CN116832326A

CN116832326A - Transcranial electrical stimulation electrode optimization method and device, electronic equipment and storage medium

Info

Publication number: CN116832326A
Application number: CN202310811223.5A
Authority: CN
Inventors: 张利; 谢旭; 王敏敏; 张劼; 程瑞动; 朱根应; 曾雅琴; 闻万顺; 田亮; 叶青; 梁嘉瑶; 赵智勇; 张韶岷; 叶祥明
Original assignee: Zhejiang University ZJU; Zhejiang Provincial Peoples Hospital
Current assignee: Zhejiang University ZJU; Zhejiang Provincial Peoples Hospital
Priority date: 2023-07-04
Filing date: 2023-07-04
Publication date: 2023-10-03

Abstract

The embodiment of the disclosure relates to the technical field of transcranial electrical stimulation of human bodies, and provides an electrode optimization method and device for transcranial electrical stimulation, electronic equipment and storage medium, wherein the method comprises the following steps: constructing a head electric field model based on head image data of the individual; determining a target brain region to be electrically stimulated based on the head structure image magnetic resonance data and the head function magnetic resonance data of the individual; determining an electrode optimization constraint condition based on the head state of the individual and the actual condition of the transcranial electrical stimulation equipment; performing multi-objective optimization on an optimization target of a target brain region by utilizing an electrode optimization constraint condition to obtain an electrode optimization result; the optimization targets of the target brain region comprise the electric field intensity and the focusing property of the target brain region; and quantitatively evaluating the optimization effect of the electrode optimization result by using the head electric field model. The embodiment of the disclosure provides a more personalized and accurate transcranial electric stimulation optimization scheme for individuals, so that the effect of transcranial electric stimulation in clinical brain function rehabilitation application is effectively improved.

Description

Transcranial electrical stimulation electrode optimization method and device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of transcranial electrical stimulation of human bodies, in particular to an electrode optimization method and device for transcranial electrical stimulation, electronic equipment and a storage medium.

Background

Brain function impairment is a common clinical problem, bringing a huge burden to society and families. There are many causes of brain injury, and it is common that violence acts on organic injuries of brain tissue caused by head, cerebral apoplexy, etc. When a patient suffers from brain injury, the clinical manifestations will vary depending on where the injury occurs. For example, if the injury occurs in the motor innervation area of the brain, the patient may experience limb movement impairment. If the injury occurs in other areas of the brain, the patient may suffer from cognitive dysfunction such as memory impairment, attention impairment, executive dysfunction, or mental impairment, hearing impairment, swallowing impairment, etc. Therefore, how to efficiently perform brain function rehabilitation has important clinical value and social significance.

The transcranial electric stimulation method is a noninvasive brain function nerve regulation and control method, and the brain-related nerve activity is regulated by applying low-intensity stimulation current to the brain through electrodes placed on the surface of the scalp, so that the aim of treating and improving the brain function is fulfilled. Because of the characteristics of noninvasive, portable and easy operation, the transcranial electric stimulation method has been widely applied to the aspects of cerebral apoplexy movement and cognitive function rehabilitation, conscious disturbance wake-promoting treatment, post-apoplexy affective disorder and the like.

Conventional transcranial electrical stimulation typically uses two relatively large sponge electrodes (typically 20cm in area ² ～35cm ² ) For treatment, the electrode coverage area is large, the therapeutic electric field generated in the relevant brain region is relatively diffuse, the targeting and focusing of the overall stimulation are poor, and meanwhile, the brain tissue structures of different patients are obviously different, so that the distribution of the therapeutic field generated by transcranial electric stimulation among different patients is obviously different. In addition, cerebral infarction, cerebral hemorrhage, cerebral edema, cerebral trauma, skull repair and the like of patients with cerebral functional injury in clinic often occur, and the conditions further enlarge individual variability of the traditional transcranial electrical stimulation treatment, so that individual accuracy cannot be realized during transcranial electrical stimulation intervention treatment, and the clinical effectiveness of transcranial electrical stimulation is improved.

In order to improve and promote the therapeutic effect of transcranial electrical stimulation on patients with brain function injury and achieve better brain function rehabilitation, some optimizing methods of transcranial electrical stimulation are proposed. For example, the university of new york city team in the united states proposes the use of smaller electrodes(typically 1cm in area) ² ～2cm ² ) The traditional large electrode is replaced to perform intervention stimulation, and meanwhile, the targeting of stimulation is improved by adopting a 4*1 multi-electrode surrounding electrode combination. And the team also proposes reconstructing brain head model based on individual magnetic resonance images, improving the target area intensity or focusing degree of stimulation based on an electrode optimization algorithm, and the related optimization method can more accurately adjust the treatment field distribution of transcranial electric stimulation so as to achieve better stimulation effect.

However, the current transcranial electrical stimulation optimization method is mostly carried out based on the head mould of a normal patient, and is lack of consideration for cases of cerebral infarction, cerebral hemorrhage, cerebral edema, bone flap removal, scalp injury and the like possibly occurring in patients with brain function injury. Meanwhile, the existing transcranial electric stimulation optimization algorithm mostly presets or ignores one optimization target (such as target area treatment field intensity or focusing performance) to optimize the other target, and is essentially a single-target optimization problem, and finally, only a locally optimal solution under specific conditions is obtained, so that different requirements of clinical application personnel on electric field intensity and focusing performance cannot be met.

Disclosure of Invention

The present disclosure aims to solve at least one of the problems in the prior art, and provides a transcranial electrical stimulation electrode optimization method and device, an electronic device, and a storage medium.

In one aspect of the present disclosure, there is provided an electrode optimization method of transcranial electrical stimulation, the electrode optimization method comprising:

constructing a head electric field model based on head image data of the individual;

determining a target brain region to be electrically stimulated based on the individual's head structure like magnetic resonance data and head functional magnetic resonance data;

determining an electrode optimization constraint condition based on the head state of the individual and the actual condition of the transcranial electrical stimulation equipment;

Performing multi-objective optimization on the optimization target of the target brain region by utilizing the electrode optimization constraint condition to obtain an electrode optimization result; the optimization targets of the target brain region comprise the electric field intensity and the focusing property of the target brain region;

quantitatively evaluating the optimization effect of the electrode optimization result by utilizing the head electric field model;

the constructing a head electric field model based on the head image data of the individual comprises the following steps:

acquiring the head image data;

performing head normal brain tissue segmentation and head brain organic change region segmentation according to the head image data to obtain a plurality of brain tissue structure regions;

respectively endowing each brain tissue structure area with corresponding dielectric characteristics;

placing candidate stimulation electrodes on the scalp of the individual;

and carrying out finite element calculation based on the dielectric characteristics and the candidate stimulation electrodes to obtain head induced electric field transmission matrixes of the individual under the electric stimulation of different electrode unit currents, so as to obtain the head electric field model.

Optionally, the determining a target brain region to be electrically stimulated based on the head structure image magnetic resonance data and the head function magnetic resonance data of the individual comprises:

Analyzing the head functional magnetic resonance data to determine the brain activity intensity and functional connection intensity of the relevant brain region;

registering the head structural image magnetic resonance data with the head functional magnetic resonance data, acquiring the space coordinate information of a relevant brain region corresponding to the head functional magnetic resonance data, and determining the target brain region by combining a brain map.

Optionally, the head functional magnetic resonance data comprises resting state functional magnetic resonance data and task state functional magnetic resonance data;

the analyzing the head functional magnetic resonance data to determine brain activity intensity and functional connection intensity of a relevant brain region comprises:

analyzing the resting state functional magnetic resonance data and determining resting state brain activity, local consistency, low-frequency amplitude, degree of center and functional connection strength of a relevant brain region;

and analyzing the task state functional magnetic resonance data and determining the activation and functional connection strength.

Optionally, the determining the electrode optimization constraint condition based on the head state of the individual and the actual condition of the transcranial electrical stimulation device includes:

determining a candidate scalp region for placement of an electrode based on the scalp state of the individual;

determining the region of head brain organic change as a non-target brain region based on a brain tissue state of the individual;

And determining the single-channel current intensity and the number of electrodes adopted by the electrical stimulation intervention based on the electrical stimulation tolerance condition of the individual and the actual condition of the transcranial electrical stimulation equipment.

Optionally, the performing multi-objective optimization on the optimization objective of the objective brain region by using the electrode optimization constraint condition to obtain an electrode optimization result includes:

determining an optimization objective function corresponding to the target brain region, wherein the optimization objective function is expressed as an electrode optimization formula shown in the following formula:

wherein s represents the current level of the electrode,represents the required electric field intensity distribution of the target brain region, lambda represents the weight parameter, E _{0.25d_max} Representing the electric field intensity at a coordinate point having a maximum distance of 1/4 from the target point;

determining a multi-target electrode optimization solving constraint condition based on the electrode optimization constraint condition;

and calculating and solving the optimization objective function based on the multi-objective electrode optimization solution constraint condition to obtain an optimization solution of the optimization objective function under the multi-objective electrode optimization solution constraint condition, wherein the optimization solution is used as the electrode optimization result.

Optionally, the determining the multi-objective electrode optimization solution constraint condition based on the electrode optimization constraint condition includes:

Determining candidate electrodes according to the candidate scalp areas;

determining a candidate electric field transmission matrix according to the non-target brain region;

determining the number of electrodes, the total electrode current and the single electrode current used for restraining the optimized objective function according to the single-channel current intensity and the number of electrodes adopted by the electrical stimulation intervention;

the input current magnitude is defined as the output current magnitude according to the current conservation equation.

Optionally, the quantitatively evaluating the optimizing effect of the electrode optimizing result by using the head electric field model includes:

and calculating the electric field intensity and the focusing property of the target brain region corresponding to the electrode optimization result by using the head electric field model, and checking the electrode optimization effect corresponding to the electrode optimization result through the whole brain electric field distribution.

In another aspect of the present disclosure, there is provided an electrode optimizing device for transcranial electrical stimulation, the electrode optimizing device comprising:

the building module is used for building a head electric field model based on the head image data of the individual;

a first determining module for determining a target brain region to be electrically stimulated based on head structure image magnetic resonance data and head function magnetic resonance data of the individual;

The second determining module is used for determining an electrode optimization constraint condition based on the head state of the individual and the actual condition of the transcranial electric stimulation equipment;

the optimization module is used for performing multi-objective optimization on the optimization objective of the objective brain region by utilizing the electrode optimization constraint condition to obtain an electrode optimization result; the optimization targets of the target brain region comprise the electric field intensity and the focusing property of the target brain region;

the evaluation module is used for quantitatively evaluating the optimization effect of the electrode optimization result by utilizing the head electric field model;

the building module is used for building a head electric field model based on head image data of an individual, and comprises the following steps:

the construction module is used for: acquiring the head image data; performing head normal brain tissue segmentation and head brain organic change region segmentation according to the head image data to obtain a plurality of brain tissue structure regions; respectively endowing each brain tissue structure area with corresponding dielectric characteristics; placing candidate stimulation electrodes on the scalp of the individual; and carrying out finite element calculation based on the dielectric characteristics and the candidate stimulation electrodes to obtain head induced electric field transmission matrixes of the individual under the electric stimulation of different electrode unit currents, so as to obtain the head electric field model.

Optionally, the first determining module is configured to determine a target brain region to be electrically stimulated based on the head structure image magnetic resonance data and the head function magnetic resonance data of the individual, and includes:

the first determining module is configured to: analyzing the head functional magnetic resonance data to determine the brain activity intensity and functional connection intensity of the relevant brain region; registering the head structural image magnetic resonance data with the head functional magnetic resonance data, acquiring the space coordinate information of a relevant brain region corresponding to the head functional magnetic resonance data, and determining the target brain region by combining a brain map.

the first determining module is used for analyzing the head functional magnetic resonance data and determining the brain activity intensity and functional connection intensity of the relevant brain region, and comprises the following steps:

the first determining module is used for analyzing the resting state functional magnetic resonance data and determining resting state brain activity, local consistency, low-frequency amplitude, degree of center and functional connection strength of a relevant brain region; and analyzing the task state functional magnetic resonance data and determining the activation and functional connection strength.

Optionally, the second determining module is configured to determine an electrode optimization constraint condition based on a head state of the individual and a transcranial electrical stimulation device actual condition, including:

the second determining module is configured to: determining a candidate scalp region for placement of an electrode based on the scalp state of the individual; determining the region of head brain organic change as a non-target brain region based on a brain tissue state of the individual; and determining the single-channel current intensity and the number of electrodes adopted by the electrical stimulation intervention based on the electrical stimulation tolerance condition of the individual and the actual condition of the transcranial electrical stimulation equipment.

Optionally, the optimizing module is configured to perform multi-objective optimization on the optimization objective of the objective brain area by using the electrode optimization constraint condition, to obtain an electrode optimization result, where the optimizing module includes:

the optimizing module is used for:

Optionally, the optimizing module is configured to determine a multi-objective electrode optimization solution constraint condition based on the electrode optimization constraint condition, and includes:

the optimizing module is used for: determining candidate electrodes according to the candidate scalp areas; determining a candidate electric field transmission matrix according to the non-target brain region; determining the number of electrodes, the total electrode current and the single electrode current used for restraining the optimized objective function according to the single-channel current intensity and the number of electrodes adopted by the electrical stimulation intervention; the input current magnitude is defined as the output current magnitude according to the current conservation equation.

Optionally, the evaluation module is configured to quantitatively evaluate an optimization effect of the electrode optimization result by using the head electric field model, and includes:

The evaluation module is used for: and calculating the electric field intensity and the focusing property of the target brain region corresponding to the electrode optimization result by using the head electric field model, and checking the electrode optimization effect corresponding to the electrode optimization result through the whole brain electric field distribution.

In another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the transcranial electrical stimulation electrode optimization method described hereinabove.

In another aspect of the present disclosure, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, implements the transcranial electrical stimulation electrode optimization method described above.

Compared with the prior art, the method has the following beneficial effects:

1. from the practical point of clinical application of brain functions, the situations that the prior transcranial electrical stimulation optimization mostly ignores cerebral infarction, cerebral hemorrhage, cerebral edema, bone flap removal, scalp injury and the like possibly occurring in individuals with brain functions are overcome, and a more personalized and accurate transcranial electrical stimulation optimization scheme can be provided by pertinently combining multimode image data of the individuals of the patients;

2. Through individual head electric field model construction and a multi-target optimization algorithm, the electric field intensity and the focusing of a target area, namely a target brain area, are optimized, and finally an optimal solution for balancing the two optimization targets can be obtained, so that a more accurate transcranial electric stimulation intervention scheme is provided for an individual through the optimal solution, and the effect of transcranial electric stimulation in clinical brain function rehabilitation application is effectively improved.

Drawings

One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which the figures do not depict a proportional limitation unless expressly stated otherwise.

FIG. 1 is a flow chart of a method for optimizing electrodes for transcranial electrical stimulation according to one embodiment of the present disclosure;

FIG. 2 is a flow chart of a method of optimizing electrodes for transcranial electrical stimulation according to another embodiment of the present disclosure;

FIG. 3 is a schematic illustration of brain tissue structure segmentation provided in another embodiment of the present disclosure;

FIG. 4 is a schematic illustration of candidate electrode limitations provided in accordance with another embodiment of the present disclosure;

FIG. 5 is a perspective view of results of electric field simulation using multi-target electrode optimization results of transcranial electrical stimulation provided in accordance with another embodiment of the present disclosure;

FIG. 6 is a schematic image of an electric field simulation using electrode optimization results with different weights lambda according to another embodiment of the present disclosure;

FIG. 7 is a schematic structural view of an electrode optimizing device for transcranial electrical stimulation according to another embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of an electronic device according to another embodiment of the present disclosure.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art will understand that in various embodiments of the present disclosure, numerous technical details have been set forth in order to provide a better understanding of the present disclosure. However, the technical solutions claimed in the present disclosure can be implemented without these technical details and with various changes and modifications based on the following embodiments. The following divisions of the various embodiments are for convenience of description, and should not be construed as limiting the specific implementations of the disclosure, and the various embodiments may be mutually combined and referred to without contradiction.

One embodiment of the present disclosure relates to a method for optimizing electrodes for transcranial electrical stimulation, the flow of which is shown in fig. 1, comprising:

step S110, a head electric field model is constructed based on the head image data of the individual.

Specifically, the head image data may be magnetic resonance image data or electronic computed tomography (Computed Tomography, CT) image data, or the like. The magnetic resonance image data may be one or more of T1 image data, T2 image data, functional magnetic resonance imaging (Functional Magnetic Resonance Imaging, fMRI) data, diffusion tensor imaging (Diffusion Tensor Imaging, DTI) data. Where T1 is the longitudinal relaxation time and T2 is the transverse relaxation time. When the magnetic resonance image data is T1 image data, the resolution of the image is better than 1 x 1mm ³ 。

Illustratively, step S110 includes the steps of:

step S111, acquiring head image data. Specifically, step S111 may acquire head image data of the individual through a medical imaging apparatus such as a magnetic resonance imaging apparatus or a CT apparatus.

Step S112, head normal brain tissue segmentation and head brain organic change region segmentation are carried out according to the head image data, and a plurality of brain tissue structure regions are obtained. Specifically, the head normal brain tissue segmentation refers to the segmentation of normal brain tissue structures such as scalp, cerebrospinal fluid, skull, white matter and gray matter of the brain, and the like, so as to obtain corresponding brain tissue structure regions. The segmentation of the organic change region of the head and the brain refers to the segmentation of brain tissue structures of brain injuries such as cerebral infarction, cerebral hemorrhage, cerebral edema, bone flap removal, scalp injury and the like, so as to obtain corresponding brain tissue structure regions. Step S112 may employ image segmentation manually or with related software. For example, normal brain tissue segmentation of the head may be achieved by registration of the T1 image with the T2 image or registration of the T1 image with the CT image, soft brain tissue segmentation is achieved using a magnetic resonance imaging (Magnetic Resonance Imaging, MRI) image, and brain tissue structure segmentation such as skull is achieved using the CT image.

Step S113, corresponding dielectric properties are respectively given to each brain tissue structure region. Specifically, step S113 may respectively impart isotropic conductivity to each brain tissue structure region. The anisotropic conductivity of the brain tissue structure region corresponding to the white matter can also be converted by a diffusion tensor imaging (Diffusion Tensor Imaging, DTI) coefficient, namely a DTI tensor coefficient.

Step S114, placing candidate stimulation electrodes on the scalp of the individual. Specifically, step S114 may be performed by placing the MRI data anatomical landmark points according to the 10-10 International Standard lead, or manually.

And step S115, performing finite element calculation based on dielectric characteristics and candidate stimulation electrodes to obtain head induced electric field transmission matrixes of individuals under the electric stimulation of different electrode unit currents, and obtaining a head electric field model. Specifically, step S115 may perform finite element calculation on a head model corresponding to the image data of the head of the individual based on the dielectric characteristics corresponding to each brain tissue structure region and the candidate stimulation electrodes corresponding to each brain tissue structure region, so as to obtain a head induced electric field transmission matrix of the individual under the electrical stimulation of different electrode unit currents, and obtain a head electric field model.

The head normal brain tissue segmentation and the head brain organic change region segmentation are carried out according to head image data, corresponding dielectric characteristics are respectively endowed to each brain tissue structure region obtained by segmentation, finite element calculation is carried out based on the dielectric characteristics and candidate stimulation electrodes placed on the scalp of an individual, head induced electric field transmission matrixes of the individual under the electric stimulation of different electrode unit currents are obtained, a head electric field model is obtained, and conditions of cerebral infarction, cerebral hemorrhage, cerebral edema, bone flap removal, scalp injury and the like possibly occurring in a brain function damaged individual can be fully considered when the head electric field model of the individual is modeled, so that the actual optimization effect of the follow-up electrode optimization result is further improved.

Step S120, determining a target brain region to be electrically stimulated based on the head structure image magnetic resonance data and the head function magnetic resonance data of the individual. In particular, the head functional magnetic resonance data comprises resting state functional magnetic resonance data and task state functional magnetic resonance data. Step S120 includes the steps of:

step S121, analyzing the head functional magnetic resonance data, and determining the brain activity intensity and functional connection intensity of the relevant brain region, specifically including: analyzing resting state functional magnetic resonance data, and determining resting state brain activity, local consistency, low-frequency amplitude, degree of center and functional connection strength of a relevant brain region; and analyzing task state functional magnetic resonance data and determining the activation and functional connection strength.

Step S122, registering the head structure image magnetic resonance data with the head function magnetic resonance data, obtaining the space coordinate information of the relevant brain region corresponding to the head function magnetic resonance data, and determining the target brain region to be electrically stimulated by combining the brain map.

The target brain region to be electrically stimulated is determined based on the head structure image magnetic resonance data and the head function magnetic resonance data of the individual, so that the determination of the target brain region can be more accurate, and the actual optimization effect of the follow-up electrode optimization result is further improved.

Step S130, determining electrode optimization constraint conditions based on the head state of the individual and the actual situation of the transcranial electrical stimulation device. Illustratively, step S130 includes the steps of:

step S131, determining a candidate scalp area for placing the electrode based on the scalp state of the individual. Specifically, step S131 may define a scalp region where the electrode can be placed, i.e., a candidate scalp region, according to whether or not the scalp of the individual has skin damage, skull damage, or the like.

Step S132, determining the head brain organic change region as a non-target brain region based on the brain tissue state of the individual. Specifically, step S132 may define the corresponding region as the non-target brain region according to the presence or absence of cerebral infarction, edema, tumor, and the like in the brain tissue of the individual.

Step S133, determining the single-channel current intensity and the number of electrodes adopted by the electric stimulation intervention based on the electric stimulation tolerance condition of the individual and the actual condition of the transcranial electric stimulation equipment. Wherein, the electrical stimulation tolerance of the individual refers to the electrical stimulation intensity that the individual is able to tolerate. The practical situation of the transcranial electric stimulation device refers to the number and the performance of channels of the transcranial electric stimulation device.

Step S140, performing multi-objective optimization on the optimization target of the target brain region by utilizing the electrode optimization constraint condition to obtain an electrode optimization result; the optimization objectives of the target brain region include the electric field strength and focusing properties of the target brain region.

Specifically, in step S140, two targets, i.e., the electric field intensity and the focusing property, of the target brain region are mainly optimized, so as to maximize the electric field intensity of the target region, i.e., the target brain region, while minimizing the electric field intensity of the non-target brain region, so as to improve the focusing property.

Illustratively, step S140 includes the steps of:

step S141, determining an optimization objective function corresponding to the target brain region, wherein the optimization objective function is expressed as an electrode optimization formula of the following formula:

wherein s represents the current level of the electrode,represents the electric field intensity distribution required by the target brain region, lambda represents the weight parameter, E _{0.25d_max} The electric field strength at a coordinate point having a distance of 1/4 x maximum distance from the target point is represented.

Specifically, to reduce the amount of computation and improve the computation efficiency, the electric field intensity of the minimized non-target brain region is characterized as the electric field intensity at the coordinate point where the distance between the minimized non-target brain region and the target point is 1/4×the maximum distance. On this basis, an optimized objective function shown in the above formula can be obtained. The weight parameter lambda is used for balancing two optimization targets of electric field intensity and focusing property. The larger λ represents the higher the focus of the target brain region; the smaller λ represents the greater electric field strength of the target brain region.

Step S142, determining a multi-target electrode optimization solving constraint condition based on the electrode optimization constraint condition. Specifically, step S142 may determine a multi-objective electrode optimization solution constraint based on the electrode optimization constraint determined in step S130.

Illustratively, step S142 specifically includes the steps of: determining candidate electrodes according to the candidate scalp regions; determining a candidate electric field transmission matrix according to the non-target brain region; determining the number of electrodes, the total electrode current and the single electrode current used for restraining the optimized objective function according to the single-channel current intensity and the number of electrodes adopted by the electrical stimulation intervention; the input current magnitude is defined as the output current magnitude according to the current conservation equation. Thus, the multi-objective electrode optimization solution constraint comprises: the method comprises the following steps of candidate electrode constraint, candidate electric field transmission matrix constraint, electrode number constraint, total electrode current size constraint, single electrode current size constraint and constraint that the input current size is equal to the output current size, namely the sum of the current sizes is 0.

The determination flow of the number of electrodes used for constraining the optimization objective function is as follows: firstly, an electrode optimization result when the optimization objective function is unconstrained, namely, an electrode optimization result of the optimization objective function when the electrode number is not limited is obtained through an algorithm, then a specific number of electrodes with the largest current are selected from the electrode optimization result to serve as electrodes used for constraining the optimization objective function, and the selected electrodes are utilized to optimize the optimization objective function again to obtain a final electrode optimization result. Where a specific number refers to the number of electrodes that are desired to be used for the final constraint of the optimization objective function.

And step S143, calculating and solving the optimization objective function based on the multi-objective electrode optimization solution constraint condition to obtain an optimization solution of the optimization objective function under the multi-objective electrode optimization solution constraint condition, and taking the optimization solution as an electrode optimization result. For example, a heuristic algorithm may be employed to computationally solve the optimization objective function.

The electric field intensity and the focusing property of the target brain region are optimized by utilizing the electrode optimization constraint condition to obtain an electrode optimization result, and the obtained electrode optimization result can be an optimal solution of two optimization targets of balanced electric field intensity and focusing property, so that a more accurate transcranial electric stimulation intervention scheme can be provided for an individual through the optimal solution, and the clinical brain function rehabilitation application effect is further effectively improved.

And S150, quantitatively evaluating the optimization effect of the electrode optimization result by using the head electric field model.

Illustratively, step S150 includes: and calculating the electric field intensity and the focusing property of a target brain region corresponding to the electrode optimization result by using the head electric field model, and checking the electrode optimization effect corresponding to the electrode optimization result through the whole brain electric field distribution.

Compared with the prior art, the embodiment of the disclosure starts from the clinical application practice of brain functions, overcomes the situations that the past transcranial electric stimulation optimization mostly ignores cerebral infarction, cerebral hemorrhage, cerebral edema, bone flap removal, scalp injury and the like possibly occurring in individuals with brain functions, can pertinently combine the multimode image data of the individuals of the patients, and optimizes two optimization targets of electric field intensity and focusing property of a target brain region through the construction of an individual head electric field model and a multi-target optimization algorithm to finally obtain an optimal solution for balancing the two optimization targets, thereby providing a more personalized and accurate transcranial electric stimulation optimization scheme for the individuals based on the optimal solution, and further effectively improving the effect of transcranial electric stimulation in clinical brain function rehabilitation application.

In order to enable a person skilled in the art to better understand the above embodiments, a specific example will be described below.

As shown in fig. 2, the method for optimizing the transcranial electric stimulation electrode mainly comprises five steps of head model construction based on individual image data, target brain region determination, optimization constraint condition determination, multi-target electrode optimization and quantitative evaluation of optimization effect, wherein:

the head model construction based on the individual image data specifically comprises the following steps:

(1.1) acquiring individual head image data through magnetic resonance and CT scanning to obtain related MRI image data and CT images, wherein the MRI image data is one or more of T1, T2, fMRI and DTI, and the resolution of the T1 image is better than 1 x 1mm ³ 。

(1.2) dividing the normal brain tissue of the head according to the image data, wherein a manual or automatic dividing method can be adopted, and dividing is mainly carried out through T1/T2 image registration or T1/CT image registration. For example, as shown in fig. 3, using t1\t2 image registration, normal brain segmentation can be performed using SPM12 tool software to segment the head tissue into several major brain tissue components such as scalp, skull, cerebrospinal fluid, gray matter, white matter, ventricle, etc.

(1.3) dividing the region of the organic change of the head brain according to the image data, wherein the region can be divided manually or automatically, the brain soft tissue adopts MRI images, and the skull and the like adopt CT images. For example, as shown in fig. 3, an interactive linear and generic optimization solver (Linear Interactive and General Optimizer, lingo) may be employed to segment regions of organic changes in the head brain based on the image data.

(1.4) imparting the node parameters of the head tissue structure, imparting the corresponding dielectric properties of different tissue structures, mainly imparting isotropic conductivity, and converting the anisotropic conductivity of the white matter region by the DTI tensor coefficient to the white matter region.

(1.5) candidate electrode placement: candidate stimulation electrodes are placed on the scalp of an individual, and anatomical marker points can be placed according to 10-10 international standard leads through MRI data, or can be placed manually.

And (1.6) performing finite element calculation on the head model corresponding to the image data of the head of the individual to obtain a head induced electric field transmission matrix A under the electric stimulation of different electrode unit currents.

The target brain area determination specifically comprises the following steps:

(2.3) analyzing individual head functional magnetic resonance data for which resting brain activity, local consistency, low frequency amplitude, degree of centrality and functional connectivity can be analyzed; for task state functional magnetic resonance data, the activation and functional connection can be analyzed.

And (2.4) registering the head structure image magnetic resonance data and the functional magnetic resonance data of the individual to acquire the space coordinate information of the target brain region of the functional image data, and determining the target brain region of the stimulation by combining the brain map.

The optimization constraint condition determination specifically comprises the following steps:

(3.1) scalp optional area limitation: considering the scalp state of an individual, whether there is skin injury or skull injury, a scalp area where electrodes can be placed is defined. For example, as shown in fig. 4, considering the situations of bone flap removal, scalp injury and the like possibly occurring in the patient with brain function injury, the injury site (dark gray area in the figure) generally does not suggest to place the electrode for electrical stimulation, so that the solution space of the electrode solution of the injury site can be automatically shielded in the subsequent electrical stimulation optimizing step through the candidate electrode limitation.

(3.2) non-target brain region definition: considering the brain tissue state of an individual, the existence of non-target brain areas such as cerebral infarction, edema, tumor and the like.

(3.3) transcranial electrical stimulation intervention parameter definition: the single-channel current intensity and the number of electrodes of the electric stimulation intervention are defined by considering the electric stimulation tolerance condition (stimulation intensity) of an individual and the actual equipment condition (channel number and performance of the electric stimulation equipment) of transcranial electric stimulation.

The multi-target electrode optimization specifically comprises the following steps:

(4.1) determining an optimized objective function, namely a multi-objective optimized objective function, and mainly optimizing two objectives of the electric field intensity and the focusing property of the target brain region so as to minimize the electric field intensity of a non-target region while maximizing the electric field intensity of the target region, namely the target brain region, so as to improve the focusing property; to reduce the amount of computation and increase the computational efficiency, the electric field intensity of the minimized non-target region is characterized as being at a coordinate point where the distance from the target point is 1/4 of the maximum distance The electric field strength. The total electrode optimization formula, namely the optimization objective function, is:wherein s represents the current level of the electrode,represents the electric field intensity distribution required by the target brain region, lambda represents the weight parameter, E _{0.25d_max} The electric field strength at a coordinate point having a distance of 1/4 x maximum distance from the target point is represented.

The weight parameter lambda is used to balance the two optimization objectives, namely electric field strength and focusing. The larger λ represents the higher focusing property. The smaller λ represents the larger target electric field strength.

(4.2) determining multi-target electrode optimization solving constraint conditions, and defining a target brain region according to the target brain region determining step; determining a candidate electrode sa according to the scalp region where the electrode can be placed determined in the optimization constraint condition step (3.1); determining a candidate electric field transfer matrix Aa according to the non-target brain region determined in the optimization constraint condition step (3.2); determining the number of electrodes used in constraint, the total electrode current magnitude Itotal and the single electrode current magnitude Imax according to the optimization constraint condition step (3.3), wherein the flow of the number of electrodes used in constraint is as follows: firstly, an unconstrained electrode optimization result is obtained through an algorithm, then a specific number of electrodes with the largest current (namely, the number of electrodes which are finally wanted to be used) are selected from the optimization result, and the algorithm is used for optimizing based on the selected electrodes again to obtain a final electrode optimization result. Other constraints also include that the input current is equal in magnitude to the output current, i.e., the sum of the current magnitudes is 0, to conform to the current conservation equation.

(4.3) calculating and solving the transcranial electrical stimulation optimal solution, wherein a heuristic algorithm can be adopted.

The quantitative evaluation of the optimizing effect comprises the following steps:

simulation model calculation and result evaluation visualization: the electric field intensity and the focusing performance of a target brain region of an electrode optimization result are calculated mainly through an electric field simulation model, and the electrode optimization effect is checked through whole brain electric field distribution.

For example, fig. 5 is a perspective view of results of electric field simulation using multi-target electrode optimization results of transcranial electrical stimulation. As shown in FIG. 5, the electric field intensity represented by the color of the brain region template in the figure can be indicated by a color bar (colorbar) of the right electric field intensity, and the display range of the electric field intensity is 0-0.3V/m. The portions of the circles in fig. 5 represent the electrodes used, the color of which corresponds to the magnitude of the current flowing into or out of the electrode, which can be indicated by the color bar of the left current magnitude.

Fig. 6 is an electric field simulation image corresponding to the electrode optimization result when the weight parameter λ takes different values. The left part of fig. 6 is an image with λ taking a larger value, and the right part of fig. 6 is an image with λ taking a smaller value. As can be seen by comparison, when the weight parameter lambda is larger, the obtained electric field distribution is more diffuse, the focusing performance is relatively poorer, but the electric field intensity on the target point is larger (the electric field on the left image target point is 0.2989V/m, and the electric field on the right image target point is 0.2156V/m); in contrast, when the weight parameter lambda takes a smaller value, the focusing property of the electric field distribution is better, but the electric field intensity of the target point is lower.

Another embodiment of the present disclosure relates to an electrode optimizing device for transcranial electrical stimulation, as shown in fig. 7, comprising:

a construction module 701, configured to construct a head electric field model based on head image data of an individual;

a first determining module 702 for determining a target brain region to be electrically stimulated based on head structure of an individual like magnetic resonance data and head function magnetic resonance data;

a second determining module 703, configured to determine an electrode optimization constraint condition based on a head state of the individual and an actual condition of the transcranial electrical stimulation device;

the optimizing module 704 is configured to perform multi-objective optimization on an optimization objective of the objective brain region by using an electrode optimization constraint condition, so as to obtain an electrode optimization result; the optimization targets of the target brain region comprise the electric field intensity and the focusing property of the target brain region;

and the evaluation module 705 is used for quantitatively evaluating the optimization effect of the electrode optimization result by using the head electric field model.

Illustratively, the constructing module 701 is configured to construct a head electric field model based on head image data of an individual, including:

the construction module 701 is configured to: acquiring head image data; performing head normal brain tissue segmentation and head brain organic change region segmentation according to the head image data to obtain a plurality of brain tissue structure regions; respectively endowing each brain tissue structure area with corresponding dielectric characteristics; placing candidate stimulation electrodes on the scalp of the individual; and carrying out finite element calculation based on the dielectric characteristics and the candidate stimulation electrodes to obtain head induced electric field transfer matrixes of individuals under the electric stimulation of different electrode unit currents, so as to obtain a head electric field model.

Illustratively, the first determining module 702 is configured to determine a target brain region to be electrically stimulated based on head structure of an individual like magnetic resonance data and head function magnetic resonance data, including:

the first determining module 702 is configured to: analyzing the head functional magnetic resonance data and determining the brain activity intensity and functional connection intensity of the relevant brain region; registering the head structure image magnetic resonance data with the head function magnetic resonance data, acquiring the space coordinate information of the relevant brain region corresponding to the head function magnetic resonance data, and determining the target brain region by combining the brain map.

The head functional magnetic resonance data includes resting state functional magnetic resonance data and task state functional magnetic resonance data. The first determining module 702 is configured to analyze the head functional magnetic resonance data and determine brain activity intensity and functional connection intensity of a related brain region, and includes:

the first determining module 702 is configured to analyze the resting state functional magnetic resonance data and determine resting state brain activity, local consistency, low frequency amplitude, degree of center and functional connection strength of the brain region; and analyzing task state functional magnetic resonance data and determining the activation and functional connection strength.

Illustratively, the second determining module 703 is configured to determine the electrode optimization constraint based on the head state of the individual and the transcranial electrical stimulation device actual conditions, including:

The second determining module 703 is configured to: determining a candidate scalp region for placement of the electrode based on the scalp state of the individual; determining a region of brain organic change of the head as a non-target brain region based on a brain tissue state of the individual; and determining the single-channel current intensity and the number of electrodes adopted by the electric stimulation intervention based on the electric stimulation tolerance condition of the individual and the actual condition of the transcranial electric stimulation equipment.

Illustratively, the optimizing module 704 is configured to perform multi-objective optimization on an optimization objective of a target brain area using an electrode optimization constraint condition, to obtain an electrode optimization result, including:

the optimization module 704 is configured to:

determining an optimization objective function corresponding to a target brain region, wherein the optimization objective function is expressed as an electrode optimization formula of the following formula:

wherein s represents the current level of the electrode,represents the electric field intensity distribution required by the target brain region, lambda represents the weight parameter, E _{0.25d_max} Representing the electric field intensity at a coordinate point having a maximum distance of 1/4 from the target point;

based on the multi-target electrode optimization solution constraint condition, calculating and solving the optimization objective function to obtain an optimization solution of the optimization objective function under the multi-target electrode optimization solution constraint condition, and taking the optimization solution as an electrode optimization result.

Illustratively, the optimization module 704 is configured to determine a multi-objective electrode optimization solution constraint based on the electrode optimization constraint, including:

the optimization module 704 is configured to: determining candidate electrodes according to the candidate scalp regions; determining a candidate electric field transmission matrix according to the non-target brain region; determining the number of electrodes, the total electrode current and the single electrode current used for restraining the optimized objective function according to the single-channel current intensity and the number of electrodes adopted by the electrical stimulation intervention; the input current magnitude is defined as the output current magnitude according to the current conservation equation.

Illustratively, the evaluation module 705 is configured to quantitatively evaluate an optimization effect of an electrode optimization result using a head electric field model, including:

the evaluation module 705 is configured to: and calculating the electric field intensity and the focusing property of a target brain region corresponding to the electrode optimization result by using the head electric field model, and checking the electrode optimization effect corresponding to the electrode optimization result through the whole brain electric field distribution.

The specific implementation method of the transcranial electric stimulation electrode optimization device provided by the embodiment of the present disclosure may be described with reference to the transcranial electric stimulation electrode optimization method provided by the embodiment of the present disclosure, and will not be described herein.

Another embodiment of the present disclosure relates to an electronic device, as shown in fig. 8, comprising:

at least one processor 801; the method comprises the steps of,

a memory 802 communicatively coupled to the at least one processor 801; wherein,,

the memory 802 stores instructions executable by the at least one processor 801, the instructions being executable by the at least one processor 801 to enable the at least one processor 801 to perform the transcranial electrical stimulation electrode optimization method described in the above embodiments.

Where the memory and the processor are connected by a bus, the bus may comprise any number of interconnected buses and bridges, the buses connecting the various circuits of the one or more processors and the memory together. The bus may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or may be a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over the wireless medium via the antenna, which further receives the data and transmits the data to the processor.

The processor is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory may be used to store data used by the processor in performing operations.

Another embodiment of the present disclosure relates to a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method for optimizing electrodes for transcranial electrical stimulation described in the above embodiments.

That is, it will be understood by those skilled in the art that all or part of the steps of the method described in the above embodiments may be implemented by a program stored in a storage medium, including several instructions for causing a device (which may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the method described in the various embodiments of the disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific embodiments for carrying out the present disclosure, and that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure.

Claims

1. A method of optimizing an electrode for transcranial electrical stimulation, the method comprising:

acquiring the head image data;

placing candidate stimulation electrodes on the scalp of the individual;

2. The method of claim 1, wherein the determining a target brain region to be electrically stimulated based on the individual's head structure like magnetic resonance data and head function magnetic resonance data comprises:

3. The electrode optimization method of claim 2, wherein the head functional magnetic resonance data comprises resting state functional magnetic resonance data and task state functional magnetic resonance data;

4. The method of electrode optimization according to claim 1, wherein the determining an electrode optimization constraint based on the head state of the individual and the transcranial electrical stimulation device reality comprises:

5. The method of optimizing electrodes according to claim 4, wherein the performing multi-objective optimization on the optimization objective of the objective brain region using the electrode optimization constraint condition to obtain an electrode optimization result comprises:

6. The method of claim 5, wherein determining a multi-objective electrode optimization solution constraint based on the electrode optimization constraint comprises:

determining candidate electrodes according to the candidate scalp areas;

7. The method according to any one of claims 1 to 6, wherein the quantitatively evaluating the optimization effect of the electrode optimization result using the head electric field model comprises:

8. An electrode optimizing device for transcranial electrical stimulation, the electrode optimizing device comprising:

9. An electronic device, comprising:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the transcranial electrical stimulation electrode optimization method of any one of claims 1 to 7.

10. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method for optimizing electrodes for transcranial electrical stimulation according to any one of claims 1 to 7.