Uri T Eden

École Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland

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Publications (57)143.57 Total impact

  • Society for Neuroscience Annual Meeting, Washington D.C.; 10/2014
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    ABSTRACT: A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach - linking statistical, computational, and experimental neuroscience - provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.
    PLoS ONE 01/2014; 9(1):e85269. · 3.53 Impact Factor
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    ABSTRACT: The rat hippocampus and entorhinal cortex have been shown to possess neurons with place fields that modulate their firing properties under different behavioral contexts. Such context-dependent changes in neural activity are commonly studied through electrophysiological experiments in which a rat performs a continuous spatial alternation task on a T-maze. Previous research has analyzed context-based differential firing during this task through the characterization of differences in the mean firing activity between left-turn and right-turn experimental trials. In this paper, we draw upon findings that demonstrate considerable trial-to-trial variability of neural activity in these regions and broaden the definition of differential firing to include context-dependent changes in the stochastic structure of the trial-to-trial rate variability. We develop qualitative and quantitative methods to characterize and compare changes in trial-to-trial variability in the CA1 region of hippocampus and in the dorsocaudal medial entorhinal cortex (dcMEC) between turn direction contexts during a spatial alternation task on a T-maze. We identify a subset of cells with context-dependent changes in firing rate variability. Additionally, we show that dcMEC populations encode turn direction uniformly throughout the T-maze stem, whereas CA1 populations encode context at major waypoints in the spatial trajectory. Our results suggest scenarios in which individual cells that sparsely provide information on turn direction might combine in the aggregate to produce a robust population encoding. © 2014 Wiley Periodicals, Inc.
    Hippocampus 01/2014; · 5.49 Impact Factor
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    ABSTRACT: The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.
    Frontiers in Computational Neuroscience 01/2014; 8:31. · 2.48 Impact Factor
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    ABSTRACT: Understanding the role of rhythmic dynamics in normal and diseased brain function is an important area of research in neural electrophysiology. Identifying and tracking changes in rhythms associated with spike trains present an additional challenge, because standard approaches for continuous-valued neural recordings-such as local field potential, magnetoencephalography, and electroencephalography data-require assumptions that do not typically hold for point process data. Additionally, subtle changes in the history dependent structure of a spike train have been shown to lead to robust changes in rhythmic firing patterns. Here, we propose a point process modeling framework to characterize the rhythmic spiking dynamics in spike trains, test for statistically significant changes to those dynamics, and track the temporal evolution of such changes. We first construct a two-state point process model incorporating spiking history and develop a likelihood ratio test to detect changes in the firing structure. We then apply adaptive state-space filters and smoothers to track these changes through time. We illustrate our approach with a simulation study as well as with experimental data recorded in the subthalamic nucleus of Parkinson's patients performing an arm movement task. Our analyses show that during the arm movement task, neurons underwent a complex pattern of modulation of spiking intensity characterized initially by a release of inhibitory control at 20-40 ms after a spike, followed by a decrease in excitatory influence at 40-60 ms after a spike.
    Chaos (Woodbury, N.Y.) 12/2013; 23(4):046102. · 1.80 Impact Factor
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    ABSTRACT: Grid cells in the medial entorhinal cortex fire in an array of locations falling on the vertices of tightly packed equilateral triangles as an animal explores an environment. This is thought to provide a dense neural representation of the environment. This unique, spatially tuned firing pattern has lead to grid cells being implicated in spatial memory and navigation. With increasing emphasis being placed on understanding how the brain mediates path integration, understanding the firing properties of grid cells is a chief goal. Despite this importance however, there is still a paucity of rigorous statistical techniques for analyzing the firing fields of grid cells and, therefore, it remains very challenging to test hypotheses about how the grid cell spatial firing pattern may change under different manipulations. In order to address this issue, we have developed a technique to determine multiple features of grid cells via automated likelihood fitting. Specifically, we constructed point process models that describe the spiking of each grid cell as a function of the positions of a rat in an open field environment. The statistical model incorporates a number of unknown parameters including parameters related to grid spacing, field size, position, and orientation, among others. Using both simulated data from mechanistic models of grid cells and real data recorded from the medial entorhinal cortex while a rat foraged for food, we computed multidimensional likelihood surfaces as functions of the statistical model parameters. We show that these likelihoods have a complicated, multi-modal structure. We show, in simulation, that with an appropriate initial guess of the model parameters based on simple visualization tools, a gradient ascent procedure will reliably attain the global maximum of the likelihood surface. We use the likelihood to estimate the parameters that best define each grid cell’s firing field, and construct confidence intervals. Using these methods, we also define a maximum likelihood ratio test, to determine significant differences between grid firing fields. We have developed MATLAB code to implement these methods, which can be adapted to most experimental preparations
    Society for Neuroscience, San Diego, CA; 11/2013
  • M A Kramer, U T Eden
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    ABSTRACT: Brain voltage activity displays distinct neuronal rhythms spanning a wide frequency range. How rhythms of different frequency interact - and the function of these interactions - remains an active area of research. Many methods have been proposed to assess the interactions between different frequency rhythms, in particular measures that characterize the relationship between the phase of a low frequency rhythm and the amplitude envelope of a high frequency rhythm. However, an optimal analysis method to assess this cross-frequency coupling (CFC) does not yet exist. Here we describe a new procedure to assess CFC that utilizes the generalized linear modeling (GLM) framework. We illustrate the utility of this procedure in three synthetic examples. The proposed GLM-CFC procedure allows a rapid and principled assessment of CFC with confidence bounds, scales with the intensity of the CFC, and accurately detects biphasic coupling. Compared to existing methods, the proposed GLM-CFC procedure is easily interpretable, possesses confidence interval s that are easy and efficient to compute, and accurately detects biphasic coupling. The GLM-CFC statistic provides a method for accurate and statistically rigorous assessment of CFC.
    Journal of neuroscience methods 09/2013; · 2.30 Impact Factor
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    ABSTRACT: Electrical neurostimulation techniques, such as deep brain stimulation (DBS) and transcranial magnetic stimulation (TMS), are increasingly used in the neurosciences, e.g., for studying brain function, and for neurotherapeutics, e.g., for treating depression, epilepsy, and Parkinson's disease. The characterization of electrical properties of brain tissue has guided our fundamental understanding and application of these methods, from electrophysiologic theory to clinical dosing-metrics. Nonetheless, prior computational models have primarily relied on ex-vivo impedance measurements. We recorded the in-vivo impedances of brain tissues during neurosurgical procedures and used these results to construct MRI guided computational models of TMS and DBS neurostimulatory fields and conductance-based models of neurons exposed to stimulation. We demonstrated that tissues carry neurostimulation currents through frequency dependent resistive and capacitive properties not typically accounted for by past neurostimulation modeling work. We show that these fundamental brain tissue properties can have significant effects on the neurostimulatory-fields (capacitive and resistive current composition and spatial/temporal dynamics) and neural responses (stimulation threshold, ionic currents, and membrane dynamics). These findings highlight the importance of tissue impedance properties on neurostimulation and impact our understanding of the biological mechanisms and technological potential of neurostimulatory methods.
    NeuroImage 07/2013; · 6.25 Impact Factor
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    ABSTRACT: We develop a particle filter algorithm to simultaneously estimate and track the instantaneous peak frequency, amplitude, and bandwidth of multiple concurrent non-stationary components of an EEG signal in the time-frequency domain. We use this method to characterize human EEG activity during anesthesia-induced unconsciousness.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:417-420.
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    ABSTRACT: Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.
    PLoS Computational Biology 07/2013; 9(7):e1003138. · 4.87 Impact Factor
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    ABSTRACT: The instantaneous phase of neural rhythms is important to many neuroscience-related studies. In this letter, we show that the statistical sampling properties of three instantaneous phase estimators commonly employed to analyze neuroscience data share common features, allowing an analytical investigation into their behavior. These three phase estimators-the Hilbert, complex Morlet, and discrete Fourier transform-are each shown to maximize the likelihood of the data, assuming the observation of different neural signals. This connection, explored with the use of a geometric argument, is used to describe the bias and variance properties of each of the phase estimators, their temporal dependence, and the effect of model misspecification. This analysis suggests how prior knowledge about a rhythmic signal can be used to improve the accuracy of phase estimates.
    Neural Computation 01/2013; · 1.76 Impact Factor
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    ABSTRACT: Why seizures spontaneously terminate remains an unanswered fundamental question of epileptology. Here we present evidence that seizures self-terminate via a discontinuous critical transition or bifurcation. We show that human brain electrical activity at various spatial scales exhibits common dynamical signatures of an impending critical transition-slowing, increased correlation, and flickering-in the approach to seizure termination. In contrast, prolonged seizures (status epilepticus) repeatedly approach, but do not cross, the critical transition. To support these results, we implement a computational model that demonstrates that alternative stable attractors, representing the ictal and postictal states, emulate the observed dynamics. These results suggest that self-terminating seizures end through a common dynamical mechanism. This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature.
    Proceedings of the National Academy of Sciences 12/2012; · 9.81 Impact Factor
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    ABSTRACT: Many experiments in neuroscience have compared the strength of association between neural spike trains and rhythms present in local field potential (LFP) recordings. The measure employed in these comparisons, "spike-field coherence", is a frequency dependent measure of linear association, and is shown to depend on overall neural activity (Lepage et al., 2011). Dependence upon overall neural activity, that is, dependence upon the total number of spikes, renders comparison of spike-field coherence across experimental context difficult. In this paper, an inferential procedure based upon a generalized linear model is shown to be capable of separating the effects of overall neural activity from spike train-LFP oscillatory coupling. This separation provides a means to compare the strength of oscillatory association between spike train-LFP pairs independent of differences in spike counts. Following a review of the generalized linear modelling framework of point process neural activity a specific class of generalized linear models are introduced. This model class, using either a piece-wise constant link function, or an exponential function to relate an LFP rhythm to neural response, is used to develop hypothesis tests capable of detecting changes in spike train-LFP oscillatory coupling. The performance of these tests is validated, both in simulation and on real data. The proposed method of inference provides a principled statistical procedure by which across-context change in spike train-LFP rhythmic association can be directly inferred that explicitly handles between-condition differences in total spike count.
    Journal of neuroscience methods 11/2012; · 2.30 Impact Factor
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    ABSTRACT: A method is presented capable of disambiguating the relative influence of statistical covariates upon neural spiking activity. The method, an extension of the generalized linear model (GLM) methodology introduced in Truccolo et al. (2005) to analyze neural spiking data, exploits projection operations motivated by a geometry present in the Fisher information of the GLM maximum likelihood parameter estimator. By exploiting these projections, neural activity can be divided into three categories. These three categories, neural activity due solely to a set of covariates of interest, neural activity due solely to a set of uninteresting, or nuisance, covariates, and neural activity that cannot be unequivocally assigned to either set of covariates, can be associated with physical variables such as time, position, head-direction and velocity. This association allows the analysis of neural activity that can, for example, be due solely to temporal influence, irrespective of other, identified, influences. The method is applied in simulation to a rat exploring a temporally modulated place field. A portion of the analysis reported in MacDonald et al. (2011), using the methodology described herein, is reproduced. This analysis demonstrates the temporal bridging of a delay period in a sequential memory task by firing activity of cells present in the rodent hippocampus that cannot be explained by rodent position, head direction or velocity.
    Journal of neuroscience methods 01/2012; 205(2):295-304. · 2.30 Impact Factor
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    ABSTRACT: Accurately describing the spiking patterns of neurons in the subthalamic nucleus (STN) of patients suffering from Parkinson's disease (PD) is important for understanding the pathogenesis of the disease and for achieving the maximum therapeutic benefit from deep brain stimulation (DBS). We analyze the spiking activity of 24 subthalamic neurons recorded in Parkinson's patients during a directed hand movement task by using a point process generalized linear model (GLM). The model relates each neuron's spiking probability simultaneously to factors associated with movement planning and execution, directional selectivity, refractoriness, bursting, and oscillatory dynamics. The model indicated that while short-term history dependence related to refractoriness and bursting are most informative in predicting spiking activity, nearly all of the neurons analyzed have a structured pattern of long-term history dependence such that the spiking probability was reduced 20-30 ms and then increased 30-60 ms after a previous spike. This suggests that the previously described oscillatory firing of neurons in the STN of Parkinson's patients during volitional movements is composed of a structured pattern of inhibition and excitation. This point process model provides a systematic framework for characterizing the dynamics of neuronal activity in STN.
    Frontiers in Integrative Neuroscience 01/2012; 6:28.
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    ABSTRACT: Visual cues open a unique window to the understanding of Parkinson's disease (PD). These cues can temporarily but dramatically improve PD motor symptoms. Although details are unclear, cues are believed to suppress pathological basal ganglia (BG) activity through activation of corticostriatal pathways. In this study, we investigated human BG neurophysiology under different cued conditions. We evaluated bursting, 10-30 Hz oscillations (OSCs), and directional tuning (DT) dynamics in the subthalamic nucleus (STN) activity while seven patients executed a two-step motor task. In the first step (predicted +cue), the patient moved to a target when prompted by a visual go cue that appeared 100% of the time. Here, the timing of the cue is predictable and the cue serves an external trigger to execute a motor plan. In the second step, the cue appeared randomly 50% of the time, and the patient had to move to the same target as in the first step. When it appeared (unpredicted +cue), the motor plan was to be triggered by the cue, but its timing was not predictable. When the cue failed to appear (unpredicted -cue), the motor plan was triggered by the absence of the visual cue. We found that during predicted +cue and unpredicted -cue trials, OSCs significantly decreased and DT significantly increased above baseline, though these modulations occurred an average of 640 ms later in unpredicted -cue trials. Movement and reaction times were comparable in these trials. During unpredicted +cue trials, OSCs, and DT failed to modulate though bursting significantly decreased after movement. Correspondingly, movement performance deteriorated. These findings suggest that during motor planning either a predictably timed external cue or an internally generated cue (generated by the absence of a cue) trigger the execution of a motor plan in premotor cortex, whose increased activation then suppresses pathological activity in STN through direct pathways, leading to motor facilitation in PD.
    Frontiers in Integrative Neuroscience 01/2012; 6:40.
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    Liang Meng, Mark A Kramer, Uri T Eden
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    ABSTRACT: Realistic computational models of neuronal activity typically involve many variables and parameters, most of which remain unknown or poorly constrained. Moreover, experimental observations of the neuronal system are typically limited to the times of action potentials, or spikes. One important component of developing a computational model is the optimal incorporation of these sparse experimental data. Here, we use point process statistical theory to develop a procedure for estimating parameters and hidden variables in neuronal computational models given only the observed spike times. We discuss the implementation of a sequential Monte Carlo method for this procedure and apply it to three simulated examples of neuronal spiking activity. We also address the issues of model identification and misspecification, and show that accurate estimates of model parameters and hidden variables are possible given only spike time data.
    Journal of Neural Engineering 11/2011; 8(6):065006. · 3.28 Impact Factor
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    ABSTRACT: Over the past two decades, the increased ability to analyze network relationships among neural structures has provided novel insights into brain function. Most network approaches, however, focus on static representations of the brain's physical or statistical connectivity. Few studies have examined how brain functional networks evolve spontaneously over long epochs of continuous time. To address this, we examine functional connectivity networks deduced from continuous long-term electrocorticogram recordings. For a population of six human patients, we identify a persistent pattern of connections that form a frequency-band-dependent network template, and a set of core connections that appear frequently and together. These structures are robust, emerging from brief time intervals (~100 s) regardless of cognitive state. These results suggest that a metastable, frequency-band-dependent scaffold of brain connectivity exists from which transient activity emerges and recedes.
    Journal of Neuroscience 11/2011; 31(44):15757-67. · 6.91 Impact Factor
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    ABSTRACT: The hippocampus is critical to remembering the flow of events in distinct experiences and, in doing so, bridges temporal gaps between discontiguous events. Here, we report a robust hippocampal representation of sequence memories, highlighted by "time cells" that encode successive moments during an empty temporal gap between the key events, while also encoding location and ongoing behavior. Furthermore, just as most place cells "remap" when a salient spatial cue is altered, most time cells form qualitatively different representations ("retime") when the main temporal parameter is altered. Hippocampal neurons also differentially encode the key events and disambiguate different event sequences to compose unique, temporally organized representations of specific experiences. These findings suggest that hippocampal neural ensembles segment temporally organized memories much the same as they represent locations of important events in spatially defined environments.
    Neuron 08/2011; 71(4):737-49. · 15.77 Impact Factor
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    ABSTRACT: Neurological disease is often associated with changes in firing activity in specific brain areas. Accurate statistical models of neural spiking can provide insight into the mechanisms by which the disease develops and clinical symptoms manifest. Point process theory provides a powerful framework for constructing, fitting, and evaluating the quality of neural spiking models. We illustrate an application of point process modeling to the problem of characterizing abnormal oscillatory firing patterns of neurons in the subthalamic nucleus (STN) of patients with Parkinson's disease (PD). We characterize the firing properties of these neurons by constructing conditional intensity models using spline basis functions that relate the spiking of each neuron to movement variables and the neuron's past firing history, both at short and long time scales. By calculating maximum likelihood estimators for all of the parameters and their significance levels, we are able to describe the relative propensity of aberrant STN spiking in terms of factors associated with voluntary movements, with intrinsic properties of the neurons, and factors that may be related to dysregulated network dynamics.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:757-60.

Publication Stats

1k Citations
143.57 Total Impact Points

Institutions

  • 2013
    • École Polytechnique Fédérale de Lausanne
      Lausanne, Vaud, Switzerland
  • 2007–2013
    • Boston University
      • Department of Mathematics and Statistics
      Boston, MA, United States
  • 2004–2010
    • Massachusetts Institute of Technology
      • • Department of Brain and Cognitive Sciences
      • • Department of Electrical Engineering and Computer Science
      • • Division of Health Sciences and Technology
      Cambridge, MA, United States
  • 2002–2009
    • Massachusetts General Hospital
      Boston, Massachusetts, United States
  • 2005
    • Harvard Medical School
      Boston, Massachusetts, United States
    • Brown University
      • Department of Neuroscience
      Providence, RI, United States