Neural Dynamics Simulation Method and Regulation Method, Device, and Medium for Brain Diseases

Abstract

A neural dynamics simulation method and regulation method, device, and medium for brain diseases belong to the technical field of neural dynamics simulation and regulation of brain diseases. The simulation method includes: S1. Obtain multimodal image data of a brain disease patient; S2. Construct a human brain structural connection matrix SC for reflecting the brain's structural connection situation and a human brain functional connection matrix FC for reflecting the brain's functional activity situation; S3. Calculate a degree-coupled network based on the human brain structural connection matrix SC; S4. Establish a spatial autoregressive model based on the degree-coupled network; S5. Optimize the parameters of the degree-coupled network and the spatial autoregressive model to obtain optimal parameters. By integrating the topological features of the brain structure, extracting the dynamic and structural features that can understand the brain function, and establishing the connection between the dynamic brain function and the relatively static anatomical structure based on the spatial autoregressive signal propagation mechanism, the present application can effectively improve the accuracy of neural dynamics simulation of brain diseases.

Description

Technical Field

[0001] The present application relates to the technical field of neurodynamics simulation and regulation of brain diseases, and particularly relates to a neurodynamics simulation method, a regulation method, a device, and a medium for brain diseases. 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-state neural activities 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 unknown, its diagnosis and treatment are difficult to substantially improve the disease outcome. In addition, the selection of targets in surgical treatment also mostly depends on clinical experience, and the individual differences and heterogeneity of the disease seriously affect the success rate and risk degree of the operation. Modeling the neural information interaction between neurons, neural clusters or brain regions through neurodynamics provides an important method for reconstructing the pathological process of brain diseases. Simulating and regulating specific brain regions of individual brain disease patients based on the pathological reconstruction model is of great significance for clinical auxiliary diagnosis and treatment, preoperative guidance, surgical prediction, etc.

[0003] The current mainstream methods of brain neurodynamics models include the Spiking Neuron Model, the Dynamic Mean Filed, the Neural Mass Model, the Wilson-Cowan, the Kuramoto model, etc., but their applications in the neurodynamics reconstruction and regulation of brain diseases are few, and the influence of the topological characteristics of the brain anatomical structure on the functional signal simulation is not considered, resulting in poor simulation effects of the final neurodynamics signals. In addition, in order to establish a detailed and physiologically characteristic simulation model, the current neurodynamics models model neurons, neural clusters or brain regions, with many model 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 neurodynamics models in the existing technologies and effectively simulate and regulate the neurodynamics signals of brain diseases. Summary of the Invention

[0004] The present application aims to at least solve one of the technical problems in the related technologies to some extent. For this purpose, the present application provides a neurodynamics simulation method, a regulation method, a device, and a medium for brain diseases. By integrating the topological characteristics of the brain structure, extracting the dynamic and structural characteristics that can understand the brain function, and establishing the connection between the dynamic brain function and the relatively static anatomical structure based on the spatial autoregressive signal propagation mechanism, the accuracy of the neurodynamics simulation of brain diseases can be effectively improved.

[0005] To achieve the above object, in a first aspect, the present application provides a method for simulating the neurodynamics of brain diseases, including the following steps:

[0006] S1. Obtain multimodal imaging data of a brain disease patient, including T1 data, dMRI data, and fMRI data;

[0007] S2. Preprocess the multimodal imaging data to construct a human brain structural connection matrix SC for reflecting the brain's structural connection situation and a human brain functional connection matrix FC for reflecting the brain's 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;

[0008] 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;

[0009] S4. Establish a spatial autoregressive model based on the degree-coupling network to calculate the dynamic simulation BOLD signal of any brain region;

[0010] 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.

[0011] Preferably, the step of preprocessing the multimodal imaging data includes: partitioning the multimodal imaging data based on a brain partition template at the standard space macroscale to obtain multiple brain regions Nr.

[0012] Preferably, the step of calculating the 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.

[0013] ​Preferably, the steps of establishing a spatial autoregressive model based on the degree-coupled network include: In the spatial autoregressive model, the dynamic simulation BOLD signal of the i-th brain region is affected by the signals of all brain regions connected to it, that is: , where y i represents the dynamic simulation BOLD signal of the i-th brain region, k is the spatial autoregressive coefficient, representing the global signal transmission index. The larger k is, the greater the global coupling strength between brain regions. 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; the parameters k and σ are used to balance the signals from other brain regions v and the endogenous noise signal of this brain region

[0014] Preferably, the steps of optimizing the parameters of the degree-coupled network and the spatial autoregressive model include:

[0015] S501. Parameter initialization processing, initialize the local loss factor β and the spatial autoregressive coefficient k, 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;

[0016] S502. Based on the initialized parameters, calculate the degree-coupled network and the corresponding whole-brain dynamic simulation BOLD signal;

[0017] S503. Calculate the simulated functional connectivity strength between any two brain regions based on the Pearson correlation coefficient and the whole-brain dynamic simulation BOLD signal; and calculate the similarity between the simulated functional connectivity strength and the actual human brain functional connectivity matrix FC;

[0018] S504. Adjust the local loss factor β and the spatial autoregressive coefficient k, repeat steps S501 - S503 until the similarity between the simulated functional connectivity strength between any two brain regions calculated based on the dynamic simulation BOLD signal calculated under the corresponding parameters and the actual human brain functional connectivity matrix FC is the highest.

[0019] Preferably, the step S503 includes:

[0020] Calculate the simulated functional connectivity strength between any two brain regions based on the Pearson correlation coefficient and the whole-brain dynamic simulation BOLD signal;

[0021] Repeat steps S501 and S502 R times, and the finally obtained simulated functional connectivity strength is the average of the R simulated functional connectivity strengths;

[0022] Calculate the similarity between the finally obtained simulated functional connection strength and the actual human brain functional connection matrix FC.

[0023] In a second aspect, the present application provides a method for regulating brain disease neurodynamics, including:

[0024] S6. Establish a regulation model of the human brain structural connection matrix SC for simulating the impact of changes or resection of diseased brain regions on brain function; wherein, the regulation mechanism of the regulation model is achieved by changing the connection weights of the human brain structural connection matrix SC;

[0025] S7. Regulate the connection weights of the human brain structural connection matrix SC of an individual brain disease patient;

[0026] S8. Based on the regulated human brain structural connection matrix SC, calculate the corresponding degree-coupled network and simulated neurodynamics BOLD signal by combining the optimal parameters obtained by the brain disease neurodynamics simulation method described in any one of the above, and calculate the simulated functional connection strength between any two brain regions after regulation based on the Pearson correlation coefficient;

[0027] S9. Based on the simulated functional connection strength, extract network topological characteristics to reflect the regulated brain function; and calculate the feature difference E between the topological characteristics of the simulated functional network and the topological characteristics of the healthy subject's brain functional network, where the feature difference E is one or more of the small-world property difference, modularity property difference, global efficiency difference, and topological feature difference;

[0028] S10. Select the regulated brain region, adjust the regulation parameters of the regulation model, and repeat steps S7 - S9 until the feature difference E is reduced to a certain set value.

[0029] Preferably, the step of regulating the connection weights of the human brain structural connection matrix SC of an individual brain disease patient includes:

[0030] If the i-th brain region is regulated, then virtual perturbations are performed on the connection weights SC ij (j ∈ [1, Nr]) between the i-th brain region and other brain regions, the perturbation coefficient is set to f , and the offset is set to θ, then the regulated human brain structural connection matrix is expressed as (j ∈ [1, Nr]), where represents the regulated human brain structural connection matrix, and SC ij represents the connection weights between the i-th brain region and other brain regions.

[0031] In a third aspect, the present application provides an electronic device, including: 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.

[0032] In a fourth aspect, the present application provides a computer-readable storage medium, including a computer program, which, when running on an electronic device, causes the electronic device to execute the steps of the brain disease neurodynamics simulation method or the brain disease neurodynamics regulation method described in any one of the above.

[0033] Based on the above technical solutions, the brain disease neurodynamics simulation method, regulation method, device, and medium of the present application, compared with the prior art, at least have one of the following beneficial effects:

[0034] 1. By integrating the topological features of the brain structure, the brain disease neurodynamics simulation method of the present application extracts the dynamic and structural features that can understand the brain function, and based on the spatial autoregressive signal propagation mechanism, establishes the connection between the dynamic brain function and the relatively static anatomical structure, which can effectively improve the accuracy of brain disease neurodynamics simulation.

[0035] 2. The brain disease neurodynamics simulation method of the present application provides a simulation model based on the degree-coupled network and the spatial autoregressive model. This simulation method has few parameters and fast simulation speed, can effectively improve the accuracy of brain disease neurodynamics simulation, and makes up for the problems of the current neurodynamics model with more parameters, high computational complexity, poor reconstruction and simulation effects in brain disease neurodynamics, being unfavorable for brain disease pathology simulation and analysis, and being difficult to achieve disease simulation regulation and auxiliary diagnosis and treatment.

[0036] 3. The brain disease neurodynamics regulation method of the present application establishes a regulation strategy based on the simulation model, simulates the repair process of specific brain regions of an individual brain, and predicts through neural computing whether the repair of specific brain regions will improve the functional neurodynamics of brain diseases. 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 disease diagnosis and treatment, providing preoperative guidance, preoperative planning, preoperative prediction, etc. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is a flowchart of a brain disease neurodynamics simulation method provided in the first embodiment of the present application;

[0039] Figure 2 It is a flowchart of the optimization process in a brain disease neurodynamics simulation method of the present application;

[0040] Figure 3 It is a flowchart of a brain disease neurodynamics regulation method provided in the second embodiment of the present application;

[0041] Figure 4 It is a structural block diagram of a brain disease neurodynamics simulation and regulation device based on the degree-coupled spatial autoregressive model provided in the fifth embodiment of the present application. Detailed implementation manners

[0042] To make the objectives, technical solutions and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0043] The terms "first", "second", "third", "fourth", "fifth", "sixth", "seventh" and "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 do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here.

[0044] In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0045] The basic idea of the present application is to fuse the topological features of the brain structure, extract the dynamic features and structural connection features that can understand the brain function, and establish a connection between the dynamic brain function and the relatively static anatomical structure based on the spatial autoregressive signal propagation mechanism, thereby effectively improving the accuracy of brain disease neurodynamics simulation, effectively reconstructing and regulating the neurodynamics signals of brain diseases, and making up for the problems of existing neurodynamics models with more parameters, high computational complexity, being not conducive to brain disease pathology simulation and analysis, and being difficult to achieve disease simulation regulation and auxiliary diagnosis and treatment. Embodiments

[0046] Such as Figure 1As shown, in order to construct an accurate neurodynamics simulation model for brain diseases, the inventor of the present invention has conducted in-depth research on the structural connections of the brain and proposed a neurodynamics simulation method for brain diseases, including the following steps:

[0047] S1. Obtain multimodal imaging data of brain disease patients, including T1 data, dMRI data, and fMRI data;

[0048] S2. Preprocess the multimodal imaging data to construct a human brain structural connection matrix SC for reflecting the brain structural 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;

[0049] 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;

[0050] S4. Establish a spatial autoregressive model based on the degree-coupling network to calculate the dynamic simulation BOLD signal of any brain region;

[0051] 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.

[0052] Specifically, in step S1, diffusion magnetic resonance imaging (dMRI) is a special magnetic resonance imaging technique that can measure the diffusion motion of water molecules in biological tissues. In the brain, the diffusion direction of water molecules is closely related to the orientation of white matter fibers. Therefore, the trajectory of white matter fibers can be inferred from dMRI data. The acquisition of dMRI data requires setting appropriate parameters, such as the diffusion gradient direction (usually 64 or more directions), the 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.

[0053] Functional magnetic resonance imaging (fMRI) indirectly reflects neural activity by detecting changes in blood oxygenation levels in the brain. When neuronal activity in a certain area of the brain increases, local blood flow increases, leading to a change in the ratio of oxyhemoglobin to deoxyhemoglobin, 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 slices, etc., to ensure that the functional activity signals of the brain can be captured.

[0054] T1 data refers to magnetic resonance imaging (MRI) data obtained through T1-weighted imaging (T1WI). It is a commonly used sequence in MRI scans and is mainly used to obtain anatomical information of the brain. T1-weighted imaging mainly reflects the differences in the longitudinal relaxation time (T1 value) of tissues. In T1 images, different tissue types such as brain tissue (e.g., gray matter, white matter) and cerebrospinal fluid will show different signal intensities, where white matter usually has a stronger signal than gray matter, and cerebrospinal fluid has a weaker signal. T1 data is mainly used for:

[0055] 1. Visualization of brain anatomy: Provide detailed images of high-resolution brain structures such as gray matter, white matter, and ventricles.

[0056] 2. Brain tissue segmentation: Divide the brain 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.

[0057] 3. Spatial normalization: Register individual brain images to a standard space (such as the MNI template) for group analysis and comparison.

[0058] Preferably, the step of preprocessing the multi-modal image data includes: based on a brain partition template at the macro scale of the standard space, performing partition processing on the multi-modal image data to obtain multiple brain regions Nr, where Nr is the number of partitions. Usually, standard brain partition templates (such as the AAL template, Desikan-Killiany template, etc.) are used.

[0059] Among them, the human brain structural connection matrix SC represents the normalized fiber connection quantity between two brain regions i and j. The fiber connection quantity between brain regions can be obtained through fiber tracking of dMRI data. Fiber tracking is a computational method for estimating the orientation and connection of nerve fibers in the brain. The fiber tracking data can include the fiber connection quantity between two 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, and 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 fiber connection quantity, log normalization is adopted. If the human brain structural connection matrix SC is the fiber connection density or fiber connection probability, min-max normalization is adopted.

[0060] The steps for constructing the human brain structural connection matrix SC for reflecting the brain structural connection and the human brain functional connection matrix FC for reflecting the brain functional activity specifically include: Based on the brain partition template Template at the macroscopic scale of the standard space (MNI), the structural connection network SC = W ij (i, j ∈ [1, Nr]) and the human brain functional connection network FC = fc ij (i, j ∈ [1, Nr]) of the brain disease patients are obtained respectively, where Nr represents the number of segmented brain regions. The SC is the normalized fiber connection quantity between two brain regions i and j, that is where Count ij represents the fiber quantity connecting brain regions i and j. The FC is the Pearson correlation coefficient of the BOLD time series between two brain regions i and j.

[0061] Preferably, the steps for calculating the degree coupling network based on the human brain structural connection matrix SC include: If the normalized fiber connection quantity between two brain regions i and j is not zero, 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. 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. Since the obtained functional network is an undirected network, the coupling strength between i and j is defined as and The average, i.e.: . To reduce the influence of weak connections or even spurious connections on the degree-coupled network, a certain proportion of weak connections in the structural network are removed before calculating the degree-coupled network PRv . Only strong connections are used to calculate the node degree and degree-coupling strength, highlighting the role of strong connections. It is verified on multiple existing databases PRv and setting it to 20% gives relatively stable results.

[0062] Preferably, the steps of establishing a spatial autoregressive model based on the degree-coupled network include: In the spatial autoregressive model, the dynamic simulation BOLD signal of the i-th brain region is affected by the signals of all brain regions connected to it, i.e.: , where y i represents the dynamic simulation BOLD signal of the i-th brain region, k is the spatial autoregressive coefficient, representing the global signal transmission index. The larger k is, the greater the global coupling strength between brain regions. 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; the parameters k and σ are used to balance the signals from other brain regions v and the endogenous noise signal of this brain region

[0063] Furthermore, in this embodiment, to reduce the computational complexity and improve the efficiency of model simulation, the dynamic simulation BOLD signal Y is further assumed to be a multivariate normal matrix with a mean of 0 and a variance 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.

[0064] Thus, the method for simulating the neurodynamics of brain diseases provided in this embodiment extracts dynamic and structural features that can understand brain functions by integrating the topological features of the brain structure. Based on the spatial autoregressive signal propagation mechanism, it establishes the connection between the dynamic brain function and the relatively static anatomical structure, which can effectively improve the accuracy of simulating the neurodynamics of brain diseases.

[0065] As Figure 2 shown, preferably, the steps of optimizing the parameters of the degree-coupled network and the spatial autoregressive model include:

[0066] S501. Parameter initialization processing, initializing the local loss factor β and the spatial autoregressive coefficient k, setting the length of the total simulated BOLD time series to L, and randomizing the Gaussian white noise of each brain region with a mean of 0 and a variance of 1; among them, the selection of the length of the simulated BOLD time series is usually not less than 2000 to enable the system to reach a balanced state and reduce the influence of noise.

[0067] S502. Calculate the degree-coupled network and the corresponding BOLD signals of the whole-brain dynamics simulation based on the initialized parameters;

[0068] S503. Calculate the simulated functional connectivity strength between any two brain regions based on the Pearson correlation coefficient and the BOLD signals of the whole-brain dynamics simulation; and calculate the similarity between the simulated functional connectivity strength and the actual human brain functional connectivity matrix FC; wherein, the simulated functional connectivity strength between any two brain regions is expressed as , is the simulated functional connectivity strength, and the superscript iter = 1 represents the first iteration, where X and Y represent the dynamic simulated BOLD sequences from brain regions i and j, X t and Y t represent the numerical values of the simulated BOLD time series of brain regions i and j at time t, and are the average values within the time series of L;

[0069] Among them, calculate the simulated functional connectivity and 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 respectively represent the vectorized simulated functional connectivity matrix and the vectorized actual human brain functional connectivity matrix FC, num represents the element index value in the vectorized functional connectivity, and Lm represents the total number of elements in the vectorized functional connectivity matrix, and respectively represent the average values of the elements in the simulated functional connectivity and the actual functional connectivity. The larger the correlation coefficient r, the higher the similarity between the simulated functional connectivity and the actual human brain functional connectivity, and the better the simulation effect of the model on the neurodynamics of brain diseases.

[0070] S504. Adjust the local loss factor β and the spatial autoregressive coefficient k, and repeat steps S501 - S503 until the similarity between the simulated functional connectivity strength between any two brain regions calculated based on the BOLD signals of the dynamics simulation under the corresponding parameters and the actual human brain functional connectivity matrix FC is the highest. And obtain the optimal parameters (β opt , k opt ) and the optimal simulated functional connectivity FCs opt . To ensure the optimization result and improve the operation efficiency at the same time, the optimization algorithm used can be a neural network optimization algorithm, a genetic algorithm, a particle swarm optimization algorithm, etc.

[0071] Preferably, the step S503 includes:

[0072] Calculate the simulated functional connectivity strength between any two brain regions based on the Pearson correlation coefficient and the whole-brain dynamics simulation BOLD signal;

[0073] Repeat steps S501 and S502 for R times, and the finally obtained simulated functional connectivity strength is the average of the R simulated functional connectivity strengths;

[0074] Calculate the similarity between the finally obtained simulated functional connectivity strength and the actual human brain functional connectivity matrix FC.

[0075] The purpose of repeating steps S501 and S502 for R times is to reduce the influence of accidental factors and randomness. The generation of the simulated BOLD signal involves random Gaussian white noise. Each time of simulation, the specific implementation of the noise is different, which will lead to slight differences in the BOLD signal and functional connectivity strength of each simulation. By repeating the simulation R times and taking the average, these random fluctuations can be smoothed out, making the finally simulated functional connectivity closer to the real system behavior rather than the result under a specific random noise. And the result of a single simulation may be affected by accidental factors, while taking the average of multiple simulations can enhance the credibility of the result. In scientific research and engineering applications, it is usually hoped to verify the consistency and reliability of the result through multiple tests. In this application, by repeating the simulation R times, the stability of the simulated functional connectivity can be ensured, and the result deviation caused by accidental factors in a single simulation can be avoided.

[0076] This embodiment provides a simulation model based on the degree-coupled network and the spatial autoregressive model. This simulation method has few parameters and fast simulation speed, and can effectively improve the accuracy of brain disease neurodynamics simulation, making up for the problems of the current neurodynamics model with more parameters, high computational complexity, poor effect in brain disease neurodynamics reconstruction and simulation, being not conducive to brain disease pathology simulation and analysis, and being difficult to achieve disease simulation regulation and auxiliary diagnosis and treatment.

[0077] As Figure 3 shown, this embodiment provides a method for regulating brain disease neurodynamics, including:

[0078] S6. Establish a regulation model of the human brain structural connectivity matrix SC to simulate the impact of the change or resection of diseased brain regions on brain function; wherein, the regulation mechanism of the regulation model is realized by changing the connection weights of the human brain structural connectivity matrix SC;

[0079] S7. Regulate the connection weights of the human brain structural connectivity matrix SC of an individual brain disease patient;

[0080] S8. Based on the regulated human brain structural connection matrix SC, calculate the corresponding degree-coupling network and simulated neurodynamic BOLD signals according to the optimal parameters obtained by the neurodynamic simulation method of brain diseases described in any one of the above, and calculate the simulated functional connection strength between any two regulated brain regions based on the Pearson correlation coefficient;

[0081] S9. Based on the simulated functional connection strength, extract network topological characteristics to reflect the regulated brain function; and calculate the feature difference E between the topological characteristics of this simulated functional network and the topological characteristics of the healthy subject's brain functional network. The feature difference E is one or more of the small-world attribute difference, modularity attribute difference, global efficiency difference, and topological feature difference;

[0082] S10. Select the regulated brain region, adjust the regulation parameters of the regulation model, and repeat steps S7 - S9 until the feature difference E is reduced to a certain set value.

[0083] Among them, the topological characteristics of the healthy subject's brain functional network can be obtained in the following way:

[0084] Obtain the fMRI data of multiple healthy subjects, and perform data preprocessing to obtain the BOLD signals and human brain functional connection matrix FC of each healthy subject;

[0085] Extract the topological characteristics of the human brain functional connection matrix of healthy subjects to reflect brain function, including but not limited to small-world attributes, modularity attributes, global efficiency, shortest path length, clustering coefficient, centrality; among them, the topological characteristics of the human brain functional connection matrix are the average of the topological characteristics of all healthy subjects.

[0086] Preferably, the steps for regulating the connection weights of the human brain structural connection matrix SC of an individual brain disease patient include:

[0087] If regulating the i-th brain region, then virtually perturb the connection weights SC ij (j ∈ [1, Nr]) of the i-th brain region and other brain regions, set the perturbation coefficient to f , set the bias to θ, then the regulated human brain structural connection matrix is expressed as (j ∈ [1, Nr]), where, represents the regulated human brain structural connection matrix, and SC ij represents the connection weights between the i-th brain region and other brain regions. The regulation methods include but are not limited to the following:

[0088] a. If f > 1, θ > 0, then , simulate the repair of the damaged brain region i of the brain disease patient by enhancing the connection strength with other brain regions;

[0089] b. If f < 0 and θ = 0, then , the influence of brain region i on other brain regions is simulated to be weakened by reducing the connection strength with other brain regions;

[0090] c. If SC ij = 0 and θ > 0, then , then the reconstruction of the interrupted structural connection is simulated;

[0091] d. If f = 0 and θ = 0, then = 0 (j ϵ [1, Nr]), and the resection of brain region i is simulated by setting the connection strength between brain region i and all other brain regions to 0. The regulation of the structural connection can be carried out on a single brain region or on multiple brain regions simultaneously. The choice of the regulation method is based on the clinical prior knowledge of brain diseases or existing research results.

[0092] The regulation method of this embodiment establishes a regulation strategy based on the above simulation model, simulates the repair process of specific brain regions of an individual brain, and predicts whether the repair of specific brain regions will improve the functional neurodynamics of brain diseases through neural computation. 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, preoperative prediction, etc., especially for brain diseases with unclear exact etiology and pathogenesis and diseases with similar clinical manifestations, such as Parkinson's disease, Alzheimer's disease, schizophrenia, depression, etc.

[0093] An embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the program to implement the steps of any one of the above-mentioned brain disease neurodynamics simulation methods or brain disease neurodynamics regulation methods, and achieve the following functions: The brain disease neurodynamics simulation method extracts dynamic and structural features that can understand brain functions by integrating the topological features of the brain structure, and based on the spatial autoregressive signal propagation mechanism, establishes the connection between the dynamic brain function and the relatively static anatomical structure, which can effectively improve the accuracy of brain disease neurodynamics simulation. A simulation model based on the degree-coupling network and the spatial autoregressive model is provided. This simulation method has few parameters and a fast simulation speed, which can effectively improve the accuracy of brain disease neurodynamics simulation, and make up for the problems of the current neurodynamics model having more parameters, high computational complexity, poor reconstruction and simulation effects in brain disease neurodynamics, being not conducive to brain disease pathology simulation and analysis, and being difficult to achieve 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 individual brain, and predicts whether the repair of a specific brain region will improve the functional neurodynamics of the brain disease through neural computation. 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 disease diagnosis and treatment, providing preoperative guidance, preoperative planning, preoperative prediction, etc.

[0094] An embodiment of the present application provides a computer-readable storage medium, including a computer program. When the computer program runs on an electronic device, the electronic device is caused to execute the steps of any one of the above-mentioned brain disease neurodynamics simulation methods or brain disease neurodynamics regulation methods, and achieve the following functions: The brain disease neurodynamics simulation method extracts dynamic and structural features that can understand brain functions by integrating topological features of the brain structure. Based on the spatial autoregressive signal propagation mechanism, a connection between dynamic brain functions and relatively static anatomical structures is established, which can effectively improve the accuracy of brain disease neurodynamics simulation. A simulation model based on the degree-coupling network and the spatial autoregressive model is provided. This simulation method has fewer parameters and a fast simulation speed, which can effectively improve the accuracy of brain disease neurodynamics simulation, and make up for the problems of the current neurodynamics model having more parameters, high computational complexity, poor reconstruction and simulation effects in brain disease neurodynamics, being unfavorable for brain disease pathology simulation and analysis, and being difficult to achieve 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 specific brain regions of an individual brain, and predicts whether the repair of a specific brain region will improve the functional neurodynamics of brain diseases through neural computation. 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 disease diagnosis and treatment, providing preoperative guidance, preoperative planning, preoperative prediction, etc.

[0095] As Figure 4 shown, an embodiment of the present application provides a brain disease neurodynamics simulation and regulation device based on a degree-coupling spatial autoregressive model. The device includes:

[0096] A first acquisition module, configured to collect multi-modal image data of a brain disease patient, including T1 data, dMRI data, and fMRI data.

[0097] A first processing module, configured to preprocess the acquired T1 data, dMRI data, and fMRI data based on neuroimage processing software, and obtain fiber tracking data reflecting the connection relationship between brain anatomical regions and BOLD signals reflecting the dynamic functional changes of the brain.

[0098] A second processing module, configured to respectively obtain the structural connection network SC = W ij (i, j ϵ [1, Nr]) and the human brain functional connection network FC = fc ij (i, j ϵ [1, Nr]) of the brain disease patient based on the brain partition template Template at the macroscopic scale of the standard space (MNI), where Nr represents the number of segmented brain regions, and the SC is the normalized fiber connection number between two brain regions i and j, that is , where 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 pairwise brain regions i and j.

[0099] The third processing module is used to calculate the degree coupling network based on the structural connection network, describing the proportion of information transmitted from brain region i to brain region j. If w ij > 0, the coupling strength between i and j is: , otherwise . S j represents the number of brain regions j connected to brain region i, that is, 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 signal transmission from i to j, with a value range of 0 to 1. The smaller the parameter β , 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 average, that is: . To reduce the influence of weak connections or even spurious connections on the degree coupling network, a certain proportion PRv of weak connections in the structural network is removed before calculating the degree coupling network, and only strong connections are used to calculate the node degree and degree coupling strength, highlighting the role of strong connections. PRv is set to 20% (verified on multiple existing databases, PRv setting it to 20% has relatively stable effects).

[0100] The fourth processing module is used to establish a spatial autoregressive model based on the degree coupling 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 region i, k is the spatial autoregressive coefficient, representing the global signal transmission index. The larger k is, the greater the global coupling strength between brain regions. 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; the parameters k and σ are used to balance the signal from other brain regions v i.

[0101] Further, in this embodiment, to reduce the computational complexity and improve the efficiency of model simulation, the kinetic simulation BOLD signal Y is further assumed to be a multivariate normal matrix with a mean of 0 and a variance 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.

[0102] A simulation module for initializing the parameters (β, k) = (0.01, 0.01) and calculating the degree 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 kinetic BOLD signal of the simulated whole brain . The selection of the length of the simulated BOLD time series is usually not less than 2000 to enable the system to reach an equilibrium state and reduce the influence of noise.

[0103] A fifth processing module for calculating 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 simulated BOLD sequences from brain regions i and j, and X t and Y t represent the numerical values of the simulated BOLD time series of brain regions i and j at time t, and are the averages of the time series within L.

[0104] A first loop module for repeating steps S501 and S502 R times to obtain the final simulated functional connection, which is the average of the R simulated functional connections. And calculate the similarity r between the final simulated functional connection and the actual human brain functional connection FC. The similarity is characterized by the Pearson correlation coefficient, that is , where A and B represent the vectorized simulated functional connection matrix and the 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 represent the averages of the elements in the simulated functional connection and the actual functional connection respectively. The larger the correlation coefficient r, the higher the similarity between the simulated functional connection and the actual human brain functional connection, and the better the simulation effect of the model on the neurodynamics of brain diseases.

[0105] A second loop module: adjust the parameters (β, k), repeat steps S7 - S9 to obtain the simulated BOLD signal and simulated functional connection of brain disease patients until the similarity between the simulated functional connection and the actual functional connection reaches the optimum, and obtain the optimal parameters (β opt , k optand optimal simulation function connected to FCs opt . To ensure the optimization result while improving the operation efficiency, the optimization algorithm used can be a neural network optimization algorithm, a genetic algorithm, a particle swarm optimization algorithm, etc.

[0106] Regulation module: Establish a regulation model for the structural connection SC, and regulate the structural connection of individual brain disease patients based on the prior knowledge of brain diseases to simulate the impact of the change or resection of the diseased brain region on brain function. The regulation mechanism of the structural connection regulation model is realized by changing the connection weight of the structural connection. The evaluation of the brain function characteristics is quantitatively described through topological features such as separation and integration, including small-world property, modular property, global efficiency, shortest path length, clustering coefficient, centrality, etc.

[0107] The regulation module specifically includes:

[0108] The first acquisition sub-module: Specifically used to acquire the fMRI data of multiple healthy subjects from a pre-set human brain neuroimaging database, and perform data preprocessing to obtain the BOLD signal of each healthy subject and the brain functional connection network FC_con = fc_con ij (i, j ϵ [1, Nr]);

[0109] The second acquisition sub-module: 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 property, modular property, global efficiency, shortest path length, clustering coefficient, centrality, etc. The topological features of the brain functional network are the average of the topological features of all healthy subjects;

[0110] The first processing sub-module: Specifically used to regulate the structural connection of individual brain disease patients. The method used is to realize it by changing the connection weight of the structural connection, that is: if the i brain region is regulated, then the connection weight SC ij (j ϵ [1, Nr]) of the i brain region and other brain regions is virtually perturbed, the perturbation coefficient is set to f , the bias is set to θ, then the regulated human brain structural connection matrix is expressed as (j ϵ [1, Nr]), where represents the regulated human brain structural connection matrix, and SC ij represents the connection weight between the i brain region and other brain regions. The regulation methods include but are not limited to the following:

[0111] e. If f > 1, θ > 0, then , by enhancing the connection strength with other brain regions to simulate the repair of the damaged brain region i of the brain disease patient;

[0112] f. If f < 0 and θ = 0, then , the influence of brain region i on other brain regions is simulated to be weakened by reducing the connection strength with other brain regions;

[0113] g. If SC ij = 0 and θ > 0, then , then the reconstruction of the interrupted structural connection is simulated;

[0114] If f = 0 and θ = 0, then = 0 (j ∈ [1, Nr]), and the resection of brain region i is simulated by setting the connection strength between brain region i and all other brain regions to 0. The regulation of the structural connection can be the regulation of a single brain region or the simultaneous regulation of multiple brain regions. The choice of the regulation method is based on the clinical prior knowledge of brain diseases or the existing research results.

[0115] The second processing sub-module: Specifically, it is used to repeatedly calculate the degree-coupling network based on the regulated structural connection by the method of the third processing module, and re-obtain the regulated simulated neurodynamic BOLD signal through the BOLD time series simulation method described in the simulation module based on the optimal parameters (β opt , k opt ), and re-obtain the regulated simulated functional connection network based on the simulated functional connection calculation method described in the fifth processing module and the first loop module;

[0116] The third processing sub-module: Specifically, it is used to extract the network topological characteristics from the simulated functional connection network obtained by the second processing sub-module to reflect the regulated brain function, including but not limited to small-world property, modularity property, global efficiency, shortest path length, clustering coefficient, centrality, etc.; and calculate the difference E between the characteristics of this simulated functional network and the brain function characteristics of the healthy subjects obtained by the second acquisition sub-module. The characteristic difference E can be one or more of the topological characteristic differences such as small-world property difference, modularity property difference, global efficiency difference, etc.;

[0117] The fourth processing sub-module: Specifically, it is used to select the regulated brain region, adjust the parameter ( f， θ), and repeat the first, second, and third processing sub-modules until the difference E between the brain function network characteristics of the regulated brain disease patients and the brain function characteristics of the healthy subjects is reduced to a certain set value.

[0118] A method for simulating and regulating the neurodynamics of brain diseases based on a degree-coupled spatial autoregressive model provided by an embodiment of the present application. By integrating the topological features of the brain structure, it extracts the dynamic and structural features that can understand brain functions, and establishes the connection between the dynamic brain function and the relatively static anatomical structure 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 neurodynamics simulation of brain diseases, and make up for the problems of current neurodynamics models with more parameters, high computational complexity, poor reconstruction and simulation effects in the neurodynamics of brain diseases, being not conducive to the simulation and analysis of brain disease pathology, and being difficult to achieve 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 an individual brain structure, and predicts whether the repair of a specific brain region will improve the functional neurodynamics of brain diseases through the established simulation model. 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 early disease diagnosis and treatment, providing preoperative guidance, preoperative planning, preoperative prediction, etc., especially for brain diseases with unclear exact etiology and pathogenesis and diseases with similar clinical manifestations, such as Parkinson's disease, Alzheimer's disease, schizophrenia, depression, etc.

[0119] The method for regulating the neurodynamics of brain diseases provided by 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 individual 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 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 disease diagnosis and treatment, providing preoperative guidance, preoperative planning, preoperative prediction, etc.

[0120] It should also be noted that: when the above-mentioned method for simulating the neurodynamics of brain diseases and the method for regulating the neurodynamics of brain diseases perform the simulation of the neurodynamics of brain diseases or the regulation simulation, only the above-mentioned division of each functional module is used as an example for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the embodiments of the method for simulating the neurodynamics of brain diseases and the method for regulating the neurodynamics of brain diseases provided by the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiments, which will not be elaborated here.

[0121] The serial numbers of the above embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0122] Those of ordinary skill in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware or by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, or the like.

[0123] The above describes specific embodiments of the embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0124] In the description of the embodiments of the present invention, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection 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 expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can 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.

[0125] Any process or method description in a flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the embodiments of the present invention includes additional implementations, where the functions can be performed in a manner that is not shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0126] The above embodiments are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within 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 coupling 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; Among them, 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, where L is the length of the total simulated BOLD time series.

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 Perform virtual perturbation, and set the perturbation coefficient 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. A computer device comprising a memory and a processor, wherein the memory is used to store a computer program, wherein: The processor is used to execute the computer program to implement the steps of the brain disease neurodynamic simulation method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program runs on a computer or a processor, the computer or the processor executes the steps of the brain disease neurodynamic simulation method according to any one of claims 1 to 6.

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