Brain disease neurodynamics simulation method, regulation and control method, equipment and medium

Abstract

The invention discloses a brain disease neurodynamics simulation method, a brain disease neurodynamics regulation and control method, equipment and a medium, and belongs to the technical field of brain disease neurodynamics simulation and regulation and control. The simulation method comprises the following steps: S1, acquiring multi-modal image data of a brain disease patient; s2, constructing a human brain structure connection matrix SC for reflecting a brain structure connection condition and a human brain function connection matrix FC for reflecting a brain function activity condition; s3, calculating a degree coupling network based on the human brain structure connection matrix SC; s4, establishing a spatial autoregression model based on the degree coupling network; and S5, performing optimization processing on parameters of the degree coupling network and the spatial autoregression model to obtain optimal parameters. According to the method, the topological features of the brain structure are fused, the dynamic features and the structural features capable of understanding the brain function are extracted, the relation between the dynamic brain function and the relatively static anatomical structure is established based on the spatial autoregression signal propagation mechanism, and the accuracy of brain disease neurodynamics simulation can be effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of brain disease neurodynamics simulation and regulation, and in particular to a brain disease neurodynamics simulation method and regulation method, equipment, and medium. Background Art

[0002] The brain is a very complex system. In recent years, researchers have found that in many brain diseases, the anatomical structure and resting neural activity of the brain will change, such as Alzheimer's disease, Parkinson's disease, schizophrenia, depression, autism, etc. Because its etiology and influencing factors are not very clear, and the pathogenesis is still unclear, its diagnosis and treatment are difficult to substantially improve the outcome of the disease. In addition, the selection of targets in surgical treatment also depends on clinical experience. The individual differences and heterogeneity of the disease seriously affect the success rate and risk level of the operation. The simulation of neural information interaction between neurons, neural clusters or brain regions through neurodynamic modeling provides an important method for the reconstruction of the pathological process of brain diseases. The simulation and regulation of specific brain areas of individualized brain disease patients based on pathological reconstruction models are of great significance for clinical auxiliary diagnosis and treatment, preoperative guidance, and surgical prediction.

[0003] The current mainstream brain neurodynamic model methods include the spiking neuron model, the dynamic mean field model, the neural mass model, the Wilson-Cowan model, the Kuramoto model, etc. However, they are rarely used in the reconstruction and regulation of neurodynamics in brain diseases, and the influence of the topological characteristics of the brain's anatomical structure on the simulation of functional signals is not considered, resulting in poor simulation of the final neurodynamic signal. In addition, in order to establish a detailed simulation model that conforms to physiological characteristics, the current neurodynamic model models neurons, neural clusters or brain regions. The model has many parameters and high computational complexity, which is not conducive to the pathological analysis and reconstruction of brain diseases, and it is difficult to achieve disease simulation regulation and auxiliary diagnosis and treatment. Therefore, it is urgent to improve the neurodynamic model in the existing technology and effectively simulate and regulate the neurodynamic signals of brain diseases. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the present application provides a method for simulating brain disease neurodynamics and a control method, device, and medium. The present application extracts dynamic features and structural features that can understand brain functions by integrating the topological features of brain structures, and establishes a connection between dynamic brain functions and relatively static anatomical structures based on the spatial autoregressive signal propagation mechanism, which can effectively improve the accuracy of brain disease neurodynamic simulation.

[0005] To achieve the above object, in a first aspect, the present application provides a method for simulating neurodynamics of brain diseases, including the following steps: S1. Obtain multi-modal imaging data of a brain disease patient, including T1 data, dMRI data, and fMRI data; S2. Preprocess the multi-modal imaging data to construct a human brain structural connection matrix SC for reflecting the brain structure connection situation and a human brain functional connection matrix FC for reflecting the brain functional activity situation. The human brain structural connection matrix SC represents the normalized fiber connection quantity between two brain regions i and j, and the human brain functional connection matrix FC is the Pearson correlation coefficient of the BOLD time series between two brain regions; S3. Calculate a degree coupling network based on the human brain structural connection matrix SC to describe the proportion of information transmitted from brain region i to brain region j; S4. Establish a spatial autoregressive model based on the degree coupling network to calculate the dynamic simulation BOLD signal of any brain region; S5. Optimize the parameters of the degree coupling network and the spatial autoregressive model to obtain optimal parameters. Among them, the similarity between the simulated functional connection strength between any two brain regions calculated based on the Pearson correlation coefficient and the dynamic simulation BOLD signal calculated under the optimal parameters and the actual human brain functional connection matrix FC is the highest.

[0006] Preferably, the step of preprocessing the multi-modal imaging data includes: partitioning the multi-modal imaging data based on a brain partition template at a standard space macroscopic scale to obtain a plurality of brain regions Nr.

[0007] Preferably, the step of calculating a degree coupling network based on the human brain structural connection matrix SC includes: if the normalized fiber connection quantity between the two brain regions i and j is not zero, then the coupling strength between i and j is: , otherwise, , represents the coupling strength between i and j, S j represents the node degree of brain region j connected to brain region i; represents the sum of the node degrees of all nodes connected to brain region i, and the parameter β describes the local loss factor of signal transmission from i to j, and its value range is from 0 to 1. The smaller the parameter β , the smaller the signal transmission loss in the network.

[0008] Preferably, the step of establishing a spatial autoregressive model based on the degree coupling network includes: in the spatial autoregressive model, the dynamic simulation BOLD signal of brain region i is affected by the signals of all brain regions connected to it, that is: , where y i represents the dynamic simulation BOLD signal of brain area i, k is the spatial autoregressive coefficient, which represents the global signal transmission index. The larger the k, the greater the global coupling strength between brain areas. The selection of k is between 0 and 1; V=( v i) represents uncorrelated Gaussian white noise with a mean of 0 and a variance of 1; σ is the noise level; parameters k and σ are used to balance signals from other brain regions and the endogenous noise signal of the brain region v i.

[0009] Preferably, the step of optimizing the parameters of the degree-coupled network and the spatial autoregressive model comprises: S501, parameter initialization processing, local loss factor β Initialize with spatial autoregressive coefficient k, set the length of the total simulated BOLD time series to L, and randomize Gaussian white noise in each brain region with mean 0 and variance 1; S502, calculating the degree-coupled network and the corresponding whole-brain dynamics simulation BOLD signal based on the initialized parameters; S503, calculating the simulated functional connection strength between any two brain regions based on the Pearson correlation coefficient and the whole-brain dynamics simulated BOLD signal; and calculating the similarity between the simulated functional connection strength and the actual human brain functional connection matrix FC; S504, local loss factor β and the spatial autoregression coefficient k are adjusted, and steps S501-S503 are repeated until the simulated functional connection strength between any two brain regions calculated based on the dynamic simulation BOLD signal calculated under the corresponding parameters has the highest similarity with the actual human brain functional connection matrix FC.

[0010] Preferably, the step S503 includes: Calculate the simulated functional connection strength between any two brain regions based on the Pearson correlation coefficient and the whole-brain dynamics simulated BOLD signal; Repeat steps S501 and S502 R times, and the final simulated functional connection strength obtained is the average value of the R simulated functional connection strengths; Calculate the similarity between the final simulated functional connection strength and the actual human brain functional connection matrix FC.

[0011] In a second aspect, the present application provides a method for regulating neurodynamics of brain diseases, comprising: S6. Establishing a control model of the human brain structural connection matrix SC to simulate the changes in diseased brain regions or the effects of resection on brain function; wherein the control mechanism of the control model is achieved by changing the connection weights of the human brain structural connection matrix SC; S7, regulating the connection weights of the human brain structural connection matrix SC of individual brain disease patients; S8. Based on the regulated human brain structural connection matrix SC, the corresponding degree coupling network and simulated neurodynamic BOLD signal are calculated in combination with the optimal parameters obtained by any of the above-mentioned brain disease neurodynamic simulation methods, and the simulated functional connection strength between any two brain regions after regulation is calculated based on the Pearson correlation coefficient; S9. Based on the simulated functional connection strength, extract network topological characteristics to reflect the brain function after regulation; and calculate the characteristic difference E between the simulated functional network topological characteristics and the healthy subject's brain functional network topological characteristics, where the characteristic difference E is one or more of the small-world attribute difference, modular attribute difference, global efficiency difference and topological feature difference; S10, selecting the brain area to be regulated, adjusting the regulation parameters of the regulation model, and repeating steps S7-S9 until the characteristic difference E is reduced to a certain set value.

[0012] Preferably, the step of regulating the connection weights of the human brain structural connection matrix SC of the individualized brain disease patient comprises: If brain region i is regulated, the connection weight SC between brain region i and other brain regions ij (jϵ[1, Nr]) performs a virtual perturbation, and the perturbation coefficient is set to f , the bias is set to θ, then the regulated human brain structure connection matrix is ​​expressed as (jϵ[1, Nr]), where Represents the structural connection matrix of the human brain after regulation, SC ij Represents the connection weight between brain region i and other brain regions.

[0013] In a third aspect, the present application provides an electronic device comprising: 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 steps of the brain disease neurodynamic simulation method or the brain disease neurodynamic regulation method as described in any one of the above items.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, comprising a computer program, which, when executed on an electronic device, enables the electronic device to execute the steps of any one of the above-mentioned methods for simulating brain disease neurodynamics or regulating brain disease neurodynamics.

[0015] Based on the above technical solutions, it can be seen that the brain disease neurodynamics simulation method and control method, device, and medium of the present application have at least one of the following beneficial effects compared with the prior art: 1. The brain disease neurodynamic simulation method of the present application integrates the topological features of the brain structure, extracts the dynamic features and structural features that can understand the brain function, and establishes the connection between the dynamic brain function and the relatively static anatomical structure based on the spatial autoregressive signal propagation mechanism, which can effectively improve the accuracy of the brain disease neurodynamic simulation.

[0016] 2. The brain disease neurodynamics simulation method of the present application provides a simulation model based on a degree-coupled network and a spatial autoregressive model. The simulation method has few parameters and fast simulation speed, and can effectively improve the accuracy of brain disease neurodynamics simulation, make up for the current neurodynamics model with many parameters, high computational complexity, poor reconstruction and simulation of brain disease neurodynamics, and is not conducive to brain disease pathology simulation and analysis, and difficult to achieve disease simulation regulation and auxiliary diagnosis and treatment.

[0017] 3. The neurodynamic regulation method for brain diseases in this application establishes a regulation strategy based on a simulation model, simulates the repair process of specific brain regions of an individualized brain, and predicts whether the repair of specific brain regions will improve the functional neurodynamics of brain diseases through neural calculations. This simulation and regulation model can be applied to the reconstruction and analysis of the pathological process of brain diseases, and is of great significance for assisting early diagnosis and treatment of diseases, providing preoperative guidance, preoperative planning, and preoperative prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 is a flow chart of a method for simulating neurodynamics of brain diseases provided in Example 1 of the present application; Figure 2 This is a flowchart of optimization processing in a brain disease neurodynamics simulation method of the present application; Figure 3 This is a flow chart of a method for regulating neurodynamics of brain diseases provided in Example 2 of the present application; Figure 4 It is a structural block diagram of a brain disease neurodynamics simulation and control device based on a degree-coupled spatial autoregressive model provided in Example 5 of the present application. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0021] The terms "first", "second", "third", "fourth", "fifth", "sixth", "seventh", "eighth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0022] In addition, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus.

[0023] The basic idea of ​​this application is to extract the dynamic characteristics and structural connection characteristics that can understand brain functions by integrating the topological features of the brain structure, and to establish the connection between dynamic brain functions and relatively static anatomical structures based on the spatial autoregressive signal propagation mechanism, thereby effectively improving the accuracy of neurodynamic simulation of brain diseases, effectively reconstructing and regulating the neurodynamic signals of brain diseases, and making up for the problems of existing neurodynamic models with many parameters, high computational complexity, being unfavorable for pathological simulation and analysis of brain diseases, and being difficult to achieve disease simulation regulation and auxiliary diagnosis and treatment. Example

[0024] like Figure 1 As shown, in order to construct an accurate neurodynamic simulation model of brain diseases, the inventors conducted in-depth research on brain structural connections and proposed a neurodynamic simulation method for brain diseases, including the following steps: S1. Obtain multimodal imaging data of patients with brain diseases, including T1 data, dMRI data, and fMRI data; S2. Preprocess the multimodal imaging data to construct a human brain structural connection matrix SC for reflecting brain structural connectivity and a human brain functional connection matrix FC for reflecting brain functional activity, wherein the human brain structural connection matrix SC represents the normalized fiber connection number between two brain regions i and j, and the human brain functional connection matrix FC is the Pearson correlation coefficient of the BOLD time series between two brain regions; S3, calculating a degree coupling network based on the human brain structural connection matrix SC to describe the proportion of information transmitted from brain region i to brain region j; S4, establishing a spatial autoregressive model based on the degree-coupled network to calculate the dynamic simulation BOLD signal of any brain region; S5. Optimizing the parameters of the degree-coupled network and the spatial autoregressive model to obtain optimal parameters, wherein the simulated functional connection strength between any two brain regions calculated based on the Pearson correlation coefficient and the dynamic simulation BOLD signal calculated under the optimal parameters has the highest similarity to the actual human brain functional connection matrix FC.

[0025] Specifically, in step S1, diffusion magnetic resonance imaging (dMRI) is a special magnetic resonance imaging technology that can measure the diffusion movement of water molecules in biological tissues. In the brain, the diffusion direction of water molecules is closely related to the direction of white matter fibers, so the trajectory of white matter fibers can be inferred from dMRI data. dMRI data acquisition requires setting appropriate parameters, such as diffusion gradient direction (usually 64 or more directions), diffusion sensitivity coefficient (b value, usually between 1000-3000 s / mm²), etc., to ensure that the diffusion information of water molecules can be accurately captured.

[0026] Functional magnetic resonance imaging (fMRI) indirectly reflects neural activity by detecting changes in blood oxygen levels in the brain. When neuronal activity in a certain area of ​​the brain increases, local blood flow increases, causing the ratio of oxygenated hemoglobin to deoxygenated hemoglobin to change, which can be detected by fMRI. fMRI data acquisition requires setting appropriate parameters, such as repetition time (TR, usually between 2-3 seconds), echo time (TE), number of scanning layers, etc., to ensure that the functional activity signals of the brain can be captured.

[0027] T1 data refers to magnetic resonance imaging (MRI) data obtained through T1-weighted imaging (T1WI). It is a commonly used sequence in MRI scans, mainly used to obtain anatomical information of the brain. T1-weighted imaging mainly reflects the difference in longitudinal relaxation time (T1 value) of tissues. In T1 images, different tissue types such as brain tissue (such as gray matter, white matter) and cerebrospinal fluid will show different signal intensities, among which white matter usually has stronger signals than gray matter and cerebrospinal fluid has weaker signals. T1 data is mainly used for: 1. Visualization of brain anatomical structures: Provides high-resolution detailed images of brain gray matter, white matter, ventricles, and other structures.

[0028] 2. Brain tissue segmentation: The brain is divided into different tissue types (such as gray matter, white matter, cerebrospinal fluid) and regions of interest (ROI) through image segmentation algorithms, providing a basis for subsequent functional and structural analysis.

[0029] 3. Spatial normalization: aligning individual brain images to a standard space (such as the MNI template) to facilitate group analysis and comparison.

[0030] Preferably, the step of preprocessing the multimodal image data includes: based on a standard spatial macro-scale brain partition template, partitioning the multimodal image data to obtain a plurality of brain regions Nr, where Nr is the number of partitions. A standard brain partition template (such as an AAL template, a Desikan-Killiany template, etc.) is usually used.

[0031] Among them, the human brain structural connection matrix SC represents the normalized number of fiber connections between brain regions i and j. The number of fiber connections between brain regions can be obtained by fiber tracking using dMRI data. Fiber tracking is a computational method used to estimate the direction and connection of nerve fibers in the brain. Fiber tracking data may include the number of fiber connections between brain regions, and / or fiber connection density, and / or fiber connection probability. The network weights of the human brain structural connection matrix SC are normalized. The normalization method is determined according to the weights of the human brain structural connection matrix SC. If the human brain structural connection matrix SC is the number of fiber connections, log normalization is used. If the human brain structural connection matrix SC is the fiber connection density or fiber connection probability, maximum and minimum value normalization is used.

[0032] The steps of constructing a human brain structural connection matrix SC for reflecting brain structural connectivity and a human brain functional connection matrix FC for reflecting brain functional activity specifically include: based on the standard space (MNI) macroscopic scale brain partition template Template, obtaining the structural connection network SC=[ W ij ](i, jϵ[1, Nr])and human brain functional connectivity network FC=[ fc ij ](i, jϵ[1, Nr]), where Nr represents the number of brain regions after segmentation, and SC is the number of normalized fiber connections between two brain regions i and j, that is, ,in Count ij represents the number of fibers connecting brain regions i and j. The FC is the Pearson correlation coefficient of the BOLD time series between brain regions i and j.

[0033] Preferably, the step of calculating the degree of coupling network based on the human brain structural connection matrix SC includes: if the number of normalized fiber connections between the two brain regions i and j is not zero, then the coupling strength between i and j is: ,otherwise, , represents the coupling strength between i and j,S j represents the node degree of brain region j connected to brain region i; represents the sum of the node degrees of all nodes connected to brain region i, and the parameter β Describes the local loss factor of the signal transmitted from i to j, ranging from 0 to 1, and the parameter β The smaller the value, the smaller the signal transmission loss in the network. Since the obtained functional network is an undirected network, the coupling strength between i and j is defined as and The average of In order to reduce the impact of weak connections or even false connections on the degree-coupled network, a certain proportion of the structural network is removed before calculating the degree-coupled network. PR Only strong connections are used to calculate node degrees and degree coupling strength, highlighting the role of strong connections. This method is verified on multiple existing databases. PR Setting it to 20% will give a more stable effect.

[0034] Preferably, the step of establishing a spatial autoregressive model based on the degree-coupled network includes: in the spatial autoregressive model, the dynamic simulation BOLD signal of brain area i is affected by the signals of all brain areas connected to it, that is: , where y i represents the dynamic simulation BOLD signal of brain area i, k is the spatial autoregressive coefficient, which represents the global signal transmission index. The larger the k, the greater the global coupling strength between brain areas. The selection of k is between 0 and 1; V=( v i) represents uncorrelated Gaussian white noise with a mean of 0 and a variance of 1; σ is the noise level, which can be set to 1; parameters k and σ are used to balance signals from other brain regions and the endogenous noise signal of the brain region v i.

[0035] Furthermore, in this embodiment, in order to reduce the computational complexity and improve the efficiency of model simulation, the dynamic simulation BOLD signal Y is further assumed to have a mean of 0 and a variance of The multivariate normal matrix of , where I represents the identity matrix, “T” is the transpose of the matrix, C is the degree coupling matrix, and σ is the noise level, which can be set to 1.

[0036] Therefore, the brain disease neurodynamic simulation method provided in this embodiment integrates the topological features of the brain structure, extracts the dynamic features and structural features that can understand the brain function, and establishes the connection between dynamic brain function and relatively static anatomical structure based on the spatial autoregressive signal propagation mechanism, which can effectively improve the accuracy of brain disease neurodynamic simulation.

[0037] like Figure 2As shown, preferably, the step of optimizing the parameters of the degree coupling network and the spatial autoregressive model includes: S501, parameter initialization processing, local loss factor β The spatial autoregressive coefficient k is initialized, the length of the total simulated BOLD time series is set to L, and the Gaussian white noise of each brain region is randomized with a mean of 0 and a variance of 1. The length of the simulated BOLD time series is usually not less than 2000, so that the system can reach a balanced state and reduce the impact of noise.

[0038] S502, calculating the degree-coupled network and the corresponding whole-brain dynamics simulation BOLD signal based on the initialized parameters; S503, calculating the simulated functional connection strength between any two brain regions based on the Pearson correlation coefficient and the whole-brain dynamics simulation BOLD signal; and calculating the similarity between the simulated functional connection strength and the actual human brain functional connection matrix FC; wherein the simulated functional connection strength between any two brain regions is expressed as , To simulate the functional connection strength, the superscript iter=1 indicates the first iteration, where X and Y represent the dynamic simulated BOLD sequences from brain areas i and j, and X t and Y t represents the simulated BOLD time series values ​​of brain regions i and j at time t, and is the average value of the time series within L; Among them, the calculation simulation function connection The similarity r with the actual human brain functional connectivity FC. The similarity is characterized by the Pearson correlation coefficient, that is, , where A and B represent the vectorized simulation function connection matrix and vectorized actual human brain functional connection matrix FC, num represents the element index value in the vectorized functional connection, Lm represents the total number of elements in the vectorized functional connection matrix, and They represent the average values ​​of the elements in the simulated functional connections and the actual functional connections, respectively. The larger the correlation coefficient r, the higher the similarity between the simulated functional connections and the actual human brain functional connections, and the better the simulation effect of the model on the neural dynamics of brain diseases.

[0039] S504, local loss factor β and spatial autoregression coefficient k, and repeat steps S501-S503 until the simulated functional connection strength between any two brain regions calculated based on the dynamic simulation BOLD signal calculated under the corresponding parameters is the highest similarity to the actual human brain functional connection matrix FC. And obtain the optimal parameter (β opt , kopt ) and optimal simulation function connection FCs opt In order to ensure the optimization results and improve the operating efficiency, the optimization algorithm used can be a neural network optimization algorithm, a genetic algorithm, a particle swarm optimization algorithm, etc.

[0040] Preferably, the step S503 includes: Calculate the simulated functional connection strength between any two brain regions based on the Pearson correlation coefficient and the whole-brain dynamics simulated BOLD signal; Repeat steps S501 and S502 R times, and the final simulated functional connection strength obtained is the average value of the R simulated functional connection strengths; Calculate the similarity between the final simulated functional connection strength and the actual human brain functional connection matrix FC.

[0041] The purpose of repeating steps S501 and S502 R times is to reduce the influence of accidental factors and randomness. The generation of simulated BOLD signals involves random Gaussian white noise. The specific implementation of noise is different each time the simulation is performed, which will cause slight differences in the BOLD signal and functional connection strength of each simulation. By repeating the simulation R times and averaging, these random fluctuations can be smoothed out, so that the final simulated functional connection is closer to the actual system behavior, rather than the result under a specific random noise. And the results of a single simulation may be affected by accidental factors, and averaging multiple simulations can enhance the credibility of the results. In scientific research and engineering applications, it is usually hoped to verify the consistency and reliability of the results through multiple experiments. In the present application, by repeating the simulation R times, the stability of the simulated functional connection can be ensured, and the deviation of the results due to accidental factors in a single simulation can be avoided.

[0042] This embodiment provides a simulation model based on a degree-coupled network and a spatial autoregressive model. The simulation method has few parameters and a fast simulation speed, and can effectively improve the accuracy of the simulation of neurodynamics of brain diseases, make up for the problems of the current neurodynamic model having many parameters, high computational complexity, poor reconstruction and simulation of neurodynamics of brain diseases, being unfavorable for the simulation and analysis of brain disease pathology, and being difficult to achieve disease simulation regulation and auxiliary diagnosis and treatment.

[0043] like Figure 3 As shown, this embodiment provides a method for regulating neurodynamics of brain diseases, comprising: S6. Establishing a control model of the human brain structural connection matrix SC to simulate the changes in diseased brain regions or the effects of resection on brain function; wherein the control mechanism of the control model is achieved by changing the connection weights of the human brain structural connection matrix SC; S7, regulating the connection weights of the human brain structural connection matrix SC of individual brain disease patients; S8. Based on the regulated human brain structural connection matrix SC, the corresponding degree coupling network and simulated neurodynamic BOLD signal are calculated in combination with the optimal parameters obtained by any of the above-mentioned brain disease neurodynamic simulation methods, and the simulated functional connection strength between any two brain regions after regulation is calculated based on the Pearson correlation coefficient; S9. Based on the simulated functional connection strength, extract network topological characteristics to reflect the brain function after regulation; and calculate the characteristic difference E between the simulated functional network topological characteristics and the healthy subject's brain functional network topological characteristics, where the characteristic difference E is one or more of the small-world attribute difference, modular attribute difference, global efficiency difference and topological feature difference; S10, selecting the brain area to be regulated, adjusting the regulation parameters of the regulation model, and repeating steps S7-S9 until the characteristic difference E is reduced to a certain set value.

[0044] Among them, the acquisition of brain functional network topology characteristics of healthy subjects can be carried out in the following ways: Obtain fMRI data from multiple healthy subjects, and perform data preprocessing to obtain the BOLD signal and human brain functional connectivity matrix FC of each healthy subject; The topological characteristics of the human brain functional connection matrix of healthy subjects are extracted to reflect brain function, including but not limited to small-world properties, modular properties, global efficiency, shortest path length, clustering coefficient, and centrality; among them, the topological characteristics of the human brain functional connection matrix are the average values ​​of the topological characteristics of all healthy subjects.

[0045] Preferably, the step of regulating the connection weights of the human brain structural connection matrix SC of the individualized brain disease patient comprises: If brain region i is regulated, the connection weight SC between brain region i and other brain regions ij (jϵ[1, Nr]) performs a virtual perturbation, and the perturbation coefficient is set to f , the bias is set to θ, then the regulated human brain structure connection matrix is ​​expressed as (jϵ[1, Nr]), where Represents the structural connection matrix of the human brain after regulation, SC ij Represents the connection weight between brain region i and other brain regions. The regulation methods include but are not limited to the following: a. If f>1, θ>0, then , by enhancing the strength of connections with other brain regions, simulating the repair of damaged brain regions i in patients with brain diseases; b. If f<0, θ=0, then , by reducing the strength of connections with other brain regions, it simulates weakening the influence of brain region i on other brain regions; c. If SC ij=0,θ>0, then , then the reconstruction of the interrupted structural connection is simulated; d. If f=0, θ=0, then =0(jϵ[1, Nr]), simulating the removal of brain region i by setting the connection strength of brain region i to all other brain regions to 0. The regulation of structural connectivity can be regulated on a single brain region or on multiple brain regions at the same time. The choice of regulation method is based on clinical prior knowledge of brain diseases or existing research results.

[0046] The control method of this embodiment establishes a control strategy based on the above-mentioned simulation model, simulates the repair process of a specific brain region of an individualized brain, and predicts whether the repair of a specific brain region will improve the functional neurodynamics of brain diseases through neural calculation. This simulation and control model can be applied to the reconstruction and analysis of the pathological process of brain diseases, and is of great significance for assisting early diagnosis and treatment of diseases, providing preoperative guidance, preoperative planning, and preoperative prediction, especially for brain diseases whose exact causes and pathogenesis are not yet clear, and diseases with similar clinical manifestations, such as Parkinson's disease, Alzheimer's disease, schizophrenia, depression, etc.

[0047] The embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the brain disease neurodynamics simulation method or the brain disease neurodynamics regulation method described in any one of the above items, and implements the following functions: The brain disease neurodynamics simulation method extracts the dynamic features and structural features that can understand the brain function by integrating the topological features of the brain structure, and establishes the connection between the dynamic brain function and the relatively static anatomical structure based on the spatial autoregressive signal propagation mechanism, which can effectively improve the accuracy of the brain disease neurodynamics simulation. Provide a simulation model based on a degree-coupled network and a spatial autoregressive model, the simulation method has few parameters and a fast simulation speed, which can effectively improve the accuracy of the brain disease neurodynamics simulation, and make up for the current neurodynamics model with many parameters, high computational complexity, poor reconstruction and simulation effect of brain disease neurodynamics, and the problem of being unfavorable for brain disease pathological simulation and analysis, and the problem of difficulty in realizing disease simulation regulation and auxiliary diagnosis and treatment. The brain disease neurodynamics regulation method establishes a regulation strategy based on the simulation model, simulates the repair process of a specific brain region of an individualized brain, and predicts whether the repair of a specific brain region will improve the functional neurodynamics of the brain disease through neural calculation. This simulation and control model can be applied to the reconstruction and analysis of the pathological process of brain diseases, and is of great significance in assisting the early diagnosis and treatment of diseases, providing preoperative guidance, preoperative planning, and preoperative prediction.

[0048] The embodiment of the present application provides a computer-readable storage medium, including a computer program. When the computer program is run on an electronic device, the electronic device executes the steps of the brain disease neurodynamic simulation method or the brain disease neurodynamic regulation method described in any one of the above items, and realizes the following functions: The brain disease neurodynamic simulation method extracts the dynamic characteristics and structural characteristics that can understand the brain function by integrating the topological characteristics of the brain structure, and establishes the connection between the dynamic brain function and the relatively static anatomical structure based on the spatial autoregressive signal propagation mechanism, which can effectively improve the accuracy of brain disease neurodynamic simulation. Provide a simulation model based on a degree-coupled network and a spatial autoregressive model. The simulation method has few parameters and fast simulation speed, which can effectively improve the accuracy of brain disease neurodynamic simulation, make up for the current neurodynamic model with many parameters, high computational complexity, poor reconstruction and simulation effect of brain disease neurodynamics, and is not conducive to the simulation and analysis of brain disease pathology, and it is difficult to achieve disease simulation regulation and auxiliary diagnosis and treatment. The brain disease neurodynamic regulation method establishes a regulation strategy based on the simulation model, simulates the repair process of a specific brain area of ​​an individualized brain, and predicts whether the repair of a specific brain area will improve the functional neurodynamics of brain diseases through neural calculation. This simulation and control model can be applied to the reconstruction and analysis of the pathological process of brain diseases, and is of great significance in assisting the early diagnosis and treatment of diseases, providing preoperative guidance, preoperative planning, and preoperative prediction.

[0049] like Figure 4 As shown, the embodiment of the present application provides a brain disease neurodynamics simulation and control device based on a degree-coupled spatial autoregressive model, the device comprising: The first acquisition module is used to collect multimodal imaging data of patients with brain diseases, including T1 data, dMRI data and fMRI data.

[0050] The first processing module is used to pre-process the acquired T1 data, dMRI data and fMRI data based on the neuroimaging processing software, and obtain the fiber tracking data reflecting the connection relationship between the anatomical regions of the brain and the BOLD signal reflecting the dynamic functional changes of the brain.

[0051] The second processing module is used to obtain the structural connection network SC=[ W ij ](i, jϵ[1, Nr])and human brain functional connectivity network FC=[ fc ij ](i, jϵ[1, Nr]), where Nr represents the number of brain regions after segmentation, and SC is the number of normalized fiber connections between two brain regions i and j, that is, ,in Countij represents the number of fibers connecting brain regions i and j. The FC is the Pearson correlation coefficient of the BOLD time series between brain regions i and j.

[0052] The third processing module is used to calculate the degree coupling network based on the structural connection network to describe the proportion of information transmitted from brain area i to brain area j. w ij >0, then the coupling strength between i and j is: ,otherwise . S j represents the number of brain regions j connected to brain region i, i.e., the node degree; represents the sum of the degrees of all nodes connected to brain region i, and the parameter β Describes the local loss factor of the signal transmitted from i to j, ranging from 0 to 1, and the parameter β The smaller the value, the smaller the signal transmission loss in the network. Since the obtained functional network is an undirected network, the coupling strength between i and j is defined as and The average of In order to reduce the impact of weak connections or even false connections on the degree-coupled network, a certain proportion of the structural network is removed before calculating the degree-coupled network. PR Only strong connections are used to calculate node degrees and degree coupling strength, highlighting the role of strong connections. PR Set to 20% (verified on multiple existing databases, PR Setting it to 20% will give a more stable effect).

[0053] The fourth processing module is used to establish a spatial autoregressive model based on the degree-coupled network and calculate the dynamic simulation BOLD signal Y=(y i ). In the spatial autoregressive model, the dynamic signal of brain region i is affected by the signals of all brain regions connected to it, that is: , where y i represents the dynamic simulation BOLD signal of brain area i, k is the spatial autoregressive coefficient, which represents the global signal transmission index. The larger the k, the greater the global coupling strength between brain areas. The selection of k is between 0 and 1; V=( v i) represents uncorrelated Gaussian white noise with a mean of 0 and a variance of 1; σ is the noise level, which can be set to 1; parameters k and σ are used to balance signals from other brain regions and the endogenous noise signal of the brain region v i.

[0054] Furthermore, in this embodiment, in order to reduce the computational complexity and improve the efficiency of model simulation, the dynamic simulation BOLD signal Y is further assumed to have a mean of 0 and a variance of The multivariate normal matrix of , where I represents the identity matrix, “T” is the transpose of the matrix, C is the degree coupling matrix, and σ is the noise level, which can be set to 1.

[0055] Simulation module, used to initialize parameters (β, k) = (0.01, 0.01) and calculate the degree of coupling network Set the length of the total simulated BOLD time series to L, randomize the Gaussian white noise of each brain region, with a mean of 0 and a variance of 1, and calculate the dynamic BOLD signal of the simulated whole brain The length of the simulated BOLD time series is usually selected to be no less than 2000, so that the system can reach a balanced state and reduce the influence of noise.

[0056] The fifth processing module is used to calculate the simulated functional connection strength between any two brain regions based on the Pearson correlation coefficient and the simulated BOLD signal , where X and Y represent the dynamic simulation BOLD sequences from brain regions i and j, X t and Y t represents the simulated BOLD time series values ​​of brain regions i and j at time t, and is the average value of the time series within L.

[0057] The first loop module is used to repeat steps S501 and S502 R times to obtain the final simulated functional connectivity, which is the average of the R simulated functional connectivity. The similarity r between the final simulated functional connectivity and the actual human brain functional connectivity FC is calculated. The similarity is characterized by the Pearson correlation coefficient, that is, , where A and B represent the vectorized simulation function connection matrix and vectorized actual human brain functional connection matrix FC, num represents the element index value in the vectorized functional connection, Lm represents the total number of elements in the vectorized functional connection matrix, and They represent the average values ​​of the elements in the simulated functional connections and the actual functional connections, respectively. The larger the correlation coefficient r, the higher the similarity between the simulated functional connections and the actual human brain functional connections, and the better the simulation effect of the model on the neural dynamics of brain diseases.

[0058] Second loop module: adjust the parameters (β, k), repeat steps S7-S9 to obtain the simulated BOLD signal and simulated functional connectivity of patients with brain diseases, until the similarity between the simulated functional connectivity and the actual functional connectivity is optimal, and obtain the optimal parameters (β opt , k opt) and optimal simulation function connection FCs opt In order to ensure the optimization results and improve the operating efficiency, the optimization algorithm used can be a neural network optimization algorithm, a genetic algorithm, a particle swarm optimization algorithm, etc.

[0059] Regulation module: Establish a regulation model of structural connection SC, regulate the structural connection of individualized brain disease patients based on prior knowledge of brain diseases, so as to simulate the effect of changes or resection of diseased brain areas on brain function. The regulation mechanism of the structural connection regulation model is achieved by changing the connection weights of structural connections. The evaluation of brain functional characteristics is quantitatively described by topological features such as separation and integration, including small-world properties, modular properties, global efficiency, shortest path length, clustering coefficient, centrality, etc.

[0060] The control module specifically includes: The first acquisition submodule is specifically used to obtain fMRI data of multiple healthy subjects from a pre-set human brain neuroimaging database, and perform data preprocessing to obtain the BOLD signal and brain functional connection network of each healthy subject FC_con=[ fc_con ij ](i, jϵ[1,Nr]); The second acquisition submodule is specifically used to extract the topological characteristics of the brain functional network of healthy subjects to reflect brain function, including but not limited to small-world properties, modular properties, global efficiency, shortest path length, clustering coefficient, centrality, etc. The topological characteristics of the brain functional network are the average values ​​of the topological characteristics of all healthy subjects; The first processing submodule is specifically used to regulate the structural connections of individual brain disease patients by changing the connection weights of the structural connections, that is, if the i brain area is regulated, the connection weights SC between the i brain area and other brain areas are changed. ij (jϵ[1, Nr]) performs a virtual perturbation, and the perturbation coefficient is set to f , the bias is set to θ, then the regulated human brain structure connection matrix is ​​expressed as (jϵ[1, Nr]), where Represents the structural connection matrix of the human brain after regulation, SC ij Represents the connection weight between brain region i and other brain regions. The regulation methods include but are not limited to the following: e. If f>1, θ>0, then , by enhancing the strength of connections with other brain regions, simulating the repair of damaged brain regions i in patients with brain diseases; f. If f<0, θ=0, then , by reducing the strength of connections with other brain regions, it simulates weakening the influence of brain region i on other brain regions; g. If SCij =0,θ>0, then , then the reconstruction of the interrupted structural connection is simulated; If f=0,θ=0, then =0(jϵ[1, Nr]), simulating the removal of brain region i by setting the connection strength of brain region i to all other brain regions to 0. The regulation of structural connectivity can be regulated on a single brain region or on multiple brain regions at the same time. The choice of regulation method is based on clinical prior knowledge of brain diseases or existing research results.

[0061] The second processing submodule is specifically used to repeatedly calculate the degree coupling network based on the structure connection after regulation through the method described in the third processing module, and based on the optimal parameter (β opt , k opt ) reacquiring the regulated simulated neural dynamics BOLD signal through the BOLD time series simulation method described in the simulation module, and reacquiring the regulated simulated functional connection network based on the simulation functional connection calculation method described in the fifth processing module and the first circulation module; The third processing submodule is specifically used to extract network topological characteristics based on the simulated functional connection network obtained by the second processing submodule to reflect the brain function after regulation, including but not limited to small-world properties, modular properties, global efficiency, shortest path length, clustering coefficient, centrality, etc.; and calculate the difference E between the simulated functional network characteristics and the brain function characteristics of healthy subjects obtained by the second acquisition submodule. The characteristic difference E can be one or more of the topological characteristic differences such as small-world property difference, modular property difference, global efficiency difference, etc.; The fourth processing submodule is specifically used to select the brain area to be regulated and adjust the parameters ( f， θ), repeat the first, second, and third processing submodules until the difference E between the brain functional network characteristics of patients with brain diseases and the brain functional characteristics of healthy subjects after regulation is reduced to a certain set value.

[0062] The embodiment of the present application provides a method for simulating and regulating neurodynamics of brain diseases based on a degree-coupled spatial autoregressive model. By fusing the topological features of the brain structure, the dynamic features and structural features that can be used to understand brain functions are extracted, and the connection between dynamic brain functions and relatively static anatomical structures is established based on the spatial autoregressive signal propagation mechanism. The simulation model has few parameters and fast simulation speed, which can effectively improve the accuracy of neurodynamic simulation of brain diseases, make up for the problems of the current neurodynamic model with many parameters, high computational complexity, poor reconstruction and simulation of neurodynamics of brain diseases, and disadvantages in pathological simulation and analysis of brain diseases, as well as difficulty in achieving disease simulation regulation and auxiliary diagnosis and treatment. At the same time, the embodiment of the present application establishes a regulation strategy to simulate the repair process of individualized brain structures, and uses the established simulation model to perform neural calculations to predict whether repair of specific brain areas will improve the functional neurodynamics of brain diseases. The embodiments of the present invention can be applied to the reconstruction and analysis of the pathological process of brain diseases, and are of great significance for assisting the early diagnosis and treatment of diseases, providing preoperative guidance, preoperative planning, preoperative prediction, etc., especially for brain diseases whose exact causes and pathogenesis are not yet very clear, and diseases with similar clinical manifestations, such as Parkinson's disease, Alzheimer's disease, schizophrenia, depression, etc.

[0063] The method for regulating brain disease neurodynamics provided in this embodiment of the present application establishes a regulation strategy based on a simulation model, simulates the repair process of a specific brain region of an individualized brain, and predicts through neural calculation whether the repair of a specific brain region will improve the functional neurodynamics of brain disease. This simulation and regulation model can be applied to the reconstruction and analysis of the pathological process of brain disease, and is of great significance for assisting early diagnosis and treatment of diseases, providing preoperative guidance, preoperative planning, preoperative prediction, etc.

[0064] It should also be noted that: the brain disease neurodynamics simulation method and brain disease neurodynamics control method provided in the above embodiments are only illustrated by the division of the above functional modules when performing brain disease neurodynamics simulation or control simulation. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the brain disease neurodynamics simulation method and the brain disease neurodynamics control method provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0065] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0066] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0067] The above describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] In the description of the embodiments of the present invention, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of the present invention. In the embodiments of the present invention, the schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in the embodiments of the present invention and the features of the different embodiments or examples, without contradiction.

[0069] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred implementation of the embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0070] The above embodiments are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for simulating brain disease neurodynamics, characterized in that: The steps include: S1. Obtain multimodal imaging data of patients with brain diseases, including T1 data, dMRI data, and fMRI data; S2. Preprocess the multimodal imaging data to construct a human brain structural connection matrix SC for reflecting brain structural connectivity and a human brain functional connection matrix FC for reflecting brain functional activity, wherein the human brain structural connection matrix SC represents the normalized fiber connection number between two brain regions i and j, and the human brain functional connection matrix FC is the Pearson correlation coefficient of the BOLD time series between two brain regions; S3, calculating a degree coupling network based on the human brain structural connection matrix SC to describe the proportion of information transmitted from brain region i to brain region j; S4, establishing a spatial autoregressive model based on the degree-coupled network to calculate the dynamic simulation BOLD signal of any brain region; S5. Optimizing the parameters of the degree-coupled network and the spatial autoregressive model to obtain optimal parameters, wherein the simulated functional connection strength between any two brain regions calculated based on the Pearson correlation coefficient and the dynamic simulation BOLD signal calculated under the optimal parameters has the highest similarity to the actual human brain functional connection matrix FC.

2. The brain disease neurodynamics simulation method according to claim 1, characterized in that: The step of preprocessing the multimodal image data includes: based on a standard spatial macro-scale brain partition template, partitioning the multimodal image data to obtain a plurality of brain regions Nr.

3. The brain disease neurodynamics simulation method according to claim 2, characterized in that: The step of calculating the degree coupling network based on the human brain structural connection matrix SC includes: if the number of normalized fiber connections between the two brain regions i and j is not zero, then the coupling strength between i and j is: ,otherwise, , represents the coupling strength between i and j, S j represents the node degree of brain region j connected to brain region i; represents the sum of the node degrees of all nodes connected to brain region i, and the parameter β Describes the local loss factor of the signal transmitted from i to j, ranging from 0 to 1, and the parameter β The smaller it is, the less signal loss there is in the network.

4. The brain disease neurodynamics simulation method according to claim 3, characterized in that: The step of establishing a spatial autoregressive model based on the degree-coupled network includes: in the spatial autoregressive model, the dynamic simulation BOLD signal of brain area i is affected by the signals of all brain areas connected to it, that is: , where y i represents the dynamic simulation BOLD signal of brain area i, k is the spatial autoregressive coefficient, which represents the global signal transmission index. The larger the k, the greater the global coupling strength between brain areas. The selection of k is between 0 and 1; V=( v i) represents uncorrelated Gaussian white noise with a mean of 0 and a variance of 1; σ is the noise level; parameters k and σ are used to balance signals from other brain regions and the endogenous noise signal of the brain region v i.

5. The brain disease neurodynamics simulation method according to claim 4, characterized in that: The step of optimizing the parameters of the degree-coupled network and the spatial autoregressive model comprises: S501, parameter initialization processing, local loss factor β Initialize with spatial autoregressive coefficient k, set the length of the total simulated BOLD time series to L, and randomize Gaussian white noise in each brain region with mean 0 and variance 1; S502, calculating the degree-coupled network and the corresponding whole-brain dynamics simulation BOLD signal based on the initialized parameters; S503, calculating the simulated functional connection strength between any two brain regions based on the Pearson correlation coefficient and the whole-brain dynamics simulated BOLD signal; and calculating the similarity between the simulated functional connection strength and the actual human brain functional connection matrix FC; S504, local loss factor β and the spatial autoregression coefficient k are adjusted, and steps S501-S503 are repeated until the simulated functional connection strength between any two brain regions calculated based on the dynamic simulation BOLD signal calculated under the corresponding parameters has the highest similarity with the actual human brain functional connection matrix FC.

6. The brain disease neurodynamics simulation method according to claim 5, characterized in that: The step S503 includes: Calculate the simulated functional connection strength between any two brain regions based on the Pearson correlation coefficient and the whole-brain dynamics simulated BOLD signal; Repeat steps S501 and S502 R times, and the final simulated functional connection strength obtained is the average value of the R simulated functional connection strengths; Calculate the similarity between the final simulated functional connection strength and the actual human brain functional connection matrix FC.

7. A method for regulating neurodynamics of brain diseases, characterized in that: include: S6. Establishing a control model of the human brain structural connection matrix SC to simulate the changes in diseased brain regions or the effects of resection on brain function; wherein the control mechanism of the control model is achieved by changing the connection weights of the human brain structural connection matrix SC; S7, regulating the connection weights of the human brain structural connection matrix SC of individual brain disease patients; S8. Based on the regulated human brain structural connection matrix SC, the corresponding degree coupling network and simulated neurodynamic BOLD signal are calculated in combination with the optimal parameters obtained by the brain disease neurodynamic simulation method according to any one of claims 1 to 6, and the simulated functional connection strength between any two brain regions after regulation is calculated based on the Pearson correlation coefficient; S9. Based on the simulated functional connection strength, extract network topological characteristics to reflect the brain function after regulation; and calculate the characteristic difference E between the simulated functional network topological characteristics and the healthy subject's brain functional network topological characteristics, where the characteristic difference E is one or more of the small-world attribute difference, modular attribute difference, global efficiency difference and topological feature difference; S10, selecting the brain area to be regulated, adjusting the regulation parameters of the regulation model, and repeating steps S7-S9 until the characteristic difference E is reduced to a certain set value.

8. The method for regulating brain disease neurodynamics according to claim 7, characterized in that: The steps of regulating the connection weights of the human brain structural connection matrix SC of an individualized brain disease patient include: If brain region i is regulated, the connection weight SC between brain region i and other brain regions ij (jϵ[1, Nr]) performs a virtual perturbation, and the perturbation coefficient is set to f , the bias is set to θ, then the regulated human brain structure connection matrix is ​​expressed as (jϵ[1, Nr]), where Represents the structural connection matrix of the human brain after regulation, SC ij Represents the connection weight between brain region i and other brain regions.

9. An electronic device comprising 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 steps of the brain disease neurodynamics simulation method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: It comprises a computer program, which, when running on an electronic device, enables the electronic device to execute the steps of the brain disease neurodynamics simulation method according to any one of claims 1 to 6.

Images