Parameter determination method of multi-modal transcranial neuromodulation and related equipment

Abstract

The application provides a multi-modal transcranial neuromodulation parameter determination method and related equipment, wherein the method comprises: determining a target point region, a first path region and a second path region on a head simulation model, the head simulation model comprising electrical distribution parameters and acoustic distribution parameters of a plurality of tissue layers; obtaining a first high-energy region of a first physical field on the head simulation model under a first candidate parameter, and a second high-energy region of a second physical field on the head simulation model under a second candidate parameter; constructing a joint optimization objective function based on the first high-energy region and the second high-energy region; and optimizing the first candidate parameter and the second candidate parameter based on the joint optimization objective function to obtain a first target parameter and a second target parameter. Through the determination method of the application, the collaborative optimization of the multi-physical field path can be realized, and the targeting and safety of the multi-modal transcranial neuromodulation can be improved.

Description

Technical Field

[0001] This application relates to the field of parameter determination technology for multimodal transcranial nerve modulation, and in particular to a method and related equipment for determining parameters of multimodal transcranial nerve modulation. Background Technology

[0002] While traditional invasive deep brain stimulation (TES) can precisely target specific brain regions, it carries risks such as surgery, infection, and high costs. Therefore, non-invasive transcranial neuromodulation techniques have become an important research direction. Transcranial electrical stimulation (tES) achieves non-invasive modulation of brain regions by applying electrical currents to the scalp surface, offering advantages such as high safety and low cost. However, due to the high electrical impedance of the scalp and skull, the current from traditional tES is difficult to reach deep brain regions, resulting in significant limitations in stimulation depth and spatial focusing.

[0003] Therefore, Temporal Interference Stimulation (TIS) was developed. It applies two sets of slightly different high-frequency currents to the scalp surface, utilizing the low-pass filtering properties of neuronal membranes to generate a low-frequency envelope electric field in the deep target area of ​​the brain, thus achieving effective stimulation of deep brain regions. Focused Ultrasound (FUS) is another important non-invasive modulation technique. It utilizes the excellent penetrability of ultrasound waves to achieve deep targeted modulation through beam focusing, achieving millimeter-level spatial accuracy.

[0004] However, the spatial focusing accuracy of TIS is limited by the electric field distribution, and the focusing volume is usually on the order of several cubic centimeters. Moreover, the high-frequency carrier electric field has the greatest intensity in the superficial region, and superficial tissues face a high risk of electric field exposure. Although FUS has high spatial accuracy, ultrasound has significant reflection and refraction at the skull interface, and the transcranial focusing accuracy is greatly affected by the individual skull structure. It is also difficult to guarantee the neural efficiency and consistency of sound beam modulation alone.

[0005] In response to this, multimodal transcranial neuromodulation has been proposed. This method utilizes multiple physical fields acting on the brain to achieve complementary advantages. However, in multimodal modulation, the parameters and spatial paths of each physical field are typically configured independently. Due to the lack of a unified optimization framework, the spatial distribution of these physical fields within the brain is difficult to coordinate effectively, thus limiting the targeting and safety of multimodal transcranial neuromodulation. Summary of the Invention

[0006] In view of this, this application provides a method and related equipment for determining parameters of multimodal transcranial nerve modulation, which can realize the synergistic optimization of multi-physical field paths and improve the targeting and safety of multimodal transcranial nerve modulation.

[0007] In a first aspect, embodiments of this application provide a method for determining parameters of multimodal transcranial nerve modulation, the method comprising: A target region, a first path region, and a second path region are determined on a head simulation model. The head simulation model includes electrical and acoustic distribution parameters of multiple tissue layers. The first path region is used for planning a first physical field, and the second path region is used for planning a second physical field. The first physical field and the second physical field are different physical fields. Obtain a first high-energy region of a first physical field on the head simulation model under a first candidate parameter, and a second high-energy region of a second physical field on the head simulation model under a second candidate parameter, wherein the first candidate parameter is related to the target region and the first path region, and the second candidate parameter is related to the target region and the second path region; Based on the first high-energy region and the second high-energy region, a joint optimization objective function is constructed; The first candidate parameters and the second candidate parameters are optimized based on the joint optimization objective function to obtain the first objective parameters and the second objective parameters.

[0008] Therefore, in the embodiments of this application, by obtaining the high-energy regions of the first and second physical fields respectively, constructing a joint optimization objective function based on the two high-energy regions, and then jointly optimizing the candidate parameters of the two physical fields based on this objective function, the parameters of the two physical fields are no longer configured independently, but are collaboratively planned within the same optimization framework, so that the optimized spatial distribution of each physical field in the brain forms an effective coordination. This effectively solves the problem of difficult spatial distribution coordination caused by the independent configuration of each physical field in multimodal transcranial neuromodulation, improving the targeting and safety of multimodal transcranial neuromodulation.

[0009] In conjunction with the first aspect, in one possible implementation, the step of constructing a joint optimization objective function based on the first high-energy region and the second high-energy region includes: Determine the first deviation between the first intensity of the first physical field in the target region and a preset first intensity threshold under the first candidate parameters; Determine the second deviation between the second intensity of the second physical field in the target region and a preset second intensity threshold under the second candidate parameter; Based on the degree of spatial overlap between the first high-energy region and the second high-energy region, a path overlap penalty term is determined. Based on the degree to which the first candidate parameter and the second candidate parameter deviate from the preset safety limit, a safety constraint item is determined; Based on the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term, the joint optimization objective function is constructed.

[0010] Therefore, in the embodiments of this application, the joint optimization objective function includes a first deviation and a second deviation of the target region, a path overlap penalty term, and a safety constraint term. The deviation is used to drive the physical field intensity of the target region to reach the effective stimulation threshold; the path overlap penalty term is used to drive the two high-energy regions to separate spatially; and the safety constraint term is used to ensure that the parameters do not exceed the safety boundary. Through the joint constraints of the above four terms, the optimization process simultaneously considers the target stimulation effect, non-target area risk avoidance, and safety assurance, forming a reasonable trade-off among multi-dimensional objectives and improving the reliability and clinical usability of the joint optimization results.

[0011] In conjunction with the first aspect, in one possible implementation, the path overlap penalty term is determined in the following manner: The path separation index is determined based on the spatial overlap between the first high-energy region and the second high-energy region. The path overlap penalty term is determined based on the path separation index, and the path overlap penalty term is negatively correlated with the path separation index.

[0012] Therefore, in the embodiments of this application, the path overlap penalty term is determined by the path separation index and is negatively correlated with it. Since the path separation index characterizes the degree of spatial overlap between two high-energy regions, a larger penalty term when the two high-energy regions overlap significantly drives the algorithm to increase the separation; conversely, a smaller penalty term when the two high-energy regions are sufficiently separated. This achieves precise quantification and effective constraint of the spatial overlap between high-energy regions of two physical fields, providing a quantitative means for avoiding non-target area risks.

[0013] In conjunction with the first aspect, in one possible implementation, determining the path separation index based on the spatial overlap between the first high-energy region and the second high-energy region includes: Determine the intersection and union of the first spatial range of the first high-energy region and the second spatial range of the second high-energy region; The ratio of the intersection range to the union range is determined as the degree of spatial overlap. The path separation index is determined based on the degree of spatial overlap, and the path separation index is negatively correlated with the degree of spatial overlap.

[0014] Therefore, in the embodiments of this application, by determining the intersection and union of the first spatial range of the first high-energy region and the second spatial range of the second high-energy region, the ratio of the intersection to the union is used as the degree of spatial overlap, and then the path separation index is determined based on this degree of overlap. Since the ratio of the intersection to the union can accurately reflect the overlap ratio of the two spatial ranges, the path separation index has a clear geometric meaning and is calculable, providing a quantitative basis for separation in subsequent joint optimization.

[0015] In conjunction with the first aspect, in one possible implementation, constructing the joint optimization objective function based on the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term includes: The joint optimization objective function is determined by weighted summation of the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term.

[0016] Therefore, in the embodiments of this application, a joint optimization objective function is constructed by weighted summing of the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term. Since each term has different physical meanings and dimensions, the relative importance of each term in the optimization objective can be flexibly adjusted through weighted summation, making the joint optimization objective function a single quantitative indicator that can comprehensively measure multi-dimensional optimization needs. This transforms a multi-objective optimization problem into a single-objective optimization problem, reducing the complexity of the optimization solution.

[0017] In conjunction with the first aspect, in one possible implementation, optimizing the first candidate parameters and the second candidate parameters based on the joint optimization objective function to obtain the first objective parameters and the second objective parameters includes: Using the first candidate parameter and the second candidate parameter as initial values, and taking the maximization of the first strength and the second strength, the minimization of the path overlap penalty term, and the minimization of the safety constraint term as the optimization direction, the first candidate parameter and the second candidate parameter are iteratively optimized until the convergence condition is met, thus obtaining the first target parameter and the second target parameter. The convergence conditions include: The number of iterations is greater than or equal to a preset iteration threshold; or, The difference between the current value of the joint optimization objective function and the value of the previous iteration is less than a preset difference threshold.

[0018] Therefore, in the embodiments of this application, by using the first and second candidate parameters as initial values, iterative optimization is performed with the optimization direction of maximizing the first and second intensities and minimizing the path overlap penalty term, and the target parameter is output when the convergence condition is met. Specifically, maximizing the intensity drives the target physical field to reach an effective stimulation level, and minimizing the path overlap penalty term drives the high-energy regions of the two physical fields to separate spatially. This achieves the synergistic goal of "target convergence and non-target separation" within a unified optimization framework, improving the targeting and safety of multimodal transcranial nerve modulation.

[0019] In conjunction with the first aspect, in one possible implementation, the first physical field is an electric field or an induced electric field, and the second physical field is a sound field.

[0020] Therefore, in the embodiments of this application, the first physical field is defined as an electric field or an induced electric field, and the second physical field is defined as an acoustic field. Thus, the method of this application can be applied to time-interference electrical stimulation modes, as well as transcranial magnetoacoustic stimulation or transcranial magnetoacoustic stimulation modes, possessing cross-modal versatility and capable of covering various transcranial neuromodulation techniques based on the principle of acoustic-electric / magnetic-acoustic coupling.

[0021] Secondly, embodiments of this application provide a parameter determination device for multimodal transcranial nerve modulation, the device comprising: The determination module is used to determine the target area, the first path area, and the second path area on the head simulation model. The head simulation model includes electrical distribution parameters and acoustic distribution parameters of multiple tissue layers. The first path area is used for planning the first physical field, and the second path area is used for planning the second physical field. The first physical field and the second physical field are different physical fields. The acquisition module is used to acquire a first high-energy region of a first physical field on the head simulation model under a first candidate parameter, and a second high-energy region of a second physical field on the head simulation model under a second candidate parameter, wherein the first candidate parameter is related to the target region and the first path region, and the second candidate parameter is related to the target region and the second path region; The optimization module is used to construct a joint optimization objective function based on the first high-energy region and the second high-energy region; and to optimize the first candidate parameters and the second candidate parameters based on the joint optimization objective function to obtain the first objective parameter and the second objective parameter.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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

[0026] 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.

[0027] Figure 1 A schematic diagram of the structure of a neural modulation system provided for an embodiment of this application; Figure 2 A flowchart illustrating a method for determining parameters of multimodal transcranial nerve modulation provided for embodiments of this application; Figure 3 A flowchart illustrating another method for determining parameters of multimodal transcranial nerve modulation provided for an embodiment of this application; Figure 4 A functional unit block diagram of a parameter determination device for multimodal transcranial nerve modulation provided for the embodiments of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided for an embodiment of this application. Detailed Implementation

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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 mutually exclusive, independent, or alternative implementation. It will be explicitly and implicitly understood by those skilled in the art that the implementations described herein can be combined with other implementations.

[0033] First, the neuromodulation system to which the parameter determination method for multimodal transcranial neuromodulation proposed in this application is applicable will be described. See [link to relevant documentation]. Figure 1 , Figure 1 A schematic diagram of a neural modulation system provided for an embodiment of this application, such as... Figure 1 As shown, the neural modulation system typically includes a neural modulation device 110 and a server 120.

[0034] Specifically, server 120 is used to provide data and services to neuromodulation device 110 so that neuromodulation device 110 can implement a parameter determination method for multimodal transcranial neuromodulation provided in this application to determine a first target parameter and a second target parameter.

[0035] In this embodiment, server 120 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.

[0036] In this embodiment, the neural modulation device 110 can be deployed independently of the server 120 or integrated into the server 120 and regarded as a functional module of the server 120. This application does not limit this.

[0037] Understandable Figure 1 The form and number of the neural modulation device 110 and server 120 in the neural modulation system shown are for illustrative purposes only and do not constitute a limitation on the implementation of this application.

[0038] For example, a neuromodulation system may also include user equipment or other devices.

[0039] For example, in addition to the neuromodulation device 110 shown, the neuromodulation system may include other neuromodulation devices.

[0040] For example, the neural modulation system may include other servers besides the server 120 shown.

[0041] 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.

[0042] See Figure 2 , Figure 2 A flowchart illustrating a method for determining parameters of multimodal transcranial nerve modulation provided in this application embodiment, which can be applied to... Figure 1 The neural modulation system shown can be specifically executed by the neural modulation device 110. The method includes the following steps: S201: Determine the target region, the first path region, and the second path region on the head simulation model.

[0043] In this embodiment, a target region, a first path region, and a second path region are defined on the head simulation model as the spatial basis for subsequent multi-physics joint optimization. The head simulation model is a high-fidelity three-dimensional digital model containing multiple tissue layers, constructed based on individual subject image data. This model includes both electrical and acoustic distribution parameters for each tissue layer, supporting subsequent joint simulation calculations of the first and second physical fields. The first path region is used for planning the first physical field, and the second path region is used for planning the second physical field; the first and second physical fields are different physical fields.

[0044] It should be noted that in this application, the first physical field can refer to an electric field or an induced electric field, and the second physical field can refer to a sound field. Of course, other physical fields that can act on transcranial nerve modulation can also be applied, and this application does not limit them.

[0045] The following will take the scenario where the first physical field is an electric field or an induced electric field and the second physical field is a sound field as an example to explain in detail the construction method of the head simulation model and the determination method of the target area, the first path area and the second path area.

[0046] (I) Construction of the head simulation model: In this embodiment, the head simulation model is constructed based on the subject's medical imaging data. Specifically, magnetic resonance imaging (MRI) data and computed tomography (CT) data of the subject's head can be acquired. MRI data is used to segment brain tissue (gray matter, white matter) and soft tissue structures such as cerebrospinal fluid; CT data is used to accurately model the geometric structure and acoustic properties of the skull, because the skull has high contrast in CT images, clearly showing the boundaries and density distribution of bony structures.

[0047] After acquiring image data, image segmentation algorithms can be used to segment multiple tissue layers from the image data. For example, deep learning-based segmentation networks (such as U-Net) or automatic segmentation tools (such as TotalSegmentator) can be used for tissue segmentation. The segmented tissue layers may include, but are not limited to: scalp, skull, cerebrospinal fluid, gray matter, white matter, and air (sinus cavities). After segmentation, a three-dimensional geometric model is constructed based on the segmentation results of each tissue layer, and the model is meshed to generate a three-dimensional mesh model suitable for finite element simulation, which serves as the computational domain for subsequent multiphysics simulations.

[0048] After completing tissue segmentation and constructing the 3D geometric model, corresponding electrical and acoustic parameters are assigned to each tissue layer. Electrical parameters include the electrical conductivity of each tissue layer, while acoustic parameters include sound velocity, density, and attenuation coefficient. Different tissue layers exhibit significant differences in these parameters; accurate assignment is crucial for individualized simulation. For example, the electrical and acoustic parameters for each tissue layer can be set according to the numerical ranges shown in Table 1 below: Table 1

[0049] It should be noted that the parameter ranges for each tissue layer in Table 1 above are merely illustrative examples. In practical applications, they can be appropriately adjusted based on individual differences in the subjects, the specific characteristics of the image data, and the requirements of the simulation tools used. In particular, the electrical conductivity and acoustic properties of the skull can be spatially mapped based on the Hounsfield Unit (HU) values ​​of CT images. Since the density and porosity of the skull vary in different locations, the HU values ​​can be used to establish a quantitative relationship between the electrical conductivity and acoustic parameters at each voxel point of the skull, thereby achieving individualized and non-uniform assignment of skull parameters. Similarly, the electrical conductivity of the white matter can be further considered in light of its anisotropic characteristics, i.e., the electrical conductivity along the direction of the nerve fiber bundle differs from that perpendicular to the fiber bundle direction. The head simulation model constructed in this way simultaneously includes the geometric structural information, electrical distribution parameters, and acoustic distribution parameters of multiple tissue layers, providing a unified computational domain and parameter basis for the subsequent joint simulation of the first and second physical fields.

[0050] (II) Determination of the target area: In this embodiment, the target region is the brain region requiring neuromodulation. The specific location of the target region can be determined based on clinical treatment goals and neuroanatomical knowledge. For example, for the treatment of Parkinson's disease, the target region may include the subthalamic nucleus; for the treatment of epilepsy or Alzheimer's disease, the target region may include the hippocampus; and for the treatment of essential tremor, the target region may include deep brain nuclei such as the ventral intermediate nucleus. In practice, the location and extent of the target region can be determined by a self-learning model or by the user, based on the subject's medical history, by marking three-dimensional coordinates on a head simulation model.

[0051] Specifically, the target region can be manually marked by the user based on the subject's individual anatomical structure, or it can be determined using an automatic registration method based on a standard brain atlas (such as the MNI standard space). When using an automatic registration method, the subject's MRI images can be registered to the standard space, and then the target coordinates in the standard space can be used to calculate the individual target region coordinates from the subject's individual space. Furthermore, the target region can be a point coordinate or a set of voxels with a certain spatial range (e.g., a spherical region centered on the target with a radius of several millimeters), which can be determined based on actual stimulation requirements and the input requirements of subsequent optimization algorithms.

[0052] (III) Determination of the first path region and the second path region: In this embodiment, the first path region is used for planning a first physical field, and the second path region is used for planning a second physical field. For example, the first physical field can be an electric field or an induced electric field, and the second physical field can be a sound field.

[0053] In this embodiment, the determination of the path region aims to provide spatial constraints for subsequent joint optimization. Specifically, the first path region and the second path region represent the spatial ranges that the first and second physical fields are expected to traverse along their propagation paths, respectively. In practice, the path regions can be predetermined based on the configuration of the physical field generating device and the propagation characteristics of the physical fields.

[0054] Specifically, when the first physical field is an electric field, the first path region can refer to the spatial range through which the electric field propagates from the scalp surface electrode through tissue layers such as the skull to the deep target area. Since the distribution of the electric field in a conductive medium is influenced by the electrode location, current amplitude, and tissue conductivity, the first path region can be initially estimated based on candidate electrode locations and the conductivity distribution of the head simulation model. For example, multiple candidate electrode locations can be arranged on the scalp surface, and the electric field distribution generated by each electrode in the brain can be calculated through rapid simulation. The spatial region where the field strength exceeds a certain preset threshold is determined as the first path region.

[0055] In the case where the first physical field is an induced electric field, the first path region can refer to the spatial range within the brain where the induced electric field may be distributed. The induced electric field is generated by the magnetoacoustic-electric effect of the static magnetic field and focused ultrasound. Its distribution is related to the configuration of the magnetic field and the position of the ultrasound focal point, and can be estimated based on the configuration parameters of the magnetic field generating device and the initial focusing position of the ultrasound transducer.

[0056] In the case where the second physical field is a sound field, the second path region can refer to the spatial channel through which the ultrasound beam propagates from the transducer array through the skull to the deep target area. Because ultrasound exhibits significant reflection and refraction at the skull interface, the actual propagation path of the sound beam may deviate from the geometric focusing position of the transducer. Therefore, the determination of the second path region can take into account the influence of the acoustic properties of the skull on the propagation of the sound beam. For example, based on the geometric parameters of the transducer and CT image data of the skull, the propagation path and energy distribution range of the ultrasound beam within the brain can be estimated using beam tracking or rapid sound field simulation methods, and this range can be determined as the second path region.

[0057] It should be noted that the first and second path regions in the above example are both determined based on preliminary estimations of the physical fields. Their purpose is to provide initial spatial constraints and optimization directions for subsequent joint optimization. During the subsequent joint optimization process, the actual spatial distribution of the first and second physical fields will dynamically change based on the iterative adjustments of the candidate parameters by the optimization algorithm. The final first and second objective parameters will be determined under the guidance of the joint optimization objective function. Therefore, the first and second path regions can be understood as the expected path range or path constraints set before the optimization begins, rather than pre-defined limitations on the final optimization result.

[0058] Through the above methods, the embodiments of this application define the target region, the first path region, and the second path region on the head simulation model, providing a spatial basis for subsequently obtaining the high-energy regions of the first and second physical fields, constructing a joint optimization objective function, and performing joint iterative optimization. The constructed head simulation model simultaneously includes electrical and acoustic distribution parameters of multiple tissue layers, enabling subsequent joint simulations of the electric / induced electric field and sound field to be performed within the same computational domain. This avoids simulation errors caused by model inconsistencies and lays the foundation for accurate parameter determination in multimodal transcranial nerve modulation.

[0059] S202: Obtain the first high-energy region of the first physical field on the head simulation model under the first candidate parameters, and the second high-energy region of the second physical field on the head simulation model under the second candidate parameters.

[0060] In this embodiment, after determining the target region, the first path region, and the second path region, a first high-energy region of the first physical field on the head simulation model and a second high-energy region of the second physical field on the head simulation model are obtained. The first candidate parameter is related to the target region and the first path region, and the second candidate parameter is related to both the target region and the second path region.

[0061] Specifically, after determining the target region, the first path region, and the second path region, the spatial distribution of the first physical field on the head simulation model is first calculated based on the first candidate parameters, and the spatial distribution of the second physical field on the head simulation model is calculated based on the second candidate parameters. Then, the first high-energy region is determined according to the spatial range in the first spatial distribution that meets the preset energy conditions (such as the field strength exceeding a certain proportion of the peak value); the second high-energy region is determined according to the spatial range in the second spatial distribution that meets the preset energy conditions.

[0062] The following section will provide a detailed explanation of how the spatial distribution of the first and second physical fields on the head simulation model is calculated, as well as how the high-energy region is determined.

[0063] (a) The first physical field: In this embodiment, the first physical field can be an electric field or an induced electric field. The methods for determining the candidate parameters and calculating their spatial distribution differ depending on the type of the first physical field. These will be explained separately below.

[0064] (1) Time-interference electrical stimulation mode: In this mode, the first physical field is an electric field. Based on the constraints of the target region and the first path region, N candidate electrode positions can be arranged on the scalp surface to form a multi-channel electrode array, where N is an integer greater than or equal to 4. Each electrode channel can independently set the current amplitude, carrier frequency, and initial phase. By configuring the above parameters of each channel, the spatial distribution of the electric field in the brain can be controlled.

[0065] In this embodiment, when determining the first candidate parameter, the first candidate parameter is related to the target region and the first path region. Specifically, the goal can be to maximize the electric field intensity in the target region, while the electrode parameters are initially optimized so that the distribution of the electric field in the first path region meets preset requirements (e.g., so that the high-energy region of the first physical field is mainly located in the first path region, or so that the electric field intensity in the first path region is not lower than a preset threshold), providing favorable initial conditions for subsequent joint optimization.

[0066] After determining the first candidate parameters, the first spatial distribution of the first physical field can be calculated on the head simulation model. Specifically, numerical calculation methods such as the finite-difference time-domain (FDTD) method or the finite element method (FEM) can be used to solve the electric field equation on the head simulation model and calculate the electric field distribution generated by each electrode channel in the brain. For the time-interference electrical stimulation mode, the total electric field is a linear superposition of the contributions of the electric fields of each channel, which can be expressed by formula (1): Formula (1) Among them, E i (r) represents the electric field generated by the i-th electrode channel at position r, f i Let h be the carrier frequency of the i-th channel. i Let t be the initial phase, t be time, and N be the total number of electrode channels. The electric field contribution of each channel can be calculated independently based on the conductivity distribution of the head simulation model, and then weighted and superimposed according to the current amplitude, frequency, and phase of each channel.

[0067] After obtaining the spatial distribution of the total electric field, it is also necessary to calculate the effective stimulation field of the time-interference electrical stimulation, i.e., the low-frequency envelope electric field. Since the neuronal membrane has low-pass filtering properties, it cannot respond to high-frequency electric fields at the kHz level, but it can respond to the low-frequency envelope generated after high-frequency electric field interference. The amplitude of the envelope electric field can be expressed by formula (2): Formula (2) Wherein, the envelope frequency Δf = |f i -f j The frequency can be set within the range of 1-200Hz to match the response band of the neuron.

[0068] (2) Transcranial magnetoacoustic stimulation mode: In this mode, the first physical field is the induced electric field, and the first candidate parameters include the configuration parameters of the static magnetic field generator and the preliminary configuration parameters of the ultrasonic transducer. The static magnetic field can be generated by a permanent magnet or an electromagnetic coil, and the magnetic field strength is preferably 0.1-3T. The direction of the magnetic field should ensure that the magnetic field lines cover the target area and the ultrasonic beam path area.

[0069] In this embodiment, when determining the first candidate parameter, the first candidate parameter is related to the target region and the first path region. Specifically, the goal can be to maximize the induced electric field intensity in the target region, while simultaneously performing preliminary optimization of the magnetic field configuration parameters so that the distribution of the induced electric field in the first path region meets preset requirements, providing favorable initial conditions for subsequent joint optimization.

[0070] After determining the first candidate parameters, the first spatial distribution of the first physical field can be calculated on the head simulation model. Specifically, the induced electric field is generated by the magnetoacoustic effect, that is, under the condition of the presence of a static magnetic field, the ion vibration caused by the ultrasonic beam generates a Lorentz force in the magnetic field, thereby generating an induced electric field in the target area, which can be expressed by formula (3): Formula (3) Where Φ is the scalar potential and A is the magnetic vector potential. In solving this problem, the ultrasonic propagation equation and the induced electric field equation need to be solved simultaneously. Considering the coupling effect between the sound field and the induced electric field, a numerical calculation method (such as the finite element method) is used to obtain the spatial distribution of the induced electric field on the head simulation model.

[0071] In this embodiment, after determining the first spatial distribution of the first physical field, an objective function for optimizing the path of the first physical field can be further constructed, so that the first physical field generates a field strength that satisfies a preset threshold at the deep target point, while ensuring that the high-energy region of the first physical field avoids the acoustic channel region (i.e., the second path region) reserved for the second physical field as much as possible. Specifically, the objective function for optimizing the path of the first physical field can be expressed by formula (4): Formula (4) Where X is the set of configuration parameters for the first physical field generator, and E target (X) represents the electric field strength in the target region, Ω US λ is the weighting coefficient, representing the acoustic channel region (i.e., the second path region) reserved for the second physical field. The first term of the objective function represents maximizing the field strength in the target region, and the second term represents minimizing the energy of the first physical field in the acoustic channel region.

[0072] In different modes, X and E have different specific meanings: In the time-interference electrostimulation mode, X includes the current amplitude, carrier frequency and initial phase of each channel, and E is the electric field strength; in the transcranial magnetic acoustic stimulation or transcranial magnetic acoustic stimulation mode, X includes magnetic field configuration parameters (such as magnetic field strength and direction), and E is the induced electric field strength.

[0073] Based on the objective function for optimizing the first physical field path described above, corresponding constraints need to be set to ensure the physical and physiological feasibility of the solution. For example, the constraints may include: the field strength in the target region is not lower than a preset effective stimulation threshold (E0). target ≥E threshold To ensure the effectiveness of the stimulus, all physical parameters must be within preset safety limits to ensure safety. The above optimization problem can be solved using convex optimization methods or surrogate model optimization algorithms to obtain the optimized first candidate parameters.

[0074] Based on the spatial distribution of the first physical field obtained from the above calculations, the first high-energy region of the first physical field can be determined. Specifically, the first high-energy region refers to the spatial region where the intensity of the first physical field exceeds a preset threshold. For example, the spatial region where the field intensity exceeds 50% of the peak value can be determined as the first high-energy region. Of course, the specific value of the preset threshold can be flexibly adjusted according to the actual application scenario and the definition of the high-energy region, and this application does not impose any restrictions on it.

[0075] (ii) The second physical field: In this embodiment, the second physical field is a sound field, specifically an ultrasonic sound field generated by focused ultrasound (FUS).

[0076] In this embodiment, when determining the second candidate parameter, the second candidate parameter is related to the target region and the second path region. Specifically, the goal can be to achieve a preset threshold for the sound pressure in the target region, while simultaneously optimizing the parameters of the ultrasonic transducer to ensure that the sound field distribution in the second path region meets the preset requirements, thus providing favorable initial conditions for subsequent joint optimization.

[0077] In this embodiment, M ultrasonic transducer elements can be arranged on the scalp surface or periphery to form an ultrasonic phased array unit, where M is an integer greater than or equal to 2. Each element can be independently set with excitation delay and amplitude. By adjusting the phase and amplitude of each element, the ultrasonic beam can be focused at a deep target point.

[0078] To compensate for the phase distortion and focus shift caused by the skull, the excitation delay and amplitude of each array element can be calculated using the time-reversal method. Specifically, the time-reversal method is based on the reciprocity principle of the sound field. By simulating the process of sound waves propagating backward from the target point to the transducer array, the required excitation delay and amplitude of each array element are calculated to compensate for the distortion effect caused by the skull on the sound beam propagation. The excitation delay of each array element can be specifically expressed by formula (5): Formula (5) Where, d i Let c be the geometric distance from the i-th element to the target point. avg For the average speed of sound, Δq i skull The phase delay compensation amount introduced for the skull. The phase delay compensation amount introduced for the skull can be calculated point-by-point based on the sound velocity distribution of the skull in the CT image data.

[0079] After determining the second candidate parameters (excitation delay and amplitude of each array element), the second spatial distribution of the second physical field can be calculated on the head simulation model. Specifically, the viscoelastic finite-difference time-domain (FDTD) method can be used to numerically solve the transcranial sound field propagation. The viscoelastic finite-difference time-domain method introduces a viscoelastic constitutive relation on the basis of the traditional finite-difference time-domain method, which can accurately simulate the propagation characteristics of sound waves in lossy media (especially the skull), including sound velocity changes, energy attenuation, and waveform distortion. The sound field propagation can be expressed by formula (6): Formula (6) Where p is density, u is particle displacement, σ is stress tensor (including viscoelastic constitutive relation), and f sourceThe sound source term applied to the transducer. When solving, the acoustic parameters (sound velocity, density, attenuation coefficient) of each tissue layer in the head simulation model need to be used as input to accurately simulate the refraction, reflection and attenuation effects of the sound beam during transcranial propagation.

[0080] In this embodiment, after determining the second spatial distribution of the second physical field, an objective function for optimizing the path of the second physical field can be further constructed to concentrate the ultrasonic beam along the second path region to the deep target point, while ensuring that the high sound pressure region avoids the high field strength region of the first physical field (i.e., the first path region) as much as possible. Specifically, the objective function for optimizing the path of the first physical field can be expressed by formula (7): Formula (7) Where Y is the set of configuration parameters for the ultrasonic transducer (including the excitation delay and amplitude of each array element), P target (Y) represents the sound pressure in the target region, Ω F μ is a weighting coefficient reserved for the high-energy region of the first physical field (i.e., the first path region). The first term of the objective function represents maximizing the sound pressure in the target region, and the second term represents minimizing the sound pressure energy of the second physical field in the first path region.

[0081] Based on the objective function for optimizing the second physical field path described above, corresponding constraints need to be set to ensure the safety and effectiveness of ultrasonic stimulation. For example, the constraints may include: the sound pressure in the target area is not lower than a preset effective stimulation threshold (e.g., ≥0.5 MPa) to ensure the effectiveness of ultrasonic stimulation; the Mechanical Index (MI) does not exceed 1.9 to ensure the safety of ultrasonic stimulation; and the tissue temperature rise caused by the ultrasonic transducer excitation does not exceed a preset safety limit (e.g., ΔT≤2°C). The above optimization problem can be solved using a multi-objective optimization algorithm (e.g., genetic algorithm, particle swarm optimization algorithm) to obtain the optimized second candidate parameters.

[0082] Based on the spatial distribution of the second physical field calculated above, the second high-energy region of the second physical field can be determined. Specifically, the second high-energy region refers to the spatial region where the sound pressure exceeds a preset threshold. For example, the spatial region where the sound pressure exceeds 50% of the peak value can be determined as the second high-energy region. Of course, the specific value of the preset threshold can be flexibly adjusted according to the actual application scenario and the definition of the high-energy region, and this application does not impose any restrictions on it.

[0083] Through the above method, this application's implementation obtains a first high-energy region of the first physical field on the head simulation model under the first candidate parameters, and a second high-energy region of the second physical field on the head simulation model under the second candidate parameters. These two high-energy regions reflect the spatial energy concentration regions of the first and second physical fields under the current candidate parameter configuration, providing a quantitative basis for subsequently constructing a joint optimization objective function.

[0084] S203: Construct a joint optimization objective function based on the first high-energy region and the second high-energy region.

[0085] In this embodiment, after obtaining the first high-energy region of the first physical field on the head simulation model and the second high-energy region of the second physical field on the head simulation model, a joint optimization objective function is constructed based on the first and second high-energy regions. The joint optimization objective function is used to comprehensively evaluate the stimulation effect of the target region, the spatial overlap of the high-energy regions of the two physical fields, and the degree to which the candidate parameters meet the safety limits during the joint optimization of the first and second candidate parameters.

[0086] Specifically, the construction of the joint optimization objective function includes the following parts.

[0087] (a) Determine the first and second deviations of the target area: In this embodiment, the deviation between the first intensity of the first physical field in the target region and a preset first intensity threshold under the first candidate parameters is determined and denoted as the first deviation. The first deviation measures the deviation of the actual field strength of the first physical field in the target region from the minimum field strength required for effective stimulation. For example, if the first intensity is lower than the first intensity threshold, the first deviation is positive, indicating that the stimulation intensity in the target region is insufficient; if the first intensity reaches or exceeds the first intensity threshold, the first deviation is zero or close to zero, indicating that the stimulation intensity in the target region meets the requirements. The specific calculation method of the first deviation can be defined according to actual needs. For example, it can be defined as the normalized value of the difference between the first intensity threshold and the first intensity, or it can be defined as the square of the difference or other forms of functions. This application does not limit this.

[0088] Similarly, the degree of deviation between the second intensity of the second physical field in the target region and a preset second intensity threshold under the second candidate parameters is determined and denoted as the second deviation. The second deviation measures the deviation of the actual sound pressure of the second physical field in the target region from the minimum sound pressure required for effective stimulation. For example, the second intensity threshold can be set to 0.5 MPa. When the second intensity is below this threshold, the second deviation is positive; when the second intensity reaches or exceeds this threshold, the second deviation is zero or close to zero.

[0089] In the above manner, the first deviation and the second deviation quantify the stimuli intensity of the first physical field and the second physical field in the target area, respectively, providing a quantitative basis for the evaluation of the target stimulation effect in subsequent joint optimization.

[0090] (ii) Determine the path overlap penalty: In this embodiment, a path overlap penalty term can be determined based on the degree of spatial overlap between the first high-energy region and the second high-energy region. The path overlap penalty term is used to penalize the spatial overlap between the two high-energy regions in joint optimization, thereby driving the two high-energy regions to be spatially separated as much as possible.

[0091] Specifically, the path separation index can first be determined based on the spatial overlap between the first and second high-energy regions. Then, a path overlap penalty term is determined based on the path separation index, and this penalty term is negatively correlated with the path separation index.

[0092] For example, in this embodiment, the first high-energy region is defined as the spatial region where the intensity of the first physical field exceeds 50% of its peak value, denoted as Ω. F high The second high-energy region is defined as the spatial region where the intensity of the second physical field (sound pressure) exceeds 50% of its peak value, denoted as Ω. P high Then, the intersection and union of the first spatial range of the first high-energy region and the second spatial range of the second high-energy region can be determined. The ratio of the intersection range to the union range is determined as the degree of spatial overlap. Finally, a path separation index is determined based on the degree of spatial overlap, which is negatively correlated with the degree of spatial overlap.

[0093] Specifically, the path separation index S sep It can be expressed by formula (8): Formula (8) Among them, S sep The value range is [0,1]. The larger the value, the higher the path separation between the two high-energy regions, that is, the lower the spatial overlap; conversely, the smaller the value, the more serious the overlap between the two high-energy regions.

[0094] In this embodiment, the path overlap penalty term is negatively correlated with the path separation index; that is, the larger the path separation index, the smaller the path overlap penalty term; and the smaller the path separation index, the larger the path overlap penalty term. For example, the path overlap penalty term can be expressed as (1 S sep ) or (1 S sep )2 Equal to S sep The function form of negative correlation.

[0095] (iii) Determine safety constraints: In this embodiment, a safety constraint term can be determined based on the degree to which the first candidate parameter and the second candidate parameter deviate from a preset safety limit. The safety constraint term is used to penalize combinations of candidate parameters that exceed the safety limit during joint optimization, ensuring that the final output target parameter meets the safety requirements.

[0096] For example, safety limits may include, but are not limited to, one or more of the following: the upper limit of the current amplitude of each channel, the safe range of the static magnetic field strength, the upper limit of the mechanical index (MI) (e.g., not exceeding 1.9), and the upper limit of tissue temperature rise (e.g., ΔT ≤ 2℃). When the candidate parameter is within the safety limit, the safety constraint term takes the value of zero; when the candidate parameter deviates from the safety limit, the safety constraint term takes a positive value, and the greater the deviation, the larger the value of the safety constraint term. By incorporating the safety constraint term into the joint optimization objective function, unsafe parameter combinations can be automatically avoided during the optimization process.

[0097] In this embodiment, after determining the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term, the joint optimization objective function can be constructed. Specifically, the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term can be weighted and summed to determine the joint optimization objective function. That is, the joint optimization objective function can be expressed by formula (9): Formula (9) Among them, L F For the first deviation, L P For the second deviation, (1 S sep ) represents the path overlap penalty term, L safe For safety constraints, λ F , λ P , λ sep , λ safe These are the weighting coefficients for each item. The weighting coefficients are used to adjust the relative importance of each item in the joint optimization and can be flexibly configured according to the actual application scenario and clinical needs. For example, if a higher target stimulation intensity is required, λ can be appropriately increased. F and λ P If higher requirements are needed for path separation, λ can be appropriately increased. sep If higher security is required, λ can be appropriately increased. safe .

[0098] In this way, the joint optimization objective function unifies the target stimulus effect (characterized by the first and second deviations), non-target risk avoidance (characterized by the path overlap penalty term), and safety assurance (characterized by the safety constraint term) into a single objective function, achieving a quantifiable and comprehensive evaluation of multi-dimensional optimization objectives. This joint optimization objective function will serve as the evaluation criterion for joint iterative optimization in subsequent steps, driving the first and second candidate parameters to evolve towards the optimal combination within a unified optimization framework.

[0099] S204: Optimize the first candidate parameters and the second candidate parameters based on the joint optimization objective function to obtain the first objective parameters and the second objective parameters.

[0100] In this embodiment, after constructing the joint optimization objective function, the first candidate parameter and the second candidate parameter can be optimized based on the joint optimization objective function to obtain the first objective parameter and the second objective parameter.

[0101] In this embodiment, the first candidate parameter and the second candidate parameter constitute the initial parameter configuration before joint optimization. Using the first candidate parameter and the second candidate parameter as initial values, and with the optimization direction of maximizing the first strength and the second strength, minimizing the path overlap penalty term, and minimizing the safety constraint term, the first candidate parameter and the second candidate parameter are jointly iteratively optimized.

[0102] In this embodiment, the Alternating Direction Method of Multipliers (ADMM) or a co-evolutionary algorithm can be used for iterative solution. Specifically, each iteration includes two stages: In the first stage, the second candidate parameter is fixed, and the first candidate parameter is optimized to maximize the first intensity of the first physical field in the target region, while minimizing the spatial overlap between the first high-energy region and the current second high-energy region; In the second stage, the first candidate parameter is fixed, and the second candidate parameter is optimized to maximize the second intensity of the second physical field in the target region, while minimizing the spatial overlap between the second high-energy region and the current first high-energy region. The two stages are executed alternately to achieve coordinated adjustment of the two physical field parameters.

[0103] In each iteration, based on the current first and second candidate parameters, the first and second high-energy regions are recalculated, and the path separation index S is calculated. sep The objective function value is jointly optimized. During the iteration process, the path separation index S... sep The overlap between the two high-energy regions gradually decreases, meaning the spatial overlap gradually decreases; the value of the joint optimization objective function gradually decreases, meaning the overall cost gradually decreases.

[0104] During the iteration process, it is necessary to determine whether the convergence condition is met. The convergence condition can include any of the following: the number of iterations reaches a preset upper limit; or, the rate of change of the path separation index is less than a preset threshold; or, the difference between the current value of the joint optimization objective function and the value of the previous iteration is less than a preset change threshold. When any of the above convergence conditions is met, the iteration terminates.

[0105] When the iteration terminates, the current first candidate parameter is determined as the first target parameter, and the current second candidate parameter is determined as the second target parameter. Simultaneously, the predicted target physical field intensity, sound pressure, and path spatial separation index S are output. sep .

[0106] Therefore, the implementation method of this application achieves collaborative planning of the first and second physical fields under a unified joint optimization framework. The optimized first and second target parameters enable the two physical fields to effectively converge in the target region to meet the stimulation intensity requirements, while forming spatial separation in the non-target region to avoid unintended coupling effects, thereby improving the targeting and safety of multimodal transcranial neuromodulation.

[0107] As can be seen, the methods of various embodiments of this application obtain the high-energy regions of the first and second physical fields, construct a joint optimization objective function based on the two high-energy regions, and then jointly optimize the candidate parameters of the two physical fields based on the objective function. In this process, the parameters of the two physical fields are no longer configured independently, but are collaboratively planned within the same optimization framework, enabling the optimized spatial distribution of each physical field in the brain to form an effective coordination. Therefore, this effectively solves the problem of difficult spatial distribution coordination caused by the independent configuration of each physical field in multimodal transcranial neuromodulation, improving the targeting and safety of multimodal transcranial neuromodulation.

[0108] See Figure 3 , Figure 3 A flowchart illustrating another method for determining parameters of multimodal transcranial nerve modulation provided in this application embodiment, which can be applied to... Figure 1 The neural modulation system shown can be specifically executed by the neural modulation device 110. The method includes the following steps: S301: Determine the target region, the first path region, and the second path region on the head simulation model.

[0109] S302: Obtain the first high-energy region of the first physical field on the head simulation model under the first candidate parameters, and the second high-energy region of the second physical field on the head simulation model under the second candidate parameters.

[0110] S303: Construct a joint optimization objective function based on the first high-energy region and the second high-energy region.

[0111] S304: Optimize the first candidate parameters and the second candidate parameters based on the joint optimization objective function to obtain the first objective parameters and the second objective parameters.

[0112] In this embodiment, the specific implementation of steps S301-S304 can be referred to the description of steps S201-S204, and will not be repeated here.

[0113] S305: Based on the first and second target parameters, spatial distribution characterization and quantitative evaluation of multimodal transcranial neural modulation are performed.

[0114] In this embodiment, after obtaining the first target parameter and the second target parameter, the spatial distribution of multimodal transcranial nerve modulation can be characterized and quantitatively evaluated based on the first target parameter and the second target parameter, so as to intuitively present the stimulation effect of the target area and the risk exposure of the non-target area.

[0115] Specifically, the spatial distribution of the first physical field calculated based on the first objective parameter and the spatial distribution of the second physical field calculated based on the second objective parameter can be interpolated onto the same three-dimensional spatial grid. By interpolating to the same spatial grid, the data of different physical fields at various spatial locations can be aligned in subsequent calculations, providing a unified computational domain for joint effect calculations.

[0116] After completing the spatial distribution interpolation, a single-mode normalization effect index can be constructed. Specifically, the spatial distributions of the first and second physics fields can be mapped to a unified normalization scale. The single-mode normalization effect index E of the first physics field... norm (r) can be expressed by formula (10): Formula (10) Among them, F field (r) represents the field strength of the first physical field at position r, F th k is the effective stimulus threshold of the first physical field. F This is the steepness coefficient. This function is a sigmoid function, which can simulate the nonlinear response characteristics of a neuron to the intensity of a physical field. That is, the response is extremely low when the field strength is below the threshold, and the response saturates rapidly when the field strength exceeds the threshold.

[0117] Similarly, the single-mode normalization effect exponent P of the second physics field norm (r) can be expressed by formula (11): Formula (11) Among them, Pac (r) represents the sound pressure value of the second physical field (ultrasonic sound pressure) at position r, P th k is the effective stimulus threshold of the second physical field (e.g., 0.5 MPa). P This is the steepness coefficient.

[0118] After obtaining the single-mode normalized effect exponents of the two physical fields, the acoustic-electric / magnetoacoustic-electric coupling gain factor can be further calculated. The coupling gain factor characterizes the additional gain due to acoustic-electric or magnetoacoustic-electric effects when the first and second physical fields are spatially superimposed. Coupling gain factor Γ C (r) can be expressed by formula (12): Formula (12) Among them, G max The maximum gain coefficient, which can range from 0.3 to 0.8, represents the additional contribution of the coupling effect relative to the single-modal stimulus.

[0119] Based on the obtained single-modality normalized effect index and coupling gain factor, the equivalent neuromodulation intensity (ENI) can be calculated. ENI is a unified quantitative index used to comprehensively measure the combined contribution of the first physical field, the second physical field, and the coupling effect between them to neuromodulation. The equivalent neuromodulation intensity ENI(r) can be expressed by formula (13): Formula (13) In this formula, α, β, γ, and δ are weighting coefficients that satisfy α + β + γ + δ = 1, and θ is an exponent greater than 1. The first term represents the individual contribution of the first physical field, the second term represents the individual contribution of the second physical field, the third term represents the linear gain contribution of the acoustic-electric / magnetoacoustic-electric coupling effect, and the fourth term represents the superlinear gain contribution under the synergistic effect of the two physical fields. Since θ > 1, in regions where the intensity of both physical fields is high, the fourth term produces a significant enhancement effect, reflecting the synergistic advantage of multimodal joint stimulation.

[0120] In this embodiment, based on the calculated three-dimensional spatial distribution of the equivalent neural modulation intensity ENI(r), various visualization maps can be generated. For example, a three-dimensional volumetric drawing can be generated to visually display the distribution of ENI throughout the entire brain; multi-layered slice maps (such as sagittal, coronal, and transverse planes) can be generated to observe the distribution details of ENI in the target region and surrounding areas from different angles; and a target-side lobe comparison map can be generated to visually compare the differences in ENI between the target region and the side lobe region.

[0121] Furthermore, various quantitative evaluation indicators can be calculated based on the three-dimensional spatial distribution of ENI. For example, at least one of the following indicators may be included: Target Effectiveness Index (ENI(r)). target The metrics include: 1) the absolute modulation intensity of the target region; 2) the effective modulation volume, used to measure the spatial range of ENI exceeding a preset threshold; 3) the target-background contrast, used to characterize the difference in modulation intensity between the target region and the surrounding background region; 4) the sidelobe risk index, used to assess the risk of abnormally elevated ENI in non-target regions; and 5) the synergistic gain contribution rate, used to quantify the proportion of coupling effects and superlinear synergistic terms in the total ENI. These quantitative assessment indicators can assist clinicians in comprehensively evaluating multimodal stimulation protocols preoperatively.

[0122] In the above manner, the embodiments of this application map physical fields with different physical dimensions to a unified equivalent neuromodulation intensity (ENI) scale, realizing intuitive visualization and quantitative evaluation of the multimodal joint modulation effect. This helps clinicians to assess whether the target point has reached an effective stimulation intensity, whether there is an unexpected exposure risk in the non-target area, and whether the path separation strategy is successful in a single preoperative assessment. It provides a complete evaluation tool for the formulation and optimization of multimodal transcranial neuromodulation programs.

[0123] 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.

[0124] 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.

[0125] When using integrated units, Figure 4This is a functional unit block diagram of a parameter determination device for multimodal transcranial nerve modulation proposed in this application. The parameter determination device 400 for multimodal transcranial nerve modulation includes a determination module 401, an acquisition module 402, and an optimization module 403.

[0126] In this embodiment, the determining module 401, the acquiring module 402, and the optimizing module 403 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 them.

[0127] In this embodiment, the parameter determination device 400 for multimodal transcranial nerve modulation may further include a storage unit for storing computer program code or instructions executed by the multimodal transcranial nerve modulation parameter determination device 400. The storage unit may be a memory.

[0128] In this embodiment, the parameter determination device 400 for multimodal transcranial nerve modulation can be a chip or a chip module.

[0129] In this embodiment, the determining module 401, the acquiring module 402, and the optimizing module 403 can be integrated into the communication unit. The communication unit can be a communication interface, transceiver, transceiver circuit, etc.

[0130] In this embodiment, the determining module 401, the acquiring module 402, and the optimizing module 403 can be integrated into the processing unit.

[0131] 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.

[0132] In this embodiment, the multimodal transcranial nerve modulation parameter determination device 400 is used to perform any of the steps performed by network devices / chips / chip modules, etc., as described in the above method embodiments.

[0133] In specific implementation, the determining module 401, the acquiring module 402, and the optimizing module 403 are used to execute any of the steps 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.

[0134] The determination module 401 is used to determine the target area, the first path area and the second path area on the head simulation model. The head simulation model includes electrical distribution parameters and acoustic distribution parameters of multiple tissue layers. The first path area is used for planning the first physical field, and the second path area is used for planning the second physical field. The first physical field and the second physical field are different physical fields. The acquisition module 402 is used to acquire a first high-energy region of a first physical field on the head simulation model under a first candidate parameter, and a second high-energy region of a second physical field on the head simulation model under a second candidate parameter. The first candidate parameter is related to the target region and the first path region, and the second candidate parameter is related to the target region and the second path region. The optimization module 403 is used to construct a joint optimization objective function based on the first high-energy region and the second high-energy region; and to optimize the first candidate parameters and the second candidate parameters based on the joint optimization objective function to obtain the first objective parameter and the second objective parameter.

[0135] In this embodiment, regarding the construction of a joint optimization objective function based on the first high-energy region and the second high-energy region, the optimization module 403 is specifically used for: Determine the first deviation between the first intensity of the first physical field in the target region and a preset first intensity threshold under the first candidate parameters; Determine the second deviation between the second intensity of the second physical field in the target region and a preset second intensity threshold under the second candidate parameter; Based on the degree of spatial overlap between the first high-energy region and the second high-energy region, a path overlap penalty term is determined. Based on the degree to which the first candidate parameter and the second candidate parameter deviate from the preset safety limit, a safety constraint item is determined; Based on the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term, the joint optimization objective function is constructed.

[0136] In this embodiment, the optimization module 403 determines the path overlap penalty term in the following manner: The path separation index is determined based on the spatial overlap between the first high-energy region and the second high-energy region. The path overlap penalty term is determined based on the path separation index, and the path overlap penalty term is negatively correlated with the path separation index.

[0137] In this embodiment, regarding the determination of the path separation index based on the spatial overlap between the first high-energy region and the second high-energy region, the optimization module 403 is specifically used for: Determine the intersection and union of the first spatial range of the first high-energy region and the second spatial range of the second high-energy region; The ratio of the intersection range to the union range is determined as the degree of spatial overlap. The path separation index is determined based on the degree of spatial overlap, and the path separation index is negatively correlated with the degree of spatial overlap.

[0138] In this embodiment, regarding the construction of the joint optimization objective function based on the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term, the optimization module 403 is specifically used for: The joint optimization objective function is determined by weighted summation of the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term.

[0139] In this embodiment, regarding the optimization of the first candidate parameters and the second candidate parameters based on the joint optimization objective function to obtain the first objective parameters and the second objective parameters, the optimization module 403 is specifically used for: Using the first candidate parameter and the second candidate parameter as initial values, and taking the maximization of the first strength and the second strength, the minimization of the path overlap penalty term, and the minimization of the safety constraint term as the optimization direction, the first candidate parameter and the second candidate parameter are iteratively optimized until the convergence condition is met, thus obtaining the first target parameter and the second target parameter. The convergence conditions include: The number of iterations is greater than or equal to a preset iteration threshold; or, The difference between the current value of the joint optimization objective function and the value of the previous iteration is less than a preset difference threshold.

[0140] In this embodiment, the first physical field is an electric field or an induced electric field, and the second physical field is a sound field.

[0141] It should be noted that, Figure 4 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.

[0142] See Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. The electronic device 500 may include a processor 510, a memory 520, and a communication bus for connecting the processor 510 and the memory 520.

[0143] Optionally, the memory 520 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 520 is used to store program code executed by the electronic device 500 and data transmitted therefrom.

[0144] In this embodiment, the electronic device 500 also includes a communication interface for receiving and sending data.

[0145] In this embodiment, the electronic device 500 can be the terminal device, network device, reader, or A-IoT device described above.

[0146] In this embodiment, the processor 510 may be one or more CPUs. If the processor 510 is a CPU, the CPU may be a single-core CPU or a multi-core CPU.

[0147] In this embodiment, the processor 510 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.

[0148] In a specific implementation, the processor 510 in the electronic device 500 executes the computer program or instructions 521 stored in the memory 520 to perform the following operations: A target region, a first path region, and a second path region are determined on a head simulation model. The head simulation model includes electrical and acoustic distribution parameters of multiple tissue layers. The first path region is used for planning a first physical field, and the second path region is used for planning a second physical field. The first physical field and the second physical field are different physical fields. Obtain a first high-energy region of a first physical field on the head simulation model under a first candidate parameter, and a second high-energy region of a second physical field on the head simulation model under a second candidate parameter, wherein the first candidate parameter is related to the target region and the first path region, and the second candidate parameter is related to the target region and the second path region; Based on the first high-energy region and the second high-energy region, a joint optimization objective function is constructed; The first candidate parameters and the second candidate parameters are optimized based on the joint optimization objective function to obtain the first objective parameters and the second objective parameters.

[0149] It should be noted that, Figure 5 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The steps of the methods or algorithms described in the embodiments of 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 the 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.

[0155] 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)).

[0156] 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.

[0157] 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 parameters of multimodal transcranial nerve modulation, characterized in that, The method includes: A target region, a first path region, and a second path region are determined on a head simulation model. The head simulation model includes electrical and acoustic distribution parameters of multiple tissue layers. The first path region is used for planning a first physical field, and the second path region is used for planning a second physical field. The first physical field and the second physical field are different physical fields. Obtain a first high-energy region of a first physical field on the head simulation model under a first candidate parameter, and a second high-energy region of a second physical field on the head simulation model under a second candidate parameter, wherein the first candidate parameter is related to the target region and the first path region, and the second candidate parameter is related to the target region and the second path region; Based on the first high-energy region and the second high-energy region, a joint optimization objective function is constructed; The first candidate parameters and the second candidate parameters are optimized based on the joint optimization objective function to obtain the first objective parameters and the second objective parameters.

2. The method according to claim 1, characterized in that, The step of constructing a joint optimization objective function based on the first high-energy region and the second high-energy region includes: Determine the first deviation between the first intensity of the first physical field in the target region and a preset first intensity threshold under the first candidate parameters; Determine the second deviation between the second intensity of the second physical field in the target region and a preset second intensity threshold under the second candidate parameter; Based on the degree of spatial overlap between the first high-energy region and the second high-energy region, a path overlap penalty term is determined. Based on the degree to which the first candidate parameter and the second candidate parameter deviate from the preset safety limit, a safety constraint item is determined; Based on the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term, the joint optimization objective function is constructed.

3. The method according to claim 2, characterized in that, The path overlap penalty term is determined in the following way: The path separation index is determined based on the spatial overlap between the first high-energy region and the second high-energy region. The path overlap penalty term is determined based on the path separation index, and the path overlap penalty term is negatively correlated with the path separation index.

4. The method according to claim 3, characterized in that, The determination of the path separation index based on the spatial overlap between the first high-energy region and the second high-energy region includes: Determine the intersection and union of the first spatial range of the first high-energy region and the second spatial range of the second high-energy region; The ratio of the intersection range to the union range is determined as the degree of spatial overlap. The path separation index is determined based on the degree of spatial overlap, and the path separation index is negatively correlated with the degree of spatial overlap.

5. The method according to claim 2, characterized in that, The joint optimization objective function is constructed based on the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term, including: The joint optimization objective function is determined by weighted summation of the first deviation, the second deviation, the path overlap penalty term, and the safety constraint term.

6. The method according to claim 2, characterized in that, The optimization of the first candidate parameters and the second candidate parameters based on the joint optimization objective function to obtain the first objective parameters and the second objective parameters includes: Using the first candidate parameter and the second candidate parameter as initial values, and taking the maximization of the first strength and the second strength, the minimization of the path overlap penalty term, and the minimization of the safety constraint term as the optimization direction, the first candidate parameter and the second candidate parameter are iteratively optimized until the convergence condition is met, thus obtaining the first target parameter and the second target parameter. The convergence conditions include: The number of iterations is greater than or equal to a preset iteration threshold; or, The difference between the current value of the joint optimization objective function and the value of the previous iteration is less than a preset difference threshold.

7. The method according to claim 1, characterized in that, The first physical field is an electric field or an induced electric field, and the second physical field is a sound field.

8. A parameter determination device for multimodal transcranial nerve modulation, characterized in that, The device includes: The determination module is used to determine the target area, the first path area, and the second path area on the head simulation model. The head simulation model includes electrical distribution parameters and acoustic distribution parameters of multiple tissue layers. The first path area is used for planning the first physical field, and the second path area is used for planning the second physical field. The first physical field and the second physical field are different physical fields. The acquisition module is used to acquire a first high-energy region of a first physical field on the head simulation model under a first candidate parameter, and a second high-energy region of a second physical field on the head simulation model under a second candidate parameter, wherein the first candidate parameter is related to the target region and the first path region, and the second candidate parameter is related to the target region and the second path region; The optimization module is used to construct a joint optimization objective function based on the first high-energy region and the second high-energy region; and to optimize the first candidate parameters and the second candidate parameters based on the joint optimization objective function to obtain the first objective parameter and the second objective parameter.

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-readable 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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