zhe Chen, Tianjin Feng, Z. Houkes
Conference Papers Proceedings of International Forum on Multimedia Image Processing, 1999

Abstract

Incorporating a priori knowledge into initialized weights for neural classifier

Zhe Chen, Tianjin Feng, Z. Houkes
Conference Papers Proceedings of IEEE Joint Conference on Neural Networks, 2000, Pages 291-286

Abstract

A new view of regularization theory

Zhe Chen, Simon Haykin
Conference Papers Proceedings of IEEE Conference on Systems, Man and Cybernetics, 2001, Pages 1642-1647

Abstract

Theory of Monte Carlo sampling-based ALOPEX algorithms for neural networks

Zhe Chen, Suzanna Becker, Simon Haykin
Conference Papers Proceedings of IEEE ICASSP, 2004, Pages 501-504

Abstract

A new neural equalizer for decision-feedback equalization

Zhe Chen, A.C. de C. Lima
Conference Papers Proceedings of IEEE Workshop on Machine Learning for Signal Processing, 2004, Pages 675-684

Abstract

Improved particle filtering schemes for target tracking

Zhe Chen, A. Cichocki, T.M. Rutkowski
Conference Papers Proceedings of IEEE ICASSP, 2005, Pages 145-148

Abstract

Analysis of feasible solutions of the ICA problem under the one-bit-matching condition

Jinwen Ma, Zhe Chen, Shun-ichi Amari
Conference Papers Proceedings of 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA), 2006, Pages 838-845

Abstract

Constrained non-negative matrix factorization method for EEG analysis in early detection of Alzheimer disease

Zhe Chen, A. Cichocki, T.M. Rutkowski
Conference Papers Proceedings of IEEE ICASSP, 2006, Pages 893-896

Abstract

Contrast functions of non-circular and circular sources separation in complex-valued ICA

Zhe Chen, Jinwen Ma
Conference Papers Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN), 2006, Pages 465-472

Abstract

An empirical quantitative EEG analysis for evaluating clinical brain death

Zhe Chen, Jianting Cao
Conference Papers Proceedings of IEEE EMBC, 2007, Pages 3880-3883

Abstract

ICA and complexity measures of EEG analysis in brain death determination

Jianting Cao, Zhe Chen
Conference Papers Proceedings of 1st Conference on Cognitive Neurodynamics (ICCN'07), 2007, Pages 699-703

Abstract

Modified modulated Hebb-Oja learning rule: A method for biologically plausible principal component analysis

M. Jankovic, P. Martinez, Zhe Chen, A. Cichocki
Conference Papers Proceedings of 14th International Conference on Neural Information Processing (ICONIP2007), 2008, Pages 527-536

Abstract

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

Jordi Sole-Casals, F. Vialatte, Zhe Chen, A. Cichocki
Conference Papers Proceedings of 1st International Conference on Bio-inspired Systems and Signal Processing, 2009, Pages 232-235

Abstract

A study of probabilistic models for characterizing human heart beat dynamics in autonomic blockade control

Wei Wu, Zhe Chen, Emery N. Brown, Riccardo Barbieri
Conference Papers Proceedings of IEEE ICASSP, 2008, Pages 481-484

Abstract

A study of probabilistic models for characterizing human heart beat dynamics in autonomic blockade control

Zhe Chen, Emery N. Brown, Riccardo Barbieri
Conference Papers Proceedings of IEEE EMBC, 2008, Pages 2781-2784

Abstract

Assessment of hippocampal and autonomic neural activity by point process models

Riccardo Barbieri, Zhe Chen, Emery N. Brown
Conference Papers Proceedings of IEEE EMBC, 2008, Pages 3679

Abstract

A point process approach to assess dynamic baroreflex gain

Zhe Chen, Emery N. Brown, Riccardo Barbieri
Conference Papers Computers in Cardiology, 2008, Pages 805-808

Abstract

Coherency and sharpness measures by using ICA algorithms: an investigation for Alzheimer's disease discrimination

Jordi Sole-Casals, F. Vialatte, A. Cichocki, Zhe Chen
Conference Papers Proceedings of 2nd International Conference on Bio-inspired Systems and Signal Processing, 2009, Pages 468-475

Abstract

Assessment of baroreflex control of heart rate during general anesthesia using a point process method

Wei Wu, Zhe Chen, P.L. Purdon, E.T. Pierce, P.G. Harrell, Emery N. Brown, Riccardo Barbieri
Conference Papers Proceedings of IEEE ICASSP, 2009, Pages 333-336

Abstract

A unified point process framework for assessing heartbeat dynamics and cardiovascular control

Zhe Chen, Emery N. Brown, Riccardo Barbieri
Conference Papers Proceedings of IEEE 35th Annual Northeast Bioengineering Conference, 2008

Abstract

Linear and nonlinear quantification of respiratory sinus arrhythmia during propofol general anesthesia

Zhe Chen, P.L. Purdon, E.T. Pierce, P.G. Harrell, J. Walsh, A.F. Salazar, C.L. Tavares, Emery N. Brown, Riccardo Barbieri
Conference Papers Proceedings of IEEE EMBC, 2009, Pages 5336-5339

Abstract

A probabilistic framework for learning robust common spatial patterns

Wei Wu, Zhe Chen, Shangkai Gao, Emery N. Brown
Conference Papers Proceedings of IEEE EMBC, 2009, Pages 4658-4661

Abstract

Robustness in signal processing is crucial for the purpose of reliably interpreting physiological features from noisy data in biomedical applications. We present a robust algorithm based on the reformulation of a well-known spatial filtering and feature extraction algorithm named Common Spatial Patterns (CSP). We cast the problem of learning CSP into a probabilistic framework, which allows us to gain insights into the algorithm. To address the overfitting problem inherent in CSP, we propose an expectation-maximization (EM) algorithm for learning robust CSP using from a Student-t distribution. The efficacy of the proposed robust algorithm is validated with both simulated and real EEG data.

A regularized point process generalized linear model for assessing the functional connectivity in the cat motor cortex

Zhe Chen, David Putrino, Demba Ba, S. Ghosh, Riccardo Barbieri, Emery N. Brown
Conference Papers Proceedings of IEEE EMBC, 2009, Pages 5006-5009

Abstract

Variational Bayesian inference for point process generalized linear models in neural spike train analysis

Zhe Chen, Fabian Kloosterman, Matthew A. Wilson, Emery N. Brown
Conference Papers Proceedings of IEEE ICASSP, 2010, Pages 2086-2089

Abstract

Hierarchical Bayesian modeling of inter-trial variability and variational Bayesian learning of common spatial patterns from multichannel EEG

Wei Wu, Zhe Chen, Shangkai Gao, Emery N. Brown
Conference Papers Proceedings of IEEE ICASSP, 2010, Pages 501-504

Abstract

In numerous neuroscience studies, multichannel EEG data are often recorded over multiple trial periods under the same experimental condition. To date, little effort is aimed to learn spatial patterns from EEG data to account for trial-to-trial variability. In this paper, a hierarchical Bayesian framework is introduced to model inter-trial source variability while extracting common spatial patterns under multiple experimental conditions in a supervised manner. We also present a variational Bayesian algorithm for model inference, by which the number of sources can be determined effectively via automatic relevance determination (ARD). The efficacy of the proposed learning algorithm is validated with both synthetic and real EEG data. Using two brain-computer interface (BCI) motor imagery data sets we show the proposed algorithm consistently outperforms the common spatial patterns (CSP) algorithm while attaining comparable performance with a recently proposed discriminative approach.

A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis

Zhe Chen, Patrick L. Purdon, Emery N. Brown, Riccardo Barbieri
Conference Papers Proceedings of IEEE EMBC, 2010, Pages 3567-3570

Abstract

Instantaneous assessment of autonomic cardiovascular control during general anesthesia

Zhe Chen, Luca Citi, Patrick L. Purdon, Emery N. Brown, Riccardo Barbieri
Conference Papers Proceedings of IEEE EMBC, 2011, Pages 8444-8447

Abstract

Assessing neuronal interactions of cell assemblies during general anesthesia

Zhe Chen, Sujith Vijayan, S. Ching, G. Hale, F. Flores, Matthew A. Wilson, Emery N. Brown
Conference Papers Proceedings of IEEE EMBC, 2011, Pages 4175-4178

Abstract

Transductive neural decoding for unsorted neuronal spikes of rat hippocampus

Zhe Chen, Fabian Kloosterman, Stuart Layton, Matthew A. Wilson
Conference Papers Proceedings of IEEE EMBC, 2012, Pages 1310-1313

Abstract

Neural decoding is an important approach for extracting information from population codes. We previously proposed a novel transductive neural decoding paradigm and applied it to reconstruct the rat’s position during navigation based on unsorted rat hippocampal ensemble spiking activity. Here, we investigate several important technical issues of this new paradigm using one data set of one animal. Several extensions of our decoding method are discussed.

Sparse Bayesian inference methods for decoding 3D reach and grasp kinematics and joint angles with primary motor cortical ensembles

Zhe Chen, Kazutaka Takahashi, Nicholas G. Hatsopopulos
Conference Papers Proceedings of IEEE EMBC, 2013, Pages 5930-5933

Abstract

A variational nonparametric Bayesian approach for inferring rat hippocampal population codes

Zhe Chen, Matthew A. Wilson
Conference Papers Proceedings of IEEE EMBC, 2013, Pages 7092-7095

Abstract

Integrating neural spiking and LFP activity to decode kinematics of the arm and hand during unconstrained reach to grasp movement

M.D. Best, K. Takahashi, Zhe Chen, N. Huh, K.A. Brown, N.G. Hatsopopulos
Conference Papers Proceedings of IEEE EMBS Neural Engineering Conference (NER), 2013, Pages 1425-1428

Abstract

Many current brain-machine interfaces do not consider the behavioral context of the subject, rather, they assume the subject is constantly engaged in a single task. We investigated how incorporating information about state can improve the performance of a decoder. Unit spiking activity and LFPs were recorded from chronically implanted electrode arrays in primary motor and premotor corticies while a monkey performed a reach to grasp task. We applied an unsupervised clustering technique to LFP data to identify different neural states. Then, for each state, we fit a sparse Bayesian linear model with causal interaction terms to decode the joint kinematics of many degrees of freedom in the arm and hand. We used automatic relevance determination for variable selection and to avoid overfitting. We show that the state-based decoding model improves decoding performance over a model without state information. We further show that topology of interaction terms is different across different states.

Statistical analysis of neuronal population codes for encoding acute pain

Zhe Chen, Jing Wang
Conference Papers Proceedings of IEEE ICASSP, 2016, Pages 829-833

Abstract

To date most pain studies have focused on spinal cord or peripheral pathways. However, a complete understanding of pain mechanisms requires the study of neocortex. Using an animal model of acute pain, we investigate neural codes for pain at both single-cell and population levels. We propose a statistical framework, rooted in state space analysis, for analyzing neural ensembles recorded from the rat primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) during a laser pain stimulation protocol. The 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 pain on a single or multiple-trial basis.

Neural encoding models of complex receptive fields: a comparison of nonparametric and parametric approaches

Rahul Agarwal, Zhe Chen, Sridevi S. Sarma
Conference Papers Proceedings of 50th Conference on Information Science and Systems (CISS), 2016, Pages 562-567

Abstract

Parametric models have been widely used to estimate conditional intensity functions of neuronal spike train point processes and are efficient to construct from experimental data. Furthermore, parametric models are easy to interpret. However, neurons that have more complex receptive fields may not be sufficiently characterized through parametric modeling since it imposes strict structure on the encoding fields. In this paper, we consider a pyramidal neuron recorded from the rat hippocampus, called a “place” cell, that has a diverse apparently multimodal receptive field that encodes information about the spatial position while the rat freely-forages in a circular environment. We construct encoding models for this place cell using two nonparametric modeling approaches, our recently developed band-limited maximum likelihood (BLML) estimator and a kernel density estimator (KDE); and compare them to models constructed using two parametric approaches that have been previously applied to these neurons. We found that the BLML and KDE better capture the complex receptive field of the studied cell as measured by the KS-statistic and log-likelihood.

Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes

Zhe Chen, Scott Linderman, Matthew A. Wilson
Conference Papers Proceedings of IEEE Machine Learning for Signal Processing (MLSP), 2016

Abstract

Hippocampal functions are responsible for encoding spatial and temporal dimensions of episodic memory, and hippocampal reactivation of previous awake experiences in sleep is important for learning and memory consolidation. Therefore, uncovering neural representations of hippocampal ensemble spike activity during various behavioral states would provide improved understanding of neural mechanisms of hippocampal-cortical circuits. In this paper, we propose two Bayesian nonparametric methods for this purpose: the Bayesian modeling allows to impose informative priors and constraints into the model, whereas Bayesian nonparametrics allows automatic model selection. We validate these methods to three different hippocampal ensemble recordings under different task behaviors, and provide interpretation and discussion on the derived results.

Unfolding representations of trajectory coding in neuronal population spike activity

Zhe Chen
Conference Papers Proceedings of 51st Conference on Information Science and Systems (CISS), 2017

Abstract

A fundamental goal of computational neuroscience is to understand the representation of large-scale neuronal population codes during various behavioral states. To date, a few probabilistic models have been developed for uncovering representations of neuronal population spike activity. In this paper, we propose an unsupervised learning framework for unfolding sequence representations in neuronal population codes. Specifically, we integrate temporal embedding into a hidden Markov model (HMM) and use it to unfold trajectory coding representations in neuronal population spike activity. Finally, we apply the proposed methods to two rat hippocampal recordings. Our preliminary results suggest that temporal embedding enhances the representation power of HMM-type models and improves the detection power for identifying significant hippocampal memory replay events during off-line state.

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

Zhe Chen, Sile Hu, Qiaosheng Zhang, Jing Wang
Conference Papers Proceedings of IEEE EMBS Neural Engineering Conference (NER), 2017, Pages 481-484

Abstract

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.

A real-time rodent neural interface for deciphering acute pain signals from neuronal ensemble spike activity

Sile Hu, Qiaosheng Zhang, Jing Wang, Zhe Chen
Conference Papers Proceedings of 51st Asilomar Conference on Signals, Systems, and Computers, 2017, Pages 93-97

Abstract

Latent variable models for uncovering motor cortical ensemble dynamics

Zhe Chen, Shizhao Liu, Jose Iriarte-Diaz, Nicholas G. Hatsopoulos, Callum F. Ross, Kazutaka Takahashi
Conference Papers Proceedings of 51st Asilomar Conference on Signals, Systems, and Computers, 2017, Pages 331-335

Abstract

Practical consideration of a BMI application for detecting acute pain signals

Sile Hu, Zhengdong Xiao, Qiaosheng Zhang, Louise Urien, Jing Wang, Zhe Chen
Conference Papers Proceedings of IEEE ICASSP, 2018, Pages 831-835

Abstract

Proportionate adaptation: new paradigms in adaptive filters

Zhe Chen, Steven Gay, Simon Haykin
Book Chapter Least-Mean-Square Adaptive Filters, Pages 293-334 | 2003 | Wiley | ISBN: 978-0-471-21570-7

Abstract

The machine cocktail party problem

Simon Haykin, Zhe Chen
Book Chapter New Directions in Statistical Signal Processing: From Systems to Brain, Pages 51-75 | 2006 | MIT Press | ISBN: 9780262083485

Abstract

Advanced EEG signal processing in brain death diagnosis

Zhe Chen, Jianting Cao
Book Chapter Signal Processing Techniques for Knowledge Extraction and Information Fusion, Pages 275-298 | 2008 | Springer | ISBN: 978-0-387-74367-7

Abstract

In this chapter, we present several electroencephalography (EEG) signal processing and statistical analysis methods for the purpose of clinical diagnosis of brain death, in which an EEG-based preliminary examination system was developed during the standard clinical procedure. Specifically, given the reallife recorded EEG signals, a robust principal factor analysis (PFA) associated with independent component analysis (ICA) approach is applied to reduce the power of additive noise and to further separate the brain waves and interference signals. We also propose a few frequency-based and complexity-based statistics for quantitative EEG analysis with an aim to evaluate the statistical significance differences between the coma patients and quasi-brain-death patients. Based on feature selection and classification, the system may yield a binary decision from the classifier with regard to the patient's status. Our empirical data analysis has shown some promising directions for real-time EEG analysis in clinical practice.

State-space modeling of neural spike train and behavioral data

Zhe Chen, Riccardo Barbieri, Emery N. Brown
Book Chapter Statistical Signal Processing for Neuroscience and Neurotechnology, Pages 175-218 | 2010 | Academic Press | ISBN: 9780123750273

Abstract

Generalized linear model for point process analyses of neural spiking activity

Zhe Chen, Emery N. Brown
Book Chapter Encyclopedia Article of Computational Neuroscience | 2014 | Springer | ISBN: 978-1-4614-6676-5

Abstract

State space models for the analysis of neural spike train and behavioral data

Zhe Chen, Emery N. Brown
Book Chapter Encyclopedia Article of Computational Neuroscience | 2014 | Springer | ISBN: 978-1-4614-6676-5

Abstract

Introduction

Zhe Chen
Book Chapter Advanced State Space Methods for Neural and Clinical Data, Pages 1-13 | 2015 | Cambridge University Press | ISBN: 9781139941433

Abstract

Probabilistic approaches to uncover rat hippocampal population codes

Zhe Chen, Fabian Kloosterman, Matthew A. Wilson
Book Chapter Advanced State Space Methods for Neural and Clinical Data, Pages 186-206 | 2015 | Cambridge University Press | ISBN-10: 9781139941433

Abstract

A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions

Zhe Chen, Riccardo Barbieri
Book Chapter Advanced State Space Methods for Neural and Clinical Data, Pages 302-239 | 2015 | Cambridge University Press | ISBN-10: 9781139941433

Abstract

Introduction

Zhe Chen, Sridevi S. Sarma
Book Chapter Dynamic Neuroscience: Statistics, Modeling and Control, Pages 1-25 | 2018 | Springer | ISBN-10: 978-3-319-71976-4

Abstract

Nowadays we have witnessed an enormous amount of neural data being collected. Neural signals are stochastic and dynamic processes measured in specific neural circuits at various spatiotemporal scales. Development of efficient quantitative tools to characterize these signals and extract information that reveals circuit mechanisms is an important task in computational and statistical neuroscience. In this introductory chapter, we review important concepts and representative applications of statistics, signal processing, and control in neuroscience. Finally, we provide roadmaps for this edited book as well as pointers to the literature and other resources.

Latent variable modeling for neuronal population dynamics

Zhe Chen
Book Chapter Dynamic Neuroscience: Statistics, Modeling and Control, Pages 53-82 | 2018 | Springer | ISBN-10: 978-3-319-71976-4

Abstract

Neural activity is noisy (“stochastic”) and dynamic at various spatiotemporal scales. We consider a general class of latent variable models for characterizing neuronal population dynamics or analyzing various sorts of neural data. The inference of latent variable models can lead to novel solutions for signal detection, neural decoding, denoising, dimensionality reduction, and data visualization. We review general modeling and inference strategies for latent variable models. Finally, we illustrate our methods with several neuroscience applications using population spike trains recorded from the animal’s hippocampus and neocortices.

Neuromodulation for pain management

Jing Wang, Zhe Chen
Book Chapter Neural Interfaces: Frontiers and Applications, Pages | 2018 | Springer | ISBN-10:

Abstract