Ultrasonic stimulation desynchronization simulation method and system equipment based on neuron group model

Abstract

The present invention relates to an ultrasonic stimulation desynchronization simulation method and system based on a neuron cluster model. The method comprises: acquiring ultrasonic data, the ultrasonic data including sound pressure and / or frequency; inputting the ultrasonic data into an intra-membrane cavitation model constructed based on the intra-membrane cavitation effect to obtain a membrane capacitance displacement current; then inputting the obtained membrane capacitance displacement current into a neuron cluster model to obtain a postsynaptic membrane potential; calculating the signal sample entropy of each neuron cluster based on the postsynaptic membrane potential; and then obtaining a simulation effect based on the neuron cluster model from the signal sample entropy. The method of the present invention aims to implement ultrasonic stimulation simulation based on a neuron cluster model and explore its ability to inhibit pathological seizures and its potential application value.

Description

Technical Field

[0001] The present invention relates to the field of neuroscience, and more specifically, to a method, device, system, computer-readable storage medium, and application thereof for simulating desynchronization of ultrasonic stimulation based on a neuron group model. Background Art

[0002] Neurons are the basic units of the nervous system's structure and function. Signal transmission in the nervous system relies on clusters of neurons. Biological information in the nervous system is transmitted from one neuron to the next. Neurons are coupled through common chemical and electrical coupling methods. Electrical coupling transmits action potentials from one neuron to the next faster than chemical coupling, so it's generally believed that electrical coupling allows for synchronized movement between neurons. Synchronous discharges in coupled neuronal groups or clusters can cause neuropsychiatric disorders such as epilepsy and Parkinson's disease. Recent studies have shown that using external physical techniques to stimulate nerves (such as applied current stimulation and magnetic acoustic stimulation) can alter the firing rhythm of neurons.

[0003] In recent years, high-frequency and high-intensity ultrasound has been widely used in the medical field, such as ultrasound imaging, ultrasonic lithotripsy, and ultrasonic scalpels. As a new type of neuromodulatory technology, low-intensity transcranial ultrasound stimulation has the characteristics of being non-invasive, having a high penetration depth, and having a high spatial resolution. It has attracted much attention from scientific researchers, with research focusing on different ultrasound parameters (frequency, peak pressure amplitude, duty cycle, duration, pulse repetition frequency) and the treatment and regulation of mental illnesses. A large number of animal models and human experiments have shown that ultrasound modulation of brain tissue can induce in situ neural functional activity; low-intensity transcranial ultrasound neuromodulation can inhibit the onset of mental illnesses such as epilepsy and Parkinson's disease. In the nervous system network of an organism, a large number of neurons and neuronal groups form a complex network, and the coupling relationship between neurons is not a one-to-one coupling relationship, but a relatively complex neuronal group. Summary of the Invention

[0004] The method of the present invention is based on the theoretical basis of the ultrasonic intra-membrane cavitation effect, and combines the ultrasonic intra-membrane cavitation model and the neuron group model. This is a theoretical innovation in the theoretical explanation and mechanism of the ultrasonic intra-membrane cavitation effect in the desynchronization of neuronal and neuronal group discharges. It provides a theoretical research method for finding the optimal parameters of different ultrasonic stimulations for the treatment of mental illnesses, and explores the application value of ultrasonic stimulation in the clinical treatment of brain diseases.

[0005] The present application discloses a method for simulating desynchronization of ultrasound stimulation based on a neuron group model, comprising:

[0006] Acquiring ultrasonic data, wherein the ultrasonic data includes sound pressure and / or frequency;

[0007] Inputting the ultrasonic data into a membrane cavitation model constructed based on the membrane cavitation effect to obtain a membrane capacitance displacement current;

[0008] Inputting the membrane capacitance displacement current into the neuron group model to obtain the postsynaptic membrane potential;

[0009] Calculating the signal sample entropy of each neuron group based on the postsynaptic membrane potential;

[0010] A simulation effect of the neuron group model is obtained based on the signal sample entropy.

[0011] Furthermore, the ultrasonic data is input into the membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitance displacement current, specifically: the ultrasonic data is input into BLS, and the BLS drives the bubble to dynamically deform the bubble radius Z(t) based on the ultrasonic sound pressure. When Z(t) reaches a stable periodic solution, the capacitance C is brought into the form of Fourier series. m (Z), thereby changing the average capacitance Cm of the membrane and generating a membrane capacitance displacement current I Cm ; Preferably, the BLS equation is as follows:

[0012]

[0013]

[0014]

[0015]

[0016]

[0017] Among them, P ec is the effective pressure exerted on the membrane by the charges on both sides of the membrane, P in is the gas pressure inside the bubble, P0, P v 、P A sin(ωt) is the hydrostatic pressure in the medium outside the bubble, the saturated gas pressure inside the bubble, and the external driving force ultrasonic sound pressure, Vm is the membrane potential, t is time, f is the frequency of the ultrasonic wave, δ is the surface tension coefficient, μ is the viscosity coefficient, ɑ is the radius of the bubble boundary, Z0 is the radius of the initial bubble boundary, Δ is the initial gap between the two membranes, c is the speed of sound in the liquid medium, ε r is the relative dielectric constant of the membrane cavity, ε0 ​​is the dielectric constant of the membrane cavity, C m0 is the cell membrane capacity under initial conditions, ρ l is the tissue density of the conducting medium.

[0018] Furthermore, the neuron group model includes the Jansen-Rit model, the SR-UKF model, the Jailsen model, and the Wrendling model; preferably, the neuron group model is the Jansen-Rit model.

[0019] Specifically, the Jansen-Rit model includes a pyramidal neuron group, an excitatory interneuron group, and an inhibitory interneuron group. The pyramidal neuron group receives excitatory and inhibitory signals from the interneuron group, as well as signals from other adjacent or more distant neuron groups. The Jansen-Rit model uses the Euler method to simulate the inhibition of pathological seizures by ultrasound stimulation. Preferably, the construction process of the Jansen-Rit model is as follows:

[0020]

[0021] Where y1, y3, and y5 represent the postsynaptic membrane potential of the pyramidal neuron group, the postsynaptic membrane potential of the excitatory interneuron group, and the postsynaptic membrane potential of the inhibitory interneuron group, respectively; C1-C4 represent the coupling strength of the interaction between neuronal groups; A represents the average excitatory synaptic gain; B represents the average inhibitory synaptic gain; a represents the reciprocal of the average excitatory dendritic time constant; and b represents the reciprocal of the average inhibitory dendritic time constant.

[0022] Furthermore, the postsynaptic membrane potential includes the postsynaptic membrane potential of the pyramidal neuron group, the postsynaptic membrane potential of the excitatory interneuron group, the postsynaptic membrane potential of the inhibitory interneuron group, and the postsynaptic membrane potential difference between the excitatory interneuron group and the inhibitory interneuron group.

[0023] The signal sample entropy of each neuron group is calculated based on the postsynaptic membrane potential, and then the simulation effect of the neuron group model is obtained by quantifying the signal sample entropy. The calculation formula of the signal sample entropy is:

[0024]

[0025] Where m represents the embedding dimension of the signal and r represents the similarity threshold.

[0026] An ultrasonic closed-loop stimulation desynchronization simulation system based on a neuron group model, characterized in that the system comprises:

[0027] An acquisition module, which acquires ultrasonic data, wherein the ultrasonic data includes sound pressure and / or frequency;

[0028] A first generation module inputs the ultrasonic data into a membrane cavitation model constructed based on the membrane cavitation effect to obtain a membrane capacitance displacement current;

[0029] The second generation module inputs the membrane capacitance displacement current into the neuron group model to obtain the postsynaptic membrane potential;

[0030] a calculation module, calculating the signal sample entropy of each neuron group based on the postsynaptic membrane potential;

[0031] An output module obtains a simulation effect of the neuron group model based on the signal sample entropy.

[0032] An ultrasonic stimulation desynchronization simulation device based on a neuron group model, the device comprising:

[0033] memory and processor;

[0034] The memory is used to store program instructions;

[0035] The processor is used to call program instructions, and when the program instructions are executed, is used to execute the above-mentioned ultrasound stimulation desynchronization simulation method based on the neuron group model.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned ultrasound stimulation desynchronization simulation method based on a neuron group model.

[0037] The above-mentioned equipment is of great significance in assisting research on the desynchronization of excitable neurons by ultrasound. This research ranges from the microscopic cellular level to the macroscopic level of psychiatric diseases such as epilepsy and Parkinson's disease, using neuronal population models to explore and analyze the desynchronization regulation effect of low-intensity transcranial ultrasound stimulation.

[0038] Application of the above device in analyzing the synchronization or asynchrony of neurons;

[0039] Application of the above-mentioned device in suppressing seizures of psychiatric diseases such as epilepsy and Parkinson's disease; optionally, the application includes exploring optimal parameters for transcranial ultrasound stimulation in treating seizures of psychiatric diseases such as epilepsy and Parkinson's disease;

[0040] The above-mentioned device provides a new approach in the theoretical exploration of transcranial ultrasound stimulation for the treatment of other psychiatric diseases, and promotes the application of ultrasound stimulation in the clinical treatment of functional brain disorders.

[0041] This system is crucial for assisting research on the desynchronization of excitable neurons by ultrasound. This research ranges from microscopic cellular levels to macroscopic levels of psychiatric disorders like epilepsy and Parkinson's disease, using mathematical models to explore and analyze the desynchronization effects of low-intensity transcranial ultrasound stimulation.

[0042] Application of the above system in analyzing the synchronization or asynchrony of neurons;

[0043] Application of the above system in suppressing epilepsy and Parkinson's disease; optionally, the application includes exploring optimal parameters for transcranial ultrasound stimulation to treat epilepsy, Parkinson's disease and other psychiatric diseases;

[0044] The above system provides a new approach in the theoretical exploration of transcranial ultrasound stimulation for the treatment of other psychiatric diseases, and promotes the application of ultrasound stimulation in the clinical treatment of functional brain diseases.

[0045] This invention uses a cavitation effect model and a neuron population model for joint training of clinically significant, high-quality ultrasound data, demonstrating its significant role in addressing the desynchronization problem of excitable neurons. Based on the theoretical foundation of the cavitation effect within ultrasound membranes, the neuron population model explores and analyzes the desynchronization regulation of low-intensity transcranial ultrasound stimulation, from the microscopic cellular level to the macroscopic level of psychiatric disorders such as epilepsy and Parkinson's disease. This not only provides a biophysical mechanism for ultrasound regulation but also offers a theoretical approach for finding optimal ultrasound parameters, further providing theoretical guidance for the treatment of psychiatric disorders. This innovative approach in the life sciences will significantly advance the synchronization analysis and state regulation research of ultrasound data.

[0046] Advantages of this application:

[0047] 1. This application innovatively discloses a new ultrasound stimulation desynchronization simulation method that introduces the intramembrane cavitation effect into a neuron population model. From the microscopic cellular level to the macroscopic level of neurological diseases such as epilepsy, this method explores and analyzes the desynchronization regulatory effects of low-intensity transcranial ultrasound stimulation based on neuronal populations. This method not only provides a biophysical mechanism for ultrasound regulation but also offers a theoretical approach for finding optimal ultrasound parameters, further providing theoretical guidance for the treatment of neurological diseases.

[0048] 2. This application innovatively uses an improved BLS intramembrane cavitation model and a combined neuron population model simulation. By solving the Fourier periodic solution and substituting it into the neuron population model, it is of great significance for exploring the optimal parameters for transcranial ultrasound stimulation in treating epileptic seizures. It also provides a new approach for theoretical exploration of transcranial ultrasound stimulation for the treatment of other diseases and promotes the application of ultrasound stimulation in the clinical treatment of functional brain disorders.

[0049] 3. The present application creatively discloses an ultrasonic stimulation desynchronization simulation device and system based on a neuron group model. Through the in-depth interpretation of ultrasonic data and synchronization state control by the neuron group model, combined with the collaborative analysis of the intramembrane cavitation model, it can effectively solve the desynchronization problem of excitable neurons and significantly improve performance, making the present application more accurately applied to the auxiliary control of the occurrence and development of mental illnesses related to ultrasonic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 is a schematic flow chart of ultrasound stimulation desynchronization simulation based on a neuron group model provided by an embodiment of the present invention;

[0052] Figure 2 This is a structural diagram of a neuron double-layer acoustic cluster model based on the intra-membrane cavitation effect provided by an embodiment of the present invention;

[0053] Figure 3 This is a simulation diagram of the ultrasonic membrane cavitation mechanism based on the membrane cavitation model provided by an embodiment of the present invention;

[0054] Figure 4 is a schematic structural diagram of a neuron group model provided by an embodiment of the present invention;

[0055] Figure 5 This is a simulation analysis diagram of the epileptic seizure inhibition effect based on the neuron group model provided by an embodiment of the present invention;

[0056] Figure 6 This is a statistical analysis chart corresponding to the calculation of sample entropy based on the neuron group model provided in an embodiment of the present invention;

[0057] Figure 7 Schematic diagram of an ultrasonic stimulation desynchronization simulation device based on a neuron group model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0059] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations are included in a specific order. However, it should be clearly understood that these operations may not be performed in the order in which they are presented herein or may be performed in parallel. The sequence numbers of the operations, such as S101, S102, etc., are merely used to distinguish different operations and do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be performed in sequence or in parallel.

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0061] Figure 1 This is a schematic flow chart of an ultrasonic stimulation desynchronization simulation based on a neuron group model provided by an embodiment of the present invention. Specifically, the method includes the following steps:

[0062] S101: Acquire ultrasonic data.

[0063] In one embodiment, the acquired ultrasonic data includes sound pressure and / or frequency, for example, ultrasonic stimulation (frequency 500KHz, sound pressure 0.15MPa) is added; optionally, the ultrasonic data also includes power density, peak pressure amplitude, duty cycle, duration, and pulse repetition frequency.

[0064] S102: Inputting the ultrasonic data into the membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitance displacement current.

[0065] In one embodiment, the ultrasonic data is input into BLS. BLS is a specific membrane cavitation model built based on the membrane cavitation effect. The bubble radius Z(t) is dynamically deformed based on the ultrasonic sound pressure. When Z(t) reaches a stable periodic solution, the capacitance C is introduced in the form of a Fourier series. m (Z), thereby changing the average capacitance C of the membrane m , generating membrane capacitance displacement current I Cm ; Preferably, the BLS equation is as follows:

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] Among them, P ec is the effective pressure exerted on the membrane by the charges on both sides of the membrane, P in is the gas pressure inside the bubble, P0, P v 、P Asin(ωt) is the hydrostatic pressure in the medium outside the bubble, the saturated gas pressure inside the bubble, and the external driving force ultrasonic sound pressure, Vm is the membrane potential, t is time, f is the frequency of the ultrasonic wave, δ is the surface tension coefficient, μ is the viscosity coefficient, ɑ is the radius of the bubble boundary, Z0 is the radius of the initial bubble boundary, Δ is the initial gap between the two membranes, c is the speed of sound in the liquid medium, ε r is the relative dielectric constant of the membrane cavity, ε0 ​​is the dielectric constant of the membrane cavity, C m0 is the cell membrane capacity under initial conditions, ρ l is the tissue density of the conducting medium.

[0072] Specifically, the BLS model (modified Rayleigh-Plesset equation for bubble dynamics) is the biophysical mechanism of the intramembrane cavitation effect (the BLS model is set according to the original parameters, and these parameters are selected based on known or measured physical bioquantities or ranges). Ultrasound is transmitted to neurons at the nanometer scale, and the positive and negative pressures cause cavitation in the phospholipid bilayer membrane. Electrically, the bilayer membrane can be approximated as a capacitor. Figure 2 This diagram illustrates the structure of a neuronal bilayer sonophore model based on intramembrane cavitation, as provided by an embodiment of the present invention. It demonstrates the biomechanical and bioelectrical structure of a bilayer of phospholipid molecules undergoing intramembrane cavitation. In the nanoscale bubble BLS model, a circular, uniform phospholipid bilayer membrane patch is surrounded by a transmembrane protein confinement ring (maximum radius 32nm). Generally, incoming ultrasound induces the formation of bubbles (radius R) between specific phospholipid bilayers (bilayer sonophores BLS) on the cell membrane, generating cavitation.

[0073] In a specific example, when the bubble radius reaches a stable periodic solution, the capacitance Cm(Z) is introduced in the form of a Fourier series, thereby changing the average capacitance Cm of the membrane and generating a membrane capacitance displacement current I Cm , which in turn causes the membrane potential Vm to change. Figure 3 The results shown are detailed simulations of the cavitation mechanism within the ultrasonic membrane. Specifically, the ODE113 function in MATLAB was used to solve the problem. The solution step was set to dt = 0.025 / fμs, where f is the frequency of the ultrasonic wave in MHz, and the charge is updated every 500μs. Figure 3 In a), we can see that the alternating positive and negative pressures of ultrasound cause the bubbles between the phospholipid bilayers to compress and expand. Substituting the Fourier series periodic solution into the HH model, we can find that the M gate is the first to respond to changes in membrane potential. When the membrane potential exceeds -50mV, an action potential is generated rapidly. Figure 3b) shows a schematic diagram of the membrane discharge process. In ultrasonic cavitation bubble dynamics, the negative pressure of ultrasound pulls the molecular layer apart, while the positive pressure compresses it. The dynamic deformation of the cavitation bubble can induce the oscillation of the membrane capacitance Cm, the accumulation of charge on the cell membrane, and the membrane capacitance displacement current I Cm Produced in I Cm Driven by ultrasound, the vacuolar membrane potential Vm oscillates between -200mV and -60mV. At the resting potential, the M gate associated with the sodium ion channel is closed, the H gate is fully open, and the P and N gates associated with the potassium ion channel are closed. When ultrasound stimulation ends and the membrane capacitance returns to the reference value, the membrane potential exceeds -50mV due to the accumulated charge, and the M gate responds rapidly, generating an action potential.

[0074] S103: Input the membrane capacitance displacement current into the neuron group model to obtain the postsynaptic membrane potential.

[0075] In one embodiment, the neuron group model includes the Jansen-Rit model, the SR-UKF model, the Jailsen model, the Jansen model, and the Wrendling model; preferably, the neuron group model is the Jansen-Rit model.

[0076] The Jansen-Rit neuron group model uses the interaction between excitatory and inhibitory neuron groups to generate neural oscillations. In 2002, Wendling et al. found that changing the gain parameters of the model can output epileptic-like discharges.

[0077] SR-UKF uses a set of precisely selected sigma points to match the statistical properties of random quantities, ensuring that after passing through a nonlinear system, the statistical properties of the estimated quantity are closely aligned with the true value. Given the parameters of the neuron population model, SR-UKF is used to estimate the external inputs and model outputs corresponding to different observations.

[0078] like Figure 4 The figure shows a schematic diagram of the structure of the Jansen-Rit neuron model. The Jansen-Rit model includes pyramidal neurons, excitatory interneurons, and inhibitory interneurons. The pyramidal neurons receive excitatory and inhibitory signals from the interneurons, as well as signals from other adjacent or more distant neuron groups. Figure 4 The h(t) function in is a linear module (excitability transfer function h e (t), inhibitory transfer function h iThe sigmoid function is a nonlinear module that converts the average membrane potential v(t) into the discharge pulse r(t). Pyramidal neurons receive excitatory and inhibitory signals from interneurons, as well as signals p(t) from other adjacent or more distant neuronal groups.

[0079] In a specific embodiment, the simulation of ultrasound stimulation to suppress epileptic seizures in the Jansen-Rit neuron group model first uses the ODE113 function in MATLAB to simultaneously solve the BLS model and the Jansen-Rit neuron group model, obtaining the stable periodic solutions Z(t) and Cm(Z), which are then substituted into the Jansen-Rit model to simulate the ultrasound stimulation to suppress epileptic seizures using the Euler method, with a time step of 1ms. Preferably, the construction process of the Jansen-Rit model is as follows:

[0080]

[0081] Where y1, y3, and y5 represent the postsynaptic membrane potential of the pyramidal neuron group, the excitatory interneuron group, and the inhibitory interneuron group, respectively. C1-C4 are the coupling strengths of the interactions between neuronal groups (C1 = 135; C2 = 108; C3 = 33.75; C4 = 33.75), the average excitatory synaptic gain A = 3.7 mV, the average inhibitory synaptic gain B = 22 mV, and the inverse of the average excitatory dendritic time constant a = 100 s. -1 , the inverse of the average inhibitory dendritic time constant b = 50 s -1 The expression of the output signal y(t) of the neuron group model is:

[0082] y(t)=y3(t)-y5(t)

[0083] S104: Calculate the signal sample entropy of each neuron group based on the postsynaptic membrane potential.

[0084] In one example, the signal sample entropy of each neuron group is calculated by postsynaptic membrane potential, where the postsynaptic membrane potential includes the postsynaptic membrane potential of the pyramidal neuron group, the postsynaptic membrane potential of the excitatory interneuron group, the postsynaptic membrane potential of the inhibitory interneuron group, and the postsynaptic membrane potential difference between the excitatory interneuron group and the inhibitory interneuron group. Preferably, the specific calculation method of the signal sample entropy is as follows:

[0085]

[0086] Where m represents the embedding dimension of the signal and r represents the similarity threshold.

[0087] S105: Obtaining a simulation effect of the neuron group model based on the signal sample entropy.

[0088] In one embodiment, the sample entropy is used to evaluate the epilepsy suppression effect of different ultrasound stimulation frequencies. Figure 5 This is a simulation analysis diagram of the epileptic seizure inhibition effect based on the neuron group model provided by an embodiment of the present invention. The Jansen-Rit neuron group model is used, the external input p(t) is set to Gaussian white noise with a mean of 90 and a standard deviation of 30, the coupling strength of the interaction between the neuron groups is C1=135; C2=108; C3=33.75; C4=33.75, the average inhibitory synaptic gain B=22mV, and the inverse of the average excitatory dendritic time constant a=100s -1 , the inverse of the average inhibitory dendritic time constant b = 50 s -1 , when the average excitatory synaptic gain A=3.25mV, as Figure 5 As shown in (a), the model simulation output is an approximate spontaneous EEG signal; when A = 3.5mV, as shown in Figure 5 As shown in (b), the model simulation output waveform shows a small amount of spikes; Figure 5 As shown in (c), when A = 3.7mV, a large number of output spikes appear, which can simulate epileptic seizures; Figure 5 As shown in (d), when A=3.5mV, ultrasound stimulation was added at the 20th second, and a large number of spike waves were suppressed and the waveform showed sporadic spike waves. It can be concluded that ultrasound stimulation can inhibit epileptic seizures.

[0089] The above method is used to assist in suppressing the synchronization of neural discharges and selecting the attack scheme of mental diseases such as epilepsy and Parkinson's disease. The intra-membrane cavitation model BLS is combined with the neuron group model. The simulation research effect is improved from Figure 6 , which is a statistical analysis diagram corresponding to the calculation of sample entropy based on the neuron group model provided by an embodiment of the present invention, introducing ultrasound stimulation into the Jansen-Rit neuron group model. Figure 6 The Jansen-Rit neuron model with 0.1-1 MHz ultrasound stimulation was applied to 10 groups of external inputs p(t), and the sample entropy was calculated and statistically analyzed. Figure 6 (a) It can be seen that under 10 different p(t), the sample entropy of each group at the same frequency is basically the same. The sample entropy increases with the increase of frequency. In the range of 0.3-0.6MHz, the sample entropy has a large growth slope and increases rapidly. In the range of 0.7-1.0MHz, the growth is slow. Figure 6The change in average sample entropy in (b) shows that the inhibitory effect of ultrasound on epileptic seizures increases with increasing ultrasound frequency within the 0.1-1.0 MHz range. Therefore, simulations of ultrasound stimulation on a Jansen-Rit neuron population model show that TUS can suppress epileptic seizures, and the inhibitory effect increases with increasing ultrasound frequency.

[0090] An embodiment of the present invention provides an ultrasound stimulation desynchronization simulation system based on a neuron group model, comprising:

[0091] An acquisition module, for acquiring ultrasonic data, where the ultrasonic data includes sound pressure and / or frequency;

[0092] The first generation module inputs the ultrasonic data into the membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitance displacement current;

[0093] The second generation module inputs the membrane capacitance displacement current into the neuron group model to obtain the postsynaptic membrane potential;

[0094] The calculation module calculates the signal sample entropy of each neuron group based on the postsynaptic membrane potential;

[0095] The output module obtains the simulation effect of the neuron group model based on the signal sample entropy.

[0096] Figure 7 An embodiment of the present invention provides an ultrasonic stimulation desynchronization simulation device based on a neuron group model, comprising: a memory and a processor; the device may also include: an input device and an output device.

[0097] The memory, processor, input device and output device can be connected via a bus or other means. Figure 7 The example shown is connected via a bus;

[0098] The memory is used to store program instructions;

[0099] The processor is used to call program instructions, and when the program instructions are executed, is used to execute the above-mentioned ultrasound stimulation desynchronization simulation method based on the neuron group model.

[0100] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned ultrasonic stimulation desynchronization simulation method based on a neuron group model is implemented.

[0101] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0103] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the purpose of this embodiment based on actual needs.

[0104] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0105] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0106] Those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be implemented by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0107] The above is a detailed introduction to a computer device provided by the present invention. For those skilled in the art, according to the concept of the embodiments of the present invention, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for simulating desynchronization of ultrasonic stimulation based on a neuron group model, characterized in that: include: Acquiring ultrasonic data, wherein the ultrasonic data includes sound pressure and / or frequency; Inputting the ultrasonic data into a membrane cavitation model constructed based on the membrane cavitation effect to obtain a membrane capacitance displacement current; Inputting the membrane capacitance displacement current into the neuron group model to obtain the postsynaptic membrane potential; Calculating the signal sample entropy of each neuron group based on the postsynaptic membrane potential; Based on the signal sample entropy, a simulation effect of the neuron group model is obtained. In the range of 0.1-1.0 MHz, the signal sample entropy increases with the increase of the frequency in the ultrasonic data, and the inhibitory effect of ultrasound on epileptic seizures is enhanced; the neuron group model is a Jansen-Rit model; the Jansen-Rit model includes a pyramidal neuron group, an excitatory interneuron group and an inhibitory interneuron group, and the pyramidal neuron group receives excitatory and inhibitory signals from the interneuron group as well as signals from other adjacent or more distant neuron groups; the Jansen-Rit model uses the Euler method to simulate the inhibition of pathological seizures by ultrasonic stimulation.

2. The ultrasonic stimulation desynchronization simulation method based on the neuron group model according to claim 1, characterized in that: The ultrasonic data is input into the membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitance displacement current. Specifically, the ultrasonic data is input into BLS. The BLS drives the bubble to dynamically deform based on the ultrasonic sound pressure. When Z(t) reaches a stable periodic solution, the capacitance C is brought into the form of a Fourier series. m (Z), thereby changing the average capacitance C of the membrane m , generating membrane capacitance displacement current I Cm .

3. The ultrasonic stimulation desynchronization simulation method based on the neuron group model according to claim 2, characterized in that: The equation for the BLS is as follows: Among them, P ec is the effective pressure exerted on the membrane by the charges on both sides of the membrane, P in is the gas pressure inside the bubble, P0, P v 、 are the hydrostatic pressure in the medium outside the bubble, the saturated gas pressure inside the bubble, and the external driving force ultrasonic sound pressure, Vm is the membrane potential, t is time, f is the frequency of the ultrasonic wave, δ is the surface tension coefficient, μ is the viscosity coefficient, ɑ is the radius of the bubble boundary, Z0 is the radius of the initial boundary of the bubble, Δ is the initial gap between the two membranes, c is the speed of sound in the liquid medium, ε r is the relative dielectric constant of the membrane cavity, ε0 ​​is the dielectric constant of the membrane cavity, C m0 is the cell membrane capacity under initial conditions, ρ l is the tissue density of the conducting medium.

4. The ultrasonic stimulation desynchronization simulation method based on the neuron group model according to claim 1, characterized in that: The postsynaptic membrane potential includes the postsynaptic membrane potential of the pyramidal neuron group, the postsynaptic membrane potential of the excitatory interneuron group, the postsynaptic membrane potential of the inhibitory interneuron group, and the postsynaptic membrane potential difference between the excitatory interneuron group and the inhibitory interneuron group.

5. Ultrasonic stimulation desynchronization simulation system based on neuron group model, characterized by: The system comprises: An acquisition module, which acquires ultrasonic data, wherein the ultrasonic data includes sound pressure and / or frequency; A first generation module inputs the ultrasonic data into a membrane cavitation model constructed based on the membrane cavitation effect to obtain a membrane capacitance displacement current; The second generation module inputs the membrane capacitance displacement current into the neuron group model to obtain the postsynaptic membrane potential; a calculation module, calculating the signal sample entropy of each neuron group based on the postsynaptic membrane potential; The output module obtains the simulation effect of the neuron group model based on the quantization of the signal sample entropy. In the range of 0.1-1.0 MHz, the signal sample entropy increases with the increase of the frequency in the ultrasonic data, and the inhibitory effect of ultrasound on epileptic seizures is enhanced; the neuron group model is a Jansen-Rit model; the Jansen-Rit model includes a pyramidal neuron group, an excitatory interneuron group and an inhibitory interneuron group, and the pyramidal neuron group receives excitatory and inhibitory signals from the interneuron group as well as signals from other adjacent or more distant neuron groups; the Jansen-Rit model uses the Euler method to simulate the inhibition of pathological seizures by ultrasonic stimulation.

6. An ultrasonic stimulation desynchronization simulation device based on a neuron group model, characterized in that: The device includes: a memory and a processor; the memory is used to store program instructions; the processor is used to call program instructions, and when the program instructions are executed, it is used to implement the ultrasonic stimulation desynchronization simulation method based on the neuron group model as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the ultrasonic stimulation desynchronization simulation method based on a neuron group model according to any one of claims 1 to 4 is implemented.

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