Transcranial magnetic stimulation simulation method under simulated microgravity

Abstract

The invention provides a transcranial magnetic stimulation simulation method under simulated microgravity. The method comprises the following steps: constructing a human head three-dimensional grid model; wherein the parameters comprise low-frequency target electrical characteristic parameters in the corresponding microgravity environment; establishing an electromagnetic simulation model; and determining a tissue area generating effective stimulation based on the model and a threshold value. Through the corrected conductivity, the model can truly reflect brain tissue electrical characteristic changes caused by microgravity, and electric field calculation errors caused by parameter solidification are overcome. And secondly, the nerve activation threshold value suitable for the microgravity environment is determined by fusing the excitability correction coefficients of the cells and the network hierarchy, so that the problem of misjudgment of the stimulation effectiveness caused by reduction of the excitability of the neurons is solved. And finally, through electromagnetic simulation coupled with the adaptive parameters, reliable prediction of induced electric field distribution and an effective stimulation area under a microgravity condition is realized.

Description

Technical Field

[0001] This invention generally relates to the field of transcranial magnetic stimulation technology, and specifically to a transcranial magnetic stimulation simulation method under microgravity. Background Technology

[0002] Transcranial magnetic stimulation (TMS) is an important non-invasive brain modulation technique. Its simulation is usually based on medical images to construct a head model, and uses fixed tissue conductivity and neural activation threshold to calculate the induced electric field and predict the stimulation effect.

[0003] However, this technology is not applicable to astronauts. This is because, in a prolonged microgravity environment, astronauts experience head-to-head fluid shifts, leading to widening of the extracellular spaces in their brain tissue, changes in macroscopic conductivity, and decreased neuronal excitability. These dynamic changes in physiological parameters render existing simulation methods based on fixed parameters calibrated on the ground inapplicable. Directly using these methods would fail to accurately predict the actual stimulation effect of the on-orbit TMS, potentially resulting in insufficient stimulation dosage or positioning errors.

[0004] Therefore, there is an urgent need for a TMS simulation method that can dynamically respond to changes in physiological state in order to solve the simulation distortion problem caused by the fixed parameters in existing technologies. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the existing technology, it is desirable to provide a method for simulating transcranial magnetic stimulation under microgravity.

[0006] This invention provides a method for simulating transcranial magnetic stimulation under microgravity, comprising: S1: Construct a three-dimensional mesh model of the human head; the three-dimensional mesh model is divided into multiple tissue regions corresponding to human brain tissue, used to simulate the human brain under microgravity environment; S2: Assign low-frequency target electrical characteristic parameters to multiple tissue regions to simulate the electrical characteristics of tissue regions under microgravity conditions; the low-frequency target electrical characteristic parameters include at least the target conductivity of human brain tissue under microgravity conditions; S3: An electromagnetic simulation model is established based on a three-dimensional mesh model with low-frequency target electrical characteristic parameters and a transcranial magnetic stimulation coil model; the electromagnetic simulation model has model parameters, which are used to substitute different values ​​to simulate different transcranial magnetic stimulation processes; S4: Obtain the specific values ​​of the model parameters and the target nerve activation threshold; the model parameters are used to input the electromagnetic simulation model to simulate the transcranial magnetic stimulation process; the target nerve activation threshold is used to determine whether the transcranial magnetic stimulation is effective; S5: Based on the specific values ​​of the model parameters, the target neural activation threshold, and the electromagnetic simulation model, determine the tissue region that generates effective stimulation.

[0007] According to the technical solution provided by the present invention, a three-dimensional mesh model of a human head is constructed, including: Acquire medical imaging data of the human head; The medical image data is preprocessed and segmented sequentially to obtain an initial three-dimensional structural model; The initial three-dimensional structural model is subjected to surface reconstruction and meshing to obtain the three-dimensional mesh model.

[0008] According to the technical solution provided by the present invention, tissue segmentation is performed on the preprocessed medical image data to obtain a three-dimensional structural model, including: The preprocessed medical image data is subjected to edge detection to obtain multiple edge points of brain tissue. By fitting edge points of multiple brain tissues, multiple initial brain tissue interfaces are obtained; The preprocessed medical image data is divided into multiple initial tissue regions based on multiple initial brain tissue interfaces; the multiple initial tissue regions constitute the initial three-dimensional structural model.

[0009] According to the technical solution provided by the present invention, the initial three-dimensional structural model is subjected to surface reconstruction and meshing processing to obtain the three-dimensional mesh model, including: Surface reconstruction of the initial three-dimensional structural model includes removing overlaps, filling holes, and smoothing the initial three-dimensional structural model to obtain a reconstructed three-dimensional model. The surface of the reconstructed 3D model is decomposed into discrete mesh structures at equal intervals to obtain a 3D mesh model; the 3D mesh model contains multiple tissue regions obtained by surface reconstruction and meshing of the initial tissue regions.

[0010] According to the technical solution provided by the present invention, obtaining low-frequency target electrical characteristic parameters includes: Obtain the initial electrical characteristic parameters under surface gravity conditions; Obtain the extracellular space volume fraction and tissue structure index under gravity conditions that are less than or equal to the gravity threshold; The initial electrical characteristic parameters are corrected based on the extracellular space volume fraction and tissue structure index to obtain the low-frequency target electrical characteristic parameters.

[0011] According to the technical solution provided by the present invention, obtaining the target neural activation threshold includes: A baseline neural activation threshold is obtained; the baseline neural activation threshold is used to determine whether transcranial magnetic stimulation is effective under the influence of gravity. Cell-level correction coefficients and network-level correction coefficients are obtained under microgravity conditions; the cell-level correction coefficients are used to characterize the effect of neuronal electrophysiological changes on the neural activation threshold; the network-level correction coefficients are used to characterize the effect of overall neural circuit excitability changes on the neural activation threshold. Based on the cell-level correction coefficient and the network-level correction coefficient, the baseline neural activation threshold is corrected to adapt to the microgravity environment, thereby obtaining the target neural activation threshold.

[0012] According to the technical solution provided by the present invention, the tissue region that generates effective stimulation is determined based on the specific values ​​of the model parameters, the target neural activation threshold, and the electromagnetic simulation model, including: The specific values ​​of the model parameters are input into the electromagnetic simulation model to simulate the transcranial magnetic stimulation process and obtain the induced electric field distribution in multiple tissue regions. The target neural activation threshold is used to determine the tissue region that generates effective stimulation.

[0013] According to the technical solution provided by the present invention, the tissue region that generates effective stimulation is determined by utilizing the target neural activation threshold, including: Tissue regions where the induced electric field distribution is greater than or equal to the target nerve activation threshold are determined to have generated effective stimulation; otherwise, they are determined to have not generated effective stimulation.

[0014] The beneficial effects of this invention are as follows: To address the problem of inaccurate stimulation effects caused by the use of fixed conductivity and neural activation thresholds in existing transcranial magnetic stimulation (TMS) simulation methods, which fail to accurately predict physiological changes in astronaut brain tissue under microgravity conditions, this invention employs a TMS simulation method based on a microgravity environment. This method includes: constructing a three-dimensional mesh model of the human head; assigning low-frequency target electrical characteristic parameters under microgravity conditions to tissue regions within the model; establishing an electromagnetic simulation model based on this parameterized model and a coil model; obtaining specific model parameters and the target neural activation threshold corrected for microgravity; and determining the tissue regions that generate effective stimulation based on the aforementioned model and thresholds. By introducing conductivity corrected based on parameters such as extracellular space volume fraction, the model can realistically reflect changes in brain tissue electrical characteristics caused by microgravity, overcoming the electric field calculation errors caused by fixed parameters. Secondly, by fusing excitability correction coefficients at the cellular and network levels, a neural activation threshold suitable for microgravity environments is dynamically determined, resolving the problem of misjudgment of stimulation effectiveness caused by reduced neuronal excitability. Finally, through electromagnetic simulation coupled with the aforementioned adaptive parameters, reliable prediction of the induced electric field distribution and effective stimulation regions under microgravity conditions is achieved. Attached Figure Description

[0015] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating a transcranial magnetic stimulation simulation method under microgravity. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] It should be noted that the microgravity environment mentioned in this invention specifically refers to an environment where the gravitational acceleration is within the range of [0, 0.1], with units of m / s². 2 Therefore, the gravity threshold mentioned in this invention is 0.1 m / s². 2 .

[0019] In addition, the low frequency mentioned in this embodiment specifically refers to 0 to 1000 Hz.

[0020] refer to Figure 1 This invention provides a method for simulating transcranial magnetic stimulation under microgravity, comprising: S1: Construct a three-dimensional mesh model of the human head; the three-dimensional mesh model is divided into multiple tissue regions corresponding to human brain tissue, used to simulate the human brain under microgravity conditions, including: Acquire medical imaging data of the human head; The medical image data is preprocessed and segmented sequentially to obtain an initial three-dimensional structural model; The initial three-dimensional structural model is subjected to surface reconstruction and meshing to obtain the three-dimensional mesh model.

[0021] Specifically, medical imaging data includes magnetic resonance imaging (MRI), computed tomography (CT), or medical digital imaging and communications (DICOM) sequences.

[0022] The preprocessing process includes denoising, registration, and resampling, with specific methods and effects as follows: Denoising: Median filtering or nonlocal mean filtering is used to suppress image noise and improve the clarity of tissue boundaries and segmentation accuracy.

[0023] Registration: Through rigid or affine transformation, multimodal images (such as MRI and CT) or sequential images are aligned to the same spatial coordinate system to ensure the consistency of subsequent tissue segmentation and parameter assignment.

[0024] Resampling: Using bilinear or trilinear interpolation, the image voxel size is unified to a preset resolution (e.g., 1×1×1 mm³), eliminating anisotropy and laying the foundation for generating a high-quality computational grid.

[0025] The preprocessing process can significantly improve the geometric fidelity and physical consistency of the 3D head model, providing a reliable model basis for subsequent accurate conductivity space mapping and electric field simulation.

[0026] Further, the preprocessed medical image data is segmented to obtain a three-dimensional structural model, including: The preprocessed medical image data is subjected to edge detection to obtain multiple edge points of brain tissue. By fitting edge points of multiple brain tissues, multiple initial brain tissue interfaces are obtained; The preprocessed medical image data is divided into multiple initial tissue regions based on multiple initial brain tissue interfaces; the multiple initial tissue regions constitute the initial three-dimensional structural model.

[0027] Edge detection methods include gradient-based operators such as the Sobel operator, Prewitt operator, and Canny operator. Edges are detected by calculating the gradient of image grayscale values, resulting in edge points for multiple brain tissues.

[0028] Edge point fitting methods include: using Bézier curves / surfaces, B-splines, or non-uniform rational B-splines (NURBS) to mathematically fit the edge points. These edge point fitting methods can generate smooth, continuous analytical surfaces, facilitating subsequent geometric operations and mesh generation.

[0029] Multiple initial brain tissue interfaces obtained by edge point fitting are actually the boundaries between various brain tissues. The area enclosed by multiple boundaries is actually the space where a part of the brain tissue is located. Therefore, the internal space enclosed by the initial brain tissue interfaces can be regarded as the initial tissue region.

[0030] Multiple initial tissue regions include at least the scalp, skull, and brain tissue; they can be further subdivided into tissue regions such as gray matter, white matter, and cerebrospinal fluid.

[0031] Through the segmentation and fitting process described above, which involves "edge points - interface - initial tissue region," this invention achieves high-fidelity conversion from discrete medical image pixels to continuous, closed three-dimensional geometric entities. Based on the continuous and smooth interface obtained through fitting, the anatomical boundaries of different tissues such as scalp, skull, gray matter, and white matter can be accurately depicted, overcoming the step-like artifacts present in traditional voxel-level segmentation and laying the foundation for subsequent generation of high-quality computational meshes.

[0032] Each initial tissue region is a closed space, which can be independently assigned differentiated electrical property parameters (such as conductivity) to ensure accurate spatial mapping of material properties in subsequent electromagnetic simulations, thereby significantly improving the anatomical realism of induced electric field calculations. This structured segmentation and reconstruction process is easy to integrate, supports automated generation from images to simulation models, reduces errors caused by human intervention, and improves the reliability and efficiency of the method in scientific research and engineering applications.

[0033] Further, the initial three-dimensional structural model is subjected to surface reconstruction and meshing processing to obtain the three-dimensional mesh model, including: Surface reconstruction of the initial three-dimensional structural model includes removing overlaps, filling holes, and smoothing the initial three-dimensional structural model to obtain a reconstructed three-dimensional model. The surface of the reconstructed 3D model is decomposed into discrete mesh structures at equal intervals to obtain a 3D mesh model; the 3D mesh model contains multiple tissue regions obtained by surface reconstruction and meshing of the initial tissue regions.

[0034] Specifically, methods for removing overlaps include: Vertex merging: Set a geometric tolerance (e.g., 0.01mm) to merge all vertices with a spatial distance less than this tolerance into one vertex, thereby eliminating redundant vertices caused by segmentation or fitting accuracy.

[0035] Remove duplicate / degenerate faces: Delete completely overlapping triangles and degenerate triangles with an area of ​​zero or close to zero.

[0036] Self-intersection detection and repair: Detects and eliminates unreasonable mutual penetration (self-intersection) between faces in a triangular mesh, usually achieved through local mesh reconstruction.

[0037] Boundary stitching: For model cracks caused by discontinuous segmentation, the surface is closed by identifying and connecting boundary edges that are close to each other.

[0038] By systematically removing geometric defects such as overlaps, cracks, and self-intersections, a closed and consistent closed surface model is generated. This is a necessary prerequisite for generating high-quality volume meshes (such as tetrahedral meshes) that can be used for finite element calculations, and can effectively avoid solution failures, convergence difficulties, or result distortions caused by mesh quality issues in subsequent electromagnetic simulation calculations.

[0039] Specifically, methods for filling holes include: The boundary-based triangular patch growth method identifies the sequence of vertices that constitute the boundary of a hole, and gradually fills the hole area by recursively adding triangular patches inside it until a complete and continuous surface is formed.

[0040] Surface interpolation or fitting: For holes with clear boundaries, interpolation fitting is performed on the boundary vertices using local polynomial surfaces or radial basis functions (RBF) to generate smooth patch surfaces, which are then triangulated.

[0041] Curvature continuity detection and smoothing: After filling geometric holes, the curvature of the newly generated patch and the surrounding original surface at the connection is detected and smoothed to avoid discontinuous sharp edges or wrinkles.

[0042] Filling the voids on the surface is a necessary step in generating a closed and geometrically continuous 3D surface model. This not only ensures the smooth progress of subsequent volume mesh generation (such as tetrahedral mesh generation) and avoids mesh generation failures caused by an incomplete model, but more importantly, it ensures the integrity and physical continuity of the computational model in terms of anatomical structure. This lays a flawless geometric foundation for the accurate calculation of electromagnetic fields at tissue boundaries, thereby guaranteeing the reliability of the entire simulation process and the credibility of the results.

[0043] Specifically, the smoothing process includes: Laplacian smoothing involves iteratively relaxing each vertex by shifting it to the average position of its neighboring vertices. This method is simple and efficient, but can lead to overall model shrinkage or excessive blurring of details.

[0044] Hyperbolic smoothing involves introducing a scaling factor into Laplacian smoothing and achieving a better balance between smoothing and volume shrinkage through low-pass filtering. This method effectively eliminates high-frequency noise while better preserving the overall volume and shape of the model, and is a commonly used improvement method.

[0045] Curvature-based smoothing involves moving vertices along their normal direction, with the amount of movement related to the local curvature; regions with higher curvature experience stronger smoothing. This method can smooth noise while better preserving original sharp features (such as brain sulci and gyri), making it a preferred approach for feature preservation.

[0046] The smoothing process described above significantly improves the element quality of the surface mesh (such as the minimum angle of triangles and aspect ratio), eliminating "ill-conditioned" meshes that may lead to singularities in subsequent finite element calculations. More importantly, it generates geometric representations that more closely resemble the smooth boundaries of real biological tissues, thereby improving the accuracy of boundary condition handling in electromagnetic simulations and ultimately enhancing the computational accuracy and physical reliability of the predicted results for the induced electric field and effective stimulation region.

[0047] The methods for decomposing the surface of the reconstructed 3D model into discrete mesh structures at equal intervals include: The leading edge advancement method involves generating new triangular elements by gradually advancing inward from the surface boundary, while continuously updating the "leading edge" boundary. This method can effectively control the local mesh size, generate high-quality triangles with high boundary fit, and is suitable for complex anatomical surfaces.

[0048] Constrained Delaunay triangulation involves performing Delaunay triangulation on the surface point set to generate a triangular mesh, while ensuring that the original boundary constraints are not violated. This method generates triangles with maximum and minimum angle properties (i.e., avoiding elongated triangles as much as possible), resulting in high-quality meshes and a mature and stable algorithm.

[0049] The spherical projection / parameterization method involves parametrically mapping a 3D surface to a 2D plane (or a unit sphere), performing regular triangulation in the 2D domain, and then mapping it back to the 3D surface. This method is easy to generate meshes with good uniformity, but it may produce distortions when mapping complex topologies (such as highly wrinkled cortical surfaces).

[0050] Mesh remapping and homogenization involve iteratively optimizing the initial mesh generated by the methods described above through techniques such as edge folding / splitting, vertex redistribution (e.g., optimization based on centroid Voronoi subdivision), or local Laplacian smoothing. This method can significantly improve the dimensional uniformity and shape quality of mesh cells (e.g., triangle angles approaching 60°), and is a common step in obtaining high-quality computational meshes.

[0051] The high-quality, highly uniform surface triangular meshes generated by these methods are not only the foundation for 3D model visualization and geometric analysis, but also an indispensable prerequisite for subsequent automatic generation of volume meshes (such as tetrahedral meshes). Uniform and regular surface meshes ensure the quality and efficiency of volume mesh generation, thus providing a stable and accurate spatial discretization basis for subsequent finite element electromagnetic simulations, ensuring the convergence and reliability of electric field distribution calculations.

[0052] S2: Assign low-frequency target electrical characteristic parameters to multiple tissue regions to simulate the electrical characteristics of tissue regions under microgravity conditions; the low-frequency target electrical characteristic parameters include at least the target conductivity of human brain tissue under microgravity conditions; Furthermore, the low-frequency target electrical characteristic parameters are obtained, including: Obtain the initial electrical characteristic parameters under surface gravity conditions; Obtain the extracellular space volume fraction and tissue structure index under gravity conditions that are less than or equal to the gravity threshold; The initial electrical characteristic parameters are corrected based on the extracellular space volume fraction and tissue structure index to obtain the low-frequency target electrical characteristic parameters.

[0053] The principle is that the headward shift of body fluids induced by the microgravity environment directly alters the extracellular volume fraction (α) and tissue structure index (m) of brain tissue. Based on the low-frequency equivalent medium theory of biological tissue conductivity, the macroscopic conductivity of tissue is a function of extracellular fluid conductivity, α, and m. The corrected target conductivity can be calculated by substituting the values ​​of α and m under low gravity into the theoretical model. This step ensures that the material properties upon which the electromagnetic simulation is based are consistent with the actual physiological state of the astronauts.

[0054] Specifically, the gravity threshold is 0.1 m / s². 2 The specific values ​​of extracellular space volume fraction α and tissue structure index m were obtained experimentally. During the experiment, an environment with an equivalent gravity less than the gravity threshold was constructed (e.g., an equivalent weightless environment of falling), and the extracellular space volume fraction and tissue structure index of the sample cells were measured.

[0055] The low-frequency target electrical characteristic parameters are calculated using the following formula:

[0056] in, For the target conductivity, α represents the cerebrospinal fluid conductivity in the surface environment, α represents the extracellular space volume fraction, and m represents the tissue structure index.

[0057] For example, the cerebrospinal fluid conductivity in a surface environment is set as It is approximately 1.7 S / m.

[0058] By introducing conductivity corrected based on microgravity physiological parameters (including extracellular space volume fraction and tissue structure index), this step enables the tissue electromagnetic properties of the simulation model to truly reflect the astronaut's physiological state in orbit, providing an accurate material property basis for subsequent high-fidelity electromagnetic field calculations and fundamentally overcoming the errors caused by fixed parameters.

[0059] In some implementations, the electrical properties also include dielectric constant and magnetic permeability. Brain tissue is a non-magnetic material, and its magnetic properties are not altered by the change from surface gravity to a microgravity environment. The dielectric constant is minimally affected by microgravity, and its impact on the calculation of the induced electric field intensity is negligible.

[0060] In quasi-static electromagnetic simulations, the dielectric constant typically has a relatively small impact on the calculation of the induced electric field. Incorporating it, along with permeability, into the same correction framework primarily ensures that all electromagnetic material properties are updated synchronously and coordinately as the state changes. This avoids the inherent physical inconsistencies that might arise from dynamically correcting some parameters (conductivity) while others remain static, thus guaranteeing the stability of the numerical solution process and the overall consistency of the simulation system.

[0061] S3: An electromagnetic simulation model is established based on a three-dimensional mesh model with low-frequency target electrical characteristic parameters and a transcranial magnetic stimulation coil model; the electromagnetic simulation model has model parameters, which are used to substitute different values ​​to simulate different transcranial magnetic stimulation processes; Specifically, the coil model includes: coil geometry, number of turns, conductor cross-section, and material.

[0062] Model parameters include spatial relationships, excitation parameters, boundary conditions, and solution domain settings.

[0063] The spatial relationships include: the position, orientation, and distance of the coil relative to the head mold; Excitation parameters include: pulse waveform (single-phase / dual-phase), peak current, frequency, or rise time parameter; Boundary conditions and solution domain settings include: air domain, insulating / conductor boundary, and mesh refinement region.

[0064] S3 specifically includes: Import the 3D mesh model of the head and the coil model into the electromagnetic simulation software; Assign microgravity-corrected conductivity (target conductivity) and other electromagnetic parameters (such as dielectric constant) to each tissue region (such as scalp, skull, gray matter, white matter, and cerebrospinal fluid). The coil material is set to copper (high conductivity), and the excitation current path is set; Define the current excitation in the coil model, and input a low-frequency pulse waveform and a current peak value; Configure the connection method for multi-turn coils; Select the "Electromagnetic Field" or "Quasi-Static Electromagnetic Field" module; Set the solution domain to a composite structure of "air + tissue + coil"; Set the outer boundary to "Insulation" or "Perfect Matching Layer (PML)"; the final electromagnetic simulation model is obtained.

[0065] The electromagnetic simulation model established in this embodiment couples the aforementioned anatomically reconstructed and parameterized three-dimensional head mesh with a physically realistic coil model within a unified mathematical-physical framework. Its core effect lies in the fact that, for the first time, microgravity-corrected tissue electrical property parameters are systematically integrated into the simulation system, constructing a high-fidelity computing platform that accurately reflects the interaction between electromagnetic fields and biological tissues under specific physiological conditions. This model not only fully characterizes the spatiotemporal characteristics of coil excitation and key physical details such as the electromagnetic continuity conditions of tissue boundaries, but also...

[0066] S4: Obtain the specific values ​​of the model parameters and the target nerve activation threshold; the model parameters are used to input the electromagnetic simulation model to simulate the transcranial magnetic stimulation process; the target nerve activation threshold is used to determine whether the transcranial magnetic stimulation is effective; Further, the activation threshold of the target nerve is obtained, including: A baseline neural activation threshold is obtained; the baseline neural activation threshold is used to determine whether transcranial magnetic stimulation is effective under the influence of gravity. Cell-level correction coefficients and network-level correction coefficients are obtained under microgravity conditions; the cell-level correction coefficients are used to characterize the effect of neuronal electrophysiological changes on the neural activation threshold; the network-level correction coefficients are used to characterize the effect of overall neural circuit excitability changes on the neural activation threshold. Based on the cell-level correction coefficient and the network-level correction coefficient, the baseline neural activation threshold is corrected to adapt to the microgravity environment, thereby obtaining the target neural activation threshold.

[0067] Specifically, the baseline neural activation threshold is used to determine whether transcranial magnetic stimulation is effective, and it is a known quantity.

[0068] The specific values ​​of the cell-level correction coefficient and the network-level correction coefficient were obtained from neuronal physiological experiments.

[0069] Cell-level correction coefficients include action potential threshold, membrane time constant, and excitability; network-level correction coefficients include changes in cortical microstimulation threshold and changes in population response threshold.

[0070] The target neural activation threshold is calculated using the following formula:

[0071] in, The initial neural activation threshold, The target neural activation threshold, This is a cell-level correction factor. This represents the network layer correction coefficient.

[0072] For example, ≈33.4V / m, ≈1.25, =1.5.

[0073] Microgravity directly reduces neuronal excitability (e.g., increases action potential threshold). Simulating this using fixed activation thresholds from Earth's normal operating conditions would severely overestimate the effectiveness of transcranial magnetic stimulation (TMS), leading to misjudgments of "effective stimulation." This invention introduces a dual-coefficient correction mechanism at both the cellular and network levels to quantitatively transform the microscopic electrophysiological changes and macroscopic network excitability changes induced by microgravity into a dynamic upregulation of the baseline threshold. Therefore, it can realistically reflect the more "sluggish" neural state of astronauts in orbit, ensuring that the prediction of the "effective stimulation region" possesses physiological authenticity from the root of the biological effects of stimulation.

[0074] S5: Based on the specific values ​​of the model parameters, the target neural activation threshold, and the electromagnetic simulation model, determine the tissue region that generates effective stimulation, including: The specific values ​​of the model parameters are input into the electromagnetic simulation model to simulate the transcranial magnetic stimulation process and obtain the induced electric field distribution in multiple tissue regions. Using the target neural activation threshold, the tissue region that generates effective stimulation is determined, including: Tissue regions where the induced electric field distribution is greater than or equal to the target nerve activation threshold are determined to have generated effective stimulation; otherwise, they are determined to have not generated effective stimulation.

[0075] Based on the target neural activation threshold corrected for microgravity environment, the distribution of induced electric field calculated by high-fidelity simulation is quantitatively discriminated, realizing reliable prediction of induced electric field distribution and effective stimulation area under microgravity conditions, providing key technical support for precise regulation of brain function in aerospace environment.

[0076] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention is not limited to the specific combination of the above-described technical features, but also includes other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A method for simulating transcranial magnetic stimulation under microgravity, characterized in that, include: S1: Construct a three-dimensional mesh model of the human head; the three-dimensional mesh model is divided into multiple tissue regions corresponding to human brain tissue, used to simulate the human brain under microgravity environment; S2: Assign low-frequency target electrical characteristic parameters to multiple tissue regions to simulate the electrical characteristics of tissue regions under microgravity conditions; The low-frequency target electrical characteristic parameters include at least the target conductivity of human brain tissue under microgravity conditions. S3: An electromagnetic simulation model is established based on a three-dimensional mesh model with low-frequency target electrical characteristic parameters and a transcranial magnetic stimulation coil model; the electromagnetic simulation model has model parameters, which are used to substitute different values ​​to simulate different transcranial magnetic stimulation processes; S4: Obtain the specific values ​​of the model parameters and the target nerve activation threshold; the model parameters are used to input the electromagnetic simulation model to simulate the transcranial magnetic stimulation process; the target nerve activation threshold is used to determine whether the transcranial magnetic stimulation is effective; S5: Based on the specific values ​​of the model parameters, the target neural activation threshold, and the electromagnetic simulation model, determine the tissue region that generates effective stimulation.

2. The method for simulating transcranial magnetic stimulation under microgravity according to claim 1, characterized in that, Constructing a 3D mesh model of the human head includes: Acquire medical imaging data of the human head; The medical image data is preprocessed and segmented sequentially to obtain an initial three-dimensional structural model; The initial three-dimensional structural model is subjected to surface reconstruction and meshing to obtain the three-dimensional mesh model.

3. The method for simulating transcranial magnetic stimulation under microgravity according to claim 2, characterized in that, The preprocessed medical image data is segmented to obtain a three-dimensional structural model, including: The preprocessed medical image data is subjected to edge detection to obtain multiple edge points of brain tissue. By fitting edge points of multiple brain tissues, multiple initial brain tissue interfaces are obtained; The preprocessed medical image data is divided into multiple initial tissue regions based on multiple initial brain tissue interfaces; the multiple initial tissue regions constitute the initial three-dimensional structural model.

4. The method for simulating transcranial magnetic stimulation under microgravity according to claim 3, characterized in that, The initial three-dimensional structural model is subjected to surface reconstruction and meshing to obtain the three-dimensional mesh model, including: Surface reconstruction of the initial three-dimensional structural model includes removing overlaps, filling holes, and smoothing the initial three-dimensional structural model to obtain a reconstructed three-dimensional model. The surface of the reconstructed 3D model is decomposed into discrete mesh structures at equal intervals to obtain a 3D mesh model; the 3D mesh model contains multiple tissue regions obtained by surface reconstruction and meshing of the initial tissue regions.

5. The method for simulating transcranial magnetic stimulation under microgravity according to claim 1, characterized in that, Obtain the low-frequency target electrical characteristic parameters, including: Obtain the initial electrical characteristic parameters under surface gravity conditions; Obtain the extracellular space volume fraction and tissue structure index under gravity conditions that are less than or equal to the gravity threshold; The initial electrical characteristic parameters are corrected based on the extracellular space volume fraction and tissue structure index to obtain the low-frequency target electrical characteristic parameters.

6. The method for simulating transcranial magnetic stimulation under microgravity according to claim 1, characterized in that, Obtain the target neural activation threshold, including: A baseline neural activation threshold is obtained; the baseline neural activation threshold is used to determine whether transcranial magnetic stimulation is effective under the influence of gravity. Cell-level correction coefficients and network-level correction coefficients are obtained under microgravity conditions; the cell-level correction coefficients are used to characterize the effect of neuronal electrophysiological changes on the neural activation threshold; the network-level correction coefficients are used to characterize the effect of overall neural circuit excitability changes on the neural activation threshold. Based on the cell-level correction coefficient and the network-level correction coefficient, the baseline neural activation threshold is corrected to adapt to the microgravity environment, thereby obtaining the target neural activation threshold.

7. The method for simulating transcranial magnetic stimulation under microgravity according to claim 1, characterized in that, Based on the specific values ​​of the model parameters, the target neural activation threshold, and the electromagnetic simulation model, the tissue region that generates effective stimulation is determined, including: The specific values ​​of the model parameters are input into the electromagnetic simulation model to simulate the transcranial magnetic stimulation process and obtain the induced electric field distribution in multiple tissue regions. The target neural activation threshold is used to determine the tissue region that generates effective stimulation.

8. A method for simulating transcranial magnetic stimulation under microgravity according to claim 7, characterized in that, Using the target neural activation threshold, the tissue region that generates effective stimulation is determined, including: Tissue regions where the induced electric field distribution is greater than or equal to the target nerve activation threshold are determined to have generated effective stimulation; otherwise, they are determined to have not generated effective stimulation.

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