Biomedical subjects

Emery N Brown

Publications and source records attributed to Emery N Brown.

At least 19 recordsLinked to original sources

PubMed2006

Bayesian analysis of interleaved learning and response bias in behavioral experiments.

Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood-based approaches in the analysis of a simulated conditioned T-maze task and of an actual object-place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.

Bayes Theorem↗

PubMed2006

Modeling the contribution of lamina 5 neuronal and network dynamics to low frequency EEG phenomena.

The Electroencephalogram (EEG) is an important clinical and research tool in neurophysiology. With the advent of recording techniques, new evidence is emerging on the neuronal populations and wiring in the neocortex. A main challenge is to relate the EEG generation mechanisms to the underlying circuitry of the neocortex. In this paper, we look at the principal intrinsic properties of neocortical cells in layer 5 and their network behavior in simplified simulation models to explain the emergence of several important EEG phenomena such as the alpha rhythms, slow-wave sleep oscillations, and a form of cortical seizure. The models also predict the ability of layer 5 cells to produce a resonance-like neuronal recruitment known as the augmenting response. While previous models point to deeper brain structures, such as the thalamus, as the origin of many EEG rhythms (spindles), the current model suggests that the cortical circuitry itself has intrinsic oscillatory dynamics which could account for a wide variety of EEG phenomena.

Animals↗

PubMed2006

Hippocampal and cortical place cell plasticity: implications for episodic memory.

In humans, the hippocampus is essential for storing episodic memories. These event memories require the rapid storage of novel associations, but little is known about the cellular correlates of such rapid plasticity. We studied patterns of activity and plasticity in the CA1 region of the hippocampus and in anatomically adjacent cortical regions as rats explored a novel arm of a maze to identify the neural correlates of hippocampally dependent memory formation. We found that hippocampal place fields exhibited three phenomena that may have direct relevance to the encoding of episodic memories: (1) very rapid plasticity upon exposure to the new environment, (2) instability in representations formed after short periods of exploration, and (3) a dissociation between the stability of a hippocampal representation and the apparent familiarity of a location. In contrast, cortical regions showed less dramatic changes. Taken together, these findings suggest that hippocampal activity undergoes a period of rapid reorganization during the encoding of novel information, and that even after this reorganization is complete, areas outside the hippocampus have not yet formed stable memories.

Animals↗

PubMed2006

Analysis of heartbeat dynamics by point process adaptive filtering.

Heartbeats are a point process yet, most of the current analysis methods do not model this important characteristic of these data. We describe human heartbeat time series as a history dependent inverse Gaussian model. We present a point process adaptive filter algorithm to estimate the model's time-varying parameters, and use it to compute new measures of heart rate variability. We apply our algorithm to analyze simulated heartbeat data and actual heartbeat data from a tilt table experiment and from healthy subjects and subjects with congestive heart failure during sleep. Our results suggest a new approach for characterizing heartbeat dynamics.

Algorithms↗

PubMed2006

A state-space analysis for reconstruction of goal-directed movements using neural signals.

The execution of reaching movements involves the coordinated activity of multiple brain regions that relate variously to the desired target and a path of arm states to achieve that target. These arm states may represent positions, velocities, torques, or other quantities. Estimation has been previously applied to neural activity in reconstructing the target separately from the path. However, the target and path are not independent. Because arm movements are limited by finite muscle contractility, knowledge of the target constrains the path of states that leads to the target. In this letter, we derive and illustrate a state equation to capture this basic dependency between target and path. The solution is described for discrete-time linear systems and gaussian increments with known target arrival time. The resulting analysis enables the use of estimation to study how brain regions that relate variously to target and path together specify a trajectory. The corresponding reconstruction procedure may also be useful in brain-driven prosthetic devices to generate control signals for goal-directed movements.

Animals↗

PubMed2005

Dynamic remodeling of dendritic arbors in GABAergic interneurons of adult visual cortex.

Despite decades of evidence for functional plasticity in the adult brain, the role of structural plasticity in its manifestation remains unclear. To examine the extent of neuronal remodeling that occurs in the brain on a day-to-day basis, we used a multiphoton-based microscopy system for chronic in vivo imaging and reconstruction of entire neurons in the superficial layers of the rodent cerebral cortex. Here we show the first unambiguous evidence (to our knowledge) of dendrite growth and remodeling in adult neurons. Over a period of months, neurons could be seen extending and retracting existing branches, and in rare cases adding new branch tips. Neurons exhibiting dynamic arbor rearrangements were GABA-positive non-pyramidal interneurons, while pyramidal cells remained stable. These results are consistent with the idea that dendritic structural remodeling is a substrate for adult plasticity and they suggest that circuit rearrangement in the adult cortex is restricted by cell type-specific rules.

Aging↗

PubMed2005

Characterizing the amplitude dynamics of the human core-temperature circadian rhythm using a stochastic-dynamic model.

Two measures, amplitude and phase, have been used to describe the characteristics of the endogenous human circadian pacemaker, a biological clock located in the hypothalamus. Although many studies of change in circadian phase with respect to different stimuli have been conducted, the physiologic implications of the amplitude changes (dynamics) of the pacemaker are unknown. It is known that phase changes of the human circadian pacemaker have a significant impact on sleep timing and content, hormone secretion, subjective alertness and neurobehavioral performance. However, the changes in circadian amplitude with respect to different stimuli are less well documented. Although amplitude dynamics of the human circadian pacemaker are observed in physiological rhythms such as plasma cortisol, plasma melatonin and core temperature data, currently methods are not available to accurately characterize the amplitude dynamics from these rhythms. Of the three rhythms core temperature is the only reliable variable that can be monitored continuously in real time with a high sampling rate. To characterize the amplitude dynamics of the circadian pacemaker we propose a stochastic-dynamic model of core temperature data that contains both stochastic and dynamic characteristics. In this model the circadian component that has a dynamic characteristic is represented as a perturbation solution of the van der Pol equation and the thermoregulatory response in the data that has a stochastic characteristic is represented as a first-order autoregressive process. The model parameters are estimated using data with a maximum likelihood procedure and the goodness-of-fit measures along with the associated standard error of the estimated parameters provided inference about the amplitude dynamics of the pacemaker. Using this model we analysed core temperature data from an experiment designed to exhibit amplitude dynamics. We found that the circadian pacemaker recovers slowly to an equilibrium level following amplitude suppression. In humans this reaction to perturbation from equilibrium value has potential physiological implications.

Body Temperature Regulation↗

PubMed2005

Functional magnetic resonance imaging activity during the gradual acquisition and expression of paired-associate memory.

Recent neurophysiological findings from the monkey hippocampus showed dramatic changes in the firing rate of individual hippocampal cells as a function of learning new associations. To extend these findings to humans, we used blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to examine the patterns of brain activity during learning of an analogous associative task. We observed bilateral, monotonic increases in activity during learning not only in the hippocampus but also in the parahippocampal and right perirhinal cortices. In addition, activity related to simple novelty signals was observed throughout the medial temporal lobe (MTL) memory system and in several frontal regions. A contrasting pattern was observed in a frontoparietal network in which a high level of activity was sustained until the association was well learned, at which point the activity decreased to baseline. Thus, we found that associative learning in humans is accompanied by striking increases in BOLD fMRI activity throughout the MTL as well as in the cingulate cortex and frontal lobe, consistent with neurophysiological findings in the monkey hippocampus. The finding that both the hippocampus and surrounding MTL cortex exhibited similar associative learning and novelty signals argues strongly against the view that there is a clear division of labor in the MTL in which the hippocampus is essential for forming associations and the cortex is involved in novelty detection. A second experiment addressed a striking aspect of the data from the first experiment by demonstrating a substantial effect of baseline task difficulty on MTL activity capable of rendering mnemonic activity as either "positive" or "negative."

Adult↗

PubMed2005

Comparison of amplitude recovery dynamics of two limit cycle oscillator models of the human circadian pacemaker.

At an organism level, the mammalian circadian pacemaker is a two-dimensional system. For these two dimensions, phase (relative timing) and amplitude of the circadian pacemaker are commonly used. Both the phase and the amplitude (A) of the human circadian pacemaker can be observed within multiple physiological measures--including plasma cortisol, plasma melatonin, and core body temperature (CBT)--all of which are also used as markers of the circadian system. Although most previous work has concentrated on changes in phase of the circadian system, critically timed light exposure can significantly reduce the amplitude of the pacemaker. The rate at which the amplitude recovers to its equilibrium level after reduction can have physiological significance. Two mathematical models that describe the phase and amplitude dynamics of the pacemaker have been reported. These models are essentially equivalent in predictions of phase and in predictions of amplitude recovery for small changes from an equilibrium value (A = 1), but are markedly different in the prediction of recovery rates when A < 0.6. To determine which dynamic model best describes the amplitude recovery observed in experimental data; both models were fit to CBT data using a maximum likelihood procedure and compared using Akaike's Information Criterion (AIC). For all subjects, the model with the lower recovery rate provided a better fit to data in terms of AIC, supporting evidence that the amplitude recovery of the endogenous pacemaker is slow at low amplitudes. Experiments derived from model predictions are proposed to test the influence of low amplitude recovery on the physiological and neurobehavioral functions.

Biological Clocks↗

PubMed2005

An analysis of hippocampal spatio-temporal representations using a Bayesian algorithm for neural spike train decoding.

Neural spike train decoding algorithms are important tools for characterizing how ensembles of neurons represent biological signals. We present a Bayesian neural spike train decoding algorithm based on a point process model of individual neurons, a linear stochastic state-space model of the biological signal, and a temporal latency parameter. The latency parameter represents the temporal lead or lag between the biological signal and the ensemble spiking activity. We use the algorithm to study whether the representation of position by the ensemble spiking activity of pyramidal neurons in the CA1 region of the rat hippocampus is more consistent with prospective coding, i.e., future position, or retrospective coding, past position. Using 44 simultaneously recorded neurons and an ensemble delay latency of 400 ms, the median decoding error was 5.1 cm during 10 min of foraging in an open circular environment. The true coverage probability for the algorithm's 0.95 confidence regions was 0.71. These results illustrate how the Bayesian neural spike train decoding paradigm may be used to investigate spatio-temporal representations of position by an ensemble of hippocampal neurons.

Algorithms↗

PubMed2005

Statistical issues in the analysis of neuronal data.

Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data.

Action Potentials↗

PubMed2005

Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity.

Analyzing the dependencies between spike trains is an important step in understanding how neurons work in concert to represent biological signals. Usually this is done for pairs of neurons at a time using correlation-based techniques. Chornoboy, Schramm, and Karr (1988) proposed maximum likelihood methods for the simultaneous analysis of multiple pair-wise interactions among an ensemble of neurons. One of these methods is an iterative, continuous-time estimation algorithm for a network likelihood model formulated in terms of multiplicative conditional intensity functions. We devised a discrete-time version of this algorithm that includes a new, efficient computational strategy, a principled method to compute starting values, and a principled stopping criterion. In an analysis of simulated neural spike trains from ensembles of interacting neurons, the algorithm recovered the correct connectivity matrices and interaction parameters. In the analysis of spike trains from an ensemble of rat hippocampal place cells, the algorithm identified a connectivity matrix and interaction parameters consistent with the pattern of conjoined firing predicted by the overlap of the neurons' spatial receptive fields. These results suggest that the network likelihood model can be an efficient tool for the analysis of ensemble spiking activity.

Action Potentials↗

PubMed2005

Behavioral and neurophysiological analyses of dynamic learning processes.

In this article, the authors address two topics relevant to the study of the brain basis of associative learning. In Part 1, they compare and contrast the patterns and time course of dynamic learning-related neural activity that have been reported in the medial temporal lobe, premotor cortex, prefrontal cortex, and striatum during various associative learning tasks. In Part 2, they examine the statistical methodologies that have been used to analyze both behavioral learning and learning-related neural activity. They describe a state-space model of behavioral learning that provides accurate estimates of dynamic learning processes and a point-process filter algorithm that tracks the dynamic changes in neural activity on a millisecond time scale. Future challenges for these statistical methodologies and their application to the study of the brain basis of associative learning are discussed.

Association Learning↗

PubMed2004

Analysis and design of behavioral experiments to characterize population learning.

In population learning studies, between-subject response differences are an important source of variance that must be characterized to identify accurately the features of the learning process common to the population. Although learning is a dynamic process, current population analyses do not use dynamic estimation methods, do not compute both population and individual learning curves, and use learning criteria that are less than optimal. We develop a state-space random effects (SSRE) model to estimate population and individual learning curves, ideal observer curves, and learning trials, and to make dynamic assessments of learning between two populations and within the same population that avoid multiple hypothesis tests. In an 80-trial study of an NMDA antagonist's effect on the ability of rats to execute a set-shift task, our dynamic assessments of learning demonstrated that both the treatment and control groups learned, yet, by trial 35, the treatment group learning was significantly impaired relative to control. We used our SSRE model in a theoretical study to evaluate the design efficiency of learning experiments in terms of the number of animals per group and number of trials per animal required to characterize learning differences between two populations. Our results demonstrated that a maximum difference in the probability of a correct response between the treatment and control group learning curves of 0.07 (0.20) would require 15 to 20 (5 to 7) animals per group in an 80 (60)-trial experiment. The SSRE model offers a practical approach to dynamic analysis of population learning and a theoretical framework for optimal design of learning experiments.

Algorithms↗

PubMed2004

A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability.

Heart rate is a vital sign, whereas heart rate variability is an important quantitative measure of cardiovascular regulation by the autonomic nervous system. Although the design of algorithms to compute heart rate and assess heart rate variability is an active area of research, none of the approaches considers the natural point-process structure of human heartbeats, and none gives instantaneous estimates of heart rate variability. We model the stochastic structure of heartbeat intervals as a history-dependent inverse Gaussian process and derive from it an explicit probability density that gives new definitions of heart rate and heart rate variability: instantaneous R-R interval and heart rate standard deviations. We estimate the time-varying parameters of the inverse Gaussian model by local maximum likelihood and assess model goodness-of-fit by Kolmogorov-Smirnov tests based on the time-rescaling theorem. We illustrate our new definitions in an analysis of human heartbeat intervals from 10 healthy subjects undergoing a tilt-table experiment. Although several studies have identified deterministic, nonlinear dynamical features in human heartbeat intervals, our analysis shows that a highly accurate description of these series at rest and in extreme physiological conditions may be given by an elementary, physiologically based, stochastic model.

Adult↗

PubMed2004

A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.

Action Potentials↗

PubMed2004

Hippocampal plasticity across multiple days of exposure to novel environments.

The hippocampus is essential for learning complex spatial relationships, but little is known about how hippocampal neural activity changes as animals learn about a novel environment. We studied the formation of new place representations in rats by examining the changes in place-specific firing of neurons in the CA1 region of the hippocampus and the relationship between these changes and behavioral change across multiple days of exposure to novel places. We found that many neurons showed very rapid changes on the first day of exposure to the novel place, including many cases in which a previously silent neuron developed a place field over the course of a single pass through the environment. Across the population, the largest changes in neural activity occurred on day 2 of exposure to a novel place, but only if the animal had little experience (<4 min) in that location on day 1. Longer exposures on day 1 were associated with smaller changes on day 2, suggesting that hippocampal neurons required 5-6 min of experience to form a stable spatial representation. Even after the representation stabilized, the animals' behavior remained different in the novel places, suggesting that other brain regions continued to distinguish novel from familiar locations. These results show that the hippocampus can form new spatial representations quickly but that stable hippocampal representations are not sufficient for a place to be treated as familiar.

Animals↗

PubMed2004

Dynamic analysis of learning in behavioral experiments.

Understanding how an animal's ability to learn relates to neural activity or is altered by lesions, different attentional states, pharmacological interventions, or genetic manipulations are central questions in neuroscience. Although learning is a dynamic process, current analyses do not use dynamic estimation methods, require many trials across many animals to establish the occurrence of learning, and provide no consensus as how best to identify when learning has occurred. We develop a state-space model paradigm to characterize learning as the probability of a correct response as a function of trial number (learning curve). We compute the learning curve and its confidence intervals using a state-space smoothing algorithm and define the learning trial as the first trial on which there is reasonable certainty (>0.95) that a subject performs better than chance for the balance of the experiment. For a range of simulated learning experiments, the smoothing algorithm estimated learning curves with smaller mean integrated squared error and identified the learning trials with greater reliability than commonly used methods. The smoothing algorithm tracked easily the rapid learning of a monkey during a single session of an association learning experiment and identified learning 2 to 4 d earlier than accepted criteria for a rat in a 47 d procedural learning experiment. Our state-space paradigm estimates learning curves for single animals, gives a precise definition of learning, and suggests a coherent statistical framework for the design and analysis of learning experiments that could reduce the number of animals and trials per animal that these studies require.

Algorithms↗