Contrasting Patterns of Receptive Field Plasticity in the Hippocampus and the Entorhinal Cortex: an Adaptive Filtering Approach

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience. May, 2002  |  Pubmed ID: 11978857

Neural receptive fields are frequently plastic: a neural response to a stimulus can change over time as a result of experience. We developed an adaptive point process filtering algorithm that allowed us to estimate the dynamics of both the spatial receptive field (spatial intensity function) and the interspike interval structure (temporal intensity function) of neural spike trains on a millisecond time scale without binning over time or space. We applied this algorithm to both simulated data and recordings of putative excitatory neurons from the CA1 region of the hippocampus and the deep layers of the entorhinal cortex (EC) of awake, behaving rats. Our simulation results demonstrate that the algorithm accurately tracks simultaneous changes in the spatial and temporal structure of the spike train. When we applied the algorithm to experimental data, we found consistent patterns of plasticity in the spatial and temporal intensity functions of both CA1 and deep EC neurons. These patterns tended to be opposite in sign, in that the spatial intensity functions of CA1 neurons showed a consistent increase over time, whereas those of deep EC neurons tended to decrease, and the temporal intensity functions of CA1 neurons showed a consistent increase only in the "theta" (75-150 msec) region, whereas those of deep EC neurons decreased in the region between 20 and 75 msec. In addition, the minority of deep EC neurons whose spatial intensity functions increased in area over time fired in a significantly more spatially specific manner than non-increasing deep EC neurons. We hypothesize that this subset of deep EC neurons may receive more direct input from CA1 and may be part of a neural circuit that transmits information about the animal's location to the neocortex.

Requirement for Hippocampal CA3 NMDA Receptors in Associative Memory Recall

Science (New York, N.Y.). Jul, 2002  |  Pubmed ID: 12040087

Pattern completion, the ability to retrieve complete memories on the basis of incomplete sets of cues, is a crucial function of biological memory systems. The extensive recurrent connectivity of the CA3 area of hippocampus has led to suggestions that it might provide this function. We have tested this hypothesis by generating and analyzing a genetically engineered mouse strain in which the N-methyl-D-asparate (NMDA) receptor gene is ablated specifically in the CA3 pyramidal cells of adult mice. The mutant mice normally acquired and retrieved spatial reference memory in the Morris water maze, but they were impaired in retrieving this memory when presented with a fraction of the original cues. Similarly, hippocampal CA1 pyramidal cells in mutant mice displayed normal place-related activity in a full-cue environment but showed a reduction in activity upon partial cue removal. These results provide direct evidence for CA3 NMDA receptor involvement in associative memory recall.

Memory of Sequential Experience in the Hippocampus During Slow Wave Sleep

Neuron. Dec, 2002  |  Pubmed ID: 12495631

Rats repeatedly ran through a sequence of spatial receptive fields of hippocampal CA1 place cells in a fixed temporal order. A novel combinatorial decoding method reveals that these neurons repeatedly fired in precisely this order in long sequences involving four or more cells during slow wave sleep (SWS) immediately following, but not preceding, the experience. The SWS sequences occurred intermittently in brief ( approximately 100 ms) bursts, each compressing the behavioral sequence in time by approximately 20-fold. This rapid encoding of sequential experience is consistent with evidence that the hippocampus is crucial for spatial learning in rodents and the formation of long-term memories of events in time in humans.

Hippocampal Memory Formation, Plasticity, and the Role of Sleep

Neurobiology of Learning and Memory. Nov, 2002  |  Pubmed ID: 12559835

Hippocampal CA3 NMDA Receptors Are Crucial for Memory Acquisition of One-time Experience

Neuron. Apr, 2003  |  Pubmed ID: 12718863

Lesion and pharmacological intervention studies have suggested that in both human patients and animals the hippocampus plays a crucial role in the rapid acquisition and storage of information from a novel one-time experience. However, how the hippocampus plays this role is poorly known. Here, we show that mice with NMDA receptor (NR) deletion restricted to CA3 pyramidal cells in adulthood are impaired in rapidly acquiring the memory of novel hidden platform locations in a delayed matching-to-place version of the Morris water maze task but are normal when tested with previously experienced platform locations. CA1 place cells in the mutant animals had place field sizes that were significantly larger in novel environments, but normal in familiar environments relative to those of control mice. These results suggest that CA3 NRs play a crucial role in rapid hippocampal encoding of novel information for fast learning of one-time experience.

Genetic Neuroscience of Mammalian Learning and Memory

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. Apr, 2003  |  Pubmed ID: 12740125

Our primary research interest is to understand the molecular and cellular mechanisms on neuronal circuitry underlying the acquisition, consolidation and retrieval of hippocampus-dependent memory in rodents. We study these problems by producing genetically engineered (i.e. spatially targeted and/or temporally restricted) mice and analysing these mice by multifaceted methods including molecular and cellular biology, in vitro and in vivo physiology and behavioural studies. We attempt to identify deficits at each of the multiple levels of complexity in specific brain areas or cell types and deduce those deficits that underlie specific learning or memory. We will review our recent studies on the acquisition, consolidation and recall of memories that have been conducted with mouse strains in which genetic manipulations were targeted to specific types of cells in the hippocampus or forebrain of young adult mice.

Dynamic Analyses of Information Encoding in Neural Ensembles

Neural Computation. Feb, 2004  |  Pubmed ID: 15006097

Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to design decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.

NMDA Receptors, Place Cells and Hippocampal Spatial Memory

Nature Reviews. Neuroscience. May, 2004  |  Pubmed ID: 15100719

A Combinatorial Method for Analyzing Sequential Firing Patterns Involving an Arbitrary Number of Neurons Based on Relative Time Order

Journal of Neurophysiology. Oct, 2004  |  Pubmed ID: 15212425

Information processing in the brain is believed to require coordinated activity across many neurons. With the recent development of techniques for simultaneously recording the spiking activity of large numbers of individual neurons, the search for complex multicell firing patterns that could help reveal this neural code has become possible. Here we develop a new approach for analyzing sequential firing patterns involving an arbitrary number of neurons based on relative firing order. Specifically, we develop a combinatorial method for quantifying the degree of matching between a "reference sequence" of N distinct "letters" (representing a particular target order of firing by N cells) and an arbitrarily long "word" composed of any subset of those letters including repeats (representing the relative time order of spikes in an arbitrary firing pattern). The method involves computing the probability that a random permutation of the word's letters would by chance alone match the reference sequence as well as or better than the actual word does, assuming all permutations were equally likely. Lower probabilities thus indicate better matching. The overall degree and statistical significance of sequence matching across a heterogeneous set of words (such as those produced during the course of an experiment) can be computed from the corresponding set of probabilities. This approach can reduce the sample size problem associated with analyzing complex firing patterns. The approach is general and thus applicable to other types of neural data beyond multiple spike trains, such as EEG events or imaging signals from multiple locations. We have recently applied this method to quantify memory traces of sequential experience in the rodent hippocampus during slow wave sleep.

Prefrontal Phase Locking to Hippocampal Theta Oscillations

Neuron. Apr, 2005  |  Pubmed ID: 15820700

The interactions between cortical and hippocampal circuits are critical for memory formation, yet their basic organization at the neuronal network level is not well understood. Here, we demonstrate that a significant portion of neurons in the medial prefrontal cortex of freely behaving rats are phase locked to the hippocampal theta rhythm. In addition, we show that prefrontal neurons phase lock best to theta oscillations delayed by approximately 50 ms and confirm this hippocampo-prefrontal directionality and timing at the level of correlations between single cells. Finally, we find that phase locking of prefrontal cells is predicted by the presence of significant correlations with hippocampal cells at positive delays up to 150 ms. The theta-entrained activity across cortico-hippocampal circuits described here may be important for gating information flow and guiding the plastic changes that are believed to underlie the storage of information across these networks.

Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

Neural Computation. Sep, 2005  |  Pubmed ID: 15992486

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.

An Analysis of Hippocampal Spatio-temporal Representations Using a Bayesian Algorithm for Neural Spike Train Decoding

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society. Jun, 2005  |  Pubmed ID: 16003890

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.

Jugular Foramen Schwannomas: Diagnosis, Management, and Outcomes

The Laryngoscope. Aug, 2005  |  Pubmed ID: 16094130

To describe the presentation, radiographic findings, and surgical management of seven patients who have been diagnosed and treated with jugular foramen schwannomas at the University of Utah.

Phase Precession of Medial Prefrontal Cortical Activity Relative to the Hippocampal Theta Rhythm

Hippocampus. 2005  |  Pubmed ID: 16149084

Theta phase-locking and phase precession are two related phenomena reflecting coordination of hippocampal place cell firing with the local, ongoing theta rhythm. The mechanisms and functions of both the phenomena remain unclear, though the robust correlation between firing phase and location of the animal has lead to the suggestion that this phase relationship constitutes a temporal code for spatial information. Recent work has described theta phase-locking in the rat medial prefrontal cortex (mPFC), a structure with direct anatomical and functional links to the hippocampus. Here, we describe an initial characterization of phase precession in the mPFC relative to the CA1 theta rhythm. mPFC phase precession was most robust during behavioral epochs known to be associated with enhanced theta-frequency coordination of CA1 and mPFC activities. Precession was coherent across the mPFC population, with multiple neurons precessing in parallel as a function of location of the animal. The existence of phase precession beyond the hippocampus implies a more global role for this phenomenon during theta rhythm-mediated coordination of neural activity.

Theta Rhythms Coordinate Hippocampal-prefrontal Interactions in a Spatial Memory Task

PLoS Biology. Dec, 2005  |  Pubmed ID: 16279838

Decision-making requires the coordinated activity of diverse brain structures. For example, in maze-based tasks, the prefrontal cortex must integrate spatial information encoded in the hippocampus with mnemonic information concerning route and task rules in order to direct behavior appropriately. Using simultaneous tetrode recordings from CA1 of the rat hippocampus and medial prefrontal cortex, we show that correlated firing in the two structures is selectively enhanced during behavior that recruits spatial working memory, allowing the integration of hippocampal spatial information into a broader, decision-making network. The increased correlations are paralleled by enhanced coupling of the two structures in the 4- to 12-Hz theta-frequency range. Thus the coordination of theta rhythms may constitute a general mechanism through which the relative timing of disparate neural activities can be controlled, allowing specialized brain structures to both encode information independently and to interact selectively according to current behavioral demands.

Reverse Replay of Behavioural Sequences in Hippocampal Place Cells During the Awake State

Nature. Mar, 2006  |  Pubmed ID: 16474382

The hippocampus has long been known to be involved in spatial navigational learning in rodents, and in memory for events in rodents, primates and humans. A unifying property of both navigation and event memory is a requirement for dealing with temporally sequenced information. Reactivation of temporally sequenced memories for previous behavioural experiences has been reported in sleep in rats. Here we report that sequential replay occurs in the rat hippocampus during awake periods immediately after spatial experience. This replay has a unique form, in which recent episodes of spatial experience are replayed in a temporally reversed order. This replay is suggestive of a role in the evaluation of event sequences in the manner of reinforcement learning models. We propose that such replay might constitute a general mechanism of learning and memory.

Neuroscience and Architecture: Seeking Common Ground

Cell. Oct, 2006  |  Pubmed ID: 17055420

As these paired Commentaries discuss, neuroscientists and architects are just beginning to collaborate, each bringing what they know about their respective fields to the task of improving the environment of research buildings and laboratories.

Coordinated Memory Replay in the Visual Cortex and Hippocampus During Sleep

Nature Neuroscience. Jan, 2007  |  Pubmed ID: 17173043

Sleep replay of awake experience in the cortex and hippocampus has been proposed to be involved in memory consolidation. However, whether temporally structured replay occurs in the cortex and whether the replay events in the two areas are related are unknown. Here we studied multicell spiking patterns in both the visual cortex and hippocampus during slow-wave sleep in rats. We found that spiking patterns not only in the cortex but also in the hippocampus were organized into frames, defined as periods of stepwise increase in neuronal population activity. The multicell firing sequences evoked by awake experience were replayed during these frames in both regions. Furthermore, replay events in the sensory cortex and hippocampus were coordinated to reflect the same experience. These results imply simultaneous reactivation of coherent memory traces in the cortex and hippocampus during sleep that may contribute to or reflect the result of the memory consolidation process.

Spatial Selectivity and Theta Phase Precession in CA1 Interneurons

Hippocampus. 2007  |  Pubmed ID: 17183531

Traditionally, most of the information processing of neural networks is thought to be carried out by excitatory cells. Likewise, recent evidence for temporal coding comes from the study of the detailed firing patterns of excitatory neurons. In the CA1 region of the rat hippocampus, pyramidal cells discharge selectively when the animal is in specific locations in its environment, and exhibit a precise relationship with the ongoing rhythmic activity of the network (phase precession). We demonstrate that during a spatial exploratory behavior on a linear track, inhibitory interneurons also show spatial selectivity and phase precession dynamics. We found that the firing rate of interneurons is modulated reliably up and down around an ongoing baseline activity level for specific locations in the environment, producing robust place-specific increases or decreases in discharge. On some sections of the track, the range of theta phases shifts progressively to earlier parts of the theta cycle as the rat advances, so that a negative correlation between phase and position could be demonstrated. Unlike pyramidal cells, phase and rate were not strongly correlated. We discuss the influence of the intrinsic firing properties of interneurons on a model of phase precession, as well as the influence of the detailed shape of the inhibitory oscillation. These results indicate that spatial selectivity and phase precession in CA1 are not properties restricted to pyramidal cells. Rather, they may be a more general expression of a common interaction between the different inputs impinging on both excitatory and inhibitory cells in CA1 and the intrinsic characteristics of those cells. Furthermore, they suggest that the role of interneurons may extend beyond a global damping of the network by participating in a finely-tuned local processing with the pyramidal cells.

Construction of Point Process Adaptive Filter Algorithms for Neural Systems Using Sequential Monte Carlo Methods

IEEE Transactions on Bio-medical Engineering. Mar, 2007  |  Pubmed ID: 17355053

The stochastic state point process filter (SSPPF) and steepest descent point process filter (SDPPF) are adaptive filter algorithms for state estimation from point process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing point process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC point process filters SMC-PPFs and SMC-PPFD, respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFs and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFs algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing point process adaptive filters using SMC methods.

Dentate Gyrus NMDA Receptors Mediate Rapid Pattern Separation in the Hippocampal Network

Science (New York, N.Y.). Jul, 2007  |  Pubmed ID: 17556551

Forming distinct representations of multiple contexts, places, and episodes is a crucial function of the hippocampus. The dentate gyrus subregion has been suggested to fulfill this role. We have tested this hypothesis by generating and analyzing a mouse strain that lacks the gene encoding the essential subunit of the N-methyl-d-aspartate (NMDA) receptor NR1, specifically in dentate gyrus granule cells. The mutant mice performed normally in contextual fear conditioning, but were impaired in the ability to distinguish two similar contexts. A significant reduction in the context-specific modulation of firing rate was observed in the CA3 pyramidal cells when the mutant mice were transferred from one context to another. These results provide evidence that NMDA receptors in the granule cells of the dentate gyrus play a crucial role in the process of pattern separation.

Hippocampal Theta Sequences

Hippocampus. 2007  |  Pubmed ID: 17663452

The activity of individual hippocampal principal neurons is spatially localized such that each neuron is active only when the animal occupies an area of the environment known as the cell's place field. Additionally, the activity of hippocampal neurons exhibits a particular temporal relationship to the hippocampal EEG, such that spikes fired by the neuron occur at progressively earlier phases of the co-occurring theta rhythm in the EEG as the animal traverses the place field. This relationship is known as theta precession. A long-standing prediction following the observation of theta precession has been that among a collection of hippocampal neurons recorded simultaneously, the neurons will fire in sequences reflecting the behavioral order of the place fields. Here we examine this prediction. We show that clear, ordered sequences occur during theta, which we name theta sequences, in which a portion of the animal's spatial experience is played out in forwards order. We further investigate the relationship of theta sequences to phase precession by shuffling spike phases in such a way as to preserve the relationship between spike phase and position. This jitter significantly reduces the prevalence of theta sequences while leaving theta phase precession intact, suggesting that the presence of theta phase precession is not trivially predictive of theta sequences. Finally, we discuss the relationship between theta sequences and individual place fields, and the possible functional role of theta sequences in navigational learning.

Firing Rate Dynamics in the Hippocampus Induced by Trajectory Learning

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience. Apr, 2008  |  Pubmed ID: 18448645

The hippocampus is essential for spatial navigation, which may involve sequential learning. However, how the hippocampus encodes new sequences in familiar environments is unknown. To study the impact of novel spatial sequences on the activity of hippocampal neurons, we monitored hippocampal ensembles while rats learned to switch from two familiar trajectories to a new one in a familiar environment. Here, we show that this novel spatial experience induces two types of changes in firing rates, but not locations of hippocampal place cells. First, place-cell firing rates on the two familiar trajectories start to change before the actual behavioral switch to the new trajectory. Second, repeated exposure on the new trajectory is associated with an increased dependence of place-cell firing rates on immediate past locations. The result suggests that sequence encoding in the hippocampus may involve integration of information about the recent past into current state.

Large-scale Chronically Implantable Precision Motorized Microdrive Array for Freely Behaving Animals

Journal of Neurophysiology. Oct, 2008  |  Pubmed ID: 18667539

Multiple single-unit recording has become one of the most powerful in vivo electro-physiological techniques for studying neural circuits. The demand has been increasing for small and lightweight chronic recording devices that allow fine adjustments to be made over large numbers of electrodes across multiple brain regions. To achieve this, we developed precision motorized microdrive arrays that use a novel motor multiplexing headstage to dramatically reduce wiring while preserving precision of the microdrive control. Versions of the microdrive array were chronically implanted on both rats (21 microdrives) and mice (7 microdrives), and relatively long-term recordings were taken.

All My Circuits: Using Multiple Electrodes to Understand Functioning Neural Networks

Neuron. Nov, 2008  |  Pubmed ID: 18995823

Much of the work in systems neuroscience thus far has focused on the brain's parts studied individually. The past 20 years has seen the advent, rise, and application of multiple-electrode technology. This allows the study of the activity of many neurons simultaneously, which in turn has provided insight into how different neuron populations interact and collaborate to produce thought and action.

Emergency Medical Services Provider Perceptions of the Nature of Adverse Events and Near-misses in Out-of-hospital Care: an Ethnographic View

Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine. Jul, 2008  |  Pubmed ID: 19086213

The objectives were to examine the perceptions of emergency medical services (EMS) providers regarding near-misses and adverse events in out-of-hospital care.

Instantaneous Frequency and Amplitude Modulation of EEG in the Hippocampus Reveals State Dependent Temporal Structure

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 2008  |  Pubmed ID: 19163009

EEG and LFP activity reflect the dynamic and organized interactions of neural ensembles; therefore, it may be possible to use the features of brain rhythms to determine the computational state of a neuronal network. When neuronal networks are activated, physical principles predict that the frequency content of the field potential should reflect the network state, per se, and ergo the state transition. A novel way for characterizing brain states is by quantifying the temporal structure of AM and FM activity (change in amplitude and frequency over time) for brain rhythms of interest. The concept of AM and FM, in the quantitative sense, is virtually unexplored in systems neuroscience. This is not surprising considering estimation of FM activity requires fine temporal and precise estimation of instantaneous frequency. For AM activity, the absolute value of the Hilbert transform is sufficient. Here, we outline a practical pole tracking algorithm which uses a Kalman filter for univariate AR processes to estimate instantaneous frequency. We demonstrate the filter performance using simulated chirp and real EEG/LFP data recorded from the rat hippocampus; and show that AM/FM activity in EEG/LFP is temporally structured and dependent on behavioral and cognitive state. This algorithm has the potential to be a practical tool for characterizing fundamental structure in electrophysiology data and classifying computational states in the brain.

A Small Molecule Inhibitor of the Wnt Antagonist Secreted Frizzled-related Protein-1 Stimulates Bone Formation

Bone. Jun, 2009  |  Pubmed ID: 19254787

Canonical Wnt signaling has been demonstrated to increase bone formation, and Wnt pathway components are being pursued as potential drug targets for osteoporosis and other metabolic bone diseases. Deletion of the Wnt antagonist secreted frizzled-related protein (sFRP)-1 in mice activates canonical signaling in bone and increases trabecular bone formation in aged animals. We have developed small molecules that bind to and inhibit sFRP-1 in vitro and demonstrate robust anabolic activity in an ex vivo organ culture assay. A library of over 440,000 drug-like compounds was screened for inhibitors of human sFRP-1 using a cell-based functional assay that measured activation of canonical Wnt signaling with an optimized T-cell factor (TCF)-luciferase reporter gene assay. One of the hits in this screen, a diarylsulfone sulfonamide, bound to sFRP-1 with a K(D) of 0.35 microM in a tryptophan fluorescence quenching assay. This compound also selectively inhibited sFRP-1 with an EC(50) of 3.9 microM in the cell-based functional assay. Optimization of this high throughput screening hit for binding and functional potency as well as metabolic stability and other pharmaceutical properties led to improved lead compounds. One of these leads (WAY-316606) bound to sFRP-1 with a K(D) of 0.08 microM and inhibited it with an EC(50) of 0.65 microM. Moreover, this compound increased total bone area in a murine calvarial organ culture assay at concentrations as low as 0.0001 microM. This work demonstrates the feasibility of developing small molecules that inhibit sFRP-1 and stimulate canonical Wnt signaling to increase bone formation.

Discrete- and Continuous-time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States

Neural Computation. Jul, 2009  |  Pubmed ID: 19323637

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.

Characterizing the Dynamic Frequency Structure of Fast Oscillations in the Rodent Hippocampus

Frontiers in Integrative Neuroscience. 2009  |  Pubmed ID: 19562084

Fast oscillations or "ripples" are found in the local field potential (LFP) of the rodent hippocampus during awake and sleep states. Ripples have been found to correlate with memory related neural processing, however, the functional role of the ripple has yet to be fully established. We applied a Kalman smoother based estimator of instantaneous frequency (iFreq) and frequency modulation (FM) to ripple oscillations recorded in-vivo from region CA1 of the rat and mouse hippocampus during slow wave sleep. We found that (1) ripples exhibit stereotypical frequency dynamics that are consistent in the rat and mouse, (2) instantaneous frequency information may be used as an additional dimension in the classification of ripple events, and (3) the instantaneous frequency structure of ripples may be used to improve the detection of ripple events by reducing Type I and Type II errors. Based on our results, we propose that high temporal and spectral resolution estimates of frequency dynamics may be used to help elucidate the mechanisms of ripple generation and memory related processing.

Measuring Instantaneous Frequency of Local Field Potential Oscillations Using the Kalman Smoother

Journal of Neuroscience Methods. Nov, 2009  |  Pubmed ID: 19699763

Rhythmic local field potentials (LFPs) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalman smoother based dynamic autoregressive model for tracking the instantaneous frequency (iFreq) and frequency modulation (FM) of noisy and non-stationary sinusoids such as those found in LFP data. We verify the performance of our algorithm using simulated data with broad spectral content, and demonstrate its application using real data recorded from behavioral learning experiments. In analyses of ripple oscillations (100-250Hz) recorded from the rodent hippocampus, our algorithm identified novel repetitive, short timescale frequency dynamics. Our results suggest that iFreq and FM may be useful measures for the quantification of small timescale LFP dynamics.

Hippocampal Replay of Extended Experience

Neuron. Aug, 2009  |  Pubmed ID: 19709631

During pauses in exploration, ensembles of place cells in the rat hippocampus re-express firing sequences corresponding to recent spatial experience. Such "replay" co-occurs with ripple events: short-lasting (approximately 50-120 ms), high-frequency (approximately 200 Hz) oscillations that are associated with increased hippocampal-cortical communication. In previous studies, rats exploring small environments showed replay anchored to the rat's current location and compressed in time into a single ripple event. Here, we show, using a neural decoding approach, that firing sequences corresponding to long runs through a large environment are replayed with high fidelity and that such replay can begin at remote locations on the track. Extended replay proceeds at a characteristic virtual speed of approximately 8 m/s and remains coherent across trains of ripple events. These results suggest that extended replay is composed of chains of shorter subsequences, which may reflect a strategy for the storage and flexible expression of memories of prolonged experience.

Lack of Kainic Acid-induced Gamma Oscillations Predicts Subsequent CA1 Excitotoxic Cell Death

The European Journal of Neuroscience. Sep, 2009  |  Pubmed ID: 19735292

Gamma oscillations are a prominent feature of hippocampal network activity, but their functional role remains debated, ranging from mere epiphenomena to being crucial for information processing. Similarly, persistent gamma oscillations sometimes appear prior to epileptic discharges in patients with mesial temporal sclerosis. However, the significance of this activity in hippocampal excitotoxicity is unclear. We assessed the relationship between kainic acid (KA)-induced gamma oscillations and excitotoxicity in genetically engineered mice in which N-methyl-D-aspartic acid receptor deletion was confined to CA3 pyramidal cells. Mutants showed reduced CA3 pyramidal cell firing and augmented sharp wave-ripple activity, resulting in higher susceptibility to KA-induced seizures, and leading to strikingly selective neurodegeneration in the CA1 subfield. Interestingly, the increase in KA-induced gamma-aminobutyric acid (GABA) levels, and the persistent 30-50-Hz gamma oscillations, both of which were observed in control mice prior to the first seizure discharge, were abolished in the mutants. Consequently, on subsequent days, mutants manifested prolonged epileptiform activity and massive neurodegeneration of CA1 cells, including local GABAergic neurons. Remarkably, pretreatment with the potassium channel blocker alpha-dendrotoxin increased GABA levels, restored gamma oscillations, and prevented CA1 degeneration in the mutants. These results demonstrate that the emergence of low-frequency gamma oscillations predicts increased resistance to KA-induced excitotoxicity, raising the possibility that gamma oscillations may have potential prognostic value in the treatment of epilepsy.

Disruption of Ripple-associated Hippocampal Activity During Rest Impairs Spatial Learning in the Rat

Hippocampus. Jan, 2010  |  Pubmed ID: 19816984

The hippocampus plays a key role in the acquisition of new memories for places and events. Evidence suggests that the consolidation of these memories is enhanced during sleep. At the neuronal level, reactivation of awake experience in the hippocampus during sharp-wave ripple events, characteristic of slow-wave sleep, has been proposed as a neural mechanism for sleep-dependent memory consolidation. However, a causal relation between sleep reactivation and memory consolidation has not been established. Here we show that disrupting neuronal activity during ripple events impairs spatial learning. We trained rats daily in two identical spatial navigation tasks followed each by a 1-hour rest period. After one of the tasks, stimulation of hippocampal afferents selectively disrupted neuronal activity associated with ripple events without changing the sleep-wake structure. Rats learned the control task significantly faster than the task followed by rest stimulation, indicating that interfering with hippocampal processing during sleep led to decreased learning.

Ambulance Personnel Perceptions of Near Misses and Adverse Events in Pediatric Patients

Prehospital Emergency Care : Official Journal of the National Association of EMS Physicians and the National Association of State EMS Directors. Oct-Dec, 2010  |  Pubmed ID: 20662679

To identify emergency medical services (EMS) provider perceptions of factors that may affect the occurrence, identification, reporting, and reduction of near misses and adverse events in the pediatric EMS patient.

Computing Confidence Intervals for Point Process Models

Neural Computation. Nov, 2011  |  Pubmed ID: 21851280

Characterizing neural spiking activity as a function of intrinsic and extrinsic factors is important in neuroscience. Point process models are valuable for capturing such information; however, the process of fully applying these models is not always obvious. A complete model application has four broad steps: specification of the model, estimation of model parameters given observed data, verification of the model using goodness of fit, and characterization of the model using confidence bounds. Of these steps, only the first three have been applied widely in the literature, suggesting the need to dedicate a discussion to how the time-rescaling theorem, in combination with parametric bootstrap sampling, can be generally used to compute confidence bounds of point process models. In our first example, we use a generalized linear model of spiking propensity to demonstrate that confidence bounds derived from bootstrap simulations are consistent with those computed from closed-form analytic solutions. In our second example, we consider an adaptive point process model of hippocampal place field plasticity for which no analytical confidence bounds can be derived. We demonstrate how to simulate bootstrap samples from adaptive point process models, how to use these samples to generate confidence bounds, and how to statistically test the hypothesis that neural representations at two time points are significantly different. These examples have been designed as useful guides for performing scientific inference based on point process models.

Assessing Neuronal Interactions of Cell Assemblies During General Anesthesia

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. Aug, 2011  |  Pubmed ID: 22255259

Understanding the way in which groups of cortical neurons change their individual and mutual firing activity during the induction of general anesthesia may improve the safe usage of many anesthetic agents. Assessing neuronal interactions within cell assemblies during anesthesia may be useful for understanding the neural mechanisms of general anesthesia. Here, a point process generalized linear model (PPGLM) was applied to infer the functional connectivity of neuronal ensembles during both baseline and anesthesia, in which neuronal firing rates and network connectivity might change dramatically. A hierarchical Bayesian modeling approach combined with a variational Bayes (VB) algorithm is used for statistical inference. The effectiveness of our approach is evaluated with synthetic spike train data drawn from small and medium-size networks (consisting of up to 200 neurons), which are simulated using biophysical voltage-gated conductance models. We further apply the analysis to experimental spike train data recorded from rats' barrel cortex during both active behavior and isoflurane anesthesia conditions. Our results suggest that that neuronal interactions of both putative excitatory and inhibitory connections are reduced after the induction of isoflurane anesthesia.

Uncovering Spatial Topology Represented by Rat Hippocampal Population Neuronal Codes

Journal of Computational Neuroscience. Feb, 2012  |  Pubmed ID: 22307459

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.