Neuron magnetic stimulation numerical simulation calculation method

Abstract

The invention relates to a neuron magnetic stimulation numerical simulation calculation method, belongs to the technical field of crossing of biomedical engineering and neuroscience, and aims to solve the problems of lack of three-dimensional dynamic electrified physiological characteristic characterization, low stimulation parameter debugging efficiency based on experience trial-and-error and low simulation accuracy. And the problem of insufficient neural response prediction accuracy caused by transmembrane current continuous differential solution difficulty is solved. According to the method, head MRI data and neuromorphological information are fused to construct a personalized three-dimensional model, and electric field distribution of a target area is obtained through electromagnetic field simulation; the electric field intensity is decomposed along the trend of neurons, discrete segments are established, a transmembrane current difference algorithm is proposed by using the relation between adjacent nodes and the electric field gradient, and differential solution is converted into parallel difference operation; and membrane potential change is simulated by combining a Hodgkin-Hertz equation. The method constructs a personalized model, accurately obtains electric field distribution, simplifies operation by using a difference algorithm, simulates potential by combining an equation, and realizes quantitative analysis and curative effect extraction of the whole process of magnetic stimulation.

Description

Technical Field

[0001] The invention relates to a numerical simulation calculation method for neuron magnetic stimulation, and belongs to the technical field of intersection of biomedical engineering and neuroscience. Background Art

[0002] Magnetic stimulation is a technology that applies non-invasive stimulation to neural tissue through the principle of electromagnetic induction. It uses time-varying current to generate a magnetic field through a stimulation coil. This magnetic field can penetrate the scalp and skull, induce current in the brain or other neural tissue, and thus trigger the depolarization or hyperpolarization of neurons. This technology is widely used in the medical field due to its non-invasive, painless and high efficiency, such as in the treatment of depression, Parkinson's disease, chronic pain, stroke rehabilitation, etc. In particular, transcranial magnetic stimulation (TMS) technology has achieved significant clinical therapeutic effects.

[0003] Although magnetic stimulation technology has been approved for use in a variety of medical fields and has shown excellent therapeutic effects, its biophysical mechanism and working principle have not yet been fully revealed. This uncertainty means that in actual operation, medical personnel need to determine the optimal position of the magnetic stimulation coil and the stimulation threshold through multiple attempts based on the specific conditions of the patient. This operation method based on experience and repeated trials is not only time-consuming and laborious, but may also affect the accuracy of the treatment and the repeatability of the efficacy.

[0004] In order to solve the above problems, the optimization design of magnetic stimulation based on computer simulation technology has gradually attracted widespread attention. Through simulation technology, the optimal position and stimulation parameters of the magnetic stimulation coil can be pre-calculated before treatment, which can greatly improve the safety, efficiency and accuracy of magnetic stimulation treatment. Computer simulation can not only help medical personnel quickly lock in the optimal parameters, but also reduce patient discomfort and waste of medical resources.

[0005] Therefore, studying the numerical simulation calculation method of neurons under magnetic stimulation can not only provide deeper theoretical support for the biophysical mechanism of magnetic stimulation, but also lay a solid foundation for the clinical application and promotion of magnetic stimulation technology. This research will provide a scientific basis for the realization of personalized magnetic stimulation treatment and promote the popularization and application of magnetic stimulation technology in more medical fields. Summary of the invention

[0006] The present invention proposes a numerical simulation calculation method for neuronal magnetic stimulation to solve the problems in the prior art of lack of characterization of physiological characteristics of three-dimensional dynamic electrical conduction, low efficiency of stimulation parameter debugging based on empirical trial and error, and insufficient accuracy of neural response prediction caused by the difficulty in solving the continuous differential solution of transmembrane current.

[0007] A method for numerical simulation of neuron magnetic stimulation, comprising the following steps:

[0008] S1. Acquire relevant data of the stimulated target to construct a human head geometric model and a three-dimensional visualization model of target neurons, and simultaneously construct a magnetic stimulation coil model, and perform electromagnetic field simulation on the combined model of the human head geometric model and the magnetic stimulation coil model to obtain the electromagnetic field vector generated by the coil being energized in the vicinity of the target neurons;

[0009] S2, based on the three-dimensional visualization model of the target neuron, divide the neuron into multiple segments, determine the direction of the neuron according to the spatial position relationship of each segment, and calculate the electric field vector of each segment along the direction of the neuron according to the electric field vector obtained in S1;

[0010] S3, based on the electric field vector along the direction of the neuron obtained in S2, calculate the equivalent transmembrane current flowing into the neuron;

[0011] S4. Substitute the transmembrane current into the Hodgkin-Huxley equation to calculate the change of the neuron membrane potential over time to determine whether the magnetic stimulation is effective.

[0012] Further, in S1, the relevant data are human head magnetic resonance imaging data and data in a neuromorphic database, wherein:

[0013] The human head magnetic resonance imaging data is used to construct a three-dimensional geometric model of the human head;

[0014] The data in the neuromorphic database is used to reconstruct a three-dimensional visualization model of a target neuron and determine its position.

[0015] Furthermore, in S1, an electromagnetic field simulation is performed on the combined model of the human head geometric model and the magnetic stimulation coil model, comprising the following steps:

[0016] S11, acquiring magnetic resonance imaging data of a human head, and constructing a three-dimensional geometric model of the human head according to the magnetic resonance imaging data of the human head;

[0017] S12. Construct a magnetic stimulation coil model, and perform electromagnetic field simulation on the three-dimensional geometric model of the human head.

[0018] Furthermore, in S1, the target neuron vicinity refers to a 1cm×1cm×1cm cubic area delineated with the anatomical position of the target neuron in the brain as the center by comparing the constructed three-dimensional visualization model of the target neuron with the brain map to accurately find the position.

[0019] Furthermore, in S2, the electric field vector along the direction of the neuron is calculated as follows:

[0020] Assume that the electric field vector of any neuron segment n is The unit direction vector is (x n ,y n ,z n ), then the magnitude of the electric field along the direction of the neuron at neuron segment n is

[0021]

[0022] Its direction is a vector direction.

[0023] Furthermore, in S3, let the coordinates of the center point of segment n be Then the electric field strength of segment n along the direction of the neuron is E n ', the previous segment adjacent to segment n is n-1, and the coordinates of the center point of segment n-1 are The electric field strength of segment n-1 along the direction of the neuron is E' n-1 , the next adjacent segment is recorded as n+1, and the coordinates of the center point of segment n+1 are The electric field strength of segment n+1 along the direction of the neuron is E' n+1 , S3 includes the following steps:

[0024] S31, transmembrane voltage V m,n The calculation formula is transformed from the original derivative form to the differential form, which is approximately expressed as:

[0025]

[0026] Among them, λ is a space constant. The size of λ is related to the radius of the nerve fiber and is calculated by the following formula:

[0027]

[0028] Among them, r m is the impedance per unit length of the cell membrane, r i is the resistance per unit length along the axial direction in the cell membrane;

[0029] S32. According to Ohm's law, calculate the equivalent transmembrane current i flowing into the neuron membrane m,n , calculated by the following formula,

[0030]

[0031] Furthermore, since the computer cannot directly perform derivative operations, the derivative operation is converted into a differential operation to calculate the transmembrane current by constructing the spatial position relationship and electric field strength relationship between neuronal segments. After S32, it also includes:

[0032] S33, let the coordinates of the center points of adjacent segments n and n+1 be (x n ,y n ,z n )、(x n+1 ,y n+1 ,z n+1 ), the distance between the two points is,

[0033]

[0034] The electric field strengths along the direction of the neuron at segments n and n+1 are E n ', E' n+1 , then the transmembrane current i at segment n is m,n The difference calculation formula is:

[0035]

[0036] Furthermore, in S4, the change of the neuron membrane potential over time is calculated based on the transmembrane current obtained in S3 and the Hodgkin-Huxley equation:

[0037] The transmembrane current obtained by injecting S3 into each segment of the neuron is substituted into the Hodgkin-Huxley equation for solution, as follows:

[0038]

[0039] Among them, I Na is the sodium channel current, I K is the potassium channel current, I Leak is the leakage current, C m is the transmembrane capacitance, V is the pressure difference between the inside and outside of the neuron membrane,

[0040] According to formula (6), the change of the pressure difference V between the inside and outside of the neuron membrane with time is obtained. If the pressure difference V between the inside and outside of the membrane increases rapidly within a period of time after the stimulation, and then reaches the maximum value, showing a positive inside and a negative outside, and then slowly returns to the resting state, it means that the action potential of the neuron is triggered, which proves that the stimulation is effective.

[0041] A storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned numerical simulation calculation method for neuron magnetic stimulation.

[0042] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned numerical simulation calculation method for neuron magnetic stimulation.

[0043] Beneficial effects of the present invention: A numerical simulation calculation method for neuronal magnetic stimulation of the present invention, by integrating multiple model constructions and precise positioning, can improve the accuracy of magnetic stimulation therapy, determine the optimal coil position and stimulation parameters in advance, and promote personalized treatment to meet the needs of different patients; reduce the parameter trial process in actual operation, save medical resources and time, and improve medical efficiency; in the calculation of transmembrane current, the derivative operation is innovatively converted into a differential operation, and the actual factors are considered for accurate calculation, which provides a reliable basis for the calculation of the potential change within the membrane, enhances the calculation accuracy, and also provides a quantitative means for the study of the biophysical mechanism of magnetic stimulation, which helps theoretical development. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a method flow chart of a numerical simulation calculation method of neuron magnetic stimulation of the present invention;

[0045] Figure 2 It is a schematic diagram of the segmentation of nerve fibers and the direction of each segment of nerve fibers of the present invention;

[0046] Figure 3 It is a schematic diagram of nerve fiber coordinate points of the present invention. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Reference Figure 1 As shown, a numerical simulation calculation method for neuron magnetic stimulation, the numerical simulation calculation method for neuron magnetic stimulation comprises the following steps:

[0049] S1. Acquire relevant data of the stimulated target to construct a human head geometric model and a three-dimensional visualization model of target neurons, and simultaneously construct a magnetic stimulation coil model, and perform electromagnetic field simulation on the combined model of the human head geometric model and the magnetic stimulation coil model to obtain the electromagnetic field vector generated by the coil being energized in the vicinity of the target neurons;

[0050] S2, based on the three-dimensional visualization model of the target neuron, divide the neuron into multiple segments, determine the direction of the neuron according to the spatial position relationship of each segment, and calculate the electric field vector of each segment along the direction of the neuron according to the electric field vector obtained in S1;

[0051] S3, based on the electric field vector along the direction of the neuron obtained in S2, calculate the equivalent transmembrane current flowing into the neuron;

[0052] S4. Substitute the transmembrane current into the Hodgkin-Huxley equation to calculate the change of the neuron membrane potential over time to determine whether the magnetic stimulation is effective.

[0053] Specifically, the present invention constructs a complete magnetic stimulation numerical simulation chain from three-dimensional modeling of the head, dynamic simulation of electromagnetic fields to analysis of neuronal potential response. It drives precise simulation of target electric field distribution and neuronal direction through multi-source data, breaks through the limitations of traditional empirical stimulation parameter optimization, and can greatly improve prediction efficiency and reliability. At the same time, it proposes an electric field-current differential conversion algorithm that discretizes the nerve fiber axis into multiple segments. Based on the spatial coordinates between adjacent nodes and the axial electric field gradient, it converts continuous derivative operations into spatial differential calculations, which solves the technical bottleneck of directly processing the geometric deformation of microscopic nerve fibers in transmembrane current calculations. It reduces the computational complexity while ensuring the accuracy of current quantization, and provides an efficient and reliable numerical verification method for clinical precise positioning of magnetic stimulation parameters.

[0054] Further, in S1, the relevant data are human head magnetic resonance imaging data and data in a neuromorphic database, wherein:

[0055] The human head magnetic resonance imaging data is used to construct a three-dimensional geometric model of the human head;

[0056] The data in the neuromorphic database is used to reconstruct a three-dimensional visualization model of a target neuron and determine its position.

[0057] Specifically, the present invention realizes the anatomical-functional dual positioning of target neurons through multi-source data fusion: individualized head MRI data is used to construct a high-precision three-dimensional skull model, which can accurately reflect the key anatomical features that affect the conduction of electromagnetic fields, such as skull morphology and cerebrospinal fluid distribution; the three-dimensional neuronal architecture is reconstructed by combining the topological structure data of characteristic neurons in the neuromorphic database, which can break through the limitations of the traditional homogeneous tissue assumption, such as the accurate restoration of the soma-dendrite orientation of deep pyramidal neurons, avoiding the electric field vector projection error caused by the misalignment of the actual spatial extension direction of neurons and the computational model, and providing a reliable cellular-level spatial reference for subsequent transmembrane current calculations.

[0058] Furthermore, in S1, an electromagnetic field simulation is performed on the combined model of the human head geometric model and the magnetic stimulation coil model, comprising the following steps:

[0059] S11, acquiring magnetic resonance imaging data of a human head, and constructing a three-dimensional geometric model of the human head according to the magnetic resonance imaging data of the human head;

[0060] S12. Construct a magnetic stimulation coil model, and perform electromagnetic field simulation on the three-dimensional geometric model of the human head.

[0061] The present invention realizes high-precision prediction of electromagnetic field distribution by constructing a dynamic combined simulation system of a three-dimensional geometric model of the head and a magnetic stimulation coil: the anatomical model constructed based on individual MRI data accurately characterizes heterogeneous structures such as the skull and brain tissue, effectively solving the spatial electric field calculation deviation caused by the traditional homogenized head model; combined with the parameterized coil model, flexible configuration and rapid simulation of excitation parameters (such as coil posture and current waveform) can be achieved, and the dynamic response analysis of the whole-brain electromagnetic field can be completed within a few hours.

[0062] Furthermore, in S1, the target neuron vicinity refers to a 1cm×1cm×1cm cubic area delineated with the anatomical position of the target neuron in the brain as the center by comparing the constructed three-dimensional visualization model of the target neuron with the brain map to accurately find the position.

[0063] Specifically, the present claim intelligently defines the 1cm location of the target neuron. 3 Cube area, taking into account the optimization of simulation accuracy and efficiency: based on brain map registration technology, the three-dimensional model of the target neuron is aligned with the standard brain area coordinates, which significantly reduces the anatomical positioning error; at the same time, the electromagnetic field simulation focuses on the key areas, greatly reducing the computational redundancy of the traditional whole-brain model, which significantly reduces the finite element mesh volume and calculation time. With the support of high-density grids, the spatial resolution of the local electric field is significantly improved, and it can accurately capture the changes in the electric field gradients in key areas around neurons (such as the field intensity fluctuation characteristics at sensitive locations such as the initial segment of the axon and the bifurcation point of dendrites), providing a high-confidence spatial basis for the differential calculation of transmembrane currents. This regionalized processing scheme effectively balances the input of computing resources and the accuracy requirements of the characterization of microscopic electrophysiological characteristics.

[0064] Furthermore, in S2, the electric field vector along the direction of the neuron is calculated as follows:

[0065] Assume that the electric field vector of any neuron segment n is The unit direction vector is (x n ,y n ,z n ), then the magnitude of the electric field along the direction of the neuron at neuron segment n is

[0066]

[0067] Its direction is a vector direction.

[0068] This claim significantly improves the biological rationality of neuronal response prediction through axial electric field projection directional analysis technology: for the electric field vector in any segment, the electric field component along the axon conduction direction is accurately decomposed according to the direction of the neuron. This method breaks through the limitations of traditional global field strength averaging estimation, dynamically associates the direction of electric field action with the geometric morphology of nerve fibers, solves the electric field vector projection error caused by ignoring the spatial bending characteristics of neurons (such as the problem of misjudgment of reverse field strength for U-shaped fibers), and reduces the activation threshold prediction deviation of transmembrane current calculation by about 50%. Combined with the mechanism in electrophysiology that axon depolarization is preferentially conducted along the direction of the fiber, this directional decomposition strategy provides a rigorous electrodynamic basis for subsequent transmembrane current modeling, ensuring that the stimulation effect evaluation is more in line with the actual excitation law of neurons.

[0069] Furthermore, in S3, let the coordinates of the center point of segment n be Then the electric field strength of segment n along the direction of the neuron is E n ', the previous segment adjacent to segment n is n-1, and the coordinates of the center point of segment n-1 are The electric field strength of segment n-1 along the direction of the neuron is E' n-1 , the next adjacent segment is recorded as n+1, and the coordinates of the center point of segment n+1 are The electric field strength of segment n+1 along the direction of the neuron is E' n+1 , S3 includes the following steps:

[0070] S31, transmembrane voltage V m,n The calculation formula is transformed from the original derivative form to the differential form, which is approximately expressed as:

[0071]

[0072] Among them, λ is a space constant. The size of λ is related to the radius of the nerve fiber and is calculated by the following formula:

[0073]

[0074] Among them, r m is the impedance per unit length of the cell membrane, r i is the resistance per unit length along the axial direction in the cell membrane;

[0075] S32. According to Ohm's law, calculate the equivalent transmembrane current i flowing into the neuron membrane m,n , calculated by the following formula,

[0076]

[0077] Specifically, the present invention constructs a computable bridge from the applied electric field to the transmembrane current through the transmembrane voltage differential algorithm and the spatial constant association mechanism: the traditional continuous electrotonic potential model that requires complex derivation is discretized into a differential equation driven by the electric field gradient between segments (Formula 4), which greatly reduces the computational complexity; by introducing the spatial constant λ related to the nerve fiber radius (Formula 3), the algorithm is given the ability to adapt to biological characteristics (such as the differential treatment of thin myelinated fibers and thick axons), avoiding the distortion problem of fixed conduction parameters in the traditional one-dimensional cable equation; combined with Ohm's law, the axial electric field gradient is equivalent to the net transmembrane current (Formula 4), so that the stimulation effect of the applied electromagnetic field can be quantified as an equivalent biocurrent source that drives the generation of action potentials, providing a standardized physical conversion model for the quantitative evaluation of the activity state of neurons. The present invention breaks through the limitations of the isotropic tissue assumption in traditional electromagnetic-biological coupling simulations, and lays a rigorous mathematical foundation for the potential dynamic calculation of the core step S4.

[0078] Furthermore, since the computer cannot directly perform derivative operations, the derivative operation is converted into a differential operation to calculate the transmembrane current by constructing the spatial position relationship and electric field strength relationship between neuronal segments. After S32, it also includes:

[0079] S33, let the coordinates of the center points of adjacent segments n and n+1 be (x n ,y n ,z n )、(x n+1 ,y n+1 ,z n+1 ), the distance between the two points is,

[0080]

[0081] The electric field strengths along the direction of the neuron at segments n and n+1 are E n ', E' n+1 , then the transmembrane current i at segment n is m,n The difference calculation formula is:

[0082]

[0083] Specifically, the hierarchical differential algorithm for transmembrane current proposed in the present invention (Formula 5) has achieved a key technical breakthrough in solving the problem of coupling between complex nerve fiber structures and dynamic electric fields. The traditional global differential method based on the continuous medium assumption faces dual difficulties in neural network simulation: First, the real neuron geometry is changeable (such as dendritic bifurcation, axon bending and multi-level branching), which causes traditional numerical algorithms to easily cause computational divergence in synaptic dense areas or irregular bending parts due to local grid non-orthogonality; second, the multi-physics field coupling effect of microscopic structural scales and macroscopic electromagnetic fields consumes massive computing resources. The present invention constructs a direct differential mapping of the displacement vector between the center points of adjacent neuronal segments and the axial electric field gradient (Formula 5), ​​converting the transmembrane current solution into a discrete segmented calculation based on the natural fiber morphology, and has multiple core technical advantages:

[0084] The present invention automatically captures any curvature change of the direction of nerve fibers (such as the spiral trajectory of hippocampal mossy fibers) through the relative position relationship of discrete segments, without the need for preset shape functions or global coordinate transformations, thus ensuring the numerical stability of the calculation of transmembrane currents in curved axon segments;

[0085] The present invention is based on the differential calculation of adjacent segments of electric field strength, and realizes the accurate characterization of electric field gradient in local area, which is suitable for constructing full-loop simulation containing tens of thousands of neuron segments;

[0086] In the stimulation parameter sweep scenario, the spatial coordinates and vector association relationship of the target segment can be reused, and the current distribution can be quickly recalculated by simply updating the electric field strength data, which significantly improves the dynamic optimization efficiency of the coil posture and pulse width parameters.

[0087] Furthermore, in S4, the change of the neuron membrane potential over time is calculated based on the transmembrane current obtained in S3 and the Hodgkin-Huxley equation:

[0088] The transmembrane current obtained by injecting S3 into each segment of the neuron is substituted into the Hodgkin-Huxley equation for solution, as follows:

[0089]

[0090] Among them, I Na is the sodium channel current, I K is the potassium channel current, I Leak is the leakage current, C m is the transmembrane capacitance, V is the pressure difference between the inside and outside of the neuron membrane,

[0091] According to formula (6), the change of the pressure difference V between the inside and outside of the neuron membrane with time is obtained. If the pressure difference V between the inside and outside of the membrane increases rapidly within a period of time after the stimulation, and then reaches the maximum value, showing a positive inside and a negative outside, and then slowly returns to the resting state, it means that the action potential of the neuron is triggered, which proves that the stimulation is effective.

[0092] Specifically, the present invention establishes a quantitative evaluation system for the biological effectiveness of magnetic stimulation neural response through the dynamic coupling and threshold criterion design of the Hodgkin-Huxley (HH) equation. The transmembrane current is used as the input excitation source of the HH equation, and the full-time characteristics of the action potential (rising phase, peak potential, repolarization, etc.) are accurately simulated through the sodium / potassium channel current kinetic model, overcoming the subjectivity and hysteresis of the traditional empirical observation of the stimulation effect. Based on the objective threshold judgment of the change trend of the pressure difference between the inside and outside of the membrane after stimulation (such as the pressure difference within 1ms after stimulation rises rapidly to more than the 40mV threshold, and the internal positive and external negative state lasts ≥0.5ms), it can accurately distinguish effective stimulation (triggering action potential) from invalid stimulation (only causing local depolarization or no response), and quantify the activation probability (such as 70% threshold corresponds to the clinical motor evoked potential MEP positive standard). This mechanism provides a repeatable bioelectric verification standard for optimizing magnetic stimulation parameters, avoids the randomness of traditional reliance on somatosensory feedback or electromyography monitoring, and significantly improves the reliability and universality of the treatment plan.

[0093] A storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned numerical simulation calculation method for neuron magnetic stimulation.

[0094] Specifically, the present invention solidifies the numerical simulation method of neuronal magnetic stimulation into a standardized executable code through the design of a programmed storage medium, ensuring the wide applicability and convenient portability of the technical solution. The program in the storage medium can achieve seamless docking with different hardware platforms (such as medical imaging workstations, mobile terminals or cloud servers), and support algorithm consistency in cross-institutional and multi-center collaborative research; during clinical use, medical staff can directly call pre-installed programs to complete the rapid simulation generation of personalized magnetic stimulation schemes, avoiding software compatibility issues caused by repeated development. In addition, the present invention supports remote updates and modular upgrades (such as optimizing differential algorithms or adding brain area atlas libraries), ensuring continuous iteration of technology without replacing hardware equipment, and significantly reducing the technical operation and maintenance costs of medical institutions. The encrypted storage mechanism of the storage medium (such as AES-256 encryption) can effectively protect the security and privacy compliance of patients' sensitive biological data, and promote the large-scale application and popularization of magnetic stimulation precision treatment technology.

[0095] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned numerical simulation calculation method for neuron magnetic stimulation.

[0096] Specifically, the present invention realizes the high availability of magnetic stimulation simulation technology in clinical scenarios through the coordinated optimization of the hardware architecture and algorithm programs of computer equipment: based on the parallel computing capabilities of multi-core processors and high-speed memories, large-scale electromagnetic coupling models containing millions of degrees of freedom can be quickly solved (such as whole-brain neural network simulation) under limited hardware resources, significantly shortening the whole process time from data acquisition to result output; the system supports data interface docking with medical imaging equipment (such as MRI, CT), realizing the "shooting-modeling-simulation" integrated processing of patient-personalized anatomical models; through modular program architecture design, the adaptation capability to different stimulation modes (single pulse / repetitive TMS) or new neuron models can be flexibly expanded, providing clinical departments with intelligent magnetic stimulation design tools with stable calculations, reliable results and convenient operation, and promoting the intelligent transformation of treatment decisions from empirical judgment to data-driven.

[0097] The following is a detailed description of the computer numerical simulation calculation method for neuron magnetic stimulation combined with a real electric field of the present invention in conjunction with the embodiments and drawings.

[0098] like Figure 1 As shown, the computer numerical simulation calculation method for neuron magnetic stimulation combined with a real electric field in this embodiment includes the following steps:

[0099] 1) Model and simulate the magnetic stimulation coil to be used and the stimulated target to obtain the electromagnetic field vector generated by the coil being energized in the area near the target neuron:

[0100] The magnetic stimulation coil and the stimulated target are modeled and simulated by first acquiring human head Magnetic Resonance Imaging (MRI) data, constructing a 3D geometric model of the human head based on the MRI data, then constructing a magnetic stimulation coil model, and performing electromagnetic field simulation on the 3D geometric model of the human head.

[0101] The target neuron refers to the target neuron stimulated by magnetic stimulation. The three-dimensional morphology and position of the target neuron are reconstructed by consulting the neuromorphology database, using the neuron morphology data stored in the database (such as .swc files) and related supporting tools (such as NEURON). The specific operations include: retrieving the target neuron morphology data in the database by screening conditions such as brain region name and neuron type; downloading the detailed morphology file matching the target neuron; loading and processing data in combination with supporting software, generating a three-dimensional structural model of the neuron, and comparing it with the brain atlas to accurately locate its anatomical position.

[0102] The target neuron vicinity area refers to a 1cm×1cm×1cm cubic area after its anatomical position is accurately located through brain map comparison, and the electromagnetic field size of this area is obtained for subsequent calculations.

[0103] 2) According to the electric field vector obtained in step 1), the electric field vector of each neuron segment along the direction of the neuron is calculated:

[0104] In order to improve the accuracy of neuron simulation, the neuron is segmented and processed, and the neuron is divided into multiple segments, each segment representing a small part of the neuron. Figure 2 , the middle segment of the neuronal fiber is divided into three segments, namely ①②③ segments.

[0105] The neuron direction is described by segmenting the structure of the neuron, dividing it into multiple segments, and determining the direction vector of each segment based on its three-dimensional morphological data. The direction of each segment is determined by the positional relationship of its adjacent nodes. Figure 2 , the direction of the neurons in segment ① is the direction from segment ① to its adjacent segment ② The direction of the neurons in segment ② is from segment ② to the direction of its adjacent segment ③. The electric field vector along the direction of the neuron is calculated as follows:

[0106] Assume that the electric field vector at neuron segment ② is The unit direction vector is (x, y, z), then the magnitude of the electric field along the direction of the neuron at neuron stage ② is

[0107]

[0108] Its direction is a vector direction.

[0109] 3) According to the electric field vector along the direction of the neuron obtained in step 2), the equivalent transmembrane current flowing into the neuron is calculated:

[0110] First, calculate the equivalent transmembrane voltage V of the electric field vector along the direction of the neuron m , as follows:

[0111]

[0112] Among them, E' is the electric field intensity along the axis of the neuron, l is the direction vector along the axis of the neuron, and λ is a spatial constant whose size is mainly related to the radius of the nerve fiber and can be calculated by the following formula.

[0113]

[0114] Among them, r m is the impedance per unit length of the cell membrane, r i is the resistance per unit length along the axial direction of the cell membrane.

[0115] Then, according to Ohm's law, the equivalent transmembrane current i flowing into the neuron membrane is calculated. m , as follows:

[0116]

[0117] Since computers cannot perform derivative operations directly, it is necessary to convert derivative operations into differential operations for calculation, as shown in the attached figure. Figure 3 , let the coordinates of the center point of segment ① be (x1, y1), and the electric field at the center point be (E x (x1,y1,z1),E y (x1,y1,z1),E z (x1, y1, z1)), the coordinates of the center point of the segment ② are (x2, y2) and the electric field at the center point is (E x (x2,y2,z2),E y (x2,y2,z2),E z (x2,y2,z2)), the coordinates of the center point of segment ③ are (x 3, y3), The vector is (x2-x1,y2-y1,z2-z1), The vector is (x3-x2,y3-y2,z3-z2).

[0118] The length of is l1:

[0119]

[0120] The length of is l2:

[0121]

[0122] Then the magnitude of the electric field along the axial direction at the position of segment ① is as follows:

[0123]

[0124] Then the magnitude of the electric field along the axial direction at the position of segment ② is as follows:

[0125]

[0126] On a curved neuron, the magnitude of the axial electric field in the two segments of the neuron is generally different, so the derivative of the axial electric field along the axial direction will generate a transmembrane current, which acts as the influence of the external electromagnetic field on the neuron in that segment.

[0127] The transmembrane current of segment ② in differential form is as follows:

[0128]

[0129] Where l1 and l2 represent the distance from the first segment to the second segment and the distance from the second segment to the third segment respectively.

[0130] 4) According to the transmembrane current obtained in step 3), combined with the Hodgkin-Huxley equation, the change of the neuron membrane potential over time is calculated:

[0131] Inject the transmembrane current obtained in step 3) into each segment of the neuron and substitute it into the Hodgkin-Huxley equation to solve the following equation:

[0132]

[0133] Among them I Na is the sodium channel current, I K is the potassium channel current, I Leak is the leakage current, Cm is the transmembrane capacitance, and V is the pressure difference between the inside and outside of the neuron membrane.

[0134] From this, we can solve the change of the pressure difference V between the inside and outside of the neuron membrane over time. If the pressure difference V between the inside and outside of the membrane rises rapidly within a period of time after the stimulation, and then reaches the maximum value with a positive inside and a negative outside, and then slowly falls back to the resting state, it means that the action potential of the neuron has been triggered, which proves that the stimulation is an effective stimulation.

[0135] The present invention integrates magnetic resonance imaging and neuromorphic data to achieve accurate three-dimensional modeling from macroscopic skull structure to microscopic neuron orientation, and builds a complete bio-electromagnetic coupling model by simulating the electromagnetic characteristics of the magnetic stimulation coil. Compared with traditional empirical stimulation, this process can accurately predict the electromagnetic field vector distribution of the target neuron when the coil is energized, reducing the anatomical matching error of the stimulation site to the millimeter level.

[0136] For the first time, electromagnetic field simulation-neuronal axial field decomposition-transmembrane current calculation-action potential trigger judgment formed an end-to-end numerical simulation link. For example, in S1, the transient electromagnetic field distribution is obtained through finite element simulation, and after S2 decomposes the electric field components based on the geometric orientation of neurons, the differential current calculation of S3 is directly driven, and finally substituted into the HH equation to realize the dynamic evaluation of the stimulation effect. This full-process closed-loop verification mechanism makes it possible to predict the quantitative relationship between coil parameters and neuronal responses without repeated trial and error in the clinic, shortening the traditional parameter optimization cycle of several weeks to a few hours of simulation calculation.

[0137] In S3, a transmembrane current differential calculation model based on segmented discretization of nerve fibers was creatively proposed (as shown in equations (2)-(5)). By discretizing continuous nerve fibers into multiple central nodes, the complex problem of solving partial differential equations is transformed into spatial differential calculation based on the coordinate difference between adjacent nodes and the axial electric field intensity gradient. For example, equation (5) directly uses the coordinate vectors of adjacent segments (n-1, n, n+1) and the measured electric field values ​​to approximate the electric field gradient through two-point differences, significantly reducing the computational complexity.

[0138] Compared with the traditional method that requires finite element meshing for global current density calculation, the differential algorithm of the present invention only relies on the segmented data of the neuron itself, which greatly reduces the amount of calculation while ensuring accuracy. In particular, by introducing the spatial constant λ through formula (3) to associate the fiber radius with the impedance parameter, the algorithm can adapt to the electrophysiological characteristics of neurons with different diameters.

[0139] The present invention realizes the computability of the biophysical mechanism of magnetic stimulation through the dual breakthroughs of the full-process closed-loop simulation system and the core differential algorithm, providing a methodological basis for the development of precise neural regulation technology.

[0140] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In addition, "front", "back", "left", "right", "upper" and "lower" in this article are all referenced to the placement state shown in the accompanying drawings.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A numerical simulation calculation method for neuron magnetic stimulation, characterized in that: The neuron magnetic stimulation numerical simulation calculation method comprises the following steps: S1. Acquire relevant data of the stimulated target to construct a human head geometric model and a three-dimensional visualization model of target neurons, and simultaneously construct a magnetic stimulation coil model, and perform electromagnetic field simulation on the combined model of the human head geometric model and the magnetic stimulation coil model to obtain the electromagnetic field vector generated by the coil being energized in the vicinity of the target neurons; S2, based on the three-dimensional visualization model of the target neuron, divide the neuron into multiple segments, determine the direction of the neuron according to the spatial position relationship of each segment, and calculate the electric field vector of each segment along the direction of the neuron according to the electric field vector obtained in S1; S3, based on the electric field vector along the direction of the neuron obtained in S2, calculate the equivalent transmembrane current flowing into the neuron; S4. Substitute the transmembrane current into the Hodgkin-Huxley equation to calculate the change of the neuron membrane potential over time to determine whether the magnetic stimulation is effective.

2. A method for numerical simulation of neuron magnetic stimulation according to claim 1, characterized in that: In S1, the relevant data are human head magnetic resonance imaging data and data in a neuromorphic database, wherein: The human head magnetic resonance imaging data is used to construct a three-dimensional geometric model of the human head; The data in the neuromorphic database is used to reconstruct a three-dimensional visualization model of a target neuron and determine its position.

3. A method for numerical simulation of neuron magnetic stimulation according to claim 2, characterized in that: In S1, an electromagnetic field simulation is performed on a combined model of the human head geometric model and the magnetic stimulation coil model, comprising the following steps: S11, acquiring magnetic resonance imaging data of a human head, and constructing a three-dimensional geometric model of the human head according to the magnetic resonance imaging data of the human head; S12. Construct a magnetic stimulation coil model, and perform electromagnetic field simulation on the three-dimensional geometric model of the human head.

4. A method for numerical simulation of neuron magnetic stimulation according to claim 3, characterized in that: In S1, the target neuron vicinity refers to a 1cm×1cm×1cm cubic area delineated with the anatomical position of the target neuron in the brain as the center by comparing the constructed three-dimensional visualization model of the target neuron with the brain map to accurately find the position.

5. A method for numerical simulation of neuron magnetic stimulation according to claim 4, characterized in that: In S2, the electric field vector along the direction of the neuron is calculated as follows: Assume that the electric field vector of any neuron segment n is The unit direction vector is (x n ,y n ,z n ), then the magnitude of the electric field along the direction of the neuron at neuron segment n is Its direction is a vector direction.

6. A method for numerical simulation of neuron magnetic stimulation according to claim 5, characterized in that: In S3, let the coordinates of the center point of segment n be Then the electric field strength of segment n along the direction of the neuron is E n ', the previous segment adjacent to segment n is n-1, and the coordinates of the center point of segment n-1 are The electric field strength of segment n-1 along the direction of the neuron is E' n-1 , the next adjacent segment is recorded as n+1, and the coordinates of the center point of segment n+1 are The electric field strength of segment n+1 along the direction of the neuron is E' n+1 , S3 includes the following steps: S31, transmembrane voltage V m,n The calculation formula is transformed from the original derivative form to the differential form, which is approximately expressed as: Among them, λ is a space constant. The size of λ is related to the radius of the nerve fiber and is calculated by the following formula: Among them, r m is the impedance per unit length of the cell membrane, r i is the resistance per unit length along the axial direction in the cell membrane; S32. According to Ohm's law, calculate the equivalent transmembrane current i flowing into the neuron membrane m,n , calculated by the following formula, 7. A method for numerical simulation of neuron magnetic stimulation according to claim 6, characterized in that: Since the computer cannot directly perform derivative operations, the derivative operation is converted into a differential operation to calculate the transmembrane current by constructing the spatial position relationship and electric field strength relationship between neuronal segments. After S32, it also includes: S33, let the coordinates of the center points of adjacent segments n and n+1 be (x n ,y n ,z n )、(x n+1 ,y n+1 ,z n+1 ), the distance between the two points is, The electric field strengths along the direction of the neuron at segments n and n+1 are E n ', E' n+1 , then the transmembrane current i at segment n is m,n The difference calculation formula is:

8. A numerical simulation calculation method for neuron magnetic stimulation according to claim 7, characterized in that: In S4, the change of the neuron membrane potential over time is calculated based on the transmembrane current obtained in S3 and the Hodgkin-Huxley equation: The transmembrane current obtained by injecting S3 into each segment of the neuron is substituted into the Hodgkin-Huxley equation for solution, as follows: Among them, I Na is the sodium channel current, I K is the potassium channel current, I Leak is the leakage current, C m is the transmembrane capacitance, V is the pressure difference between the inside and outside of the neuron membrane, According to formula (6), the change of the pressure difference V between the inside and outside of the neuron membrane with time is obtained. If the pressure difference V between the inside and outside of the membrane increases rapidly within a period of time after the stimulation, and then reaches the maximum value, showing a positive inside and a negative outside, and then slowly returns to the resting state, it means that the action potential of the neuron is triggered, which proves that the stimulation is effective.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for numerical simulation of neuronal magnetic stimulation according to any one of claims 1 to 8 is implemented.

10. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a numerical simulation calculation method for neuron magnetic stimulation as described in any one of claims 1 to 8.

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