CN116894356A - Human brain personalized time interference stimulation simulation method - Google Patents
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Abstract
The invention discloses a human brain personalized time interference stimulation simulation method, which uses a segmentation algorithm and an electrode automatic positioning and deployment algorithm based on a brain tissue segmentation template to complete automatic segmentation of a human brain MRI image and automatic deployment of electrodes, realizes rapid modeling of the human brain, uses an open source algorithm such as SPM (Statistical Parametric Mapping), ios2mesh, getdp and the like to uniformly concentrate the open source algorithm to a matlab environment for programming, and completes the full-flow simulation of time interference stimulation from the MRI image to a time interference stimulation electric field, and comprises the following specific steps: the acquired MRI image is subjected to personalized automatic segmentation by using SPM, automatic deployment of stimulation electrodes is performed on the MRI image, finite element grid generation is performed by using iso2mesh, electric field solving is performed on the finite element grid by using Getdp, and time interference stimulation electric field solving and time interference stimulation electric field imaging are performed; the user does not need to consider how to model, construct electrodes and solve the electric field, has better simulation experience, and can be suitable for various reality scenes.
Description
Technical Field
The invention belongs to the technical field of electric stimulation nerve regulation and control, and particularly relates to a human brain personalized time interference stimulation simulation method.
Background
In recent years, time-interferometry (temporal interference, TI) stimulation technology has begun to develop rapidly as a novel non-invasive brain neuromodulation approach. The time interference stimulation technology is to use two groups of high-frequency alternating current stimulation with different frequencies and low-frequency difference frequency to electrically stimulate the brain, wherein the two groups of electric stimulation respectively generate two high-frequency stimulation electric fields in the brain, and a low-frequency electric field with the frequency of the difference frequency is formed at the intersection of the two high-frequency electric fields, and the low-frequency electric field is the time interference electric field. Since neurons in the brain respond rhythmically to low frequency electric fields only, only the low frequency electric fields deep in the brain will have a stimulating effect on neurons, not on neurons in the cortex under the stimulating electrodes. And the space position of the low-frequency electric field can be changed in time by adjusting the current proportion between the two groups of high-frequency stimulation currents, so that the accurate stimulation of deep brain neurons can be realized under the noninvasive condition by the time-interference stimulation. The time intervention is favored by a plurality of scientific researchers and clinicians by the noninvasive property and deep brain focusing property of the time intervention, and has wide application prospect of noninvasive treatment of clinical mental diseases.
In the field of electric stimulation nerve regulation, the distribution of the stimulating electric field generated by the stimulating electrode is generally one of the key methods for evaluating the stimulation effectiveness, but under the current scientific and technical conditions, no safe and effective method exists for directly obtaining the stimulating electric field in the human brain, and the focusing condition of time-dependent stimulation in the human brain cannot be known. While the finite element (Finite Element Method, FEM) simulation method can just solve this problem. The finite element simulation method is a commonly used numerical analysis method, and can realize three-dimensional modeling of the human brain by constructing a polyhedral grid based on an MRI image of the human brain, and then realize calculation of an electric field in the human brain by using Dirichlet boundary conditions and Laplace current conduction equations. The finite element method has the characteristics of symmetry and positive definite coefficient matrix of algebraic equation, and can realize fast and convergent electric field simulation in a limited memory.
At present, the main simulation method of time interference stimulation is that human brain is analogous to a four-layer sphere with different conductivities, then commercial simulation software such as comsol is utilized to simulate the brain, or three-dimensional modeling is carried out on scalp, skull, cerebrospinal fluid, gray matter and white matter of the head of a human body through some modeling software, and then the model is imported into the comsol to simulate. These two methods have the following problems:
1. the first simulation method uses a simple sphere model to simulate the human brain, but the complexity of brain tissue in the human brain is far higher than that of a simple sphere, so the accuracy of using the method is not high and the public confidence is lacked.
2. Although the second method can obtain a more accurate human brain model, the reduction modeling operation of the human brain is difficult, the time cost is high, personalized stimulation is difficult to realize, and the method is more difficult to apply to a real scene.
3. Most of the current simulation software only can simulate an electric field, but cannot simulate a time interference stimulation electric field, so that after the simulation software completes the simulation, matlab or python is also required to be used for calculating the time interference stimulation electric field, and a plurality of software platforms are often required to support the simulation of the time interference stimulation, so that the simulation complexity is greatly increased.
Disclosure of Invention
Aiming at the technical problems that a sphere simulation model is simple, a human brain model is difficult to simulate and model, personalized modeling cannot be realized, simulation of time interference stimulation needs to depend on multiple platforms, and simulation is difficult in the simulation method of time interference stimulation, the invention aims to provide a human brain personalized time interference stimulation simulation method. According to the method, firstly, a brain tissue segmentation template-based segmentation algorithm and an electrode automatic positioning and deployment algorithm are used for completing automatic segmentation of a human brain MRI image and automatic deployment of an electrode, so that rapid modeling of the human brain is realized, and finally, an open source algorithm such as SPM (Statistical Parametric Mapping), ios2mesh, getdp and the like is used for uniformly concentrating the brain tissue segmentation template and the electrode automatic positioning and deployment algorithm under a matlab environment for programming, so that full-flow simulation from the MRI image to a time interference stimulation electric field is completed.
In order to achieve the above task, the present invention adopts the following technical solutions:
the human brain personalized time interference stimulation simulation method is characterized by comprising the following steps of:
the first step: personalized automatic segmentation of acquired MRI images by SPM
Step 101: automatically registering the acquired MRI image with the MNI152 standard head model through the SPM12 and obtaining a mapping matrix between the MRI image and the MNI152 standard head model;
step 102: registering the MRI image into a probability tissue model of an MNI152 standard head model through a mapping matrix to finish preliminary segmentation of the MRI image, wherein the segmented tissue types are as follows: scalp, skull, cerebrospinal fluid, grey matter, white matter, air;
step 103: performing further graphic processing on the segmented result, wherein the further graphic processing comprises smoothing processing of the segmented result, filling the segmented holes and removing voxels with abnormal segmentation;
and a second step of: automated deployment of stimulation electrodes on MRI images
Step 201: and (3) carrying out three-dimensional space positioning of electrode stimulation points on an MNI152 standard head model by referring to a 10-10 electrode system, and recording three-dimensional coordinates corresponding to each electrode.
Step 202: the three-dimensional coordinates of the electrodes on the MRI image are obtained by multiplying the mapping matrix with the three-dimensional space coordinates of the electrodes on the standard head model.
Step 203: based on the three-dimensional coordinates of the electrodes, two cylinders with the radius of 5mm and the height of 2mm are respectively constructed in the radial direction by taking the origin of an MNI coordinate system as the center and are respectively used as conductive gel and a stimulating electrode.
Step 204: the overlapping portion of the human brain and the conductive gel is post-processed, the spatial priority of the human head tissue obtained by dividing in step 103 is set to 1, the spatial priority of the conductive gel is set to 0, and voxels with high spatial priority cover voxels with low spatial priority.
And a third step of: finite element mesh generation using iso2mesh
Step 301: and (3) importing the result obtained in the step 204 into an iso2mesh for finite element grid generation, wherein the quantity and quality of the generated finite element grids can be controlled by adjusting parameters such as the maximum surface element size, the minimum angle of a curved surface triangle, the maximum distance between the circle center of a curved surface boundary and the circle center of an element boundary, the maximum distance between the circle center of the curved surface boundary and the center of a boundary sphere, the maximum radius-edge ratio, the maximum tetrahedral element volume of a target and the like in the generation process.
Step 302: and carrying out detail restoration on the generated finite element grid, wherein the detail restoration comprises the deletion of an isolated grid and the filling of a cavity area.
Fourth step: the finite element mesh was electric field solved using Getdp.
Step 401: the operation file of Getdp is written first to calculate the finite element electric field by using Getdp, and the electrode required for stimulation, the current intensity of electrode stimulation, the conductivity of human brain tissue and conductive gel, the boundary condition of finite element solution and the format and type of output file are set in the operation file according to Getdp language format.
Step 402: the time interference stimulation needs to calculate the electric fields generated by two groups of stimulation, and the stimulating electric field generated by the electrode group A and the stimulating electric field generated by the electrode group B are respectively defined as: an electric field A and an electric field B;
step 401 is repeated to calculate electric field a and electric field B, respectively.
Fifth step: solving of time-interferometry stimulating electric fields
Step 501: after the results of the electric field a and the electric field B are obtained in step 402, the time-dependent stimulus electric field is solved by using the following synthesis formula of the time-dependent stimulus electric field:
in the method, in the process of the invention,expressed as a time-dependent interferential stimulating electric field, ">Indicating the direction of the electric field +.>And->The electric field vectors representing the electric field a and the electric field B are 1*3 vectors, respectively, and represent the electric field intensities in the x, y, and z directions. />Andthe electric field moduli of electric field a and electric field B are shown, respectively. Alpha is->And->The angle between the two space vectors.
Sixth step: imaging of time-interfered stimulating electric fields
Step 601: the time-interferometry electric field values calculated in step 501 are calculated and solved based on the finite element grid, but the spatial coordinates of each finite element grid point are not continuous and regular grid grids, so that imaging is not facilitated, and therefore, the finite element grid needs to be resampled before imaging to generate a time-interferometry electric field result based on the grid grids.
Step 602: three-dimensional imaging is performed on the grid mesh-based time-interferometry stimulating electric field calculation result in step 601 using a three-dimensional imaging algorithm.
The invention relates to a personalized time interference stimulation simulation method of human brain, which brings the following technical innovation:
firstly, by mapping the MRI image of any human brain with a standard map, personalized segmentation of the MRI image of the human brain and automatic deployment of electrodes are realized, and finite element grids are generated and solved based on segmentation results.
Secondly, the current mainstream simulation method involves a plurality of commercial simulation and calculation software, which is not friendly to simulation designers, but the method is completely based on open source software and algorithm, and simulation can be completed only by using matlab, so that the professional technical threshold required by simulation is greatly reduced.
Third, by adopting the human brain personalized time interference stimulation simulation method, the full-flow calculation of the time interference stimulation simulation can be automatically completed only by providing the MRI image and the stimulation scheme, so that a user does not need to consider how to model, construct electrodes and solve an electric field, better simulation experience is achieved, and the method can be suitable for various actual scenes.
Drawings
FIG. 1 is a schematic diagram of a personalized time interferential stimulation simulation process of a human brain, according to one embodiment of the invention;
FIG. 2 is a graph of simulation results of electric field distribution when stimulus is applied to the electrode group A according to the embodiment of FIG. 1;
FIG. 3 is a graph of simulation results of electric field distribution when stimulus is applied to the electrode group B according to the embodiment of FIG. 1;
FIG. 4 is a graph of simulation results of the electric field distribution of the time-interferometry stimulation performed by the electrode sets A and B according to the embodiment of FIG. 1;
the present invention will be described in further detail with reference to the accompanying drawings and examples.
Detailed Description
The example provides a human brain personalized time interference stimulation simulation method, which is implemented according to the following steps:
1) Personalized automatic segmentation of acquired MRI images by SPM
Step 101: the acquired MRI image is automatically registered with the MNI152 standard head model by the SPM12 and a mapping matrix a between the MRI image and the MNI152 standard head model is obtained.
Step 102: registering the MRI image into a probability tissue model of an MNI152 standard head model through a mapping matrix to finish preliminary segmentation of the MRI image, wherein the segmented tissue types are as follows: scalp, skull, cerebrospinal fluid, grey matter, white matter, air.
Step 103: and carrying out further graphic processing on the segmented result, wherein the further graphic processing comprises smoothing processing of the segmented result, filling the segmented holes and removing voxels with abnormal segmentation.
2) Automated deployment of stimulation electrodes on MRI images
Step 201: and (3) carrying out three-dimensional space positioning of electrode stimulation points on an MNI152 standard head model by referring to a 10-10 electrode system, and recording three-dimensional coordinates corresponding to each electrode.
Step 202: the three-dimensional coordinates of the electrodes on the MRI image are obtained by multiplying the mapping matrix a with the three-dimensional coordinates of the electrodes on the standard head model required for stimulation. In this example, the stimulation electrodes deployed are respectively: electrode group a: f8-P8, electrode set B: F7-P7.
Step 203: based on the three-dimensional coordinates of the electrodes, two cylinders with the radius of 5mm and the height of 2mm are respectively constructed in the radial direction by taking the origin of an MNI coordinate system as the center and are respectively used as conductive gel and a stimulating electrode.
Step 204: the overlapping portion of the human brain and the conductive gel is post-processed, the spatial priority of the human head tissue obtained by dividing in step 103 is set to 1, the spatial priority of the conductive gel is set to 0, and voxels with high spatial priority cover voxels with low spatial priority.
3) Finite element mesh generation using iso2mesh
Step 301: and (3) importing the result obtained in the step 204 into an iso2mesh for finite element grid generation, wherein the quantity and quality of the generated finite element grids can be controlled by adjusting parameters such as the maximum surface element size, the minimum angle of a curved surface triangle, the maximum distance between the circle center of a curved surface boundary and the circle center of an element boundary, the maximum distance between the circle center of the curved surface boundary and the center of a boundary sphere, the maximum radius-edge ratio, the maximum tetrahedral element volume of a target and the like in the generation process. Default finite element mesh parameters are:
maximum surface element size: 5.
minimum angle of curved triangle: 30 degrees.
Maximum distance between the curved surface boundary circle center and the element boundary circle center: 0.3.
maximum distance between the center of the curved surface boundary and the center of the element boundary sphere: 0.3.
maximum radius-edge ratio: 3.
target maximum tetrahedral element volume: 10.
step 302: and carrying out detail restoration on the generated finite element grid, wherein the detail restoration comprises the deletion of an isolated grid and the filling of a cavity area.
4) The finite element mesh was electric field solved using Getdp.
Step 401: the operation file of Getdp is written first to calculate the finite element electric field by using Getdp, and the electrode required for stimulation, the current intensity of electrode stimulation, the conductivity of human brain tissue and conductive gel, the boundary condition of finite element solution and the format and type of output file are set in the operation file according to Getdp language format. In this example, the conductivities of the tissues are respectively:
white matter: 0.126S/m; ash quality: 0.276S/m; cerebrospinal fluid, 1.65S/m; skull bone: 0.01S/m; skin: 0.465S/m; air: 2.5e-14S/m; conductive gel: 0.3S/m; stimulation electrode: 5.9e7S/m.
The stimulating currents of the stimulating electrodes are respectively: f7:2mA, P7: -2mA, F8:2mA, P8: -2mA.
Step 402: as shown in fig. 1, the electric fields generated by the two groups of stimulation are calculated by the time-interferometry stimulation, and in this embodiment, the stimulating electric field a generated by the electrode group a and the stimulating electric field B generated by the electrode group B are defined as: an electric field a and an electric field B.
Step 401 is repeated to calculate electric field a and electric field B, respectively.
5) Solving of time-interferometry stimulating electric fields
Step 501: after the results of the electric field a and the electric field B are obtained in step 402, the time-interferometry electric field is solved using the following synthesis formula of the time-interferometry electric field:
in the method, in the process of the invention,expressed as a time-dependent interferential stimulating electric field, ">Indicating the direction of the electric field +.>And->The electric field vectors representing the electric field a and the electric field B are 1*3 vectors, respectively, and represent the electric field intensities in the x, y, and z directions. />Andthe electric field moduli of electric field a and electric field B are shown, respectively. Alpha is->And->Two spatial directionsThe included angle between the amounts.
The electric fields A and B generated by the stimulation of the electrode groups A and B are shown in figures 2 and 3, respectively.
6) Imaging of time-interfered stimulating electric fields
Step 601: the time-interferometry electric field values calculated in step 501 are calculated and solved based on the finite element grid, but the spatial coordinates of each finite element grid point are not continuous and regular grid grids, so that imaging is not facilitated, and therefore, the finite element grid needs to be resampled before imaging to generate a time-interferometry electric field result based on the grid grids.
Step 602: the calculation result in step 601 is three-dimensionally imaged using a three-dimensional imaging algorithm. The imaging results are shown in fig. 4.
Claims (1)
1. The human brain personalized time interference stimulation simulation method is characterized by comprising the following steps of:
the first step: personalized automatic segmentation of acquired MRI images by SPM
Step 101: automatically registering the acquired MRI image with the MNI152 standard head model through the SPM12 and obtaining a mapping matrix between the MRI image and the MNI152 standard head model;
step 102: registering the MRI image into a probability tissue model of an MNI152 standard head model through a mapping matrix to finish preliminary segmentation of the MRI image, wherein the segmented tissue types are as follows: scalp, skull, cerebrospinal fluid, grey matter, white matter, air;
step 103: performing further graphic processing on the segmented result, wherein the further graphic processing comprises smoothing processing of the segmented result, filling the segmented holes and removing voxels with abnormal segmentation;
and a second step of: automated deployment of stimulation electrodes on MRI images
Step 201: performing three-dimensional space positioning of electrode stimulation points on an MNI152 standard head model by referring to a 10-10 electrode system, and recording three-dimensional coordinates corresponding to each electrode;
step 202: multiplying the mapping matrix by the electrode three-dimensional space coordinates on the standard head model to obtain electrode three-dimensional coordinates on the MRI image;
step 203: based on the three-dimensional coordinates of the electrodes, two cylinders with the radius of 5mm and the height of 2mm are respectively constructed in the radial direction by taking the origin of an MNI coordinate system as the center and are respectively used as conductive gel and a stimulating electrode;
step 204: performing post-treatment on the overlapping part of the human brain and the conductive gel, setting the spatial priority of the human head tissue obtained by segmentation in the step 103 as 1, setting the spatial priority of the conductive gel as 0, and enabling voxels with high spatial priority to cover voxels with low spatial priority;
and a third step of: finite element mesh generation using iso2mesh
Step 301: importing the result obtained in the step 204 into an iso2mesh for finite element grid generation, and controlling the quantity and quality of the generated finite element grids by adjusting the largest surface element size, the smallest angle of a curved surface triangle, the largest distance between a curved surface boundary circle center and an element boundary circle center, the largest distance between a curved surface boundary circle center and a boundary sphere center, the largest radius-edge ratio and the largest tetrahedron element volume parameter of a target in the generation process;
step 302: performing detail restoration on the generated finite element grid, wherein the detail restoration comprises the deletion of an isolated grid and the filling of a cavity area;
fourth step: electric field solution for finite element mesh using Getdp
Step 401: calculating a finite element electric field by using Getdp, namely writing an operation file of Getdp, setting electrodes required for stimulation and current intensity of electrode stimulation, conductivity of human brain tissues and conductive gel according to Getdp language format in the operation file, and solving boundary conditions of finite elements and format and type of output file;
step 402: the time interference stimulation needs to calculate the electric fields generated by two groups of stimulation, and the stimulating electric field generated by the electrode group A and the stimulating electric field generated by the electrode group B are respectively defined as: an electric field A and an electric field B;
repeating step 401, and calculating an electric field A and an electric field B respectively;
fifth step: solving of time-interferometry stimulating electric fields
Step 501: after the results of the electric field a and the electric field B are obtained in step 402, the time-dependent stimulus electric field is solved by using the following synthesis formula of the time-dependent stimulus electric field:
in the method, in the process of the invention,expressed as a time-dependent interferential stimulating electric field, ">Indicating the direction of the electric field +.>And->The electric field vectors respectively representing the electric field A and the electric field B are 1*3 vectors, and represent the electric field intensities in the directions of x, y and z; />Andthe electric field moduli of electric field a and electric field B, respectively; alpha is->And->An included angle between the two space vectors;
sixth step: imaging of time-interfered stimulating electric fields
Step 601: the time-interferometry electric field value calculated in step 501 is obtained by performing calculation and solution based on a finite element grid, but the space coordinate of each finite element grid point is not a continuous and regular grid, so that imaging is not facilitated, and therefore resampling is required to be performed on the finite element grid before imaging, so that a time-interferometry electric field result based on the grid is generated;
step 602: the grid mesh based time interferometry electric field results of step 601 are three-dimensionally imaged using a three-dimensional imaging algorithm.
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