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Find video protocols related to scientific articles indexed in Pubmed.
Controlling Dielectric and Relaxor-Ferroelectric Properties for Energy Storage by Tuning Pb0.92La0.08Zr0.52Ti0.48O3 Film Thickness.
ACS Appl Mater Interfaces
PUBLISHED: 11-19-2014
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The energy storage properties of Pb0.92La0.08Zr0.52Ti0.48O3 (PLZT) films grown via pulse laser deposition were evaluated at variable film thickness of 125, 250, 500, and 1000 nm. These films show high dielectric permittivity up to ~1200. Cyclic I-V measurements were used to evaluate the dielectric properties of these thin films, which not only provides the total electric displacement, but also separates contributions from each of the relevant components including electric conductivity (D1), dielectric capacitance (D2), and relaxor-ferroelectric domain switching polarization (P). The results show that, as the film thickness increases, the material transits from a linear dielectric to nonlinear relaxor-ferroelectric. While the energy storage per volume increases with the film thickness, the energy storage efficiency drops from ~80% to ~30%. The PLZT films can be optimized for different energy storage applications by tuning the film thickness to optimize between the linear and nonlinear dielectric properties and energy storage efficiency.
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Modulation depth estimation and variable selection in state-space models for neural interfaces.
IEEE Trans Biomed Eng
PUBLISHED: 09-30-2014
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Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems.
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Effects of sevoflurane and propofol on frontal electroencephalogram power and coherence.
Anesthesiology
PUBLISHED: 09-19-2014
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The neural mechanisms of anesthetic vapors have not been studied in depth. However, modeling and experimental studies on the intravenous anesthetic propofol indicate that potentiation of ?-aminobutyric acid receptors leads to a state of thalamocortical synchrony, observed as coherent frontal alpha oscillations, associated with unconsciousness. Sevoflurane, an ether derivative, also potentiates ?-aminobutyric acid receptors. However, in humans, sevoflurane-induced coherent frontal alpha oscillations have not been well detailed.
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A Comparison of Propofol- and Dexmedetomidine-induced Electroencephalogram Dynamics Using Spectral and Coherence Analysis.
Anesthesiology
PUBLISHED: 09-05-2014
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Electroencephalogram patterns observed during sedation with dexmedetomidine appear similar to those observed during general anesthesia with propofol. This is evident with the occurrence of slow (0.1 to 1 Hz), delta (1 to 4 Hz), propofol-induced alpha (8 to 12 Hz), and dexmedetomidine-induced spindle (12 to 16 Hz) oscillations. However, these drugs have different molecular mechanisms and behavioral properties and are likely accompanied by distinguishing neural circuit dynamics.
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Tin dioxide@carbon core-shell nanoarchitectures anchored on wrinkled graphene for ultrafast and stable lithium storage.
ACS Appl Mater Interfaces
PUBLISHED: 05-13-2014
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The SnO2@C@GS composites as a new type of 3D nanoarchitecture have been successfully synthesized by a facile hydrothermal process followed by a sintering strategy. Such a 3D nanoarchitecture is made up of SnO2@C core-shell nanospheres and nanochains anchored on wrinkled graphene sheets (GSs). Transmission electron microscopy shows that these core-shell nanoparticles consist of 3-9 nm diameter secondary SnO2 nanoparticles embedded in about 50 nm diameter primary carbon nanospheres. Large quantities of core-shell nanoparticles are uniformly attached to the surface of wrinkled graphene nanosheets, with a portion of them further connected into nanochains. This new 3D nanoarchitecture consists of two different kinds of carbon-buffering matrixes, i.e., the carbon layer produced by glucose carbonization and the added GS template, leading to enhanced lithium storage properties. The lithium-cycling properties of the SnO2@C@GS composite have been evaluated by galvanostatic discharge-charge cycling and electrochemical impedance spectroscopy. Results show that the SnO2@C@GS composite has discharge capacities of 883.5, 845.7, and 830.5 mA h g(-1) in the 20th, 50th and 100th cycles, respectively, at a current density of 200 mA g(-1) and delivers a desirable discharge capacity of 645.2 mA h g(-1) at a rate of 1680 mA g(-1). This new 3D nanoarchitecture exhibits a high capability and excellent cycling and rate performance, holding great potential as a high-rate and stable anode material for lithium storage.
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A review of multitaper spectral analysis.
IEEE Trans Biomed Eng
PUBLISHED: 04-25-2014
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Nonparametric spectral estimation is a widely used technique in many applications ranging from radar and seismic data analysis to electroencephalography (EEG) and speech processing. Among the techniques that are used to estimate the spectral representation of a system based on finite observations, multitaper spectral estimation has many important optimality properties, but is not as widely used as it possibly could be. We give a brief overview of the standard nonparametric spectral estimation theory and the multitaper spectral estimation, and give two examples from EEG analyses of anesthesia and sleep.
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Atomic layer deposition of Al-doped ZnO/Al2O3 double layers on vertically aligned carbon nanofiber arrays.
ACS Appl Mater Interfaces
PUBLISHED: 04-23-2014
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High-aspect-ratio, vertically aligned carbon nanofibers (VACNFs) were conformally coated with aluminum oxide (Al2O3) and aluminum-doped zinc oxide (AZO) using atomic layer deposition (ALD) in order to produce a three-dimensional array of metal-insulator-metal core-shell nanostructures. Prefunctionalization before ALD, as required for initiating covalent bonding on a carbon nanotube surface, was eliminated on VACNFs due to the graphitic edges along the surface of each CNF. The graphitic edges provided ideal nucleation sites under sequential exposures of H2O and trimethylaluminum to form an Al2O3 coating up to 20 nm in thickness. High-resolution transmission electron microscopy (HRTEM) and scanning electron microscopy images confirmed the conformal core-shell AZO/Al2O3/CNF structures while energy-dispersive X-ray spectroscopy verified the elemental composition of the different layers. HRTEM selected area electron diffraction revealed that the as-made Al2O3 by ALD at 200 °C was amorphous, and then, after annealing in air at 450 °C for 30 min, was converted to polycrystalline form. Nevertheless, comparable dielectric constants of 9.3 were obtained in both cases by cyclic voltammetry at a scan rate of 1000 V/s. The conformal core-shell AZO/Al2O3/VACNF array structure demonstrated in this work provides a promising three-dimensional architecture toward applications of solid-state capacitors with large surface area having a thin, leak-free dielectric.
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State-dependent architecture of thalamic reticular subnetworks.
Cell
PUBLISHED: 03-05-2014
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Behavioral state is known to influence interactions between thalamus and cortex, which are important for sensation, action, and cognition. The thalamic reticular nucleus (TRN) is hypothesized to regulate thalamo-cortical interactions, but the underlying functional architecture of this process and its state dependence are unknown. By combining the first TRN ensemble recording with psychophysics and connectivity-based optogenetic tagging, we found reticular circuits to be composed of distinct subnetworks. While activity of limbic-projecting TRN neurons positively correlates with arousal, sensory-projecting neurons participate in spindles and show elevated synchrony by slow waves during sleep. Sensory-projecting neurons are suppressed by attentional states, demonstrating that their gating of thalamo-cortical interactions is matched to behavioral state. Bidirectional manipulation of attentional performance was achieved through subnetwork-specific optogenetic stimulation. Together, our findings provide evidence for differential inhibition of thalamic nuclei across brain states, where the TRN separately controls external sensory and internal limbic processing facilitating normal cognitive function. PAPERFLICK:
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Statistical modeling of behavioral dynamics during propofol-induced loss of consciousness.
J. Neurosci. Methods
PUBLISHED: 01-21-2014
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Accurate quantitative analysis of the changes in responses to external stimuli is crucial for characterizing the timing of loss and recovery of consciousness induced by anesthetic drugs. We studied induction and emergence from unconsciousness achieved by administering a computer-controlled infusion of propofol to ten human volunteers. We evaluated loss and recovery of consciousness by having subjects execute every 4s two interleaved computer delivered behavioral tasks: responding to verbal stimuli (neutral words or the subject's name), or less salient stimuli of auditory clicks.
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Modeling the dynamical effects of anesthesia on brain circuits.
Curr. Opin. Neurobiol.
PUBLISHED: 01-21-2014
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General anesthesia is a neurophysiological state that consists of unconsciousness, amnesia, analgesia, and immobility along with maintenance of physiological stability. General anesthesia has been used in the United States for more than 167 years. Now, using systems neuroscience paradigms how anesthetics act in the brain and central nervous system to create the states of general anesthesia is being understood. Propofol is one of the most widely used and the most widely studied anesthetics. When administered for general anesthesia or sedation, the electroencephalogram (EEG) under propofol shows highly structured, rhythmic activity that is strongly associated with changes in the patient's level of arousal. These highly structured oscillations lend themselves readily to mathematical descriptions using dynamical systems models. We review recent model descriptions of the commonly observed EEG patterns associated with propofol: paradoxical excitation, strong frontal alpha oscillations, anteriorization and burst suppression. Our analysis suggests that propofol's actions at GABAergic networks in the cortex, thalamus and brainstem induce profound brain dynamics that are one of the likely mechanisms through which this anesthetic induces altered arousal states from sedation to unconsciousness. Because these dynamical effects are readily observed in the EEG, the mathematical descriptions of how propofol's EEG signatures relate to its mechanisms of action in neural circuits provide anesthesiologists with a neurophysiologically based approach to monitoring the brain states of patients receiving anesthesia care.
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A transition in brain state during propofol-induced unconsciousness.
J. Neurosci.
PUBLISHED: 01-17-2014
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Rhythmic oscillations shape cortical dynamics during active behavior, sleep, and general anesthesia. Cross-frequency phase-amplitude coupling is a prominent feature of cortical oscillations, but its role in organizing conscious and unconscious brain states is poorly understood. Using high-density EEG and intracranial electrocorticography during gradual induction of propofol general anesthesia in humans, we discovered a rapid drug-induced transition between distinct states with opposite phase-amplitude coupling and different cortical source distributions. One state occurs during unconsciousness and may be similar to sleep slow oscillations. A second state occurs at the loss or recovery of consciousness and resembles an enhanced slow cortical potential. These results provide objective electrophysiological landmarks of distinct unconscious brain states, and could be used to help improve EEG-based monitoring for general anesthesia.
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Electrical stimulation of the ventral tegmental area induces reanimation from general anesthesia.
Anesthesiology
PUBLISHED: 01-09-2014
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Methylphenidate or a D1 dopamine receptor agonist induces reanimation (active emergence) from general anesthesia. The authors tested whether electrical stimulation of dopaminergic nuclei also induces reanimation from general anesthesia.
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Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models.
Front Comput Neurosci
PUBLISHED: 01-01-2014
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Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) models cannot represent simultaneous occurrences of spike events at an arbitrarily small time resolution. Solo recently reported a simultaneous-event multivariate point process (SEMPP) model to correct this key limitation. In this paper, we show how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM). Unlike existing approximate procedures for fitting the discrete-time SEMPP model, the mGLM is an exact algorithm. The MPP time-rescaling theorem can be used to assess model goodness-of-fit. We also derive a new marked point-process (MkPP) representation of the SEMPP model that leads to new thinning and time-rescaling algorithms for simulating an SEMPP stochastic process. These algorithms are much simpler than multivariate extensions of algorithms for simulating a univariate point process, and could not be arrived at without the MkPP representation. We illustrate the versatility of the SEMPP model by analyzing neural spiking activity from pairs of simultaneously-recorded rat thalamic neurons stimulated by periodic whisker deflections, and by simulating SEMPP data. In the data analysis example, the SEMPP model demonstrates that whisker motion significantly modulates simultaneous spiking activity at the 1 ms time scale and that the stimulus effect is more than one order of magnitude greater for simultaneous activity compared with non-simultaneous activity. Together, the mGLM, the MPP time-rescaling theorem and the MkPP representation of the SEMPP model offer a theoretically sound, practical tool for measuring joint spiking propensity in a neuronal ensemble.
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Deconvolution of serum cortisol levels by using compressed sensing.
PLoS ONE
PUBLISHED: 01-01-2014
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The pulsatile release of cortisol from the adrenal glands is controlled by a hierarchical system that involves corticotropin releasing hormone (CRH) from the hypothalamus, adrenocorticotropin hormone (ACTH) from the pituitary, and cortisol from the adrenal glands. Determining the number, timing, and amplitude of the cortisol secretory events and recovering the infusion and clearance rates from serial measurements of serum cortisol levels is a challenging problem. Despite many years of work on this problem, a complete satisfactory solution has been elusive. We formulate this question as a non-convex optimization problem, and solve it using a coordinate descent algorithm that has a principled combination of (i) compressed sensing for recovering the amplitude and timing of the secretory events, and (ii) generalized cross validation for choosing the regularization parameter. Using only the observed serum cortisol levels, we model cortisol secretion from the adrenal glands using a second-order linear differential equation with pulsatile inputs that represent cortisol pulses released in response to pulses of ACTH. Using our algorithm and the assumption that the number of pulses is between 15 to 22 pulses over 24 hours, we successfully deconvolve both simulated datasets and actual 24-hr serum cortisol datasets sampled every 10 minutes from 10 healthy women. Assuming a one-minute resolution for the secretory events, we obtain physiologically plausible timings and amplitudes of each cortisol secretory event with R (2) above 0.92. Identification of the amplitude and timing of pulsatile hormone release allows (i) quantifying of normal and abnormal secretion patterns towards the goal of understanding pathological neuroendocrine states, and (ii) potentially designing optimal approaches for treating hormonal disorders.
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Awakened by a sleeping pill.
Elife
PUBLISHED: 11-21-2013
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Characteristic changes in brain activity accompany the paradoxical increase in alertness observed in some patients with severe brain injury when they are treated with the sleeping pill zolpidem.
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Differential requirement for NMDAR activity in SAP97?-mediated regulation of the number and strength of glutamatergic AMPAR-containing synapses.
J. Neurophysiol.
PUBLISHED: 11-13-2013
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PSD-95-like, disc-large (DLG) family membrane associated guanylate kinase proteins (PSD/DLG-MAGUKs) are essential for regulating synaptic AMPA receptor (AMPAR) function and activity-dependent trafficking of AMPARs. Using a molecular replacement strategy to replace endogenous PSD-95 with SAP97?, we show that the prototypic ?-isoform of PSD-MAGUK, SAP97? has distinct NMDA receptor (NMDAR) dependent and independent roles in regulating synapse number vs synaptic strength. SAP97? enhances the number of AMPAR-containing synapses in an NMDAR dependent manner, while its effect on enhancing the size of unitary synaptic response is independent of NMDAR activity. This is in contrast to PSD-95?, whose effect on both the number and the size of unitary synaptic responses is independent of NMDAR activity. Our results suggest that SAP97? regulates synaptic AMPAR content via stabilizing the total level of GluA1-containing AMPARs whereas PSD-95? enhances synaptic AMPAR content presumably via increasing the synaptic scaffold capacity for synaptic AMPARs. Our approach delineates discrete effects of different PSD-MAGUKs on principal properties of glutamatergic synaptic transmission. Our results suggest that the molecular diversity of PSD-MAGUKs can provide rich molecular substrates for differential regulation of glutamatergic synapses in the brain.
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Likelihood Methods for Point Processes with Refractoriness.
Neural Comput
PUBLISHED: 11-08-2013
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Likelihood-based encoding models founded on point processes have received significant attention in the literature because of their ability to reveal the information encoded by spiking neural populations. We propose an approximation to the likelihood of a point-process model of neurons that holds under assumptions about the continuous time process that are physiologically reasonable for neural spike trains: the presence of a refractory period, the predictability of the conditional intensity function, and its integrability. These are properties that apply to a large class of point processes arising in applications other than neuroscience. The proposed approach has several advantages over conventional ones. In particular, one can use standard fitting procedures for generalized linear models based on iteratively reweighted least squares while improving the accuracy of the approximation to the likelihood and reducing bias in the estimation of the parameters of the underlying continuous-time model. As a result, the proposed approach can use a larger bin size to achieve the same accuracy as conventional approaches would with a smaller bin size. This is particularly important when analyzing neural data with high mean and instantaneous firing rates. We demonstrate these claims on simulated and real neural spiking activity. By allowing a substantive increase in the required bin size, our algorithm has the potential to lower the barrier to the use of point-process methods in an increasing number of applications.
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Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 10-11-2013
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We provide a method for estimating brain metabolic state based on a reduced-order model of EEG burst suppression. The model, derived from previously suggested biophysical mechanisms of burst suppression, describes important electrophysiological features and provides a direct link to cerebral metabolic rate. We design and fit the estimation method from EEG recordings of burst suppression from a neurological intensive care unit and test it on real and synthetic data.
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Reference-free harmonic regression technique to remove EEG-fMRI ballistocardiogram artifacts.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 10-11-2013
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Obtaining high quality electroencephalogram (EEG) data simultaneously with functional MRI (fMRI) recordings is increasingly relevant for the study of cognitive and clinical brain states - as EEG-fMRI offers uniquely high spatiotemporal resolution imaging of brain activity. However, the utility of this technique is limited by ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation and head movement inside the magnetic field. In this paper, we introduce a novel model-based harmonic regression technique to remove BCG artifacts from EEG recorded in the MR scanner. Our technique uses physically motivated parametric models of the BCG artifact and the true EEG signal, and incorporates maximum likelihood approaches to identify model parameters, estimate and subtract the BCG from corrupted EEG measurements. We show that this method effectively removes BCG artifacts from EEG recorded in the MR scanner, restores simulated oscillatory signatures and enables over 20-fold improvement in SNR in bands of interest. Further, unlike common BCG removal techniques that rely on cardiac or motion reference signals, our approach is reference-free and thus is useful when reference signals are corrupted or difficult to acquire.
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A brain-machine interface for control of burst suppression in medical coma.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 10-11-2013
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Burst suppression is an electroencephalogram (EEG) marker of profound brain inactivation and unconsciousness and consists of bursts of electrical activity alternating with periods of isoelectricity called suppression. Burst suppression is the EEG pattern targeted in medical coma, a drug-induced brain state used to help recovery after brain injuries and to treat epilepsy that is refractory to conventional drug therapies. The state of coma is maintained manually by administering an intravenous infusion of an anesthetic, such as propofol, to target a pattern of burst suppression on the EEG. The coma often needs to be maintained for several hours or days, and hence an automated system would offer significant benefit for tight control. Here we present a brain-machine interface (BMI) for automatic control of burst suppression in medical coma that selects the real-time drug infusion rate based on EEG observations and can precisely control the burst suppression level in real time in rodents. We quantify the burst suppression level using the burst suppression probability (BSP), the brains instantaneous probability of being in the suppressed state, and represent the effect of the anesthetic propofol on the BSP using a two-dimensional linear compartment model that we fit in experiments. We compute the BSP in real time from the EEG segmented into a binary time-series by deriving a two-dimensional state-space algorithm. We then derive a stochastic controller using both a linear-quadratic-regulator strategy and a model predictive control strategy. The BMI can promptly change the level of burst suppression without overshoot or undershoot and maintains precise control of time-varying target levels of burst suppression in individual rodents in real time.
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A brain-machine interface for control of medically-induced coma.
PLoS Comput. Biol.
PUBLISHED: 10-01-2013
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Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patients brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brains instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.
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Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression.
J Neural Eng
PUBLISHED: 09-10-2013
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Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference.
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Local cortical dynamics of burst suppression in the anaesthetized brain.
Brain
PUBLISHED: 07-25-2013
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Burst suppression is an electroencephalogram pattern that consists of a quasi-periodic alternation between isoelectric suppressions lasting seconds or minutes, and high-voltage bursts. It is characteristic of a profoundly inactivated brain, occurring in conditions including hypothermia, deep general anaesthesia, infant encephalopathy and coma. It is also used in neurology as an electrophysiological endpoint in pharmacologically induced coma for brain protection after traumatic injury and during status epilepticus. Classically, burst suppression has been regarded as a global state with synchronous activity throughout cortex. This assumption has influenced the clinical use of burst suppression as a way to broadly reduce neural activity. However, the extent of spatial homogeneity has not been fully explored due to the challenges in recording from multiple cortical sites simultaneously. The neurophysiological dynamics of large-scale cortical circuits during burst suppression are therefore not well understood. To address this question, we recorded intracranial electrocorticograms from patients who entered burst suppression while receiving propofol general anaesthesia. The electrodes were broadly distributed across cortex, enabling us to examine both the dynamics of burst suppression within local cortical regions and larger-scale network interactions. We found that in contrast to previous characterizations, bursts could be substantially asynchronous across the cortex. Furthermore, the state of burst suppression itself could occur in a limited cortical region while other areas exhibited ongoing continuous activity. In addition, we found a complex temporal structure within bursts, which recapitulated the spectral dynamics of the state preceding burst suppression, and evolved throughout the course of a single burst. Our observations imply that local cortical dynamics are not homogeneous, even during significant brain inactivation. Instead, cortical and, implicitly, subcortical circuits express seemingly different sensitivities to high doses of anaesthetics that suggest a hierarchy governing how the brain enters burst suppression, and emphasize the role of local dynamics in what has previously been regarded as a global state. These findings suggest a conceptual shift in how neurologists could assess the brain function of patients undergoing burst suppression. First, analysing spatial variation in burst suppression could provide insight into the circuit dysfunction underlying a given pathology, and could improve monitoring of medically-induced coma. Second, analysing the temporal dynamics within a burst could help assess the underlying brain state. This approach could be explored as a prognostic tool for recovery from coma, and for guiding treatment of status epilepticus. Overall, these results suggest new research directions and methods that could improve patient monitoring in clinical practice.
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Thalamocortical mechanisms for the anteriorization of ? rhythms during propofol-induced unconsciousness.
J. Neurosci.
PUBLISHED: 07-05-2013
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As humans are induced into a state of general anesthesia via propofol, the normal alpha rhythm (8-13 Hz) in the occipital cortex disappears and a frontal alpha rhythm emerges. This spatial shift in alpha activity is called anteriorization. We present a thalamocortical model that suggests mechanisms underlying anteriorization. Our model captures the neural dynamics of anteriorization when we adjust it to reflect two key actions of propofol: its potentiation of GABA and its reduction of the hyperpolarization-activated current Ih. The reduction in Ih abolishes the occipital alpha by silencing a specialized subset of thalamocortical cells, thought to generate occipital alpha at depolarized membrane potentials (>-60 mV). The increase in GABA inhibition imposes an alpha timescale on both the cortical and thalamic portions of the frontal component that are reinforced by reciprocal thalamocortical feedback. Anteriorization can thus be understood as a differential effect of anesthetic drugs on thalamic nuclei with disparate spatial projections, i.e.: (1) they disrupt the normal, depolarized alpha in posterior-projecting thalamic nuclei while (2) they engage a new, hyperpolarized alpha in frontothalamic nuclei. Our model generalizes to other anesthetics that include GABA as a target, since the molecular targets of many such anesthetics alter the model dynamics in a manner similar to that of propofol.
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The aging brain and anesthesia.
Curr Opin Anaesthesiol
PUBLISHED: 07-04-2013
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To review the neurophysiology and neuroanatomy of normal aging and the recent recommendations for the clinical management of general anesthesia and sedation in the elderly.
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Real-time closed-loop control in a rodent model of medically induced coma using burst suppression.
Anesthesiology
PUBLISHED: 06-18-2013
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A medically induced coma is an anesthetic state of profound brain inactivation created to treat status epilepticus and to provide cerebral protection after traumatic brain injuries. The authors hypothesized that a closed-loop anesthetic delivery system could automatically and precisely control the electroencephalogram state of burst suppression and efficiently maintain a medically induced coma.
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A closed-loop anesthetic delivery system for real-time control of burst suppression.
J Neural Eng
PUBLISHED: 06-07-2013
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There is growing interest in using closed-loop anesthetic delivery (CLAD) systems to automate control of brain states (sedation, unconsciousness and antinociception) in patients receiving anesthesia care. The accuracy and reliability of these systems can be improved by using as control signals electroencephalogram (EEG) markers for which the neurophysiological links to the anesthetic-induced brain states are well established. Burst suppression, in which bursts of electrical activity alternate with periods of quiescence or suppression, is a well-known, readily discernible EEG marker of profound brain inactivation and unconsciousness. This pattern is commonly maintained when anesthetics are administered to produce a medically-induced coma for cerebral protection in patients suffering from brain injuries or to arrest brain activity in patients having uncontrollable seizures. Although the coma may be required for several hours or days, drug infusion rates are managed inefficiently by manual adjustment. Our objective is to design a CLAD system for burst suppression control to automate management of medically-induced coma.
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A Subspace Pursuit-based Iterative Greedy Hierarchical solution to the neuromagnetic inverse problem.
Neuroimage
PUBLISHED: 05-31-2013
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Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent developments in source localization methodology have emphasized temporal as well as spatial constraints to improve source localization accuracy, but these methods can be computationally intense. Solutions emphasizing spatial sparsity hold tremendous promise, since the underlying neurophysiological processes generating MEG signals are often sparse in nature, whether in the form of focal sources, or distributed sources representing large-scale functional networks. Recent developments in the theory of compressed sensing (CS) provide a rigorous framework to estimate signals with sparse structure. In particular, a class of CS algorithms referred to as greedy pursuit algorithms can provide both high recovery accuracy and low computational complexity. Greedy pursuit algorithms are difficult to apply directly to the MEG inverse problem because of the high-dimensional structure of the MEG source space and the high spatial correlation in MEG measurements. In this paper, we develop a novel greedy pursuit algorithm for sparse MEG source localization that overcomes these fundamental problems. This algorithm, which we refer to as the Subspace Pursuit-based Iterative Greedy Hierarchical (SPIGH) inverse solution, exhibits very low computational complexity while achieving very high localization accuracy. We evaluate the performance of the proposed algorithm using comprehensive simulations, as well as the analysis of human MEG data during spontaneous brain activity and somatosensory stimuli. These studies reveal substantial performance gains provided by the SPIGH algorithm in terms of computational complexity, localization accuracy, and robustness.
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Encoding through patterns: regression tree-based neuronal population models.
Neural Comput
PUBLISHED: 04-22-2013
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Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding models that describe how time-varying stimulus drive modulates the expression probabilities of population-wide spike patterns. The challenge is that large populations may express an astronomical number of unique patterns, and so fitting a unique encoding model for each individual pattern is not feasible. We avoid this combinatorial problem using a dimensionality-reduction approach based on regression trees. Using the insight that some patterns may, from the perspective of encoding, be statistically indistinguishable, the tree divisively clusters the observed patterns into groups whose member patterns possess similar encoding properties. These groups, corresponding to the leaves of the tree, are much smaller in number than the original patterns, and the tree itself constitutes a tractable encoding model for each pattern. Our formalism can detect an extremely weak stimulus-driven pattern structure and is based on maximizing the data likelihood, not making a priori assumptions as to how patterns should be grouped. Most important, by comparing pattern encodings with independent neuron encodings, one can determine if neurons in the population are driven independently or collectively. We demonstrate this method using multiple unit recordings from area 17 of anesthetized cat in response to a sinusoidal grating and show that pattern-based encodings are superior to those of independent neuron models. The agnostic nature of our clustering approach allows us to investigate encoding by the collective statistics that are actually present rather than those (such as pairwise) that might be presumed.
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Real-time segmentation of burst suppression patterns in critical care EEG monitoring.
J. Neurosci. Methods
PUBLISHED: 04-09-2013
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Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients.
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Electroencephalogram signatures of loss and recovery of consciousness from propofol.
Proc. Natl. Acad. Sci. U.S.A.
PUBLISHED: 03-04-2013
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Unconsciousness is a fundamental component of general anesthesia (GA), but anesthesiologists have no reliable ways to be certain that a patient is unconscious. To develop EEG signatures that track loss and recovery of consciousness under GA, we recorded high-density EEGs in humans during gradual induction of and emergence from unconsciousness with propofol. The subjects executed an auditory task at 4-s intervals consisting of interleaved verbal and click stimuli to identify loss and recovery of consciousness. During induction, subjects lost responsiveness to the less salient clicks before losing responsiveness to the more salient verbal stimuli; during emergence they recovered responsiveness to the verbal stimuli before recovering responsiveness to the clicks. The median frequency and bandwidth of the frontal EEG power tracked the probability of response to the verbal stimuli during the transitions in consciousness. Loss of consciousness was marked simultaneously by an increase in low-frequency EEG power (<1 Hz), the loss of spatially coherent occipital alpha oscillations (8-12 Hz), and the appearance of spatially coherent frontal alpha oscillations. These dynamics reversed with recovery of consciousness. The low-frequency phase modulated alpha amplitude in two distinct patterns. During profound unconsciousness, alpha amplitudes were maximal at low-frequency peaks, whereas during the transition into and out of unconsciousness, alpha amplitudes were maximal at low-frequency nadirs. This latter phase-amplitude relationship predicted recovery of consciousness. Our results provide insights into the mechanisms of propofol-induced unconsciousness, establish EEG signatures of this brain state that track transitions in consciousness precisely, and suggest strategies for monitoring the brain activity of patients receiving GA.
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Activation of D1 dopamine receptors induces emergence from isoflurane general anesthesia.
Anesthesiology
PUBLISHED: 02-26-2013
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A recent study showed that methylphenidate induces emergence from isoflurane anesthesia. Methylphenidate inhibits dopamine and norepinephrine reuptake transporters. The objective of this study was to test the hypothesis that selective dopamine receptor activation induces emergence from isoflurane anesthesia.
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A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design.
PLoS ONE
PUBLISHED: 01-01-2013
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Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.
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Broad range of neural dynamics from a time-varying FitzHugh-Nagumo model and its spiking threshold estimation.
IEEE Trans Biomed Eng
PUBLISHED: 12-16-2011
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We study the use of the FitzHugh-Nagumo (FHN) model for capturing neural spiking. The FHN model is a widely used approximation of the Hodgkin-Huxley model that has significant limitations. In particular, it cannot produce the key spiking behavior of bursting. We illustrate that by allowing time-varying parameters for the FHN model, these limitations can be overcome while retaining its low-order complexity. This extension has applications in modeling neural spiking behaviors in the thalamus and the respiratory center. We demonstrate the use of the FHN model from an estimation perspective by presenting a novel parameter estimation method that exploits its multiple time-scale properties, and compare the performance of this method with the extended Kalman filter in several illustrative examples. We demonstrate that the dynamics of the spiking threshold can be recovered even in the absence of complete specifications for the system.
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STATE-SPACE SOLUTIONS TO THE DYNAMIC MAGNETOENCEPHALOGRAPHY INVERSE PROBLEM USING HIGH PERFORMANCE COMPUTING.
Ann Appl Stat
PUBLISHED: 11-15-2011
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Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an L(2) regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candidate intra-cortical sources. In our model, the observation model is derived from the steady-state solution to Maxwells equations while the latent model representing neural dynamics is given by a random walk process. We show that the Kalman filter (KF) and the Kalman smoother [also known as the fixed-interval smoother (FIS)] may be used to solve the ensuing high-dimensional state-estimation problem. Using a well-known relationship between Bayesian estimation and Kalman filtering, we show that the MNE estimates carry a significant zero bias. Calculating these high-dimensional state estimates is a computationally challenging task that requires High Performance Computing (HPC) resources. To this end, we employ the NSF Teragrid Supercomputing Network to compute the source estimates. We demonstrate improvement in performance of the state-space algorithm relative to MNE in analyses of simulated and actual somatosensory MEG experiments. Our findings establish the benefits of high-dimensional state-space modeling as an effective means to solve the MEG source localization problem.
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Methylphenidate actively induces emergence from general anesthesia.
Anesthesiology
PUBLISHED: 09-22-2011
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Although accumulating evidence suggests that arousal pathways in the brain play important roles in emergence from general anesthesia, the roles of monoaminergic arousal circuits are unclear. In this study, the authors tested the hypothesis that methylphenidate (an inhibitor of dopamine and norepinephrine transporters) induces emergence from isoflurane general anesthesia.
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A spatiotemporal dynamic distributed solution to the MEG inverse problem.
Neuroimage
PUBLISHED: 09-02-2011
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MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic maximum a posteriori expectation-maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods.
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Instantaneous assessment of autonomic cardiovascular control during general anesthesia.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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We present a comprehensive probabilistic point process framework to estimate and monitor the instantaneous heartbeat dynamics as related to specific cardiovascular control mechanisms and hemodynamics. Assessment of the models statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (BRS), can be rigorously derived within a parametric framework and instantaneously updated with an adaptive algorithm. Instantaneous metrics of nonlinearity, such as the bispectrum of heartbeat intervals, can also be derived. We have applied the proposed point process framework to experimental recordings from healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. Results reveal interesting dynamic trends across different pharmacological interventions, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, noninvasive assessment of general anesthesia.
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Robust time-varying multivariate coherence estimation: application to electroencephalogram recordings during general anesthesia.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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Coherence analysis characterizes frequency-dependent covariance between signals, and is useful for multivariate oscillatory data often encountered in neuroscience. The global coherence provides a summary of coherent behavior in high-dimensional multivariate data by quantifying the concentration of variance in the first mode of an eigenvalue decomposition of the cross-spectral matrix. Practical application of this useful method is sensitive to noise, and can confound coherent activity in disparate neural populations or spatial locations that have a similar frequency structure. In this paper we describe two methodological enhancements to the global coherence procedure that increase robustness of the technique to noise, and that allow characterization of how power within specific coherent modes change through time.
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Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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Accurate quantification of loss of response to external stimuli is essential for understanding the mechanisms of loss of consciousness under general anesthesia. We present a new approach for quantifying three possible outcomes that are encountered in behavioral experiments during general anesthesia: correct responses, incorrect responses and no response. We use a state-space model with two state variables representing a probability of response and a conditional probability of correct response. We show applications of this approach to an example of responses to auditory stimuli at varying levels of propofol anesthesia ranging from light sedation to deep anesthesia in human subjects. The posterior probability densities of model parameters and the response probability are computed within a Bayesian framework using Markov Chain Monte Carlo methods.
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Assessing neuronal interactions of cell assemblies during general anesthesia.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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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.
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Modeling two-photon calcium fluorescence of episodic V1 recordings using multifrequency analysis.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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The use of two-photon microscopy allows for imaging of deep neural tissue in vivo. This paper examines frequency-based analysis to two-photon calcium fluorescence images with the goal of deriving smooth tuning curves. We present a multifrequency analysis approach for improved extraction of calcium responses in episodic stimulation experiments, that is, when the stimulus is applied for a number of frames, then turned off for the next few frames, and so on. Episodic orientation stimulus was applied while recording from the primary visual cortex of an anesthetized mouse. The multifrequency model demonstrated improved tuning curve descriptions of the neurons. It also offers perspective regarding the characteristics of calcium fluorescence imaging of the brain.
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Point-process analysis of neural spiking activity of muscle spindles recorded from thin-film longitudinal intrafascicular electrodes.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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Recordings from thin-film Longitudinal Intra-Fascicular Electrodes (tfLIFE) together with a wavelet-based de-noising and a correlation-based spike sorting algorithm, give access to firing patterns of muscle spindle afferents. In this study we use a point process probability structure to assess mechanical stimulus-response characteristics of muscle spindle spike trains. We assume that the stimulus intensity is primarily a linear combination of the spontaneous firing rate, the muscle extension, and the stretch velocity. By using the ability of the point process framework to provide an objective goodness of fit analysis, we were able to distinguish two classes of spike clusters with different statistical structure. We found that spike clusters with higher SNR have a temporal structure that can be fitted by an inverse Gaussian distribution while lower SNR clusters follow a Poisson-like distribution. The point process algorithm is further able to provide the instantaneous intensity function associated with the stimulus-response model with the best goodness of fit. This important result is a first step towards a point process decoding algorithm to estimate the muscle length and possibly provide closed loop Functional Electrical Stimulation (FES) systems with natural sensory feedback information.
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Phase-based measures of cross-frequency coupling in brain electrical dynamics under general anesthesia.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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The state of general anesthesia (GA) is associated with an increase in spectral power in scalp electroencephalogram (EEG) at frequencies below 40 Hz, including spectral peaks in the slow oscillation (SO, 0.1-1 Hz) and ? (8-14 Hz) bands. Because conventional power spectral analyses are insensitive to possible cross-frequency coupling, the relationships among the oscillations at different frequencies remain largely unexplored. Quantifying such coupling is essential for improving clinical monitoring of anesthesia and understanding the neuroscience of this brain state. We tested the usefulness of two measures of cross-frequency coupling: the bispectrum-derived SynchFastSlow, which is sensitive to phase-phase coupling in different frequency bands, and modulogram analysis of coupling between SO phase and ? rhythm amplitude. SynchFastSlow, a metric that is used in clinical depth-of-anesthesia monitors, showed a robust correlation with the loss of consciousness at the induction of propofol GA, but this could be largely explained by power spectral changes without considering cross-frequency coupling. Modulogram analysis revealed two distinct modes of cross-frequency coupling under GA. The waking and two distinct states under GA could be discriminated by projecting in a two-dimensional phase space defined by the SynchFastSlow and the preferred SO phase of ? activity. Our results show that a stereotyped pattern of phase-amplitude coupling accompanies multiple stages of anesthetic-induced unconsciousness. These findings suggest that modulogram analysis can improve EEG based monitoring of brain state under GA.
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Correcting for serial dependence in studies of respiratory dynamics.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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Understanding the physiological impact of drug treatments on patients is important in assessing their performance and determining possible side effects. While this effect might be best determined in individual subjects, conventional methods assess treatment performance by averaging a physiological measure of interest before and after drug administration for n subjects. Summarizing large numbers of time-series observations in two means for each subject in this way results in significant information loss. Treatment effect can instead be analyzed in individual subjects. Because serial dependence of observations from the same animal must then be considered, methods that assume independence of observations, such as the t-test and z-test, cannot be used. We address this issue in the case of respiratory data collected from anesthetized rats that were injected with a dopamine agonist. In order to accurately assess treatment effect in time-series data, we begin by formulating a method of conditional likelihood maximization to estimate the parameters of a first-order autoregressive (AR) process. We show that treatment effect of a dopamine agonist can be determined while incorporating serial effect into the analysis. In addition, while maximum likelihood estimators of a large sample with independent observations may converge to an asymptotically normal distribution, this result of large sample theory may not hold when observations are serially dependent. In this case, a parametric bootstrap comparison can be used to approximate an appropriate measure of uncertainty.
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A state-space model of the burst suppression ratio.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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Burst suppression is an electroencephalogram pattern observed in states of severely reduced brain activity, such as general anesthesia, hypothermia and anoxic brain injuries. The burst suppression ratio (BSR), defined as the fraction of EEG spent in suppression per epoch, is the standard quantitative measure used to characterize burst suppression. We present a state space model to compute a dynamic estimate of the BSR as the instantaneous probability of suppression. We estimate the model using an approximate EM algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia. Our approach removes the need to artificially average the ratio over long epochs and allows us to make formal statistical comparisons of burst activity at different time points. Our state-space model suggests a more principled way to analyze this key EEG feature that may offer more informative assessments of its associated brain state.
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A feedback control model for cortisol secretion.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
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Existing mathematical models for cortisol secretion do not describe the entire cortisol secretion process, from the neural firing of corticotropin releasing hormone (CRH) in the hypothalamus to cortisol concentration in the plasma. In this paper, we lay the groundwork to construct a more comprehensive model, relating CRH, Adrenocorticotropic hormone (ACTH), and cortisol. We start with an existing mathematical model for cortisol secretion, and combine it with a simplified neural firing model that describes CRH and ACTH release. This simplified neural firing model is obtained using the extended FitzHugh-Nagumo (FHN) model, which includes a time-varying spiking threshold [3]. A key feature of our model is the presence of a feedback loop from cortisol secretion to ACTH secretion.
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Computing confidence intervals for point process models.
Neural Comput
PUBLISHED: 08-18-2011
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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.
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A synaptic strategy for consolidation of convergent visuotopic maps.
Neuron
PUBLISHED: 06-16-2011
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The mechanisms by which experience guides refinement of converging afferent pathways are poorly understood. We describe a vision-driven refinement of corticocollicular inputs that determines the consolidation of retinal and visual cortical (VC) synapses on individual neurons in the superficial superior colliculus (sSC). Highly refined corticocollicular terminals form 1-2 days after eye-opening (EO), accompanied by VC-dependent filopodia sprouting on proximal dendrites, and PSD-95 and VC-dependent quadrupling of functional synapses. Delayed EO eliminates synapses, corticocollicular terminals, and spines on VC-recipient dendrites. Awake recordings after EO show that VC and retina cooperate to activate sSC neurons, and VC light responses precede sSC responses within intervals promoting potentiation. Eyelid closure is associated with more protracted cortical visual responses, causing the majority of VC spikes to follow those of the colliculus. These data implicate spike-timing plasticity as a mechanism for cortical input survival, and support a cooperative strategy for retinal and cortical coinnervation of the sSC.
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Tracking brain states under general anesthesia by using global coherence analysis.
Proc. Natl. Acad. Sci. U.S.A.
PUBLISHED: 05-09-2011
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Time and frequency domain analyses of scalp EEG recordings are widely used to track changes in brain states under general anesthesia. Although these analyses have suggested that different spatial patterns are associated with changes in the state of general anesthesia, the extent to which these patterns are spatially coordinated has not been systematically characterized. Global coherence, the ratio of the largest eigenvalue to the sum of the eigenvalues of the cross-spectral matrix at a given frequency and time, has been used to analyze the spatiotemporal dynamics of multivariate time-series. Using 64-lead EEG recorded from human subjects receiving computer-controlled infusions of the anesthetic propofol, we used surface Laplacian referencing combined with spectral and global coherence analyses to track the spatiotemporal dynamics of the brains anesthetic state. During unconsciousness the spectrograms in the frontal leads showed increasing ? (8-12 Hz) and ? power (0-4 Hz) and in the occipital leads ? power greater than ? power. The global coherence detected strong coordinated ? activity in the occipital leads in the awake state that shifted to the frontal leads during unconsciousness. It revealed a lack of coordinated ? activity during both the awake and unconscious states. Although strong frontal power during general anesthesia-induced unconsciousness--termed anteriorization--is well known, its possible association with strong ? range global coherence suggests highly coordinated spatial activity. Our findings suggest that combined spectral and global coherence analyses may offer a new approach to tracking brain states under general anesthesia.
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General anesthesia and altered states of arousal: a systems neuroscience analysis.
Annu. Rev. Neurosci.
PUBLISHED: 04-26-2011
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Placing a patient in a state of general anesthesia is crucial for safely and humanely performing most surgical and many nonsurgical procedures. How anesthetic drugs create the state of general anesthesia is considered a major mystery of modern medicine. Unconsciousness, induced by altered arousal and/or cognition, is perhaps the most fascinating behavioral state of general anesthesia. We perform a systems neuroscience analysis of the altered arousal states induced by five classes of intravenous anesthetics by relating their behavioral and physiological features to the molecular targets and neural circuits at which these drugs are purported to act. The altered states of arousal are sedation-unconsciousness, sedation-analgesia, dissociative anesthesia, pharmacologic non-REM sleep, and neuroleptic anesthesia. Each altered arousal state results from the anesthetic drugs acting at multiple targets in the central nervous system. Our analysis shows that general anesthesia is less mysterious than currently believed.
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Static and dynamic autonomic response with increasing nausea perception.
Aviat Space Environ Med
PUBLISHED: 04-13-2011
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Nausea is a commonly occurring symptom typified by epigastric discomfort with urge to vomit. The relationship between autonomic nervous system (ANS) outflow and increasing nausea perception is not fully understood.
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Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis.
Early Hum. Dev.
PUBLISHED: 03-18-2011
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Cardio-respiratory interactions are weak at the earliest stages of human development, suggesting that assessment of their presence and integrity may be an important indicator of development in infants. Despite the valuable research devoted to infant development, there is still a need for specifically targeted standards and methods to assess cardiopulmonary functions in the early stages of life. We present a new methodological framework for the analysis of cardiovascular variables in preterm infants. Our approach is based on a set of mathematical tools that have been successful in quantifying important cardiovascular control mechanisms in adult humans, here specifically adapted to reflect the physiology of the developing cardiovascular system.
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Denoising two-photon calcium imaging data.
PLoS ONE
PUBLISHED: 02-17-2011
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Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models.
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A Granger causality measure for point process models of ensemble neural spiking activity.
PLoS Comput. Biol.
PUBLISHED: 02-17-2011
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The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neurons spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.
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Motor cortical networks for skilled movements have dynamic properties that are related to accurate reaching.
Neural Plast.
PUBLISHED: 02-15-2011
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Neurons in the Primary Motor Cortex (MI) are known to form functional ensembles with one another in order to produce voluntary movement. Neural network changes during skill learning are thought to be involved in improved fluency and accuracy of motor tasks. Unforced errors during skilled tasks provide an avenue to study network connections related to motor learning. In order to investigate network activity in MI, microwires were implanted in the MI of cats trained to perform a reaching task. Spike trains from eight groups of simultaneously recorded cells (95 neurons in total) were acquired. A point process generalized linear model (GLM) was developed to assess simultaneously recorded cells for functional connectivity during reaching attempts where unforced errors or no errors were made. Whilst the same groups of neurons were often functionally connected regardless of trial success, functional connectivity between neurons was significantly different at fine time scales when the outcome of task performance changed. Furthermore, connections were shown to be significantly more robust across multiple latencies during successful trials of task performance. The results of this study indicate that reach-related neurons in MI form dynamic spiking dependencies whose temporal features are highly sensitive to unforced movement errors.
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Single-neuron dynamics in human focal epilepsy.
Nat. Neurosci.
PUBLISHED: 02-15-2011
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Epileptic seizures are traditionally characterized as the ultimate expression of monolithic, hypersynchronous neuronal activity arising from unbalanced runaway excitation. Here we report the first examination of spike train patterns in large ensembles of single neurons during seizures in persons with epilepsy. Contrary to the traditional view, neuronal spiking activity during seizure initiation and spread was highly heterogeneous, not hypersynchronous, suggesting complex interactions among different neuronal groups even at the spatial scale of small cortical patches. In contrast to earlier stages, seizure termination is a nearly homogenous phenomenon followed by an almost complete cessation of spiking across recorded neuronal ensembles. Notably, even neurons outside the region of seizure onset showed significant changes in activity minutes before the seizure. These findings suggest a revision of current thinking about seizure mechanisms and point to the possibility of seizure prevention based on spiking activity in neocortical neurons.
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Human leg model predicts ankle muscle-tendon morphology, state, roles and energetics in walking.
PLoS Comput. Biol.
PUBLISHED: 02-10-2011
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A common feature in biological neuromuscular systems is the redundancy in joint actuation. Understanding how these redundancies are resolved in typical joint movements has been a long-standing problem in biomechanics, neuroscience and prosthetics. Many empirical studies have uncovered neural, mechanical and energetic aspects of how humans resolve these degrees of freedom to actuate leg joints for common tasks like walking. However, a unifying theoretical framework that explains the many independent empirical observations and predicts individual muscle and tendon contributions to joint actuation is yet to be established. Here we develop a computational framework to address how the ankle joint actuation problem is resolved by the neuromuscular system in walking. Our framework is founded upon the proposal that a consideration of both neural control and leg muscle-tendon morphology is critical to obtain predictive, mechanistic insight into individual muscle and tendon contributions to joint actuation. We examine kinetic, kinematic and electromyographic data from healthy walking subjects to find that human leg muscle-tendon morphology and neural activations enable a metabolically optimal realization of biological ankle mechanics in walking. This optimal realization (a) corresponds to independent empirical observations of operation and performance of the soleus and gastrocnemius muscles, (b) gives rise to an efficient load-sharing amongst ankle muscle-tendon units and (c) causes soleus and gastrocnemius muscle fibers to take on distinct mechanical roles of force generation and power production at the end of stance phase in walking. The framework outlined here suggests that the dynamical interplay between leg structure and neural control may be key to the high walking economy of humans, and has implications as a means to obtain insight into empirically inaccessible features of individual muscle and tendons in biomechanical tasks.
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A point process model for auditory neurons considering both their intrinsic dynamics and the spectrotemporal properties of an extrinsic signal.
IEEE Trans Biomed Eng
PUBLISHED: 02-10-2011
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We propose a point process model of spiking activity from auditory neurons. The model takes account of the neurons intrinsic dynamics as well as the spectrotemporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function. The Volterra expansion models the neurons baseline spike rate, its intrinsic dynamics-spiking history-and the stimulus effect which in this case is the analog of the spectrotemporal receptive field (STRF). We performed the model fitting efficiently in a generalized linear model framework using ridge regression to address properly this ill-posed maximum likelihood estimation problem. The model provides an excellent fit to spiking activity from 55 auditory nerve neurons. The STRF-like representation estimated jointly with the neurons intrinsic dynamics may offer more accurate characterizations of neural activity in the auditory system than current ones based solely on the STRF.
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Advance cueing produces enhanced action-boundary patterns of spike activity in the sensorimotor striatum.
J. Neurophysiol.
PUBLISHED: 02-09-2011
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One of the most characteristic features of habitual behaviors is that they can be evoked by a single cue. In the experiments reported here, we tested for the effects of such advance cueing on the firing patterns of striatal neurons in the sensorimotor striatum. Rats ran in a T-maze with instruction cues about the location of reward given at the start of the runs. This advance cueing about reward produced a highly augmented task-bracketing pattern of activity at the beginning and end of procedural task performance relative to the patterns found previously with midtask cueing. Remarkably, the largest increase in activity early during the T-maze runs was not associated with the instruction cues themselves, the earliest predictors of reward; instead, the highest peak of early activity was associated with the beginning of the motor period of the task. We suggest that the advance cueing, reducing midrun demands for decision making but adding a working-memory load, facilitated chunking of the maze runs as executable scripts anchored to sensorimotor aspects of the task and unencumbered by midtask decision-making demands. Our findings suggest that the acquisition of stronger task-bracketing patterns of striatal activity in the sensorimotor striatum could reflect this enhancement of behavioral chunking. Deficits in such representations of learned sequential behaviors could contribute to motor and cognitive problems in a range of neurological disorders affecting the basal ganglia, including Parkinsons disease.
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A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG.
Neuroimage
PUBLISHED: 01-18-2011
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Multichannel electroencephalography (EEG) offers a non-invasive tool to explore spatio-temporal dynamics of brain activity. With EEG recordings consisting of multiple trials, traditional signal processing approaches that ignore inter-trial variability in the data may fail to accurately estimate the underlying spatio-temporal brain patterns. Moreover, precise characterization of such inter-trial variability per se can be of high scientific value in establishing the relationship between brain activity and behavior. In this paper, a statistical modeling framework is introduced for learning spatio-temporal decompositions of multiple-trial EEG data recorded under two contrasting experimental conditions. By modeling the variance of source signals as random variables varying across trials, the proposed two-stage hierarchical Bayesian model is able to capture inter-trial amplitude variability in the data in a sparse way where a parsimonious representation of the data can be obtained. A variational Bayesian (VB) algorithm is developed for statistical inference of the hierarchical model. The efficacy of the proposed modeling framework is validated with the analysis of both synthetic and real EEG data. In the simulation study we show that even at low signal-to-noise ratios our approach is able to recover with high precision the underlying spatio-temporal patterns and the dynamics of source amplitude across trials; on two brain-computer interface (BCI) data sets we show that our VB algorithm can extract physiologically meaningful spatio-temporal patterns and make more accurate predictions than other two widely used algorithms: the common spatial patterns (CSP) algorithm and the Infomax algorithm for independent component analysis (ICA). The results demonstrate that our statistical modeling framework can serve as a powerful tool for extracting brain patterns, characterizing trial-to-trial brain dynamics, and decoding brain states by exploiting useful structures in the data.
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Thalamocortical model for a propofol-induced alpha-rhythm associated with loss of consciousness.
Proc. Natl. Acad. Sci. U.S.A.
PUBLISHED: 12-13-2010
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Recent data reveal that the general anesthetic propofol gives rise to a frontal ?-rhythm at dose levels sufficient to induce loss of consciousness. In this work, a computational model is developed that suggests the network mechanisms responsible for such a rhythm. It is shown that propofol can alter the dynamics in thalamocortical loops, leading to persistent and synchronous ?-activity. The synchrony that forms in the cortex by virtue of the involvement of the thalamus may impede responsiveness to external stimuli, thus providing a correlate for the unconscious state.
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A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 11-25-2010
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Modeling heartbeat variability remains a challenging signal-processing goal in the presence of highly non-stationary cardiovascular control dynamics. We propose a novel differential autoregressive modeling approach within a point process probability framework for analyzing R-R interval and blood pressure variations. We apply the proposed model to both synthetic and experimental heartbeat intervals observed in time-varying conditions. The model is found to be extremely effective in tracking non-stationary heartbeat dynamics, as evidenced by the excellent goodness-of-fit performance. Results further demonstrate the ability of the method to appropriately quantify the non-stationary evolution of baroreflex sensitivity in changing physiological and pharmacological conditions.
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Psychophysiological signals associated with affective states.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 11-25-2010
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We present a preliminary quantitative study aimed at developing an optimal standard protocol for automatic classification of specific affective states as related to human- computer interactions. This goal is mainly achieved by comparing standard psychological test-reports to quantitative measures derived from simultaneous non-invasive acquisition of psychophysiological signals of interest, namely respiration, galvanic skin response, blood volume pulse, electrocardiogram and electroencephalogram. Forty-three healthy students were exposed to computer-mediated stimuli, while wearable non-invasive sensors were applied in order to collect the physiological data. The stimuli were designed to elicit three distinct affective states: relaxation, engagement and stress. In this work we report how our quantitative analysis has helped in redefining important aspects of the protocol, and we show preliminary findings related to the specific psychophysiological patterns correlating with the three target affective states. Results further suggest that some of the quantitative measures might be useful in characterizing specific affective states.
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The Fitzhugh-Nagumo model: Firing modes with time-varying parameters & parameter estimation.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 11-25-2010
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In this paper, we revisit the issue of the utility of the FitzHugh-Nagumo (FHN) model for capturing neuron firing behaviors. It has been noted (e.g., see [6]) that the FHN model cannot exhibit certain interesting firing behaviors such as bursting. We illustrate that, by allowing time-varying parameters for the FHN model, one could overcome such limitations while still retaining the low order complexity of the FHN model. We also highlight the utility of the FHN model from an estimation perspective by presenting a novel parameter estimation method that exploits the multiple time scale feature of the FHN model, and compare the performance of this method with the Extended Kalman Filter through illustrative examples.
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Point process time-frequency analysis of respiratory sinus arrhythmia under altered respiration dynamics.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 11-25-2010
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Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heartbeat. We propose an algorithm for quantifying instantaneous RSA as applied to heart beat interval and respiratory recordings under dynamic respiration conditions. The blood volume pressure derived heart beat series (pulse intervals, PI) are modeled as an inverse gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PI and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated by a frequency domain transfer function approach. The model is statistically validated using Kolmogorov-Smirnov (KS) goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. Experimental results confirm the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states.
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Assessment of lower extremity motor adaptation via an extension of the force field adaptation paradigm.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 11-25-2010
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Lower extremity rehabilitation has seen recent increased interest. New tools are available to improve gait retraining in both adults and children. However, it remains difficult to determine optimal ways to plan interventions due to difficulties in continuously monitoring outcomes in patients undergoing rehabilitation. In this paper, we introduce an extension of the Force Field Adaptation Paradigm, used to quantitatively assess upper extremity motor adaptation, to the lower extremity. The algorithm is implemented on the Lokomat lower extremity gait orthosis and utilized to assess short-term motor adaptation. Establishing an understanding of how healthy adults motor systems adapt to external perturbations will be important to understanding how the adaptive mechanisms involved in gait are altered by disease.
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Efficient decoding with steady-state Kalman filter in neural interface systems.
IEEE Trans Neural Syst Rehabil Eng
PUBLISHED: 11-15-2010
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The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.
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Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data.
IEEE Trans Neural Syst Rehabil Eng
PUBLISHED: 10-11-2010
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The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l(2) or l(1) regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.