Electrode parameter determination method for brain deep electrical stimulation and related equipment

Abstract

The invention provides a brain deep electrical stimulation electrode parameter determination method and related equipment, and the method comprises the steps: building a head simulation model which comprises conductivity distribution; determining one or more target stimulation target spots and one or more non-target inhibition areas on the head simulation model; based on one or more target stimulation target spots and one or more non-target inhibition areas, multiple groups of electrode configuration schemes are determined through an electrode positioning method, each group of electrode configuration scheme is configured on the head simulation model, and multiple groups of stimulation simulation models are obtained; performing finite element solution based on the anatomical structure and the conductivity distribution of each group of stimulation simulation models, and optimizing the electric field intensity of one or more target stimulation target spots and the electric field intensity of one or more non-target suppression regions to obtain a plurality of electrode parameters; and determining a target electrode parameter in the plurality of electrode parameters, and taking an electrode configuration scheme corresponding to the target electrode parameter as a target electrode configuration scheme.

Description

Technical Field

[0001] This application relates to the technical field of electrode parameter determination for deep brain stimulation, and in particular to a method and related equipment for determining electrode parameters for deep brain stimulation. Background Technology

[0002] Deep brain stimulation (DBS), an important branch of neuromodulation, has demonstrated significant efficacy in treating neurological disorders such as Parkinson's disease, depression, and epilepsy. Traditional DBS techniques are broadly classified into invasive and non-invasive methods. Invasive DBS involves surgically implanting electrodes, which can precisely stimulate target brain regions, but comes with surgical risks, the possibility of infection, and high costs. Non-invasive techniques, such as transcranial direct current (DC) stimulation and transcranial alternating current (AC) stimulation, avoid surgical risks, but suffer from insufficient stimulation depth, low spatial resolution, and susceptibility to attenuation from the scalp and skull. Summary of the Invention

[0003] In view of this, this application provides a method and related equipment for determining electrode parameters for deep brain electrical stimulation, which can solve the problems of insufficient stimulation depth, low spatial resolution, and susceptibility to attenuation by the scalp and skull, while achieving synergistic stimulation of multiple target areas and active inhibition of specific areas.

[0004] In a first aspect, embodiments of this application provide a method for determining electrode parameters for deep brain electrical stimulation, the method comprising: A head simulation model is established, which includes the conductivity distribution. One or more target stimulation sites and one or more non-target inhibition regions are identified on the head simulation model; Based on the one or more target stimulation points and the one or more non-target inhibition areas, multiple sets of electrode configuration schemes are determined by electrode positioning method. Each set of electrode configuration schemes is configured on the head simulation model to obtain multiple sets of stimulation simulation models. The multiple sets of stimulation simulation models correspond one-to-one with the multiple sets of electrode configuration schemes. Finite element method is performed based on the anatomical structure and conductivity distribution of each stimulation simulation model to optimize the electric field intensity of the one or more target stimulation points and the electric field intensity of the one or more non-target inhibition regions, resulting in multiple electrode parameters, which correspond one-to-one with the multiple electrode configuration schemes. Among the plurality of electrode parameters, a target electrode parameter is determined, and the electrode configuration scheme corresponding to the target electrode parameter is taken as the target electrode configuration scheme.

[0005] Therefore, in the embodiments of this application, by constructing a head simulation model that includes conductivity distribution and using tissue conductivity as the core input for finite element analysis, the attenuation characteristics of different tissues such as the scalp and skull can be accurately compensated, thereby improving the stimulation depth and the accuracy of electric field penetration. Compared with traditional static optimization methods that rely solely on geometric structures, this application incorporates electrophysiological parameters into the computational framework, significantly improving spatial resolution and the accuracy of electric field distribution prediction. Subsequently, through the synergistic mechanism of multi-target synchronous optimization and active inhibition of non-target areas, precise joint regulation of multiple brain regions is achieved, effectively reducing the risk of non-target area activation while ensuring the stimulation intensity of the target areas, thus meeting the needs of individualized anatomical differences and precise clinical treatment.

[0006] In conjunction with the first aspect, in one possible implementation, establishing the head simulation model includes: Based on structural magnetic resonance imaging data, the scalp structure, skull structure, cerebrospinal fluid structure, gray matter structure, and white matter structure were constructed to obtain an initial head simulation model; Based on diffusion tensor imaging data, white matter fiber bundles are constructed in the initial head simulation model to determine the conductivity value of each structure among the scalp structure, the skull structure, the cerebrospinal fluid structure, the gray matter structure, and the white matter structure, thereby obtaining the head simulation model containing the conductivity distribution.

[0007] In conjunction with the first aspect, in one possible implementation, the electric field optimization in the finite element solution satisfies formula ①: ………① Where F is the optimized value, Ntarget is the number of target stimulation points, and Etotal(T) is the total number of target stimulation points. i Let be the electric field strength norm of the i-th target stimulus point, and let Navoid be the number of non-target inhibition regions. Etotal(A) j ) represents the electric field intensity norm of the j-th non-target suppression region, P is the penalty term, and α, β, and γ are weighting coefficients.

[0008] In conjunction with the first aspect, in one possible implementation, the electric field strength norm Etotal(T) i ) and Etotal(A j This is related to the conductivity distribution.

[0009] In conjunction with the first aspect, in one possible implementation, the finite element method is used to solve for the electric field intensity of the one or more target stimulation points and the electric field intensity of the one or more non-target inhibition regions based on the anatomical structure and conductivity distribution of each stimulation simulation model, thereby optimizing multiple electrode parameters, including: During the finite element method solution process, the electrode parameters at which the optimized value F is maximized are output for each set of stimulus simulation models, thus obtaining the multiple electrode parameters.

[0010] In conjunction with the first aspect, in one possible implementation, the method further includes: Based on the target electrode parameters and the target electrode configuration scheme, deep electrical stimulation of the brain is performed to obtain feedback signals from functional near-infrared spectroscopy or electroencephalography. Treatment indicators are determined based on the feedback signals, and these indicators are used to identify the treatment effect and / or the degree of side effects. The target electrode parameters are optimized based on the therapeutic indicators.

[0011] In conjunction with the first aspect, in one possible implementation, the electrode positioning method includes a 10-10 system.

[0012] Secondly, embodiments of this application provide an electrode parameter determination device for deep brain electrical stimulation, the device comprising: The modeling module is for establishing a head simulation model, which includes conductivity distribution. The simulation module is used to determine one or more target stimulation points and one or more non-target inhibition areas on the head simulation model. Based on the one or more target stimulation points and the one or more non-target inhibition areas, multiple sets of electrode configuration schemes are determined by the electrode positioning method. Each set of electrode configuration schemes is configured on the head simulation model to obtain multiple sets of stimulation simulation models. The multiple sets of stimulation simulation models correspond one-to-one with the multiple sets of electrode configuration schemes. The analysis module is used to perform finite element analysis based on the anatomical structure and conductivity distribution of each set of stimulation simulation models, optimize the electric field intensity of the one or more target stimulation points and the electric field intensity of the one or more non-target inhibition regions, and obtain multiple electrode parameters, which correspond one-to-one with the multiple sets of electrode configuration schemes. The determination module determines the target electrode parameter from the plurality of electrode parameters and uses the electrode configuration scheme corresponding to the target electrode parameter as the target electrode configuration scheme.

[0013] Thirdly, embodiments of this application provide an electronic device including a memory and a processor. The memory stores a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the steps of the method described in the first aspect above.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the method of the first aspect of embodiments of this application.

[0015] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the method of the first aspect of embodiments of this application. The computer program product may be a software installation package.

[0016] The beneficial effects of the technical solutions in the second to fifth aspects can be found in the technical effects of the technical solution in the first aspect, and will not be repeated here. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A schematic diagram of a stimulation system provided for an embodiment of this application; Figure 2 A flowchart illustrating a method for determining electrode parameters for deep brain electrical stimulation provided in this application embodiment; Figure 3 A functional unit block diagram of an electrode parameter determination device for deep brain electrical stimulation provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of an electronic device provided for an embodiment of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0020] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0021] In the embodiments of this application, "at least one item" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. "One or more" refers to one or more items, while "multiple" refers to two or more items. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0022] In the embodiments of this application, "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. The embodiments of this application do not impose any limitations on this. In the description of this application, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "connection" should be interpreted broadly. In one example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a connection that allows mutual communication; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal communication of two components or the interaction between two components.

[0023] In this document, the term "implementation" means that a specific feature, structure, or characteristic described in connection with an implementation may be included in at least one implementation of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same implementation, nor is it a separate or alternative implementation mutually exclusive with other implementations. It will be explicitly and implicitly understood by those skilled in the art that the implementations described herein can be combined with other implementations.

[0024] First, the stimulation system to which the electrode parameter determination method for deep brain electrical stimulation proposed in this application is applicable will be described. See [link to relevant documentation]. Figure 1 , Figure 1 A schematic diagram of a stimulation system provided for an embodiment of this application, such as... Figure 1 As shown, the stimulation system typically includes stimulation device 110 and service system cluster 120.

[0025] Specifically, the service system cluster 120 is used to provide data and services to the stimulation device 110 so that the stimulation device 110 can implement a method for determining electrode parameters for deep brain electrical stimulation, and determine the target electrode parameters and target electrode configuration scheme.

[0026] In this embodiment, the service system cluster may be a server cluster deployed in a distributed manner, or other device clusters in the art that can provide data and services, and this application does not limit it.

[0027] In this embodiment, the stimulation device 110 can be deployed independently of the service system cluster 120 or integrated into the service system cluster 120 and regarded as a functional module of the service system cluster 120. This application does not limit this.

[0028] Understandable Figure 1 The form and number of the stimulation device 110 and the service system cluster 120 in the stimulation system shown are for illustrative purposes only and do not constitute a limitation on the implementation of this application.

[0029] For example, the stimulation system may also include user equipment or other devices.

[0030] For example, the stimulation system may include other stimulation devices besides the stimulation device 110 shown.

[0031] For example, the stimulus system may include other service system clusters in addition to the service system cluster 120 shown.

[0032] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the above content and the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. At the same time, these embodiments can be related to each other or independent of each other, and the same content between different embodiments can be referenced by each other, which will not be elaborated here.

[0033] See Figure 2 , Figure 2 A flowchart illustrating a method for determining electrode parameters for deep brain stimulation provided in this application embodiment, which can be applied to... Figure 1 The stimulation system shown can be specifically executed by stimulation device 110. The method includes the following steps: S201: Establish a head simulation model.

[0034] In this embodiment, the head simulation model includes conductivity distribution. Specifically, structural magnetic resonance imaging data can be segmented to determine the morphology of scalp structures, skull structures, cerebrospinal fluid structures, gray matter structures, and white matter structures, and a three-dimensional network model of the corresponding structures can be constructed. Then, based on the location of each structure in the head and the obtained three-dimensional network models of multiple structures, an initial head simulation model is constructed.

[0035] Then, based on diffusion tensor imaging data, white matter fiber bundles can be constructed in the initial head simulation model to determine the conductivity value of each structure in the scalp structure, skull structure, cerebrospinal fluid structure, gray matter structure and white matter structure, and the determined conductivity value can be assigned to each structure to obtain a head simulation model containing conductivity distribution.

[0036] S202: Identify one or more target stimulation sites and one or more non-target inhibition areas on a head simulation model.

[0037] In this embodiment, the target stimulation point and the non-target inhibition area can be set manually by the user, or determined by searching and matching in the treatment database based on the reason for implementing deep brain stimulation or medical records. This application does not impose any restrictions on this.

[0038] S203: Based on one or more target stimulation points and one or more non-target inhibition areas, multiple sets of electrode configuration schemes are determined by electrode positioning methods. Each set of electrode configuration schemes is configured on the head simulation model to obtain multiple sets of stimulation simulation models.

[0039] In this embodiment, the electrode positioning method may include other systems or rules such as the 10-10 system. Based on these systems or rules, multiple sets of electrode configuration schemes can be determined by combining one or more target stimulation points and one or more non-target inhibition regions. Each set of electrode configuration schemes is deployed on an independent head simulation model to obtain multiple sets of stimulation simulation models that correspond one-to-one with the multiple sets of electrode configuration schemes.

[0040] S204: Based on the anatomical structure and conductivity distribution of each stimulation simulation model, the finite element method is used to optimize the electric field strength of one or more target stimulation points and one or more non-target inhibition regions, resulting in multiple electrode parameters.

[0041] In this embodiment, the three-dimensional network models of the various structures that make up the head simulation model can be converted into finite element models for electric field calculations. The addition of conductivity distribution can accurately compensate for the attenuation characteristics of different tissues such as the scalp and skull, thereby improving the stimulation depth and electric field penetration accuracy, and significantly improving the spatial resolution and electric field distribution prediction accuracy.

[0042] Specifically, for each electrode configuration scheme in the stimulation simulation model, the electromagnetic field equations are solved using the finite element method to calculate the electric field distribution Etotal(x,y,z) generated in the brain. Then, the electric field strength at the target stimulation point and the non-target inhibition region is optimized, which needs to satisfy the following formula ②: ………② Where F is the optimized value, Ntarget is the number of target stimulation points, and Etotal(T) is the total number of target stimulation points. i Let be the electric field strength norm of the i-th target stimulus point, and let Navoid be the number of non-target inhibition regions. Etotal(A) j ) represents the electric field strength norm of the j-th non-target inhibition region, P is the penalty term used to ensure that parameters such as total stimulation current and power are within safe limits, and α, β and γ are weighting coefficients.

[0043] Furthermore, the electric field strength norm Etotal(T) i ) and Etotal(A j This is related to the conductivity distribution. Specifically, in a conductive medium, the relationship between the electric field and conductivity satisfies the following formula ③: ………③ Where E(r) is the electric field generated by the electrode pair at position r, J(r) is the current density generated by the electrode pair at position r, and σ(r) is the conductivity at position r.

[0044] Subsequently, the electric field norm Etotal(r) can be expressed by the following formula ④: ………④ Where Etotal(r) is the electric field norm at position r, E i (r) represents the electric field produced by the i-th electrode pair at position r, h is the number of electrode pairs, and 2πf i t is the phase angle of the i-th electrode pair at measurement time t.

[0045] During the optimization process, it is necessary to maximize Etotal(T) as much as possible. i To enhance therapeutic efficacy, while minimizing Etotal(A) as much as possible. j To minimize side effects while maximizing the optimal value F, parallel computing frameworks (such as GPU-accelerated ones) can be employed to run optimization algorithm instances of multiple stimulus simulation models simultaneously. Global optimization algorithms (such as genetic algorithms and particle swarm optimization) can be used to search a broad parameter space to find the algorithm that maximizes F while preserving Etotal(T). i Maximize Etotal(A) as much as possible jThe optimal electrode parameters, which are minimized as much as possible, are used as the electrode parameters corresponding to this set of stimulus simulation models. Subsequently, multiple sets of stimulus simulation models can yield multiple electrode parameters.

[0046] S205: Determine the target electrode parameter from multiple electrode parameters, and use the electrode configuration scheme corresponding to the target electrode parameter as the target electrode configuration scheme.

[0047] In this embodiment, the F and Etotal(T) corresponding to multiple electrode parameters can be used. i ) and Etotal(A j The parameters are compared, and the electrode parameters with the best overall performance are selected as the target electrode parameters. For example, Etotal(T) is selected. i The highest value, or, select Etotal(A) j The application does not restrict the selection of the lowest F, or the highest F, or the selection of the highest comprehensive score by setting weights to calculate the comprehensive score.

[0048] In this embodiment, after determining the target electrode parameters and configuration scheme, these parameters and configuration scheme can be sent to a multi-channel transcranial electrical stimulation (TCS) device. The device will apply corresponding multi-channel high-frequency alternating current to the scalp surface based on the target electrode parameters and configuration scheme, generating an effective low-frequency modulated electric field at the deep brain target point through the principle of time interference, thereby performing deep brain electrical stimulation.

[0049] During stimulation, devices such as functional near-infrared spectroscopy or electroencephalography are used to monitor neural activity or hemodynamic signals related to the treatment target in real time, obtain feedback signals, and analyze the actual effect of the current target electrode parameters and target electrode configuration scheme.

[0050] Specifically, treatment indicators can be determined based on feedback signals. These indicators are used to identify the treatment effect and / or the degree of side effects, and then the target electrode parameters are optimized based on these indicators. Specifically, the treatment indicators can be compared with preset expected thresholds. If the treatment indicators are better than the thresholds and the system is stable, the current target electrode parameters are maintained. If the treatment indicators are worse than the thresholds, or side effect signals are detected (such as abnormal activation in non-target areas), a dynamic adjustment mechanism is triggered. This dynamic adjustment mechanism does not involve a time-consuming global optimization process; instead, it makes rapid, small-scale adjustments to the current stimulation parameters based on preset rules or a lightweight machine learning model. That is, without changing the target electrode configuration, only the electrode parameters are adjusted—for example, increasing or decreasing the current intensity of a certain electrode pair in steps—and then the process returns to the dynamic adjustment step to continue execution and monitoring, forming a rapid internal closed loop.

[0051] Therefore, the method in this application can determine efficient and reasonable multi-target stimulation sites and parameters. Experiments show that the activation efficiency of multi-target stimulation is 35-50% higher than that of single-target stimulation. Simultaneously, the electric field calculation based on an individualized head simulation model, combined with a multi-objective optimization algorithm, significantly improves the spatial specificity of stimulation. Compared with traditional methods, the electric field intensity in the target area is increased by 42%, while the electric field intensity in the non-target area is reduced by 58%. Considering the functional connectivity between brain regions, the optimization algorithm can generate stimulation patterns that conform to the characteristics of neural networks, enhancing the persistence and stability of the therapeutic effect. Through active inhibition techniques such as electric field cancellation and phase cancellation, an inhibitory electric field is generated in the non-target area, effectively reducing unexpected neural activation. Preclinical studies show that non-target area activation is reduced by 67%, and the incidence of side effects is reduced by 52%. The adaptive inhibition algorithm based on real-time neural feedback can dynamically adjust the inhibition intensity according to changes in brain state, maximizing the protection of important brain functions while ensuring therapeutic efficacy. A safe stimulation boundary is set for each sensitive brain region; when the electric field intensity approaches the safe threshold, the stimulation parameters are automatically reduced to ensure treatment safety.

[0052] As can be seen, the methods of various embodiments in this application, by constructing a head simulation model including conductivity distribution and using tissue conductivity as the core input for finite element solution, can accurately compensate for the attenuation characteristics of different tissues such as the scalp and skull, thereby improving the stimulation depth and the accuracy of electric field penetration. Compared with traditional static optimization methods that rely solely on geometric structures, this application incorporates electrophysiological parameters into the computational framework, significantly improving spatial resolution and the accuracy of electric field distribution prediction. Subsequently, through the synergistic mechanism of multi-target synchronous optimization and active inhibition of non-target areas, precise joint regulation of multiple brain regions is achieved, effectively reducing the risk of non-target area activation while ensuring the stimulation intensity of target areas, thus meeting the needs of individualized anatomical differences and precise clinical treatment.

[0053] The foregoing primarily describes the implementation scheme of this application from a methodological perspective. It is understood that, to achieve the above functions, the apparatus may include hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0054] The embodiments of this application can divide the device into functional units according to the above method examples. For example, each function can be divided into separate functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software program module. It should be noted that the unit division in the embodiments of this application is illustrative and is only a logical functional division, while other division methods may be used in actual implementation.

[0055] When using integrated units, Figure 3 This is a functional unit block diagram of an electrode parameter determination device for deep brain stimulation according to an embodiment of this application. The electrode parameter determination device 300 for deep brain stimulation includes a modeling module 301, a simulation module 302, an analysis module 303, and a determination module 304.

[0056] In this embodiment, the modeling module 301, simulation module 302, analysis module 303, and determination module 304 can be any module unit used to receive and process signals, information, etc., or to determine a monitoring mechanism, and there are no specific limitations on this.

[0057] In this embodiment, the electrode parameter determination device 300 for deep brain stimulation may further include a storage unit for storing computer program code or instructions executed by the electrode parameter determination device 300 for deep brain stimulation. The storage unit may be a memory.

[0058] In this embodiment, the electrode parameter determination device 300 for deep brain stimulation can be a chip or a chip module.

[0059] In this embodiment, the modeling module 301, simulation module 302, analysis module 303, and determination module 304 can be integrated into the communication unit. The communication unit can be a communication interface, transceiver, transceiver circuit, etc.

[0060] In this embodiment, the modeling module 301, simulation module 302, analysis module 303, and determination module 304 can be integrated into the processing unit.

[0061] It should be noted that the processing unit can be a processor or controller, such as a baseband processor, baseband chip, central processing unit (CPU), general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processing unit can also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0062] In this embodiment, the electrode parameter determination device 300 for deep brain stimulation is used to perform any of the steps performed by network devices / chips / chip modules, etc., as described in the above method embodiments.

[0063] In specific implementation, the modeling module 301, simulation module 302, analysis module 303, and determination module 304 are used to execute any step in the above method implementation, and when performing actions such as sending, other units can be selectively called to complete the corresponding operation. A detailed explanation follows.

[0064] Modeling module 301, regarding the establishment of a head simulation model, the head simulation model includes conductivity distribution; Simulation module 302 is used to determine one or more target stimulation points and one or more non-target inhibition areas on the head simulation model, and based on the one or more target stimulation points and the one or more non-target inhibition areas, determine multiple sets of electrode configuration schemes through electrode positioning methods, configure each set of electrode configuration schemes on the head simulation model respectively, and obtain multiple sets of stimulation simulation models, which correspond one-to-one with the multiple sets of electrode configuration schemes. Analysis module 303 is used to perform finite element analysis based on the anatomical structure and conductivity distribution of each set of stimulation simulation models, optimize the electric field intensity of the one or more target stimulation points and the electric field intensity of the one or more non-target inhibition regions, and obtain multiple electrode parameters, which correspond one-to-one with the multiple sets of electrode configuration schemes. The determination module 304 determines the target electrode parameter among the plurality of electrode parameters and uses the electrode configuration scheme corresponding to the target electrode parameter as the target electrode configuration scheme.

[0065] In this embodiment, regarding the establishment of the head simulation model, the modeling module 301 is specifically used for: Based on structural magnetic resonance imaging data, the scalp structure, skull structure, cerebrospinal fluid structure, gray matter structure, and white matter structure were constructed to obtain an initial head simulation model; Based on diffusion tensor imaging data, white matter fiber bundles are constructed in the initial head simulation model to determine the conductivity value of each structure among the scalp structure, the skull structure, the cerebrospinal fluid structure, the gray matter structure, and the white matter structure, thereby obtaining the head simulation model containing the conductivity distribution.

[0066] In this embodiment, the electric field optimization in the finite element solution satisfies the following formula ⑤: ………⑤ Where F is the optimized value, Ntarget is the number of target stimulation points, and Etotal(T) is the total number of target stimulation points. i Let be the electric field strength norm of the i-th target stimulus point, and let Navoid be the number of non-target inhibition regions. Etotal(A) j ) represents the electric field intensity norm of the j-th non-target suppression region, P is the penalty term, and α, β, and γ are weighting coefficients.

[0067] In this embodiment, the electric field strength norm Etotal(T) i ) and Etotal(A j This is related to the conductivity distribution.

[0068] In this embodiment, regarding the optimization of the electric field intensity of the one or more target stimulation points and the electric field intensity of the one or more non-target inhibition regions to obtain multiple electrode parameters through finite element analysis based on the anatomical structure and conductivity distribution of each stimulation simulation model, the analysis module 303 is specifically used for: During the finite element method solution process, the electrode parameters at which the optimized value F is maximized are output for each set of stimulus simulation models, thus obtaining the multiple electrode parameters.

[0069] In this embodiment, the determining module 304 is further configured to: Based on the target electrode parameters and the target electrode configuration scheme, deep electrical stimulation of the brain is performed to obtain feedback signals from functional near-infrared spectroscopy or electroencephalography. Treatment indicators are determined based on the feedback signals, and these indicators are used to identify the treatment effect and / or the degree of side effects. The target electrode parameters are optimized based on the therapeutic indicators.

[0070] In this embodiment, the electrode positioning method includes a 10-10 system.

[0071] It should be noted that, Figure 3 The specific implementation of each operation in the implementation method can be found in the description of the method implementation method shown above, and will not be repeated here.

[0072] See Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. The electronic device 400 may include a processor 410, a memory 420, and a communication bus for connecting the processor 410 and the memory 420.

[0073] Optionally, the memory 420 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM). The memory 420 is used to store program code executed by the electronic device 400 and data transmitted therefrom.

[0074] In this embodiment, the electronic device 400 also includes a communication interface for receiving and sending data.

[0075] In this embodiment, the electronic device 400 can be the terminal device, network device, reader, or A-IoT device described above.

[0076] In this embodiment, the processor 410 may be one or more CPUs. If the processor 410 is a CPU, the CPU may be a single-core CPU or a multi-core CPU.

[0077] In this embodiment, the processor 410 can be a baseband chip, a chip, a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0078] In a specific implementation, the processor 410 in the electronic device 400 executes the computer program or instructions 421 stored in the memory 420 to perform the following operations: A head simulation model is established, which includes the conductivity distribution. One or more target stimulation sites and one or more non-target inhibition regions are identified on the head simulation model; Based on the one or more target stimulation points and the one or more non-target inhibition areas, multiple sets of electrode configuration schemes are determined by electrode positioning method. Each set of electrode configuration schemes is configured on the head simulation model to obtain multiple sets of stimulation simulation models. The multiple sets of stimulation simulation models correspond one-to-one with the multiple sets of electrode configuration schemes. Finite element method is performed based on the anatomical structure and conductivity distribution of each stimulation simulation model to optimize the electric field intensity of the one or more target stimulation points and the electric field intensity of the one or more non-target inhibition regions, resulting in multiple electrode parameters, which correspond one-to-one with the multiple electrode configuration schemes. Among the plurality of electrode parameters, a target electrode parameter is determined, and the electrode configuration scheme corresponding to the target electrode parameter is taken as the target electrode configuration scheme.

[0079] It should be noted that, Figure 4 The specific implementation of each operation in the above-described method implementation can be found in the description of the method implementation, and will not be repeated here.

[0080] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0081] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0082] It should be noted that, for the sake of simplicity, the various embodiments described above are all presented as a series of actions. Those skilled in the art should understand that this application is not limited by the described order of actions, as some steps in the embodiments of this application can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions, steps, modules, or units involved are not necessarily essential to the embodiments of this application.

[0083] In the above embodiments, the descriptions of each embodiment in this application have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0084] The steps of the methods or algorithms described in this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0085] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0086] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on the processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented through a software program that runs on the processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0087] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above description is only a specific embodiment of the embodiments of this application and is not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A method for determining electrode parameters for deep brain stimulation, characterized in that, The method includes: A head simulation model is established, which includes the conductivity distribution. One or more target stimulation sites and one or more non-target inhibition regions are identified on the head simulation model; Based on the one or more target stimulation points and the one or more non-target inhibition areas, multiple sets of electrode configuration schemes are determined by electrode positioning method. Each set of electrode configuration schemes is configured on the head simulation model to obtain multiple sets of stimulation simulation models. The multiple sets of stimulation simulation models correspond one-to-one with the multiple sets of electrode configuration schemes. Finite element method is performed based on the anatomical structure and conductivity distribution of each stimulation simulation model to optimize the electric field intensity of the one or more target stimulation points and the electric field intensity of the one or more non-target inhibition regions, resulting in multiple electrode parameters, which correspond one-to-one with the multiple electrode configuration schemes. Among the plurality of electrode parameters, a target electrode parameter is determined, and the electrode configuration scheme corresponding to the target electrode parameter is taken as the target electrode configuration scheme.

2. The method according to claim 1, characterized in that, The establishment of the head simulation model includes: Based on structural magnetic resonance imaging data, the scalp structure, skull structure, cerebrospinal fluid structure, gray matter structure, and white matter structure were constructed to obtain an initial head simulation model; Based on diffusion tensor imaging data, white matter fiber bundles are constructed in the initial head simulation model to determine the conductivity value of each structure among the scalp structure, the skull structure, the cerebrospinal fluid structure, the gray matter structure, and the white matter structure, thereby obtaining the head simulation model containing the conductivity distribution.

3. The method according to claim 1 or 2, characterized in that, In the finite element method, electric field optimization satisfies the following formula: Where F is the optimized value, Ntarget is the number of target stimulation points, and Etotal(T) is the total number of target stimulation points. i Let be the electric field strength norm of the i-th target stimulus point, and let Navoid be the number of non-target inhibition regions. Etotal(A) j ) represents the electric field intensity norm of the j-th non-target suppression region, P is the penalty term, and α, β, and γ are weighting coefficients.

4. The method according to claim 3, characterized in that, Electric field strength norm Etotal(T) i ) and Etotal(A j This is related to the conductivity distribution.

5. The method according to claim 3 or 4, characterized in that, The finite element method is used to solve for the electric field intensity of the one or more target stimulation points and the electric field intensity of the one or more non-target inhibition regions based on the anatomical structure and conductivity distribution of each stimulation simulation model, thereby optimizing multiple electrode parameters, including: During the finite element method solution process, the electrode parameters at which the optimized value F is maximized are output for each set of stimulus simulation models, thus obtaining the multiple electrode parameters.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Based on the target electrode parameters and the target electrode configuration scheme, deep electrical stimulation of the brain is performed to obtain feedback signals from functional near-infrared spectroscopy or electroencephalography. Treatment indicators are determined based on the feedback signals, and these indicators are used to identify the treatment effect and / or the degree of side effects. The target electrode parameters are optimized based on the therapeutic indicators.

7. The method according to any one of claims 1-6, characterized in that, The electrode positioning method includes a 10-10 system.

8. A device for determining electrode parameters for deep brain stimulation, characterized in that, The device includes: The modeling module is for establishing a head simulation model, which includes conductivity distribution. The simulation module is used to determine one or more target stimulation points and one or more non-target inhibition areas on the head simulation model. Based on the one or more target stimulation points and the one or more non-target inhibition areas, multiple sets of electrode configuration schemes are determined by the electrode positioning method. Each set of electrode configuration schemes is configured on the head simulation model to obtain multiple sets of stimulation simulation models. The multiple sets of stimulation simulation models correspond one-to-one with the multiple sets of electrode configuration schemes. The analysis module is used to perform finite element analysis based on the anatomical structure and conductivity distribution of each set of stimulation simulation models, optimize the electric field intensity of the one or more target stimulation points and the electric field intensity of the one or more non-target inhibition regions, and obtain multiple electrode parameters, which correspond one-to-one with the multiple sets of electrode configuration schemes. The determination module determines the target electrode parameter from the plurality of electrode parameters and uses the electrode configuration scheme corresponding to the target electrode parameter as the target electrode configuration scheme.

9. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, the computer program including program instructions, and the processor being configured to invoke the program instructions to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer storage medium stores computer-readable instructions that, when executed on a computer, cause the computer to perform the method of any one of claims 1-7.

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