Biomedical subjects

Mingzhou Ding

Publications and source records attributed to Mingzhou Ding.

At least 19 recordsLinked to original sources

PubMed2006

Trial-by-trial estimation of amplitude and latency variability in neuronal spike trains.

The rate function underlying single-trial spike trains can vary from trial to trial. We propose to estimate the amplitude and latency variability in single-trial neuronal spike trains on a trial-by-trial basis. The firing rate over a trial is modeled by a family of rate profiles with trial-invariant waveform and trial-dependent amplitude scaling factors and latency shifts. Using a Bayesian inference framework we derive an iterative fixed-point algorithm from which the single-trial amplitude scaling factors and latency shifts are estimated. We test the performance of the algorithm on simulated data and then apply it to actual neuronal recordings from the sensorimotor cortex of the monkey.

Action Potentials↗

PubMed2006

Differentially variable component analysis: Identifying multiple evoked components using trial-to-trial variability.

Electric potentials and magnetic fields generated by ensembles of synchronously active neurons, either spontaneously or in response to external stimuli, provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult because detectors record signals simultaneously generated by various regions throughout the brain. We introduce a novel approach to this problem, the differentially variable component analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we demonstrate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. We then compare the source-separation capabilities of dVCA with those of principal component analysis and independent component analysis. Finally, we apply dVCA to neural ensemble activity recorded from an awake, behaving macaque-demonstrating that dVCA is an important tool for identifying and characterizing multiple components in the single trial.

Animals↗

PubMed2006

Large-scale visuomotor integration in the cerebral cortex.

Efficient visuomotor behavior depends on integrated processing by the visual and motor systems of the cerebral cortex. Yet, many previous cortical neurophysiology studies have examined the visual and motor modalities in isolation, largely ignoring questions of large-scale cross-modal integration. To address this issue, we analyzed event-related local field potentials simultaneously recorded from multiple visual, motor, and executive cortical sites in monkeys performing a visuomotor pattern discrimination task. The timing and cortical location of four aspects of event-related activities were examined: stimulus-evoked activation onset, stimulus-specific processing, stimulus category-specific processing, and response-specific processing. Activations appeared earliest in striate cortex and rapidly thereafter in other visual areas. Stimulus-specific processing began early in most visual cortical areas, some at activation onset. Early onset latencies were also observed in motor, premotor, and prefrontal areas, some as early as in striate cortex, but these early-activating frontal sites did not show early stimulus-specific processing. Response-specific processing began around 150 ms poststimulus in widespread cortical areas, suggesting that perceptual decision formation and response selection arose through concurrent processes of visual, motor, and executive areas. The occurrence of stimulus-specific and stimulus category-specific differences after the onset of response-specific processing suggests that sensory and motor stages of visuomotor processing overlapped in time.

Algorithms↗

PubMed2006

Synchronization in networks with random interactions: theory and applications.

Synchronization is an emergent property in networks of interacting dynamical elements. Here we review some recent results on synchronization in randomly coupled networks. Asymptotical behavior of random matrices is summarized and its impact on the synchronization of network dynamics is presented. Robert May's results on the stability of equilibrium points in linear dynamics are first extended to systems with time delayed coupling and then nonlinear systems where the synchronized dynamics can be periodic or chaotic. Finally, applications of our results to neuroscience, in particular, networks of Hodgkin-Huxley neurons, are included.

Action Potentials↗

PubMed2006

Stochastic modeling of neurobiological time series: power, coherence, Granger causality, and separation of evoked responses from ongoing activity.

In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

Action Potentials↗

PubMed2005

Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data.

It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.

Algorithms↗

PubMed2005

Stability of synchronous oscillations in a system of Hodgkin-Huxley neurons with delayed diffusive and pulsed coupling.

We study the synchronization dynamics for a system of two Hodgkin-Huxley (HH) neurons coupled diffusively or through pulselike interactions. By calculating the maximum transverse Lyapunov exponent, we found that, with diffusive coupling, there are three regions in the parameter space, corresponding to qualitatively distinct behaviors of the coupled dynamics. In particular, the two neurons can synchronize in two regions and desynchronize in the third. When excitatory and inhibitory pulse coupling is considered, we found that synchronized dynamics becomes more difficult to achieve in the sense that the parameter regions where the synchronous state is stable are smaller. Numerical simulations of the coupled system are presented to validate these results. The stability of a network of coupled HH neurons is then analyzed and the stability regions in the parameter space are exactly obtained.

Action Potentials↗

PubMed2004

Will a large complex system with time delays be stable?

In 1972 May showed that for a large linear system with random coupling the system size and the average coupling strength must together satisfy a simple inequality to ensure the stability of the equilibrium point. Here we extend the analysis to delay coupled systems. Our calculations establish that the same inequality obtained by May constrains the stability for systems randomly coupled through discrete and distributed delays.

Journal Article↗

PubMed2004

Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality.

Previous studies have shown that synchronized beta frequency (14-30 Hz) oscillations in the primary motor cortex are involved in maintaining steady contractions of contralateral arm and hand muscles. However, little is known about the role of postcentral cortical areas in motor maintenance and their patterns of interaction with motor cortex. We investigated the functional relations of beta-synchronized neuronal assemblies in pre- and postcentral areas of two monkeys as they pressed a hand lever during the wait period of a visual discrimination task. By using power and coherence spectral analysis, we identified a beta-synchronized large-scale network linking pre- and postcentral areas. We then used Granger causality spectra to measure directional influences among recording sites. In both monkeys, strong Granger causal influences were observed from primary somatosensory cortex to both motor cortex and inferior posterior parietal cortex, with the latter area also exerting Granger causal influences on motor cortex. Granger causal influences from motor cortex to postcentral sites, however, were weak in one monkey and not observed in the other. These results are the first, to our knowledge, to demonstrate in awake monkeys that synchronized beta oscillations bind multiple sensorimotor areas into a large-scale network during motor maintenance behavior and carry Granger causal influences from primary somatosensory and inferior posterior parietal cortices to motor cortex.

Animals↗

PubMed2004

Is partial coherence a viable technique for identifying generators of neural oscillations?

Partial coherence measures the linear relationship between two signals after the influence of a third signal has been removed. Gersch proposed in 1970 that partial coherence could be used to identify sources of driving for multivariate time series. This idea, referred to in this paper as Gersch Causality, has received wide acceptance and has been applied extensively to a variety of fields in the signal processing community. Neurobiological data from a given sensor include both the signals of interest and other unrelated processes collectively referred to as measurement noise. We show that partial-coherence-based Gersch Causality is extremely sensitive to signal-to-noise ratio; that is, for a group of three or more simultaneously recorded time series, the time series with the highest signal-to-noise ratio (i.e., relatively noise free) is often identified as the "driver" of the group, irrespective of the true underlying patterns of connectivity. This hypothesis is tested both theoretically and on experimental time series acquired from limbic brain structures during the theta rhythm.

Animals↗

PubMed2004

Neural dynamics and the fundamental mechanisms of event-related brain potentials.

Event-related potentials (ERPs) provide a critical link between the hemodynamic response, as measured by functional magnetic resonance imaging, and the dynamics of the underlying neuronal activity. Single-trial ERP recordings capture the oscillatory activity that are hypothesized to underlie both communication between brain regions and amplified processing of behaviorally relevant stimuli. However, precise interpretations of ERPs are precluded by uncertainty about their neural mechanisms. One influential theory holds that averaged sensory ERPs are generated by partial phase resetting of ongoing electroencephalographic oscillations, while another states that ERPs result from stimulus-evoked neural responses. We formulated critical predictions of each theory and tested these using direct, intracortical analyses of neural activity in monkeys. Our findings support a predominant role for stimulus-evoked activity in sensory ERP generation, and they outline both logic and methodology necessary for differentiating evoked and phase resetting contributions to cognitive and motor ERPs in future studies.

Animals↗

PubMed2004

Enhancement of neural synchrony by time delay.

In a network of neuronal oscillators with time-delayed coupling, we uncover a phenomenon of enhancement of neural synchrony by time delay: a stable synchronized state exists at low coupling strengths for significant time delays. By formulating a master stability equation for time-delayed networks of Hindmarsh-Rose neurons, we show that there is always an extended region of stable synchronous activity corresponding to low coupling strengths. Such synchrony could be achieved in the undelayed system only by much higher coupling strengths. This phenomenon of enhanced neural synchrony by delay has important implications, in particular, in understanding synchronization of distant neurons and information processing in the brain.

Animals↗

PubMed2004

Transitions to synchrony in coupled bursting neurons.

Certain cells in the brain, for example, thalamic neurons during sleep, show spike-burst activity. We study such spike-burst neural activity and the transitions to a synchronized state using a model of coupled bursting neurons. In an electrically coupled network, we show that the increase of coupling strength increases incoherence first and then induces two different transitions to synchronized states, one associated with bursts and the other with spikes. These sequential transitions to synchronized states are determined by the zero crossings of the maximum transverse Lyapunov exponents. These results suggest that synchronization of spike-burst activity is a multi-time-scale phenomenon and burst synchrony is a precursor to spike synchrony.

Action Potentials↗

PubMed2003

Estimation of single-trial multicomponent ERPs: differentially variable component analysis (dVCA).

A Bayesian inference framework for estimating the parameters of single-trial, multicomponent, event-related potentials is presented. Single-trial recordings are modeled as the linear combination of ongoing activity and multicomponent waveforms that are relatively phase-locked to certain sensory or motor events. Each component is assumed to have a trial-invariant waveform with trial-dependent amplitude scaling factors and latency shifts. A Maximum a Posteriori solution of this model is implemented via an iterative algorithm from which the component's waveform, single-trial amplitude scaling factors and latency shifts are estimated. Multiple components can be derived from a single-channel recording based on their differential variability, an aspect in contrast with other component analysis techniques (e.g., independent component analysis) where the number of components estimated is equal to or smaller than the number of recording channels. Furthermore, we show that, by subtracting out the estimated single-trial components from each of the single-trial recordings, one can estimate the ongoing activity, thus providing additional information concerning task-related brain dynamics. We test this approach, which we name differentially variable component analysis (dVCA), on simulated data and apply it to an experimental dataset consisting of intracortically recorded local field potentials from monkeys performing a visuomotor pattern discrimination task.

Algorithms↗

PubMed2003

General stability analysis of synchronized dynamics in coupled systems.

We consider the stability of synchronized states (including equilibrium point, periodic orbit, or chaotic attractor) in arbitrarily coupled dynamical systems (maps or ordinary differential equations). We develop a general approach, based on the master stability function and Gershgörin disk theory, to yield constraints on the coupling strengths to ensure the stability of synchronized dynamics. Systems with specific coupling schemes are used as examples to illustrate our general method.

Journal Article↗

PubMed2003

Analyzing stability of equilibrium points in neural networks: a general approach.

Networks of coupled neural systems represent an important class of models in computational neuroscience. In some applications it is required that equilibrium points in these networks remain stable under parameter variations. Here we present a general methodology to yield explicit constraints on the coupling strengths to ensure the stability of the equilibrium point. Two models of coupled excitatory-inhibitory oscillators are used to illustrate the approach.

Computer Simulation↗

PubMed2002

Synchronized activity in prefrontal cortex during anticipation of visuomotor processing.

It is commonly presumed, though not well established, that the prefrontal cortex exerts top-down control of sensory processing. One aspect of this control is thought to be a facilitation of sensory pathways in anticipation of such processing. To investigate the possible involvement of prefrontal cortex in anticipatory top-down control, we studied the statistical relations between prefrontal activity, recorded while a macaque monkey waited for presentation of a visual stimulus, and subsequent sensory and motor events. Local field potentials were simultaneously recorded from prefrontal, motor, occipital and temporal cortical sites in the left cerebral hemisphere. Spectral power and coherence analysis revealed that during stimulus anticipation three of five prefrontal sites participated in a coherent oscillatory network synchronized in the beta-frequency range. Pre-stimulus network power and coherence were highly correlated with the amplitude and latency of early visual evoked potential components in visual cortical areas, and with response time. The results suggest that synchronized oscillatory networks in prefrontal cortex are involved in top-down anticipatory mechanisms that facilitate subsequent sensory processing in visual cortex. They further imply that stronger top-down control leads to larger and faster sensory responses, and a subsequently faster motor response.

Animals↗

PubMed2002

Task-related power and coherence changes in neuromagnetic activity during visuomotor coordination.

We analyzed a set of full-head (66 channels, CTF Inc.) magnetoencephalography (MEG) data recorded when 5 subjects performed rhythmic right index-finger flexion and extension movements on the beat (synchronization) or off the beat (syncopation) with a visual metronome at 1 Hz. Neuromagnetic activities in the alpha (8-14 Hz), beta (15-30 Hz) and gamma (30-50 Hz) ranges were shown to correlate with different aspects of the task. Specifically, we found that, compared with the control condition in which subjects only looked at the visual metronome without making any movement, all the movement conditions were accompanied by a decrease of power in the alpha range (8-14 Hz) in sensorimotor channels of both hemispheres, and an increase of coherence among a subset of these channels. The same comparison showed that power changes in the beta range differentiate task conditions by exhibiting power increases for synchronization and power decreases for syncopation. Changes in the gamma range power were found to be related to the kinematics of movement trajectories (flexion versus extension). These results suggest that three important cortical oscillations play different functional roles in a visuomotor timing task.

Alpha Rhythm↗