Wei Xile

Modulation of electroencephalograph activity by manual acupuncture stimulation in healthy subjects: An autoregressive spectral analysis

To investigate whether and how manual acupuncture (MA) modulates brain activities, we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph (EEG) signals in healthy subjects. We adopt the autoregressive (AR) Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta (0 Hz–4 Hz), theta (4 Hz–8 Hz), alpha (8 Hz–13 Hz), and beta (13 Hz–30 Hz) bands. Our results show that MA at ST36 can significantly increase the EEG slow wave relative power (delta band) and reduce the fast wave relative powers (alpha and beta bands), while there are no statistical differences in theta band relative power between different acupuncture states. In order to quantify the ratio of slow to fast wave EEG activity, we compute the power ratio index. It is found that the MA can significantly increase the power ratio index, especially in frontal and central lobes. All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture. The proposed quantitative method of acupuncture signals may be further used to make MA more standardized.

Characteristics analysis of acupuncture electroencephalograph based on mutual information Lempel—Ziv complexity

As a convenient approach to the characterization of cerebral cortex electrical information, electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects. In this paper, a method composed of the mutual information method and Lempel—Ziv complexity method (MILZC) is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs. In the experiments, eight subjects are manually acupunctured at ‘Zusanli’ acupuncture point (ST-36) with different frequencies (i.e., 50, 100, 150, and 200 times/min) and the EEGs are recorded simultaneously. First, MILZC values are compared in general. Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies. Finally, significance index P values are used to study the spatiality of the acupuncture effect on the brain. Three main findings are obtained: (i) MILZC values increase during the acupuncture; (ii) manual acupunctures (MAs) with 100 times/min and 150 times/min are more effective than with 50 times/min and 200 times/min; (iii) contralateral hemisphere activation is more prominent than ipsilateral hemispheres. All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.

Bursting synchronization in clustered neuronal networks

Neuronal networks in the brain exhibit the modular (clustered) property, i.e., they are composed of certain subnetworks with differential internal and external connectivity. We investigate bursting synchronization in a clustered neuronal network. A transition to mutual-phase synchronization takes place on the bursting time scale of coupled neurons, while on the spiking time scale, they behave asynchronously. This synchronization transition can be induced by the variations of inter- and intracoupling strengths, as well as the probability of random links between different subnetworks. Considering that some pathological conditions are related with the synchronization of bursting neurons in the brain, we analyze the control of bursting synchronization by using a time-periodic external signal in the clustered neuronal network. Simulation results show a frequency locking tongue in the driving parameter plane, where bursting synchronization is maintained, even in the presence of external driving. Hence, effective synchronization suppression can be realized with the driving parameters outside the frequency locking region.

Adaptive synchronization control of coupled chaotic neurons in an external electrical stimulation

In this paper we present a combined algorithm for the synchronization control of two gap junction coupled chaotic FitzHugh—Nagumo (FHN) neurons in an external electrical stimulation. The controller consists of a combination of dynamical sliding mode control and adaptive backstepping control. The combined algorithm yields an adaptive dynamical sliding mode control law which has the advantage over static sliding mode-based controllers of being chattering-free, i.e., a sufficiently smooth control input signal is generated. It is shown that the proposed control scheme can not only compensate for the system uncertainty, but also guarantee the stability of the synchronized error system. In addition, numerical simulations are also performed to demonstrate the effectiveness of the proposed adaptive controller.

Closed-loop fuzzy control of Parkinsonian state based on slow variable

Closed-loop deep brain stimulation is an effective method for controlling the Parkinsonian state. However, the twin issues of how to obtain a suitable feedback variable and design a high-performance control strategy are still unresolved. This paper proposes a variable universe fuzzy closed-loop control method based on slow variable to modulate the abnormal Parkinsonian state. For highly nonlinear neural systems, in order to achieve energy optimization of the control signal, this paper designs a closed-loop control strategy of thalamic neurons by combining unscented Kalman filter with variable universe fuzzy control, with the objective of improving the firing patterns of thalamic neurons via external stimuli with lower energy consumption. Using a slow variable as the feedback variable significantly decreases the fluctuations and energy expenditure of the stimuli. Qualitative and quantitative analyses conducted demonstrate that the proposed variable universe fuzzy closed-loop control strategy based on slow variable is effective.

A novel feature extraction method for epilepsy EEG signals based on robust generalized synchrony analysis

A feature extraction method for Epilepsy diagnosis is proposed in this paper, which can be incorporated in automatic/semi-automatic epilepsy diagnosis systems to improve diagnosis efficiency from multi-channel Electroencephalogram signals. This method calculates the Robust Generalized Synchrony between pairs of Electroencephalogram channels in the first step. Then six character parameters are extracted from the Robust Generalized Synchrony values for the whole brain and the sub-brain regions. A set of Electroencephalogram data including 20 normal objects and 20 epileptic patients in interictal states were used to test the proposed method The results demonstrate that these features are effective to differentiate between epilepsy patients and the normal objects with the p-values smaller than 0.01.

Developments of neural effects inducedby noninvasive brain modulation

Noninvasive brain modulation (NBM) is such a stimulation technique that uses electric or magnetic fields to non-intrusively stimulate the central nervous system (CNS). Common NBM modalities include transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). TMS usually uses pulsed fields of 1–4 Tesla in intensity with duration less than a millisecond to stimulate brain. Such strong field is sufficient to directly evoke action potentials in relevant neural tissue, which is referred as to suprathreshold field. Unlike TMS, tDCS delivers weak constant current of 0.5–2 mA in intensity to stimulate human cortex by means of a pair of electrode pads placed on the scalp. Such weak stimulus is unable to excite neural tissue to initiate action potentials, which is referred as to subthreshold fields. Nowadays, NBM has become a promising option for the treatment, diagnosis, and rehabilitation of a number of neurologic or psychiatric disorders in clinics, such as, epilepsy, schizophrenia, Alzheimer’s disease, depression, Parkinson’s disease, stroke, as well as pain syndromes. What is more, it is also a common tool used to study brain physiology and function, which has great potentials in exploring cognitive, emotion, memory, attention, learning, perception, language, and plasticity. As a noninvasive method of intervening brain activities, the NBM technique has significantly promoted the developments of cognitive science, neurophysiology, neurology and psychiatry in recent years. Although NBM has been widely and successfully applied in clinics and basic researches, it is still largely unknown how it interacts with CNS and modulates brain function. Such insufficient understanding of the potential mechanisms underlying NBM would delay the design of more effective stimulator as well as limit the development and optimization of stimulation protocols. One common principle for NBM techniques is that they modulate neural activity and ultimately behavior through the generation of extracellular electric fields around the interested brain tissue. According to this principle, two key problems should be considered when we are probing how NBM participates in brain activity and function. One is to determine the characteristics of extracellular fields generated in the brain tissue during stimulation. The common method for addressing this problem is finite element analysis. The other one is to investigate how this induced field modulates neural activity to alter brain function and ultimately behavior. The focus of recent researches associated with this problem is to identify the spiking behaviors of neural system stimulated by electric fields as well as to explore the biophysical basis for their generation. In this paper, we first introduce the application status of NBM in neuroscience and clinics. Then, we review the main findings on the neuromodulation effects of electric field, including electrophysiological experiment and computational modeling simulation. Finally, we raise several key issues that need to be addressed in the future.

Sensitivity of firing rate in single-compartment neurons depends on induction electric field

This paper establishes a new neuron model in the effect of induction electric field (IEF) based on the Hodgkin-Huxley (HH) model to investigate how a single-compartment neuron encodes the stimulus and how the sensitivity of firing rate to input fluctuations changes under different IEF. Then add noisy direct current IEF (DCIEF) and alternating current IEF (ACIEF) to the neuron model respectively at the bifurcation point. By the data obtained and our analysis, this paper shows how the variance of IEF frequency or amplitude influences the neuronal sensitivity of firing rate.

Self-sustained firing activities of the cortical network with plastic rules in weak AC electrical fields

Both external and endogenous electrical fields widely exist in the environment of cortical neurons. The effects of a weak alternating current (AC) field on a neural network model with synaptic plasticity are studied. It is found that self-sustained rhythmic firing patterns, which are closely correlated with the cognitive functions, are significantly modified due to the self-organizing of the network in the weak AC field. The activities of the neural networks are affected by the synaptic connection strength, the external stimuli, and so on. In the presence of learning rules, the synaptic connections can be modulated by the external stimuli, which will further enhance the sensitivity of the network to the external signal. The properties of the external AC stimuli can serve as control parameters in modulating the evolution of the neural network.

Spiking patterns of a hippocampus model in electric fields

We develop a model of CA3 neurons embedded in a resistive array to mimic the effects of electric fields from a new perspective. Effects of DC and sinusoidal electric fields on firing patterns in CA3 neurons are investigated in this study. The firing patterns can be switched from no firing pattern to burst or from burst to fast periodic firing pattern with the increase of DC electric field intensity. It is also found that the firing activities are sensitive to the frequency and amplitude of the sinusoidal electric field. Different phase-locking states and chaotic firing regions are observed in the parameter space of frequency and amplitude. These findings are qualitatively in accordance with the results of relevant experimental and numerical studies. It is implied that the external or endogenous electric field can modulate the neural code in the brain. Furthermore, it is helpful to develop control strategies based on electric fields to control neural diseases such as epilepsy.