Ultrasound stimulation desynchronization simulation method and system device based on a double neuron model

Abstract

The application relates to an ultrasonic stimulation desynchronization simulation method, system and device based on a double-neuron model. The method comprises the following steps: acquiring ultrasonic wave data, wherein the ultrasonic wave data comprises sound pressure and / or frequency; inputting the ultrasonic wave data into an intramembranous cavitation model constructed based on an intramembranous cavitation effect to obtain an intramembranous capacitive displacement current; inputting the obtained intramembranous capacitive displacement current into a double-neuron model to obtain time corresponding to the peak values of two neuron potentials; calculating a phase difference according to the time corresponding to the peak values of the two neuron potentials; and obtaining that the two neurons are in a desynchronized state or a synchronized state according to the phase difference. The method aims to realize desynchronization based on ultrasonic stimulation simulation of the double-neuron model, and to explore the inhibition capacity and potential application value of the double-neuron model on morbid attacks.

Description

Technical Field

[0001] This invention relates to the field of neuroscience, and more specifically, to a method, device, system, computer-readable storage medium, and application of ultrasound stimulation desynchronization simulation based on a dual-neuron model. Background Technology

[0002] Neurons are the basic structural and functional units of the nervous system, and 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. Coupling between neurons includes common chemical coupling and electrical coupling. Action potentials are transmitted from one neuron to the next via electrical coupling faster than via chemical coupling; therefore, electrical coupling is generally considered to cause synchronous movement between neurons. Synchronous firing of coupled dineurons or neural clusters can cause neuropsychiatric disorders such as epilepsy and Parkinson's disease. Recent studies have shown that stimulating neurons using external physical techniques (e.g., applied electrical current stimulation, magnetoacoustic stimulation) can alter the firing rhythm of neurons.

[0003] In recent years, high-frequency, high-intensity ultrasound has been widely used in the medical field, such as in ultrasound imaging, ultrasonic lithotripsy, and ultrasonic scalpels. Low-intensity transcranial ultrasound stimulation (LCU), as a novel neuromodulation technique, has attracted considerable attention from researchers due to its non-invasive nature, high penetration depth, and high spatial resolution. Research focuses on the therapeutic and modulatory effects of different ultrasound parameters (frequency, peak pressure amplitude, duty cycle, duration, and pulse repetition frequency) on mental disorders. Numerous animal models and human experiments have shown that ultrasound modulation of brain tissue induces in-situ neural activity; LCU stimulating the motor cortex of healthy mice can induce motor responses in the tail, limbs, and whiskers, alter local field potentials, and enhance cerebral blood flow velocity; and LCU neuromodulation can inhibit epileptic and Parkinson's disease seizures. Summary of the Invention

[0004] This invention is based on the theoretical foundation of the intramembranous cavitation effect of ultrasound. It combines the intramembranous cavitation model and the dual-neuron model, which is a theoretical innovation in the theoretical explanation and mechanism of action of intramembranous cavitation effect of ultrasound in neuronal discharge desynchronization. It provides a theoretical research method for finding the optimal parameters of different ultrasound stimulations in the treatment of epilepsy and explores the application value of ultrasound stimulation in the clinical treatment of functional brain diseases.

[0005] This application discloses a method for desynchronizing ultrasound stimulation based on a dual-neuron model, including:

[0006] Acquire ultrasonic data, the ultrasonic data including sound pressure and / or frequency;

[0007] The ultrasonic data is input into the membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitance displacement current.

[0008] The membrane capacitance displacement current is input into the dual neuron model to obtain the time corresponding to the peak potential of the two neurons;

[0009] The phase difference between the two neurons is calculated based on the time corresponding to the peak values ​​of their potentials.

[0010] The phase difference between the two neurons determines whether they are in a desynchronized or synchronized state.

[0011] Furthermore, the step of inputting the ultrasonic data into a membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitance displacement current specifically involves: inputting the ultrasonic data into a BLS (Bubble Light System), where the BLS is based on the dynamic deformation of the bubble caused by ultrasonic sound pressure, resulting in the bubble radius Z(t). Once Z(t) reaches a stable periodic solution, it is substituted into the capacitance C in the form of a Fourier series. m (Z), thereby changing the average capacitance C of the membrane. m This generates a membrane capacitor displacement current I. Cm Preferably, the equation of the BLS is as follows:

[0012]

[0013]

[0014]

[0015]

[0016]

[0017] Among them, P ec P is the effective pressure exerted on the membrane by the charges on both sides of the membrane. in It refers to the gas pressure inside the bubble, P0, P v P A ·sin(ωt) represents the hydrostatic pressure in the medium outside the bubble, the saturated gas pressure inside the bubble, and the ultrasonic sound pressure driven by the external force, respectively; 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; α is the radius of the bubble boundary; Z0 is the initial radius of the bubble boundary; Δ is the initial gap between the two membranes; c is the velocity of sound in the liquid medium; ε r ε0 is the relative permittivity of the membrane cavity, and C is the permittivity of the membrane cavity. m0 It is the cell membrane capacity under initial conditions, ρ l It is the density of the conductive medium.

[0018] Furthermore, the dual-neuron model includes one or more of the following models: Hindmarsh-Rose, FitzHugh-Nagumo, Wilson-Cowan, Hodgkin-Huxey, McCulloch and Pitts, Map models, Morris-Lecar, and Phase Oscillator models; preferably, the dual-neuron model is Hindmarsh-Rose.

[0019] The Hindmarsh-Rose two-neuron model comprises two fully synchronized neuronal coupling systems, which transfer membrane capacitance displacement current I... Cm The input is fed into the two fully synchronized neuronal coupling systems, and the time corresponding to the peak values ​​of the two neuronal potentials is calculated based on the neuronal firing activity; preferably, the construction process of the two fully synchronized neuronal coupling systems is as follows:

[0020]

[0021] Where x is the cell membrane potential of the neuron, y represents the recovery variable related to the internal current, z represents the slow-varying regulatory current related to the potassium ion current activated by calcium ions, a, b, c, d, r, s, and χ are the construction parameters of the two fully synchronized neuronal coupling systems, I is the capacitive position current generated by the ultrasound stimulation on the neuron, and C is the coupling strength between the two neurons.

[0022] Furthermore, the specific calculation process for the phase difference between the two neurons is as follows:

[0023]

[0024] Where t1 and t2 are the times corresponding to the peak values ​​of adjacent action potentials in the first neuron; t s It is the time corresponding to the peak of the action potential in the second neuron.

[0025] Furthermore, the step of determining whether the two neurons are in a desynchronized or synchronized state based on the phase difference between the two neurons specifically means that when the phase difference between the two neurons is 0 or 2π, the neurons are in a synchronized state; when the phase difference between the two neurons is not exactly 0 or 2π, the neurons are in a desynchronized state.

[0026] Furthermore, the method for calculating the desynchronization or synchronization state of the two neurons also includes: obtaining the interpeak interval (ISI) by calculating the time difference corresponding to the peak values ​​of the two neuronal potentials. i Then, the synchronicity between the two neurons is determined based on the average interpeak duration under each stimulus; ISIi The calculation process is as follows:

[0027] ISI i =t 2i -t 1i

[0028] Among them, t 1i and t 2i These represent the times corresponding to the i-th firing spike of the two neurons, respectively.

[0029] A simulation system for ultrasound stimulation desynchronization based on a dual-neuron model, characterized in that the system comprises:

[0030] The acquisition module acquires ultrasonic data, which includes sound pressure and / or frequency.

[0031] The first generation module inputs the ultrasonic data into a membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitance displacement current.

[0032] The second generation module inputs the membrane capacitance displacement current into the dual neuron model to obtain the time corresponding to the peak value of the two neuron potentials;

[0033] The calculation module calculates the phase difference between the two neurons based on the time corresponding to the peak values ​​of the two neuron potentials;

[0034] The output module determines whether the two neurons are in a desynchronized or synchronized state based on the phase difference analysis simulation results of the two neurons.

[0035] An ultrasound stimulation desynchronization simulation device based on a dual-neuron model, the device comprising:

[0036] Memory and processor;

[0037] The memory is used to store program instructions;

[0038] The processor is used to call program instructions, which, when executed, are used to perform the aforementioned ultrasound stimulation desynchronization simulation method based on a dual-neuron model.

[0039] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for desynchronizing ultrasound stimulation based on a dual-neuron model.

[0040] The aforementioned equipment is of great significance in assisting research on the desynchronization of excitable neurons by ultrasound; the research includes exploring and analyzing the desynchronization regulation effect of low-intensity transcranial ultrasound stimulation using a dual-neuron model, from the microscopic cellular level to the macroscopic level of mental diseases such as epilepsy and Parkinson's disease.

[0041] The aforementioned equipment is used to analyze the synchronous or asynchronous states of neurons;

[0042] The above-mentioned equipment is used to suppress the onset of mental illnesses such as epilepsy and Parkinson's disease; optionally, the application includes exploring the optimal parameters for transcranial ultrasound stimulation therapy for the onset of mental illnesses such as epilepsy and Parkinson's disease.

[0043] The aforementioned equipment provides a new approach to the theoretical exploration of transcranial ultrasound stimulation for the treatment of other mental illnesses, and promotes the application of ultrasound stimulation in the clinical treatment of functional brain disorders.

[0044] The aforementioned system is of great significance in assisting research on the desynchronization of excitable neurons by ultrasound; the research includes exploring and analyzing the desynchronization regulation effect of low-intensity transcranial ultrasound stimulation using mathematical models, from the microscopic cellular level to the macroscopic level of mental diseases such as epilepsy and Parkinson's disease.

[0045] The above system is used to analyze the synchronous or asynchronous states of neurons;

[0046] The above-mentioned system is used to suppress the onset of epilepsy and Parkinson's disease; optionally, the application includes exploring the optimal parameters for transcranial ultrasound stimulation therapy for the onset of epilepsy, Parkinson's disease and other mental illnesses.

[0047] The aforementioned system provides a new approach to the theoretical exploration of transcranial ultrasound stimulation for the treatment of other mental illnesses, and promotes the application of ultrasound stimulation in the clinical treatment of functional brain disorders.

[0048] This invention employs a combined training model of cavitation effect and a dual-neuron model for clinically significant high-quality ultrasound data, playing a crucial role in addressing the desynchronization problem of excitable neurons. Based on the theoretical foundation of intramural cavitation effect in ultrasound, mathematical models are used to explore and analyze the desynchronization modulation effect of low-intensity transcranial ultrasound stimulation from the microscopic cellular level to the macroscopic level of mental illnesses such as epilepsy and Parkinson's disease. This not only provides a biophysical mechanism for ultrasound modulation but also offers a theoretical method for finding optimal ultrasound parameters, further providing theoretical guidance for the treatment of mental illnesses. This invention is innovative in the field of life sciences and will beneficially promote research on the synchronization analysis and state modulation of ultrasound data.

[0049] Advantages of this application:

[0050] 1. This application innovatively discloses a novel ultrasound stimulation desynchronization simulation method. First, it explores the desynchronization effect of ultrasound stimulation in a fully synchronized neuronal coupling system. The method is based on a dual-neuron model and combines it with an intramembrane cavitation model to deeply explore the life laws behind ultrasound data. Specifically, ultrasound data is input into the intramembrane cavitation model to obtain the membrane capacitance displacement current. Then, the obtained membrane capacitance displacement current is input into the dual-neuron model to obtain the time corresponding to the peak potential of the two neurons. Then, the phase difference between the two neurons is calculated by using the time corresponding to the peak potential of the two neurons to obtain whether the two neurons are in a desynchronized state or a synchronized state.

[0051] 2. This application innovatively uses an improved BLS membrane cavitation model to transmit ultrasonic data to nanoscale neurons through a modified Rayleigh-Plesset equation of bubble dynamics. Positive and negative pressures cause cavitation bubbles to form in the phospholipid bilayer membrane. The ultrasonic sound pressure drives the bubbles to undergo dynamic deformation, thereby changing the average capacitance of the membrane, generating capacitive displacement current, and thus changing the membrane potential. The effect is significant.

[0052] 3. This application creatively discloses an ultrasound stimulation desynchronization simulation device and system based on a dual-neuron model. Through in-depth interpretation of ultrasound data and synchronization state control using the dual-neuron model, combined with collaborative analysis using an intramembrane cavitation model, the desynchronization problem of excitable neurons can be effectively solved with significant results. This allows this application to be more accurately applied to the auxiliary control and treatment selection of mental illnesses related to ultrasound data. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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 effort.

[0054] Figure 1 This is a schematic flowchart of ultrasound stimulation desynchronization simulation based on a dual-neuron model provided in an embodiment of the present invention;

[0055] Figure 2 This is a structural diagram of a neuronal double-layer acoustic cluster model based on intramembrane cavitation effect provided in an embodiment of the present invention;

[0056] Figure 3 This is a time history diagram of synchronized or asynchronous states based on a dual-neuron model provided in an embodiment of the present invention;

[0057] Figure 4 This is a diagram showing the desynchronization effect of a dual-neuron model under different ultrasound stimuli, provided in an embodiment of the present invention.

[0058] Figure 5 This is a schematic diagram of an ultrasound stimulation desynchronization simulation device based on a dual-neuron model provided in an embodiment of the present invention. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0060] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be performed in the order they appear herein, or may be performed in parallel. The operation numbers, such as S101, S102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be performed sequentially or in parallel.

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Figure 1 This is a schematic flowchart of an ultrasound stimulation desynchronization simulation based on a dual-neuron model provided by an embodiment of the present invention. Specifically, the method includes the following steps:

[0063] S101: Acquire ultrasonic data.

[0064] In one embodiment, the acquired ultrasound data includes sound pressure and / or frequency, for example, with the addition of ultrasound stimulation (frequency 500 kHz, sound pressure 0.15 MPa); optionally, the ultrasound data also includes power density, peak pressure amplitude, duty cycle, duration, and pulse repetition frequency.

[0065] S102: Input the ultrasonic data into the membrane cavitation model based on the membrane cavitation effect to obtain the membrane capacitance displacement current.

[0066] In one embodiment, ultrasonic data is input into a BLS, which is a specific membrane cavitation model constructed based on the membrane cavitation effect. The model is based on the ultrasonic sound pressure driving the dynamic deformation of the bubble, resulting in the bubble radius Z(t). Once Z(t) reaches a stable periodic solution, the capacitance Cm(Z) is substituted into the model 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 Preferably, the BLS equation is as follows:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] Among them, P ec P is the effective pressure exerted on the membrane by the charges on both sides of the membrane. in It refers to the gas pressure inside the bubble, P0, P v P A ·sin(ωt) represents the hydrostatic pressure in the medium outside the bubble, the saturated gas pressure inside the bubble, and the ultrasonic sound pressure driven by the external force, respectively; 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; α is the radius of the bubble boundary; Z0 is the initial radius of the bubble boundary; Δ is the initial gap between the two membranes; c is the velocity of sound in the liquid medium; ε r ε0 is the relative permittivity of the membrane cavity, and C is the permittivity of the membrane cavity. m0 It is the cell membrane capacity under initial conditions, ρ l It is the density of the conductive medium.

[0073] Specifically, the BLS model (a modified Rayleigh-Plesset equation for bubble dynamics) is the biophysical mechanism of intramembrane cavitation (the BLS model uses the original parameter settings, and these parameters are selected based on known or measured physical biomass or ranges). Ultrasound waves are transmitted to neurons at the nanometer scale, and positive and negative pressures cause cavitation bubbles to form in the phospholipid bilayer membrane. Electrically, the bilayer membrane can be approximated as a capacitor. Figure 2 This is a structural diagram of a neuronal bilayer acoustic cluster model based on intramembrane cavitation effect provided in an embodiment of the present invention. It shows the biomechanical and bioelectrical structure of the intramembrane cavitation bilayer phospholipid molecular layer. In the BLS model of nanoscale bubbles, a circular, uniform phospholipid bilayer membrane patch is surrounded by a transmembrane protein constraint ring (maximum radius 32nm).

[0074] Generally, incoming ultrasound (US) induces the formation of bubbles (with radius R) between specific phospholipid bilayers (bilayer sonophores BLS) on the cell membrane, resulting in cavitation. In the dynamics of ultrasound cavitation bubbles, the negative pressure of the ultrasound waves pulls the molecular layers apart, while the positive pressure compresses them. The dynamic deformation of the cavitation bubbles induces oscillations in the membrane capacitance Cm, leading to charge accumulation on the cell membrane and a membrane capacitance displacement current I. Cm Generate, in I Cm Driven by the ultrasound, the membrane potential Vm of the cavitation 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 the ultrasound stimulation ends, the membrane capacitance returns to the reference value, and the membrane potential, due to the accumulated charge exceeding -50mV, causes the M gate to react rapidly, generating an action potential.

[0075] S103: Input the membrane capacitance displacement current into the dual neuron model to obtain the time corresponding to the peak potential of the two neurons.

[0076] In one embodiment, the two-neuron model includes one or more of the following models: Hindmarsh-Rose, FitzHugh-Nagumo, Wilson-Cowan, Hodgkin-Huxey, McCulloch and Pitts, Map models, Morris-Lecar, and Phase Oscillator models; preferably, the two-neuron model is Hindmarsh-Rose.

[0077] Hindmarsh-Rose is a classic neuron model that can effectively simulate the characteristics of neurons in the hippocampus of the brain. Existing research on it has covered issues such as bifurcation, chaos, and synchronization.

[0078] The FitzHugh-Nagumo model incorporates a simplified model of oscillatory peak discharge neurodynamics, which includes bistability.

[0079] Map models are discrete time-mapping models and simple neuron models that can generate spike states and cluster discharge states. They are computationally very fast, but lack a biophysical basis.

[0080] The Morris-Lecar model is a simplified model that reduces the number of dynamic variables in the HH model. It shows the generation of action potentials when the current I changes, leading to a saddle-node bifurcation into a limit cycle.

[0081] Furthermore, and more preferably, the Hindmarsh-Rose two-neuron model comprises two fully synchronized neuronal coupling systems, which transfer the membrane capacitance displacement current I... Cm The input is fed into two fully synchronized neuronal coupling systems, and the time corresponding to the peak potentials of the two neurons is calculated based on the neuron firing activity; preferably, the construction process of the two fully synchronized neuronal coupling systems is as follows:

[0082]

[0083] Where x is the cell membrane potential of the neuron, y represents the recovery variable related to the internal current, z represents the slow-varying regulatory current related to the potassium ion current activated by calcium ions, I is the capacitive position current generated by the ultrasound stimulation on the neuron, C is the coupling strength between the two neurons, and a, b, c, d, r, s, and χ are commonly used parameters for constructing two fully synchronized neuronal coupling systems. The specific construction parameters are as follows: a = 1.0, b = 3.0, c = 1.0, d = 5.0, r = 0.006, s = 4.0, χ = -1.6, C = 0.02, I = 1.4 mA.

[0084] In one specific embodiment, the simulation of ultrasound stimulation desynchronization in the Hindmarsh-Rose dual-neuron model was solved using the ODE113 function in MATLAB, with a solution step size set to dt = 0.025 / fμs, where f is the frequency of the ultrasound in MHz. The charge was updated every 500μs. The simulation was performed simultaneously based on the BLS model and the Hindmarsh-Rose model, which changed continuously over time. Once the bubble radius reached a stable periodic solution, the capacitance Cm(Z) was substituted into the Fourier series to change the average capacitance Cm of the membrane. The resulting membrane capacitance displacement current I was then calculated. Cm This causes a change in the membrane potential Vm, and then the obtained I Cm Substitute this into the Hindmarsh-Rose model. For example... Figure 3 This is a time history diagram of synchronized or asynchronous states based on a two-neuron model provided in an embodiment of the present invention. Figure 3 This study demonstrates how theoretical calculations of ultrasound stimulation can be incorporated into the neural firing rhythms of dual-coupled neurons, proving that the firing synchronicity of neurons changes under ultrasound stimulation. Figure 3 (a), (c), and (e) are the time history diagrams of the synchronization state of the two neurons, the trajectory diagram of (x1,x2), and the time history diagram of the x1-x2 error, respectively. Figure 3 (b), (d), and (f) are the time history diagrams of the asynchronous state of the two neurons under ultrasound stimulation, the trajectory diagram of (x1,x2), and the time history diagram of the x1-x2 error, respectively.

[0085] Specifically, Figure 3(a) shows highly synchronized firing time histories of dual-coupled neurons in the Hindmarsh-Rose dual-neuron model without ultrasound stimulation. Figure 3 (c) In the trajectory diagram of (x1,x2), the two state points x1 and x2 fall near a linear line x1-x2=0, that is, the two synchronized states are very obvious. Figure 3 The error time history graph of (e)x1-x2 shows that the error is very small under synchronization. Figure 3 (b) is a time history diagram of two neurons subjected to ultrasound stimulation (frequency 500 kHz, sound pressure 0.15 MPa), showing the time difference between the peak potentials of the two neurons. Figure 3 (d) The trajectory graph of (x1, x2) shows that the two state points x1 and x2 are scattered and disordered, and no synchronization is achieved. Figure 3 The error time history plot of (f)x1-x2 shows that the error is large, which indicates that under ultrasound stimulation, the coupled synchronous two neurons can achieve desynchronization.

[0086] S104: Calculate the phase difference between two neurons based on the time corresponding to the peak values ​​of the two neuron potentials.

[0087] In one example, the specific calculation process for the phase difference between two neurons is as follows:

[0088]

[0089] Where t1 and t2 are the times corresponding to the peak values ​​of adjacent action potentials in the first neuron; t s It is the time corresponding to the peak of the action potential in the second neuron.

[0090] In a specific example, to further investigate the firing state and desynchronization effect of dual-coupled neurons under different ultrasound pressures and frequencies (TUS), such as... Figure 4 The figure shown is a desynchronization effect diagram based on a dual-neuron model under different ultrasound stimulations provided in the embodiments of the present invention. The average interpeak duration and average phase difference under each stimulation were calculated.

[0091] S105: Based on the phase difference between the two neurons, determine whether the two neurons are in a desynchronized or synchronized state.

[0092] In one example, determining whether two neurons are in a desynchronized or synchronized state based on their phase difference is as follows: when the phase difference between the two neurons is 0 or 2π, the neurons are in a synchronized state; when the phase difference between the two neurons is not exactly 0 or 2π, the neurons are in a desynchronized state. Therefore, the more disordered the phase difference sequence of the firing sequences of the two neurons, and the larger the average phase difference, the stronger the desynchronization effect of ultrasound stimulation on the firing of the two coupled neurons.

[0093] Furthermore, the method for calculating whether two neurons are in a desynchronized or synchronized state also includes: obtaining the interpeak-to-peak interval (ISI) by calculating the time difference corresponding to the peak values ​​of the two neuron potentials. i Then, the synchronicity between the two neurons is determined based on the average interpeak duration under each stimulus; ISI i The calculation process is as follows:

[0094] ISI i =t 2i -t 1i

[0095] Among them, t 1i and t 2i These represent the times corresponding to the i-th firing spike of the two neurons, respectively.

[0096] The above methods were applied to assist in the selection of strategies for inhibiting neural discharge synchronization and the onset of mental illnesses such as epilepsy and Parkinson's disease. The intramembrane cavitation model BLS was combined with the Hindmarsh-Rose dual-neuron model. Simulation results are as follows: Figure 4 . Figure 4 (a, b) represent the ISI and ISI of different frequencies of ultrasound stimulation at a sound pressure level of 0.15 MPa, respectively, indicating the effect of desynchronization. The graph shows that the desynchronization effect increases with increasing frequency (except at 0.6MHz, where ISI = 29.104). It reaches its maximum value at 0.8MHz (at which point ISI = 117.1708). (i.e., desynchronization is most effective). Figure 4 (c, d) represent the ISI and ISI of ultrasound stimulation at different sound pressure levels with a frequency of 0.5 MHz, respectively, to achieve desynchronization. The graph shows the changes in ISI and ISI at 0.2 MPa. The desynchronization effect is most effective when the maximum value is reached. Therefore, from Figure 4 As can be seen, ultrasound stimulation at the same sound pressure level but different frequencies couples two neurons, with ISI and ISI at f = 0.8 MHz. Reaching the maximum value, the desynchronization effect is optimal; ultrasound stimulation of the same frequency but different sound pressure levels couples the firing of two neurons, and at 0.2 MPa, ISI and Reaching the maximum value has a strong desynchronization effect.

[0097] This invention provides an ultrasound stimulation desynchronization simulation system based on a dual-neuron model, comprising:

[0098] The acquisition module acquires ultrasonic data, including sound pressure and / or frequency.

[0099] The first generation module inputs the acquired ultrasonic data into the membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitance displacement current.

[0100] The second generation module inputs the obtained membrane capacitance displacement current into the dual neuron model to obtain the time corresponding to the peak potential of the two neurons.

[0101] The calculation module calculates the phase difference between the two neurons based on the time corresponding to the peak values ​​of the two neuron potentials.

[0102] The output module, based on the simulation results of the phase difference analysis of the two neurons, determines whether the two neurons are in a desynchronized state or a synchronized state.

[0103] Figure 5 This invention provides an ultrasound stimulation desynchronization simulation device based on a dual-neuron model, comprising: a memory and a processor; the device may also include: an input device and an output device.

[0104] Memory, processor, input devices, and output devices can be connected via a bus or other means. Figure 5 The example shown is a connection via a bus.

[0105] Memory is used to store program instructions;

[0106] The processor is used to call program instructions, which, when executed, are used to perform the ultrasound stimulation desynchronization simulation method based on the dual-neuron model described above.

[0107] The present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for desynchronizing ultrasound stimulation based on a dual-neuron model.

[0108] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between apparatuses or modules, and may be electrical, mechanical, or other forms.

[0110] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0111] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The aforementioned integrated modules can be implemented in hardware or as software functional modules.

[0112] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0113] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0114] The computer device provided by the present invention has been described in detail above. For those skilled in the art, there will be changes in the specific implementation and application scope based on the ideas of the embodiments of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for desynchronizing ultrasound stimulation based on a dual-neuron model, characterized in that, include: Acquire ultrasonic data, the ultrasonic data including sound pressure and / or frequency; The ultrasonic data is input into a membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitor displacement current. Specifically, the ultrasonic data is input into a BLS (Bubble Light System), which is based on the ultrasonic sound pressure driving the dynamic deformation of the bubble, resulting in the bubble radius Z(t). When Z(t) reaches a stable periodic solution, it is substituted into the capacitor C in the form of a Fourier series. m (Z), thereby changing the average capacitance C of the membrane. m This generates a membrane capacitor displacement current I. Cm ; The membrane capacitance displacement current is input into the dual neuron model to obtain the time corresponding to the peak potential of the two neurons; The phase difference between the two neurons is calculated based on the time corresponding to the peak values ​​of their potentials. The specific calculation process for the phase difference between the two neurons is as follows: in, and It is the time corresponding to the peak value of the adjacent action potential in the first neuron; It is the time corresponding to the peak of the action potential in the second neuron; The phase difference between the two neurons determines whether they are in a desynchronized or synchronized state.

2. The ultrasound stimulation desynchronization simulation method based on a dual-neuron model according to claim 1, characterized in that, The equation for BLS is as follows: Among them, P ec P is the effective pressure exerted on the membrane by the charges on both sides of the membrane. in It refers to the gas pressure inside the bubble, P0, P v , These represent the hydrostatic pressure in the medium outside the bubble, the saturated gas pressure inside the bubble, and the ultrasonic sound pressure driven by the external force, respectively. 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, α 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 velocity of sound in the liquid medium, and ε... r ε0 is the relative permittivity of the membrane cavity, and C is the permittivity of the membrane cavity. m0 It is the cell membrane capacity under initial conditions, ρ l It is the density of the conductive medium.

3. The ultrasound stimulation desynchronization simulation method based on a dual-neuron model according to claim 1, characterized in that, The dual-neuron model includes one or more of the following models: Hindmarsh-Rose, FitzHugh-Nagumo, Wilson-Cowan, Hodgkin-Huxey, McCulloch and Pitts, Map models, Morris-Lecar, and PhaseOscillator models.

4. The ultrasound stimulation desynchronization simulation method based on a dual-neuron model according to claim 3, characterized in that, The two-neuron model is Hindmarsh-Rose.

5. The ultrasound stimulation desynchronization simulation method based on a dual-neuron model according to claim 4, characterized in that, The Hindmarsh-Rose two-neuron model comprises two fully synchronized neuronal coupling systems, which transfer membrane capacitance displacement current I... Cm The data is input into the two fully synchronized neuron coupling systems, and the time corresponding to the peak value of the two neuron potentials is calculated based on the neuron's firing activity.

6. The ultrasound stimulation desynchronization simulation method based on a dual-neuron model according to claim 5, characterized in that, The construction process of the two fully synchronized neuron coupling systems is as follows: Where x is the cell membrane potential of the neuron, y represents the recovery variable related to the internal current, z represents the slow-varying regulatory current related to the potassium ion current activated by calcium ions, a, b, c, d, r, s, and χ are the construction parameters of the two fully synchronized neuronal coupling systems, I is the capacitive position current generated by the ultrasound stimulation on the neuron, and C is the coupling strength between the two neurons.

7. The ultrasound stimulation desynchronization simulation method based on a dual-neuron model according to claim 1, characterized in that, Specifically, determining whether two neurons are in a desynchronized or synchronized state based on their phase difference means that when the phase difference between the two neurons is 0 or 2π, the neurons are in a synchronized state; when the phase difference between the two neurons is not exactly 0 or 2π, the neurons are in a desynchronized state.

8. The ultrasound stimulation desynchronization simulation method based on a dual-neuron model according to claim 1, characterized in that, The calculation method for the two neurons being in a desynchronized or synchronized state also includes: obtaining the interpeak interval (ISI) by calculating the time difference corresponding to the peak values ​​of the two neuron potentials. i Then, the synchronicity between the two neurons is determined based on the average value of the interpeak duration under each stimulus. in, and These represent the times corresponding to the i-th firing spike of the two neurons, respectively.

9. A simulation system for desynchronization of ultrasound stimulation based on a dual-neuron model, characterized in that, The system includes: The acquisition module acquires ultrasonic data, which includes sound pressure and / or frequency. The first generation module inputs the ultrasonic data into a membrane cavitation model constructed based on the membrane cavitation effect to obtain the membrane capacitor displacement current. Specifically, inputting the ultrasonic data into the membrane cavitation model to obtain the membrane capacitor displacement current involves: inputting the ultrasonic data into a BLS (Bubble Light System), where the BLS is based on the ultrasonic sound pressure driving the dynamic deformation of the bubble, resulting in the bubble radius Z(t). Once Z(t) reaches a stable periodic solution, it is substituted into the capacitor C in the form of a Fourier series. m (Z), thereby changing the average capacitance C of the membrane. m This generates a membrane capacitor displacement current I. Cm ; The second generation module inputs the membrane capacitance displacement current into the dual neuron model to obtain the time corresponding to the peak value of the two neuron potentials; The calculation module calculates the phase difference between the two neurons based on the time corresponding to the peak values ​​of their potentials; the specific calculation process for the phase difference between the two neurons is as follows: in, and It is the time corresponding to the peak value of the adjacent action potential in the first neuron; It is the time corresponding to the peak of the action potential in the second neuron; The output module determines whether the two neurons are in a desynchronized or synchronized state based on the phase difference analysis simulation results of the two neurons.

10. A simulation device for ultrasound stimulation desynchronization based on a dual-neuron 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, which, when executed, are used to implement the ultrasound stimulation desynchronization simulation method based on the dual-neuron model as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the ultrasound stimulation desynchronization simulation method based on the dual-neuron model as described in any one of claims 1-8.

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