CN118634422B - Individualized non-invasive deep brain stimulation devices and related products - Google Patents
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Abstract
本申请提供了一种个体化无创脑深部电刺激装置及相关产品。对个体磁共振图像进行预处理，得到多个第一脑组织区域；对每个第一脑组织区域进行网格化处理，得到多个第一网格；获取与目标靶核团对应的多个候选电极组；确定每个候选电极组在每个第一网格处产生的电流密度峰值；基于每个候选电极组在每个第一网格处产生的电流密度峰值，确定每个候选电极组对目标靶核团的第一刺激效果；基于每个候选电极组对目标靶核团的第一刺激效果，从多个候选电极组中确定目标靶核团对应的目标电极组；通过目标电极组，对目标靶核团进行电刺激。通过无创的脑深部电刺激方式，准确地刺激到目标靶核团，提高电刺激的精度，从而提高脑深部电刺激的治疗效果。
The present application provides an individualized non-invasive deep brain electrical stimulation device and related products. Pre-process individual magnetic resonance images to obtain multiple first brain tissue regions; grid each first brain tissue region to obtain multiple first grids; obtain multiple candidate electrode groups corresponding to the target target nuclei; determine the current density peak generated by each candidate electrode group at each first grid; based on the current density peak generated by each candidate electrode group at each first grid, determine the first stimulation effect of each candidate electrode group on the target target nuclei; based on the first stimulation effect of each candidate electrode group on the target target nuclei, determine the target electrode group corresponding to the target target nuclei from multiple candidate electrode groups; and electrically stimulate the target target nuclei through the target electrode group. Through non-invasive deep brain electrical stimulation, the target target nuclei can be accurately stimulated, the accuracy of electrical stimulation can be improved, and thus the therapeutic effect of deep brain electrical stimulation can be improved.
Description
Technical Field
The application relates to the technical field of medical treatment, in particular to an individuation noninvasive deep brain electric stimulation device and related products.
Background
At present, when treating nervous system diseases such as parkinsonism, paralysis agitans, tic disease and the like, electrical stimulation is required to be performed on the deep brain to cause the response of brain neurons on the deep brain, thereby achieving the effect of nerve regulation. The prior art regulates brain neuron activity by deep brain electrical stimulation (Deep Brain Stimulation, DBS), by implanting electrodes into specific areas of the brain, and then by externally activating these electrodes. Although DBS is an effective treatment method, complications such as infection, hemorrhage or brain injury may be caused during the operation of implanting the electrode into a specific area of the brain, and there is a risk of operation and poor safety. Furthermore, a noninvasive deep brain electric stimulation mode is presented at present, and a low-frequency current is formed by the interference of two pairs of high-frequency currents, so that electric stimulation is performed on a specific area of the brain, and although the method does not have operation risks, the stimulation precision of the scheme is lower, so that the treatment effect of deep brain electric stimulation is affected.
Disclosure of Invention
The application provides a personalized noninvasive deep brain electric stimulation device and related products, which accurately stimulate target groups in a noninvasive deep brain electric stimulation mode, and improve the accuracy of deep brain electric stimulation, thereby improving the treatment effect of deep brain electric stimulation.
In a first aspect, the application provides an individualized noninvasive deep brain electric stimulation device, which comprises an individualized stimulation simulation navigation module and a stimulation module;
The personalized stimulation simulation navigation module is used for preprocessing the individual magnetic resonance image to obtain a plurality of first brain tissue areas corresponding to the individual magnetic resonance image;
the personalized stimulation simulation navigation module is used for carrying out gridding treatment on each first brain tissue region to obtain a plurality of first grids corresponding to the plurality of first brain tissue regions;
the personalized stimulation simulation navigation module is used for acquiring a plurality of candidate electrode groups corresponding to the target groups, and each candidate electrode group comprises two pairs of stimulation electrodes;
the personalized stimulation simulation navigation module is used for determining a current density peak value generated by each candidate electrode group at each first grid based on a first current density function and a second current density function, wherein the current density functions of two pairs of stimulation electrodes in each candidate electrode group are the first current density function and the second current density function respectively;
the personalized stimulation simulation navigation module is used for determining a first stimulation effect of each candidate electrode group on the target group based on a current density peak value generated by each candidate electrode group at each first grid;
the personalized stimulation simulation navigation module is used for determining a target electrode group corresponding to the target group from the plurality of candidate electrode groups based on the first stimulation effect of each candidate electrode group on the target group;
the stimulation module is used for electrically stimulating the target group through the target electrode group.
Optionally, the first current density function and the second current density function are based on, determining a current density peak value generated at each first grid for each candidate electrode group, wherein the personalized stimulation simulation navigation module is specifically used for:
Determining a first current density function and a second current density function for each candidate electrode set by finite element analysis, the third current density function and the fourth current density function generated at each first grid;
Acquiring an interference current density function formed by a third current density function and a fourth current density function generated at each first grid of each candidate electrode group;
And enveloping the interference current density function of each candidate electrode group at each first grid to obtain a current density peak value generated by each candidate electrode group at each first grid.
Optionally, the individualized stimulus simulation navigation module is specifically configured to, in terms of enveloping the interference current density function of each candidate electrode group at each first grid, obtain a current density peak value generated by each candidate electrode group at each first grid:
Performing Hilbert transformation on the interference current density function of each candidate electrode group at each first grid to obtain an analysis signal corresponding to the interference current density function of each candidate electrode group at each first grid;
determining the modulus of the analytic signal corresponding to each candidate electrode group at each first grid as an envelope curve corresponding to the interference current density function of each candidate electrode group at each first grid;
And determining the peak-to-peak value of the envelope curve corresponding to the interference current density function of each candidate electrode group at each first grid as the current density peak value generated by each candidate electrode group at each first grid.
In a second aspect, the application provides an individualized non-invasive brain deep electric stimulation method, which is applied to an individualized non-invasive brain deep electric stimulation device, wherein the individualized non-invasive brain deep electric stimulation device comprises an individualized stimulation simulation navigation module and a stimulation module; the method comprises the following steps:
preprocessing an individual magnetic resonance image to obtain a plurality of first brain tissue areas corresponding to the individual magnetic resonance image;
Performing gridding treatment on each first brain tissue region to obtain a plurality of first grids corresponding to the plurality of first brain tissue regions;
acquiring a plurality of candidate electrode groups corresponding to the target group, wherein each candidate electrode group comprises two pairs of stimulation electrodes;
Determining a current density peak generated at each first grid for each candidate electrode set based on a first current density function and a second current density function, wherein the current density functions of the two pairs of stimulation electrodes in each candidate electrode set are the first current density function and the second current density function, respectively;
determining a first stimulation effect of each candidate electrode set on the target group based on the current density peaks generated by each candidate electrode set at each first grid;
determining a target electrode group corresponding to the target group from the plurality of candidate electrode groups based on a first stimulation effect of each candidate electrode group on the target group;
and electrically stimulating the target group through the target electrode group.
In a third aspect, the present application provides an electrode set determining device for personalized noninvasive deep brain electrical stimulation, comprising: the individuation stimulation simulation navigation module;
the personalized stimulation simulation navigation module is used for preprocessing the group magnetic resonance image to obtain a plurality of second brain tissue areas corresponding to the group magnetic resonance image;
The personalized stimulation simulation navigation module is used for carrying out gridding treatment on each second brain tissue region to obtain a plurality of second grids corresponding to the plurality of second brain tissue regions;
The personalized stimulation simulation navigation module is used for randomly combining a plurality of stimulation electrodes to obtain a plurality of electrode groups, wherein each electrode group comprises two pairs of stimulation electrodes;
An individualized stimulus simulation navigation module for determining, for a first target cluster, a current density peak generated at each second grid for each electrode set based on the first current density function and the second current density function;
The individualized stimulation simulation navigation module is used for determining a second stimulation effect of each electrode group on a first target group based on a current density peak value generated at each second grid by the electric stimulation of each electrode group, wherein the first target group is any one of a plurality of target groups, and the target group is one of a plurality of target groups;
And the personalized stimulation simulation navigation module is used for determining a plurality of candidate electrode groups corresponding to the first target group from the plurality of electrode groups based on the second stimulation effect of each electrode group on the first target group.
In a fourth aspect, the present application provides a method for determining an electrode set for personalized noninvasive deep brain electrical stimulation, comprising:
preprocessing the group magnetic resonance image to obtain a plurality of second brain tissue areas corresponding to the group magnetic resonance image;
performing gridding treatment on each second brain tissue region to obtain a plurality of second grids corresponding to the plurality of second brain tissue regions;
randomly combining a plurality of stimulation electrodes to obtain a plurality of electrode groups, wherein each electrode group comprises two pairs of stimulation electrodes;
determining, for a first target cluster, a current density peak generated at each second grid for each electrode set based on the first current density function and the second current density function;
Determining a second stimulation effect of each electrode set on the first target cluster based on a current density peak generated at each second grid by the electrical stimulation of each electrode set, wherein the first target cluster is any one of the plurality of target clusters, the target cluster is one of the plurality of target clusters;
A plurality of candidate electrode sets corresponding to the first target group is determined from the plurality of electrode sets based on a second stimulation effect of each electrode set on the first target group.
In a fifth aspect, the present application provides an electronic device, comprising: a processor and a memory, the processor being connected to the memory, the memory being for storing a computer program, the processor being for executing the computer program stored in the memory to cause the electronic device to perform the method as in the second and fourth aspects.
In a sixth aspect, the present application provides a computer readable storage medium storing a computer program which when executed by a processor implements the methods of the second and fourth aspects.
In a seventh aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the methods as in the second and fourth aspects.
The embodiment of the application has the following beneficial effects:
It can be seen that, in the embodiment of the present application, the individual magnetic resonance image is preprocessed, so as to obtain a plurality of first brain tissue areas corresponding to the individual magnetic resonance image; performing gridding treatment on each first brain tissue region to obtain a plurality of first grids corresponding to the plurality of first brain tissue regions; acquiring a plurality of candidate electrode groups corresponding to a target group, wherein each candidate electrode group comprises two pairs of stimulation electrodes; determining a current density peak generated at each first grid for each candidate electrode set based on the first current density function and the second current density function, wherein the current density functions of the two pairs of stimulation electrodes in each candidate electrode set are the first current density function and the second current density function, respectively; determining a first stimulation effect of each candidate electrode set on the target group based on the current density peaks generated by each candidate electrode set at each first grid; and determining a target electrode group corresponding to the target group from the plurality of candidate electrode groups based on the first stimulation effect of each candidate electrode group on the target group. And electrically stimulating the target group through the target electrode group. According to the application, deep brain electrical stimulation is performed on a subject in a noninvasive mode, so that the possible operation risk caused by implanted deep brain electrical stimulation can be effectively avoided, and the safety of deep brain electrical stimulation treatment is improved.
Further, when the noninvasive deep brain electric stimulation is performed, a target electrode group is selected from the candidate electrode groups, namely, a first stimulation effect of each candidate electrode group on the target group is determined based on a current density peak value generated at each first grid by a first current density function and a second current density function output by two pairs of stimulation electrodes in each candidate electrode group, then a target electrode group corresponding to the target group is determined from a plurality of candidate electrode groups based on the first stimulation effect of each candidate electrode group on the target group, namely, the stimulation effect is used as a standard for determining a target electrode group corresponding to the target group, so that the target electrode group with the best stimulation effect on the target group in a plurality of candidate electrode groups is determined, then the target electrode group is used for electrically stimulating the target group, ensures that the target electrode group can accurately stimulate the target group, and the stimulation effect of the electric stimulation output by the target electrode group on the target group is optimal, thereby improving the accuracy of the noninvasive deep brain electric stimulation and the treatment effect of the noninvasive deep brain electric stimulation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of a scenario of an individualized non-invasive deep brain electrical stimulation provided by an embodiment of the present application;
fig. 2 is a schematic diagram of an individualized non-invasive deep brain stimulation device according to an embodiment of the present application;
FIG. 3 is a schematic view of another personalized non-invasive deep brain stimulation device provided by an embodiment of the present application;
FIG. 4 is a schematic diagram of a current density function according to an embodiment of the present application;
Fig. 5 is a schematic flow chart of a personalized noninvasive deep brain electrical stimulation method according to an embodiment of the present application;
Fig. 6 is a schematic diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, result, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to facilitate understanding of the technical scheme of the present application, explanation and explanation of the present application related to the related art will be first provided.
Matrix laboratory (Matrix Laboratory, MATLAB) MATLAB is a commercial mathematical software manufactured by MathWorks company, and is mainly used in the fields of data analysis, wireless communication, deep learning, image processing and computer vision, signal processing, control system and the like. The system integrates the functions of numerical analysis, matrix calculation, scientific data visualization, modeling and simulation of a nonlinear dynamic system and the like, provides a window environment easy to use, and gets rid of the editing mode of the traditional non-interactive programming language.
Target nuclei refer to target nuclei that need to be precisely affected when performing a particular medical procedure or treatment. For different therapeutic diseases, different target groups are corresponding, for example, in deep brain electrical stimulation, the target group corresponding to Parkinson is the subthalamic nucleus. The target nucleic acid is part of the brain tissue and the different target nucleic acid corresponds to different brain tissue areas, e.g. the subthalamic nucleus is part of the gray matter in the brain tissue. It should be noted that the target group in the present application refers to a target group corresponding to a disease to be treated by a subject.
An individual magnetic resonance image, which refers to an image of the brain structure of a subject acquired by magnetic resonance imaging (Magnetic Resonance Imaging, MRI) techniques, is of particular interest to the longitudinal relaxation time ( T1) weighting characteristics of the brain, and T1 weighting images reflect mainly the time required for the tissue to spin back to equilibrium in the magnetic field, , which is important for understanding normal and abnormal brain structures. T1 weighted imaging is one of the most common imaging modes in MRI, and can highlight the moisture content and fat content in brain tissue, and in brain structure images obtained by the T1 imaging mode, structures such as cerebrospinal fluid, white matter, gray matter and the like can be clearly displayed.
Group magnetic resonance image: the population magnetic resonance image is also a T1 weighted image acquired by MRI techniques, and the production of the population magnetic resonance image typically involves the use of MRI scan data of multiple individuals, , processed and integrated by specific software, to generate a standardized population magnetic resonance image, , for teaching and research reference. this population magnetic resonance image integrates common features of multiple individuals, provides a relatively universal brain structure, aids in understanding and interpretation in clinical applications. It should be noted that, in the present application, the group magnetic resonance image may also be referred to as an internationally universal standard magnetic resonance image.
A direct current motor control circuit: the most widely used direct current motor drive is an H-type circuit, and the driving circuit conveniently realizes four-quadrant operation of the direct current motor and corresponds to forward rotation, forward rotation braking, reverse rotation and reverse rotation braking respectively.
10-20 Electrode system: is a scalp electroencephalogram recording electrode placement method widely used in clinic and research at present. Its naming stems from the fact that the electrode positions are determined from the cranial anatomical landmarks, the distance between the electrodes being approximately 10% or 20% of the full circumference length.
Referring to fig. 1, fig. 1 is a schematic view of a scenario of an individualized non-invasive deep brain electrical stimulation provided by an embodiment of the present application.
As shown in fig. 1, when the individual noninvasive deep brain electric stimulation is performed on a subject, a coil and a stimulation electrode are placed on the brain of the subject, and the deep brain electric stimulation is performed on the subject through a target electrode group in the individual noninvasive deep brain electric stimulation device, wherein the stimulation electrode can be arranged on the basis of a 10-20 electrode system.
Specifically, before deep brain electrical stimulation is carried out on a subject through a target electrode group in an individualized non-invasive deep brain electrical stimulation device, preprocessing is carried out on an individual magnetic resonance image through the individualized non-invasive deep brain electrical stimulation device, and a plurality of first brain tissue areas corresponding to the individual magnetic resonance image are obtained, wherein the plurality of first brain tissue areas comprise target groups; performing gridding treatment on each first brain tissue region to obtain a plurality of first grids corresponding to the plurality of first brain tissue regions; acquiring a plurality of candidate electrode groups corresponding to a target group, wherein each candidate electrode group comprises two pairs of stimulation electrodes; determining a current density peak generated at each first grid for each candidate electrode set based on the first current density function and the second current density function, wherein the current density functions of the two pairs of stimulation electrodes in each candidate electrode set are the first current density function and the second current density function, respectively; determining a first stimulation effect of each candidate electrode set on the target group based on the current density peaks generated by each candidate electrode set at each first grid; and determining a target electrode group corresponding to the target group from the plurality of candidate electrode groups based on the first stimulation effect of each candidate electrode group on the target group. After the target electrode group is determined, the target group in the brain tissue of the subject is electrically stimulated through the target electrode group.
Referring to fig. 2, fig. 2 is a schematic diagram of an individualized non-invasive deep brain stimulation device according to an embodiment of the present application.
As shown in fig. 2, the personalized noninvasive deep brain electric stimulation device comprises a personalized stimulation simulation navigation module and a stimulation module, and in the application, the personalized stimulation simulation navigation module can be equivalent to a processing module.
When the subject is treated, the target electrode group corresponding to the target group is determined from the plurality of candidate electrode groups through the personalized stimulation simulation navigation module, and then the target electrode group is electrically stimulated by the stimulation module. It should be noted that the individualized stimulation simulation navigation module and the stimulation module in the present application may exist independently or may be integrated in one processing unit, and the present application is not limited herein.
Referring to fig. 3, fig. 3 is a schematic diagram of another personalized noninvasive deep brain stimulation device according to an embodiment of the present application.
As shown in fig. 3, the personalized noninvasive deep brain electric stimulation device comprises a personalized stimulation simulation navigation module and a stimulation module, wherein the stimulation module further comprises a main controller and two groups of current generation modules (such as a first group of current generation modules and a second group of current generation modules in fig. 3), and further each group of current generation modules comprises a waveform generator and a direct current motor control circuit (such as a first waveform generator, a second waveform generator, a first direct current motor control circuit and a second direct current motor control circuit in fig. 3). In addition, the individuation noninvasive deep brain electric stimulation device further comprises a touch screen display and keys, and each group of current generation modules further comprises: current control means (e.g. first current control means, second current control means in fig. 3), the stimulation module further comprises: electrode loop resistance detection means.
It should be noted that the touch screen display is connected with the personalized stimulation simulation navigation module, the main controller and the keys; one end of the individualized stimulation simulation navigation module is connected with the touch screen display, the other end of the individualized stimulation simulation navigation module is connected with a main controller in the stimulation module, and the main controller in the stimulation module is connected with the touch screen display, the keys, the individualized stimulation simulation navigation module, the first waveform generator in the first group of current generation modules, the second waveform generator in the second group of current generation modules and the electrode loop resistance detection device; one end of a first waveform generator in the first group of current generation modules is connected with a first current control device in the first group of current generation modules, and the other end of the first waveform generator is connected with the main controller; one end of a first current control device in the first group of current generation modules is connected with a first waveform generator in the first group of current generation modules, and the other end of the first current control device is connected with a first direct current motor control circuit. One end of a second waveform generator in the second group of current generation modules is connected with a second current control device in the second group of current generation modules, and the other end of the second waveform generator is connected with the main controller; one end of a second current control device in the second group of current generation modules is connected with a second waveform generator in the second group of current generation modules, and the other end of the second current control device is connected with a second direct current motor control circuit; the electrode loop resistance detection device is connected with the first current control device, the second current control device and the main controller.
Specifically, an operator of the personalized noninvasive deep brain electrical stimulation device can set stimulation related parameters and target clusters through a touch screen display, wherein the stimulation related parameters comprise a first current peak value, an area of a stimulation electrode, a first frequency, a second current peak value, a second frequency and the like. After the personalized stimulation simulation navigation module acquires the target group, the target electrode group is determined from a plurality of candidate electrode groups for the target group based on the individual magnetic resonance image of the subject, after the personalized stimulation simulation navigation module determines the target electrode, the stimulation module electrically stimulates the target group through the target electrode group. Specifically, the stimulation module includes: the device comprises a main controller and two groups of current generation modules, wherein the main controller is used for outputting a first stimulation current through one group of current generation modules in the two groups of current generation modules based on a first current peak value, the area of a stimulation electrode and a first frequency, the one group of current generation modules can be the first group of current generation modules in fig. 3 or the second group of current generation modules, and for convenience of description, the application takes the one group of current generation modules as the first group of current generation modules in fig. 3 as an example; and outputting a second stimulation current through another one of the two sets of current generating modules based on the second current peak value, the area of the stimulation electrode and the second frequency, wherein the another one of the two sets of current generating modules may be the first set of current generating modules or the second set of current generating modules in fig. 3, and for convenience of description, the present application is described taking the another one of the two sets of current generating modules as the second set of current generating modules in fig. 3 as an example; the peak value of the first stimulation current is the ratio of the peak value of the first current to the area, the frequency is the first frequency, the peak value of the second stimulation current is the ratio of the peak value of the second current to the area, and the frequency is the second frequency. That is, the frequency and amplitude of the output waveform of the first stimulation current and the output waveform of the first current density function are equal, and the frequency and amplitude of the output waveform of the second stimulation current and the output waveform of the second current density function are equal.
Specifically, based on the first current peak value, the area and the first frequency, the waveform generator in the current generation module is controlled to output a third stimulation current, the third stimulation current is converted by the direct current motor control circuit in the current generation module, so that the first stimulation current is output by the direct current motor control circuit in the current generation module, wherein the frequency of the third stimulation current is the first frequency, the peak value of the third stimulation current is the ratio of the first current peak value to the area, and the third stimulation current only comprises the positive half cycle of the first stimulation current. The main controller controls the waveform generator (corresponding to the first waveform generator in fig. 3) in the set of current generation modules to output the third stimulus current, wherein, in order to simplify the circuit design of the waveform generator, the first waveform generator only sends the direct current, i.e. the third stimulus current only including the positive half cycle of the first stimulus current. The third stimulus current is then converted by a dc motor control circuit (corresponding to the first dc motor control circuit in fig. 3) in the set of current generation modules, wherein the dc motor control circuit may form a full-period ac sine wave (i.e., the first stimulus current) composed of a positive half-cycle and a negative half-cycle based on the positive half-cycle current output from the waveform generator, that is, the dc motor control circuit (corresponding to the first dc motor control circuit in fig. 3) in the set of current generation modules converts the third stimulus current into the first stimulus current and then outputs the first stimulus current.
And controlling a waveform generator in the other group of current generation modules to output a fourth stimulation current based on the second current peak value, the area and the second frequency, and transforming the fourth stimulation current through a direct current motor control circuit in the other group of current generation modules to output the second stimulation current through the direct current motor control circuit in the other group of current generation modules, wherein the frequency of the fourth stimulation current is the second frequency, the peak value of the fourth stimulation current is the ratio of the second current peak value to the area, and the fourth stimulation current only comprises the positive half cycle of the second stimulation current. The main controller controls the waveform generator (corresponding to the second waveform generator in fig. 3) in the other group of current generation modules to output the fourth stimulating current, and then the fourth stimulating current is converted by the direct current motor control circuit (corresponding to the second direct current motor control circuit in fig. 3) in the other group of current generation modules, wherein the direct current motor control circuit can form a full-period alternating current sine wave (namely, the second stimulating current) consisting of a positive half cycle and a negative half cycle based on the positive half cycle current output by the waveform generator, that is, the direct current motor control circuit (corresponding to the second direct current motor control circuit in fig. 3) in the other group of current generation modules converts the fourth stimulating current into the second stimulating current, and then outputs the second stimulating current.
A set of current generation modules for outputting a first stimulation current to a target group through a pair of stimulation electrodes in the target electrode set; the direct current motor control circuit is connected with the plurality of stimulating electrodes and is used for sending current to the stimulating electrodes. Outputting a first stimulation current to a target group through a pair of stimulation electrodes in the target electrode group, specifically comprising: a direct current motor control circuit in the current generation module outputs a first stimulating current to a target group through a pair of stimulating electrodes in the target electrode group; and the other group of current generation modules is used for outputting a second stimulation current to the target group through the other pair of stimulation electrodes in the target electrode group, and particularly, a key can be pressed to electrically stimulate the target group.
Further, the stimulation module further comprises: electrode loop resistance detection means; each set of current generation modules further includes: and one end of a second current control device in the other group of current generation modules is connected with a second waveform generator in the other group of current generation modules, and the other end of the second current control device is connected with a second direct current motor control circuit. The electrode loop resistance detection device is respectively connected with a first current control device in one group of current generation modules, a second current control device in the other group of current generation modules and the main controller.
It should be noted that, the first current control device is used for monitoring the current sent by the first waveform generator in real time, the second current control device is used for monitoring the current sent by the second waveform generator in real time, and sending the current sending condition to the electrode loop resistance detection device, the electrode loop resistance detection device is used for monitoring the impedance of the current sent by the first waveform generator and the impedance of the current sent by the second waveform generator, when the impedance is higher than a preset value, information of over-high impedance is sent to the main controller, and then the main controller stops sending the electrical stimulation to the target electrode group so as to prevent burning of the skin of the subject or other dangerous behaviors.
It should be noted that the individualized stimulus simulation navigation module, the touch screen display, the key, the main controller, each current generation module, and the electrode loop resistance detection device in the present application may exist independently or may be integrated in one processing unit, and the present application is not limited herein.
It should be noted that the steps performed by the personalized non-invasive deep brain stimulation device in the present application may be divided mainly into two parts, the first being an off-line processing part and the second being an on-line processing part. The off-line processing part is to determine candidate electrode groups corresponding to each target group through the group magnetic resonance image, wherein different treatment purposes correspond to different target groups. The on-line processing part is aimed at individual magnetic resonance images of each specific subject, the treatment purpose of the subject is determined, a target group corresponding to the treatment purpose is determined, then a candidate electrode group corresponding to the target group is obtained according to the result of off-line processing, finally, a final target electrode group is determined in the candidate electrode groups corresponding to the target group, and the target group is electrically stimulated through the target electrode group, so that the treatment purpose of the subject is achieved. Accordingly, the present application will be described with reference to the specific contents of the offline processing section.
For the off-line processing part, in the aspect of determining a candidate electrode group corresponding to each target group based on the group magnetic resonance image, the specific content of the execution of the personalized stimulation simulation navigation module is as follows: preprocessing the group magnetic resonance image to obtain a plurality of second brain tissue areas corresponding to the group magnetic resonance image, wherein the second brain tissue areas comprise a plurality of target groups; performing gridding treatment on each second brain tissue region to obtain a plurality of second grids corresponding to the second brain tissue regions; randomly combining a plurality of stimulation electrodes to obtain a plurality of electrode groups, wherein each electrode group comprises two pairs of stimulation electrodes; determining a second stimulation effect of each electrode set on the first target set based on a current density peak generated at each second grid by the electrical stimulation of each electrode set for the first target set, wherein the first target set is any one of the plurality of target sets; a plurality of candidate electrode sets corresponding to the first target group is determined from the plurality of electrode sets based on the second stimulation effect of each electrode set on the first target group. Based on the method of determining the plurality of candidate electrode sets corresponding to the first target group, the plurality of candidate electrode sets corresponding to each target group are determined. That is, in the off-line process, the plurality of candidate electrode groups corresponding to each target group are determined in advance, so that the plurality of candidate electrodes corresponding to the target group, which is one of the plurality of target groups, can be directly obtained after the target group is determined in the on-line process, that is, the target group is a target group corresponding to the therapeutic purpose of the subject among the plurality of target groups.
Optionally, preprocessing the group magnetic resonance image to obtain a plurality of second brain tissue areas corresponding to the group magnetic resonance image. The group magnetic resonance image is a standardized magnetic resonance image integrating common features of a plurality of individuals, provides a relatively universal brain structure, so that the group magnetic resonance image can be subjected to simulation processing offline, and therefore the first stimulation effect of each electrode group on each target group when most subjects receive noninvasive deep brain electrical stimulation is simulated. Specifically, preprocessing the group magnetic resonance image includes: image segmentation and sequence registration are performed on the population magnetic resonance images.
Specifically, image segmentation and sequence registration of the group magnetic resonance image includes: in the group magnetic resonance image, firstly, the skull, the cerebrospinal fluid, the gray matter, the white matter and the segmented image of each target group are identified through an image segmentation technology. The purpose of the segmentation is to extract each brain tissue and target nucleic acid, providing a clear, independent image for further analysis. The image segmentation technique may be a threshold segmentation method, a cluster segmentation method, or the like, and the present application is not limited to a specific image segmentation technique. Because the segmented images obtained by the method may come from different imaging angles or sequences, spatial registration of the segmented images is required, so that the same structure in different segmented images can be accurately corresponding, consistency of the different segmented images in spatial positions can be ensured by mathematical transformation methods such as translation, rotation and scaling, the segmented images are converted into the same standard space, and a three-dimensional simulation model consisting of the segmented images of each brain tissue is obtained, so that the segmented images can be conveniently processed later.
Further, binarization processing is performed on the segmented image after segmentation registration, namely, various tissue structures in the segmented image after segmentation registration are converted into images with only two values (usually 0 and 1), so that the subsequent image analysis process is simplified. In this step, each brain tissue structure in the segmented registered segmented image can be clearly distinguished, facilitating the subsequent steps. And because small holes may be created in the brain tissue during the binarization process that should not be present. Therefore, it is necessary to fill the generated holes by smoothing to ensure continuity of brain tissue structures in the divided images, and for convenience of description, the divided image of each brain tissue after registration, binarization, and smoothing is referred to as a plurality of second brain tissue regions corresponding to the clustered magnetic resonance image, the plurality of second brain tissue regions including a plurality of target clusters. Because the target nucleic acid is present in brain tissue, the plurality of target nucleic acid is included in the plurality of second brain tissue regions. The plurality of second brain tissues corresponding to the clustered magnetic resonance images are in a three-dimensional simulation model of the same standard space.
Illustratively, gridding processing is performed on each second brain tissue region, so as to obtain a plurality of second grids corresponding to the plurality of second brain tissue regions. Specifically, gridding treatment is performed on each second brain tissue region by a finite element analysis method, that is to say, gridding is performed on the second brain tissue regions in the three-dimensional simulation model by finite element analysis software, so as to obtain a plurality of second grids corresponding to the second brain tissue regions. The size of each grid can be determined according to a preset value. Since the different second brain tissues have different relative dielectric constants and conductivities, it is necessary to correspond each second mesh to the second brain tissue region corresponding to the second mesh, thereby obtaining the relative dielectric constant and the conductivity of each second mesh.
Illustratively, the personalized noninvasive deep brain electro-stimulation device further comprises a plurality of stimulation electrodes, so that the plurality of stimulation electrodes can be randomly combined to obtain a plurality of electrode groups, wherein each electrode group comprises two pairs of stimulation electrodes, and each pair of stimulation electrodes comprises one positive electrode and one negative electrode, namely each electrode group comprises 4 stimulation electrodes. Specifically, the combination mode may be C (n, 4), where n is the number of the plurality of stimulating electrodes, and C (n, 4) represents the number of all combinations of 4 stimulating electrodes taken out of n different stimulating electrodes.
The first stimulation effect of each electrode set at each target bolus is illustratively determined based on the current density peaks generated by the electrical stimulation of each electrode set at each second grid. For convenience of explanation, the present application will be described with reference to a first target group, which is any one of a plurality of target groups, by determining a second stimulation effect of each electrode group on the first target group, with respect to a current density peak generated at each second grid based on electrical stimulation of each electrode group. The method for determining the first stimulating effect of each electrode group on each target group is similar to the method for determining the second stimulating effect of each electrode group on the first target group, and will not be repeated.
It should be noted that, the method for determining the first current density function and the second current density function in the present application specifically includes: acquiring a preset first current peak value, a preset second current peak value, a preset area of a stimulation electrode, a preset first frequency and a preset second frequency, wherein the first current peak value and the preset second current peak value can be the same or different; also, the area of each stimulation electrode may be the same or different, but the present application is not limited thereto, but for convenience of calculation and description, the present application is described by taking the example that the area of each stimulation electrode is the same. It should be noted that the present application may also determine the second frequency by acquiring the first frequency and the difference frequency, but the difference between the first frequency and the second frequency should be smaller than a preset value, so that the frequencies of the first current density function and the second current density function are similar. The method comprises the steps of determining a first current density function based on a first current peak value, an area of a stimulation electrode and a first frequency, wherein the amplitude of the first current density function (for convenience of description, the amplitude of the first current density function is called a first amplitude) is a ratio of the first current peak value to the area, the frequency is the first frequency, and the phase is a preset first phase.
The first current density function may be determined by the formula (1)To express:
Formula (1)
Wherein, At the first amplitude of the light is of a first amplitude,For the first frequency to be the first frequency,In the first phase of the phase-change phase,Is time.
And determining a second current density function based on the second current peak value, the area and the second frequency, wherein the amplitude of the second current density function (for convenience of description, the amplitude of the second current density function is called as second amplitude) is the ratio of the second current peak value to the area, the frequency is the second frequency, and the phase is a preset second phase.
The second current density function may be determined by the formula (2)To express:
Formula (2)
Wherein, At a second amplitude, f 2 is a second frequency,In the second phase of the phase-change phase,Is time.
A current density peak generated at each second grid by the electrical stimulation of each electrode set is determined based on the first current density function and the second current density function, wherein the current density functions of the two pairs of stimulation electrodes in each electrode set are the first current density function and the second current density function, respectively.
Further, determining a current density peak generated at each second grid by the electrical stimulation of each electrode group based on the first current density function and the second current density function, comprising: determining a first current density function and a second current density function for each electrode set by finite element analysis, a third current density function and a fourth current density function generated at each second grid, wherein the first current density function corresponds to the third current density function and the second current density function corresponds to the fourth current density function; specifically, the relative permittivity and conductivity of each second grid may be input into finite element analysis software to obtain a first current density function and a second current density function for each electrode set, and a third current density function and a fourth current density function generated at each second grid. The frequency and phase of the third and fourth current density functions generated at each second grid are not changed, so the third and fourth current density functions are still two electrical signals with similar frequencies, but the amplitudes of the third and fourth current density functions generated at each second grid can be obtained by finite element analysis software due to the difference in the position, relative dielectric constant and conductivity of each second grid from the stimulating electrode, and the first and second current density functions are attenuated to different extents during transmission. It should be noted that the specific finite element analysis software may be selected to have electromagnetic field analysis capabilities, and the present application is not limited herein.
As shown in FIG. 4, the third current density function (e.g., E 3 in FIG. 4) and the fourth current density function (e.g., E 4 in FIG. 4) generated by the first electrode group at the jth second grid are illustrated as examples, wherein the jth second grid is any one of the plurality of second grids, the third current density function generated by the first electrode group at the jth second grid has an amplitude of a third amplitude, the fourth current density function generated by the first electrode group at the jth second grid has an amplitude of a fourth amplitude, and the third current density function generated by the first electrode group at the jth second grid may be calculated by the third current density function in the equation (3)To express:
Formula (3)
Wherein, For the third amplitude of the light to be of the third amplitude,For the first frequency to be the first frequency,In the first phase of the phase-change phase,Is time.
The fourth current density function generated by the first electrode set at the jth second grid may be calculated by the formula (4)To express:
Formula (4)
Wherein, For a fourth amplitude of the vibration, the first amplitude,For the second frequency to be the same as the first frequency,In the second phase of the phase-change phase,Is time.
And obtaining an interference current density function formed by the third current density function and the fourth current density function generated by each electrode group at each first grid, specifically adding the third current density function and the fourth current density function generated by each first grid to obtain an interference current density function formed by the third current density function and the fourth current density function generated by each electrode group at each second grid.
Continuing with the example described above, as shown in FIG. 4, a third current density function is generated at the jth second grid for the first electrode set (as shown in FIG. 4) And a fourth current density function (as in FIG. 4) Forming an interference current density function (as in FIG. 4) The interference current density function generated by the first electrode group at the j-th second grid can be calculated by the formula (5)To express:
+ Formula (5)
Wherein, Is in formula (3)，Is in the formula (4)。
And adding the third current function and the fourth current function to obtain an interference current density function. Even if the frequencies of the two waves are not exactly the same, they can still interfere as long as the frequencies are similar, but the interference pattern formed at this time will take the form of amplitude modulation, i.e. envelope. The interference current density function of each electrode group at each second grid is enveloped to obtain a current density peak value generated by each electrode group at each second grid, when two electric signals with similar frequencies and output through the third current density function and the fourth current density function meet in a plurality of second brain tissue areas, the peaks and the troughs do not arrive completely synchronously due to the difference of the frequencies, so that vibration of certain areas is enhanced (constructive interference) at certain moments, and vibration is weakened (destructive interference) at other moments. This periodic amplitude variation forms an envelope. The envelope itself is a slowly varying waveform whose frequency is equal to the difference between the frequencies of the two waves, a phenomenon also known as beat frequency. That is, the interference current density function of each electrode group at each second grid is enveloped, so that a low-frequency electric signal with the frequency of difference frequency can be obtained, and the electric stimulation generated by the low-frequency electric signal at the second grid can reach the deep brain and can cause the response of brain neurons in the second grid, thereby achieving the effect of the electric stimulation on the nerve regulation.
Further, enveloping the interference current density function of each electrode group at each second grid to obtain a current density peak value generated by each electrode group at each second grid, including: and performing Hilbert transformation on the interference current density function of each electrode group at each second grid to obtain an analysis signal corresponding to the interference current density function of each electrode group at each second grid. Specifically, the hilbert transformation is performed on the interference current density function of each electrode group at each second grid, so as to obtain a hilbert transformed function of the interference current density function of each electrode group at each second grid (for convenience of description, the hilbert transformed function of each second grid is simply called), then the interference current density function of each electrode group at each second grid is taken as a real part, the hilbert transformed function of each second grid is taken as an imaginary part, and an analytic signal corresponding to the interference current density function of each electrode group at each second grid is formed, wherein the amplitude and the phase of the analytic signal contain all information of the interference current density function of each electrode group at each second grid. Determining the corresponding module of the analysis signal of each electrode group at each second grid as the corresponding envelope curve of the interference current density function of each electrode group at each second grid; the peak-to-peak value of the envelope curve corresponding to the interference current density function of each electrode group at each second grid is determined as the current density peak value generated by each electrode group at each second grid, i.e. the difference between the maximum value and the minimum value of the envelope curve. As shown in fig. 4, Δe is the peak-to-peak value of the envelope curve corresponding to the interference current density function of the first electrode group at the j-th second grid, and thus Δe is determined as the current density peak value generated by the first electrode group at the j-th second grid. It should be noted that, the envelope curve of the interference current density function of each electrode set at each second grid may also be directly obtained by the MATLAB tool, where the envelope curve corresponding to each electrode set is also a sine wave, the frequency of the sine wave is the difference frequency between the first frequency and the second frequency, the phase is the third phase, and the amplitude is the current density peak value generated by each electrode set at each second grid, where the difference frequency, the third phase, and the current density peak value generated by each electrode set at each second grid may be directly obtained by the MATLAB tool, which is not described herein in detail.
Continuing along with the envelope of the interference current density function of the first electrode set at the j-th second grid by way of example described above, may be determined by the method of equation (6)To express:
wherein a 5 is the current density peak generated by the first electrode set at the j-th second grid, Is the difference frequency between the first frequency and the second frequency,In the case of the third phase of the phase,Is time.
As shown in FIG. 4, the envelope of the interference current density function of the first electrode group at the jth second grid isThe current density peak generated by the first electrode set at the j-th second grid is deltae in fig. 4.
Because the current density is the distribution of the current in a unit area, when the current passes through the second brain tissue region, the current will generate charge distribution in the second brain tissue region, so as to form an electric field, and the electric field will affect the brain neurons in the second brain tissue region, so as to perform the effect of neuromodulation on the brain neurons. Further, since the higher the current density is, the higher the electric field intensity is, and the larger the influence of the electric field on the brain neurons in the second brain tissue region is, the current density is directly related to the effect of the deep brain electrical stimulation treatment, and therefore the effect of the electrical stimulation on the brain neurons can be evaluated by using the current density peak value.
For example, for a first target group, a second stimulation effect of each electrode group on the first target group is determined based on a current density peak generated at each second grid by the electrical stimulation of each electrode group. Specifically, a plurality of third grids corresponding to the first target group in the plurality of second grids can be directly obtained, the sum of current density peaks generated at each third grid by the electric stimulation of each electrode group is obtained, and the sum of current density peaks generated at each third grid by the electric stimulation of each electrode group is directly used as the second stimulation effect of each electrode group on the first target group.
Optionally, determining, for the first target group, a second stimulation effect of each electrode group on the first target group based on a current density peak generated at each second grid by the electrical stimulation of each electrode group may also include: the current density peaks of each electrode set at each second grid are ordered, in particular in order from large to small. A first threshold value for each electrode set is determined based on the ranking result of each electrode set, in particular based on a preset quantile such that a value of the preset percentage is smaller than the first threshold value, for example, for the ranking result of the first current density peaks of the first electrode set at each second grid, the first electrode set is any one of the plurality of electrode sets, the preset quantile may be eighty percent, that is, there is eighty percent of the first current density peaks being smaller than the first threshold value. And because the current density peak value can evaluate the stimulation effect of the electrical stimulation on the cerebral neurons, that is, if the current density peak value generated at a certain second grid is larger than a first threshold value under the stimulation of the first electrode group, the stimulation effect of the first electrode group on the cerebral neurons at the second grid exceeds the stimulation effect of the first electrode group on the cerebral neurons at the eighty percent other second grids, and the stimulation effect of the first electrode group on the cerebral neurons at the second grid can be considered to reach the preset standard. Therefore, the first threshold may be used to measure whether the current density peak value generated by each electrode set at the second grid meets the preset standard, i.e. if the current density peak value generated by the first electrode set at the second grid is smaller than the first threshold, the stimulation effect of the first electrode set on the brain neurons at the second grid does not meet the preset standard; if the peak value of the current density generated by the first electrode group at the second grid is larger than the first threshold value, the stimulation effect of the first electrode group on the brain neurons at the second grid reaches the preset standard. It should be noted that, the method for determining the first threshold value of each electrode set is similar to the method for determining the first threshold value of the first electrode set, and the present application is not described herein.
It can be seen that in embodiments of the present application, the current density peaks at each second grid are ordered for each electrode set; a first threshold value for each electrode set is determined based on the ranking result for each electrode set. Firstly, based on a preset quantile, the value of the preset percentage in the current density peak value of each electrode group at each second grid is smaller than the first threshold value, so that the first threshold value has the function of measuring whether the current density peak value of each electrode group at the second grid reaches a preset standard or not, and the accuracy of determining whether the current density peak value of each electrode group at the second grid reaches the preset standard or not is improved. And secondly, setting a corresponding first threshold value for each electrode group, so that errors of the first threshold values caused by different positions of the electrode groups can be eliminated, and the accuracy of the first threshold values of each electrode group can be improved.
Obtaining a fourth grid with a current density peak value larger than a first threshold value corresponding to each electrode group in a plurality of third grids under the electrical stimulation of each electrode group, wherein the plurality of third grids are second grids corresponding to the first target groups, and it should be noted that, because the first target groups belong to partial brain tissue regions in the plurality of brain tissue regions, the plurality of second grids also comprise the second grids corresponding to the first target groups, and for convenience of description, the second grids corresponding to the first target groups in the plurality of second grids are called as a plurality of third grids. Similarly, the first electrode group is illustrated, and under the electrical stimulation of the first electrode group, a fourth grid with a current density peak value greater than a first threshold value corresponding to the first electrode group is obtained from a plurality of third grids corresponding to the first target group, so as to obtain a fourth network corresponding to the first electrode group, that is, the fourth grid corresponding to the first electrode group refers to a third grid with a current density peak value greater than the first threshold value generated at each third grid by the electrical stimulation of the first electrode group. Because the peak value of the current density in the fourth grid corresponding to the first electrode group is larger than the first threshold value, under the electrical stimulation of the first electrode group, the brain neurons at the fourth grid in the first target group can obtain a better electrical stimulation effect, that is, the peak value of the current density generated by the first electrode group at the fourth grid in the first target group reaches the preset standard. It should be noted that the method for determining the fourth grid of each electrode set is similar to the method for determining the first electrode set, and the present application is not described herein. Acquiring the first number of the plurality of second grids, the second number of the plurality of third grids and the third number of the fourth grids corresponding to each electrode group; and determining a second stimulation effect of each electrode group on the first target group based on the first number, the second number, the third number of the fourth grids corresponding to each electrode group and the current density peak value of each electrode group on each second grid.
Optionally, the second stimulation effect of each electrode group on the first target bolus is determined based on the first number, the second number, the third number of fourth grids corresponding to each electrode group, and the current density peak of each electrode group at each second grid. Acquiring a first ratio of a third number to a first number of the fourth grids corresponding to each electrode group; acquiring a second ratio of a third number to a second number of the fourth grids corresponding to each electrode group; obtaining a third ratio of the sum of current density peaks of the fourth grid corresponding to each electrode group to the second number; and determining a second stimulation effect of each electrode group on the first target group based on the first ratio, the second ratio and the third ratio corresponding to each electrode group.
Specifically, the first ratio corresponding to each electrode group may represent a ratio of the third number of the fourth grids in the first target group to the first number, which reaches the preset standard, under the stimulation of each electrode group, that is, the first ratio corresponding to each electrode group is a focusing rate of each electrode group on the grids in the first target group, and thus, the first ratio corresponding to each electrode group is a focusing rate of each electrode group on the first target group. The second stimulation effect of each electrode group on the first target group can be measured by the size of the focusing rate of each electrode group on the first target group, and the larger the focusing rate of each electrode group on the first target group is, the better the second stimulation effect of each electrode group on the first target group is, and the smaller the focusing rate of each electrode group on the first target group is, the worse the second stimulation effect of each electrode group on the first target group is.
Focusing rate corresponding to each electrode groupCan be expressed by the formula (7):
; if it is Count=1, IfCount=0 Formula (7)
Wherein, For a second number of the plurality of third grids, H is a first number of the plurality of second grids, T is a first threshold,To peak the current density of the ith third grid under the electrical stimulation of each electrode group, wherein the ith third grid is any one of a plurality of third grids,To achieve a third number of fourth cells in the first target cluster meeting the predetermined criteria upon stimulation by each electrode set.
Specifically, a second ratio of the third number of the fourth grids corresponding to each electrode group to the second number is obtained, wherein the second ratio represents a ratio of the number of the fourth grids (i.e., the third number) reaching a preset standard in the first target group to the total number of the third grids (i.e., the second number) corresponding to the first target group under the stimulation of each electrode group, namely, the activation rate of each electrode group to the grids in the first target group.
The corresponding activation rate of each electrode groupCan be expressed by the formula (8):
; if it is Count=1, IfCount=0 Formula (8)
Wherein, For a second number of the plurality of third grids,As a result of the first threshold value being set,To peak the current density of the ith third grid under the electrical stimulation of each electrode group, wherein the ith third grid is any one of a plurality of third grids,To achieve a third number of fourth cells in the first target cluster meeting the predetermined criteria upon stimulation by each electrode set.
Specifically, a third ratio of the sum of the current density peaks of the fourth grid corresponding to each electrode group to the second number is obtained. I.e. the third ratio corresponding to each electrode set represents the ratio between the sum of the current density peaks in the first target cell at the fourth grid, which reaches the preset standard, and the total number of grids of the first target cell (i.e. the second number), i.e. the average current density peak of each electrode set to the grids in the first target cell, under the stimulation of each electrode set. The average current density peak represents an average current density peak at each grid in the first target cluster at which the current density peak at the fourth grid in the first target cluster reaches a preset standard under stimulation of each electrode group. Thus, the magnitude of the peak value of the average current density generated by each electrode group on the first target group can be used to measure the second stimulation effect of each electrode group on the first target group, the larger the peak value of the average current density generated by each electrode group on the first target group, the better the second stimulation effect of each electrode group on the first target group, the smaller the peak value of the average current density generated by each electrode group on the first target group, and the worse the second stimulation effect of each electrode group on the first target group.
Peak value of average current density corresponding to each electrode groupCan be expressed by the formula (9):
If (1) Then=If (1)Then=0 Formula (9)
Wherein, For a second number of the plurality of third grids,As a result of the first threshold value being set,And (c) for the current density peak value of the ith third grid under the electric stimulation of each electrode group, wherein the ith third grid is any one of a plurality of third grids.
It should be noted that the first stimulation effect only focuses on any one of the focusing rate, the activation rate and the average current density peak value, but may focus on any two of the focusing rate, the activation rate and the average current density peak value, and may focus on the focusing rate, the activation rate and the average current density peak value at the same time, and the present application is not limited in detail herein.
And determining a second stimulation effect of each electrode group on the first target group based on the first ratio, the second ratio and the third ratio corresponding to each electrode group. Normalizing the third ratio corresponding to each electrode group to obtain a fourth ratio corresponding to each electrode group, and converting the third ratio corresponding to each electrode group into a numerical interval identical to the first ratio and the second ratio, namely converting an average current density peak value generated by each electrode group in the first target group into a numerical interval identical to the focusing rate and the activation rate; and determining an evaluation score of each electrode group based on the first ratio, the second ratio, the fourth ratio and the preset weight corresponding to each electrode group, wherein the evaluation score of each electrode group is used for representing the second stimulation effect of each electrode group on the first target group. For convenience of description, the preset weight corresponding to the focusing rate of each electrode group on the first target group is referred to as a first weight, the preset weight corresponding to the activation rate of each electrode group on the first target group is referred to as a second weight, and the preset weight corresponding to the average current density peak value generated by each normalized electrode group on the first target group is referred to as a third weight.
Specifically, the first weight, the second weight, and the third weight may be set according to actual requirements, for example, if the second stimulation effect focuses only on the focusing rate (i.e., the first ratio), the second weight and the third weight may be set to zero, and the first weight is set to 1, in which case, only the focusing rate of each electrode group on the first target group may be calculated, without calculating the activation rate of each electrode group on the first target group and the average current density peak generated at the first target group by each electrode group; likewise, if the first stimulation effect is focused only on the activation rate (i.e., the second ratio), the first and third weights may be set to zero and the second weight to 1, in which case the activation rate of each electrode group for the first target cluster may be calculated only without calculating the focusing rate of each electrode group for the first target cluster and the average current density peak generated at the first target cluster by each electrode group; if the first stimulation effect focuses only on the average current density peak (i.e., the fourth ratio), the first weight and the second weight may be set to zero and the third weight may be set to 1, in which case only the average current density peak generated at the first target cluster by each electrode group may be calculated without calculating the activation rate of the first target cluster by each electrode group and the focus rate of the first target cluster by each electrode group.
It should be noted that if the second stimulation effect focuses on both the focusing rate and the activation rate and focuses on the average current density peak, the first weight, the second weight, and the third weight may be set according to actual requirements. For example, if the volume of the first target group is smaller, that is, the second number of the third grids corresponding to the first target group is smaller, in order to ensure that the electrical stimulus sent by the electrode group can make the current density peak value at the third grids in more first target groups reach the preset standard, the weight corresponding to the activation rate should be set to be the largest, that is, the second weight is greater than the first weight and the third weight. If the brain neurons in the first target cluster need a larger current density to induce the brain neurons to respond, in order to ensure that the current density peak value at the third grid in more first target clusters can reach the preset standard by the electric stimulation sent by the electrode group, and meanwhile, the current density reaching the preset standard is ensured to be as large as possible, so that the electrode group can effectively induce the brain neurons in the first target cluster, therefore, the weight corresponding to the average current density peak value should be set to be the largest, namely the third weight is larger than the first weight and the second weight. The setting conditions of the preset weights are not exemplified one by one. Further, a first product of the focusing rate of each electrode group on the first target group and the first weight, a second product of the activation rate of each electrode group on the first target group and the second weight, and a third product of the average current density peak value generated by each normalized electrode group on the first target group and the third weight are determined, and the sum of the first product, the second product and the third product is determined as an evaluation score of each electrode group.
Evaluation score of each electrode groupCan be expressed by the formula (10):
Ra+ A formula (10)
Wherein, For a first weight corresponding to the focus ratio (i.e. the first ratio),For a second weight corresponding to the activation rate (i.e. the second ratio),For a third weight corresponding to the average current density peak (i.e. the fourth ratio),For the focus rate for each electrode set, ra is the activation rate for each electrode set and a is the average current density peak for each electrode set.
It can be seen that, in the embodiment of the present application, the evaluation score of each electrode group is determined based on the first ratio, the second ratio, the fourth ratio and the preset weight corresponding to each electrode group, where the evaluation score of each electrode group is used to characterize the second stimulation effect of each electrode group on the first target group. The operator of the individual noninvasive deep brain electric stimulation device can change the preset weight according to the actual condition and stimulation requirement of the first target groups, so that the determined evaluation score can reflect different emphasis points of each first target group. For example, if the area of the first target group is smaller, the current density at the third grid corresponding to the first target group meets the preset standard as much as possible, so as to ensure that the electrical stimulation can accurately stimulate the third grid corresponding to the first target group, therefore, the weight corresponding to the activation rate should be set to be the largest, that is, the second weight is greater than the first weight and the third weight, so as to ensure that the determined evaluation score can exhibit the characteristic of each first target group, and the stimulation requirement is improved, so that the accuracy of determining the evaluation score is improved, and the accuracy of determining the plurality of candidate electrode groups corresponding to the first target group from the plurality of electrode groups based on the evaluation score is improved.
The second stimulation effect of each electrode group on the first target group is determined from the plurality of electrode groups, that is, the plurality of candidate electrode groups corresponding to the first target group are determined from the plurality of electrode groups based on the evaluation score of each electrode group. Specifically, based on the evaluation score of each electrode group, the first K electrode groups are determined as a plurality of candidate electrode groups of the first target group from among the plurality of electrode groups, ordered in order from large to small for each electrode group.
It can be seen that, in the embodiment of the present application, the population magnetic resonance image is preprocessed to obtain a plurality of second brain tissue regions corresponding to the population magnetic resonance image, where the plurality of second brain tissue regions include a plurality of target clusters; performing gridding treatment on each second brain tissue region to obtain a plurality of second grids corresponding to the second brain tissue regions; randomly combining a plurality of stimulation electrodes to obtain a plurality of electrode groups, wherein each electrode group comprises two pairs of stimulation electrodes; determining a second stimulation effect of each electrode set on the first target set based on a current density peak generated at each second grid by the electrical stimulation of each electrode set for the first target set, wherein the first target set is any one of the plurality of target sets; a plurality of candidate electrode sets corresponding to the first target group is determined from the plurality of electrode sets based on the second stimulation effect of each electrode set on the first target group. In the application, because the plurality of candidate electrode groups corresponding to each target group are determined with high calculation force, the plurality of candidate electrode groups corresponding to each target group are determined based on the group magnetic resonance image in the off-line processing stage, so that the plurality of candidate electrode groups corresponding to each target group can be directly obtained in the subsequent on-line processing stage, and the treatment efficiency of the individual noninvasive deep brain electrical stimulation is accelerated.
For the online processing part, the specific content of the execution of the personalized stimulation simulation navigation module is as follows: preprocessing an individual magnetic resonance image to obtain a plurality of first brain tissue areas corresponding to the individual magnetic resonance image, wherein the plurality of first brain tissue areas comprise target groups; performing gridding treatment on each first brain tissue region to obtain a plurality of first grids corresponding to the plurality of first brain tissue regions; acquiring a plurality of candidate electrode groups corresponding to a target group, wherein each candidate electrode group comprises two pairs of stimulation electrodes; determining a current density peak generated at each first grid for each candidate electrode set based on the first current density function and the second current density function, wherein the current density functions of the two pairs of stimulation electrodes in each candidate electrode set are the first current density function and the second current density function, respectively; determining a first stimulation effect of each candidate electrode set on the target group based on the current density peaks generated by each candidate electrode set at each first grid; and determining a target electrode group corresponding to the target group from the plurality of candidate electrode groups based on the first stimulation effect of each candidate electrode group on the target group. The stimulation module is configured to electrically stimulate the target group through the target electrode group, and a specific stimulation process is described in the description of fig. 3, which is not repeated herein. It should be noted that the personalized stimulus simulation navigation module of the online processing part may be the same as or different from the personalized stimulus simulation navigation module of the offline processing part, and the present application is not limited herein.
Specifically, when a subject needs to be treated by the personalized noninvasive deep brain electrical stimulation device, a disease that the subject needs to treat is acquired, and then a target group that needs to be stimulated for treating the disease is determined. Preprocessing an individual magnetic resonance image to obtain a plurality of first brain tissue regions corresponding to the individual magnetic resonance image, wherein the plurality of first brain tissue regions comprise target groups, and preprocessing the individual magnetic resonance image comprises the following steps: image segmentation and sequence registration are performed on individual magnetic resonance images. It should be noted that, the method for preprocessing the individual magnetic resonance image to obtain the plurality of first brain tissue areas corresponding to the individual magnetic resonance image is similar to the method for preprocessing the group magnetic resonance image to obtain the plurality of second brain tissue areas corresponding to the group magnetic resonance image, and the disclosure of the present application is not repeated here.
Specifically, gridding processing is performed on each first brain tissue region, so as to obtain a plurality of first grids corresponding to the plurality of first brain tissue regions. It should be noted that, the method of performing the gridding treatment on each first brain tissue region to obtain a plurality of first grids corresponding to the plurality of first brain tissue regions is similar to the method of performing the gridding treatment on each second brain tissue region to obtain a plurality of second grids corresponding to the plurality of second brain tissue regions, which is not described herein in detail.
Specifically, a plurality of candidate electrode groups corresponding to a target group, each including two pairs of stimulation electrodes, is acquired, the target group belonging to one of the plurality of target groups. The obtaining a plurality of candidate electrode sets corresponding to the target group includes: and acquiring the result of the offline processing, namely determining a plurality of candidate electrode groups corresponding to the target group based on the plurality of candidate electrode groups corresponding to the first target group.
Specifically, a current density peak generated at each first grid for each candidate electrode set is determined based on a first current density function and a second current density function, wherein the current density functions of the two pairs of stimulation electrodes in each candidate electrode set are the first current density function and the second current density function, respectively. In the online processing stage, the current density peak value generated by each electrode group at each first grid is not required to be determined, and only the current density peak value generated by each candidate electrode group at each first grid is required to be determined, so that the calculation efficiency is improved. Further, determining a first current density function and a second current density function for each candidate electrode set by finite element analysis, the third current density function and the fourth current density function generated at each first grid; acquiring an interference current density function formed by a third current density function and a fourth current density function generated at each first grid of each candidate electrode group; enveloping the interference current density function of each candidate electrode group at each first grid to obtain a current density peak value generated by each candidate electrode group at each first grid, and performing Hilbert transform on the interference current density function of each candidate electrode group at each first grid to obtain an analysis signal corresponding to the interference current density function of each candidate electrode group at each first grid; determining the modulus of the analytic signal corresponding to each candidate electrode group at each first grid as an envelope curve corresponding to the interference current density function of each candidate electrode group at each first grid; and determining the peak-to-peak value of the envelope curve corresponding to the interference current density function of each candidate electrode group at each first grid as the current density peak value generated by each candidate electrode group at each first grid. It should be noted that, the method for determining the current density peak value generated by each candidate electrode group at each first grid based on the first current density function and the second current density function is similar to the method for determining the current density peak value generated by the electrical stimulation of each electrode group at each second grid based on the first current density function and the second current density function, which is not described herein.
Specifically, a first stimulation effect of each candidate electrode set on the target nucleic acid is determined based on a current density peak generated by each candidate electrode set at each first grid. It should be noted that, the method for determining the first stimulation effect of each candidate electrode set on the target group based on the current density peak generated at each first grid of each candidate electrode set is similar to the method for determining the second stimulation effect of each electrode set on the first target group based on the current density peak generated at each second grid of each candidate electrode set, which is not described herein.
Specifically, determining a target electrode group corresponding to the target group from a plurality of candidate electrode groups based on a first stimulation effect of each candidate electrode group on the target group; it should be noted that, based on the first stimulation effect of each candidate electrode group on the target group, the method for determining the target electrode group corresponding to the target group from the plurality of candidate electrode groups is similar to the method for determining the plurality of candidate electrode groups corresponding to the first target group from the plurality of electrode groups based on the second stimulation effect of each electrode group on the first target group, which is not described herein again.
It can be seen that in the present application, after the candidate electrode corresponding to each target group is determined through the off-line processing stage, the on-line processing stage does not directly determine a target electrode group for each target group and then adapt to the treatment process of each subject, but also processes the individual magnetic resonance image of each different subject, determines the target candidate electrode corresponding to the target group from the candidate electrode groups, thereby eliminating the difference of brain structures among individuals and the response effect of the target groups to the electric stimulation, improving the suitability of the target electrode group of the target groups determined by each subject and the brain structures of each subject, therefore, when the target electrode group performs electric stimulation on the target group, the best nerve regulation and control effect can be exerted on brain neurons in the target group, and the treatment effect of performing noninvasive deep brain electric stimulation on each subject is improved.
Further, when the noninvasive deep brain electric stimulation is performed, a target electrode group is selected from the candidate electrode groups, namely, a first stimulation effect of each candidate electrode group on the target group is determined based on a current density peak value generated at each first grid by a first current density function and a second current density function output by two pairs of stimulation electrodes in each candidate electrode group, then a target electrode group corresponding to the target group is determined from a plurality of candidate electrode groups based on the first stimulation effect of each candidate electrode group on the target group, namely, the stimulation effect is used as a standard for determining the target electrode group corresponding to the target group, so that the target electrode group with the best stimulation effect on the target group in the plurality of candidate electrode groups can be determined conveniently, and then the target electrode group is passed, electrically stimulating the target group to ensure that the target electrode group can accurately stimulate the target group, and the stimulation effect of the electric stimulation output by the target electrode group on the target group is optimal, so that the treatment effect of the noninvasive deep brain electric stimulation is improved.
Referring to fig. 5, fig. 5 is a schematic flow chart of a personalized noninvasive deep brain electrical stimulation method according to an embodiment of the application. The method is applied to the individuation noninvasive deep brain electric stimulation device. The method includes, but is not limited to, the following steps:
S501: preprocessing the individual magnetic resonance image to obtain a plurality of first brain tissue areas corresponding to the individual magnetic resonance image.
Wherein the plurality of first brain tissue regions comprises a target nucleic acid.
S502: and carrying out gridding treatment on each first brain tissue region to obtain a plurality of first grids corresponding to the plurality of first brain tissue regions.
S503: a plurality of candidate electrode sets corresponding to a target group are acquired, each candidate electrode set including two pairs of stimulation electrodes.
S504: a current density peak generated at each first grid for each candidate electrode set is determined based on the first current density function and the second current density function.
Wherein the current density functions of the two pairs of stimulation electrodes in each candidate electrode set are a first current density function and a second current density function, respectively.
S505: a first stimulation effect of each candidate electrode set on the target group is determined based on the current density peaks generated by each candidate electrode set at each first grid.
S506: and determining a target electrode group corresponding to the target group from the plurality of candidate electrode groups based on the first stimulation effect of each candidate electrode group on the target group.
S507: and electrically stimulating the target group through the target electrode group.
The specific implementation process of step S501 to step S507 may refer to the specific function of the above-mentioned personalized stimulation simulation navigation module, and will not be described again.
Referring to fig. 6, fig. 6 is a schematic diagram of an electronic device according to an embodiment of the application. The electronic device 600 shown in fig. 6 may be the above-described personalized non-invasive deep brain electrical stimulation apparatus. The electronic device 600 shown in fig. 6 comprises a memory 601, a processor 602, a communication interface 603 and a bus 604. The memory 601, the processor 602, and the communication interface 603 are connected to each other by a bus 604.
The processor 602 may integrate the functions of the above-mentioned personalized stimulation simulation navigation module, and perform preprocessing on the individual magnetic resonance image to obtain a plurality of first brain tissue areas corresponding to the individual magnetic resonance image, where the plurality of first brain tissue areas include target groups; performing gridding treatment on each first brain tissue region to obtain a plurality of first grids corresponding to the plurality of first brain tissue regions; acquiring a plurality of candidate electrode groups corresponding to a target group, wherein each candidate electrode group comprises two pairs of stimulation electrodes; determining a current density peak generated at each first grid for each candidate electrode set based on the first current density function and the second current density function, wherein the current density functions of the two pairs of stimulation electrodes in each candidate electrode set are the first current density function and the second current density function, respectively; determining a first stimulation effect of each candidate electrode set on the target group based on the current density peaks generated by each candidate electrode set at each first grid; and determining a target electrode group corresponding to the target group from the plurality of candidate electrode groups based on the first stimulation effect of each candidate electrode group on the target group.
The communication interface 603 may integrate the functionality of the stimulation module described above for electrically stimulating a target group through a target electrode set.
The Memory 601 may be a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a random access Memory (Random Access Memory, RAM). The memory 601 may store a program which, when executed by the processor 602, the processor 602 and the communication interface 603 are adapted to perform the steps of the personalized non-invasive deep brain electrical stimulation method of an embodiment of the present application.
The processor 602 may employ a general-purpose central processing unit (Central Processing Unit, CPU), microprocessor, application SPECIFIC INTEGRATED Circuit (ASIC), graphics processor (graphics processing unit, GPU) or one or more integrated circuits for executing associated programs to perform the functions required to individualize the units in the noninvasive deep brain electrical stimulation device or to perform the individualize the noninvasive deep brain electrical stimulation method of the method embodiment of the present application.
The processor 602 may also be an integrated circuit chip with signal processing capabilities. In implementation, various steps in the personalized non-invasive deep brain stimulation method of the present application may be accomplished by instructions in the form of integrated logic circuits or software of hardware in the processor 602. The processor 602 described above may also be a general purpose processor, a digital signal processor (DIGITAL SIGNAL Processing, DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 601, and the processor 602 reads information in the memory 601, and combines the hardware thereof to perform functions required to be performed by units included in the personalized non-invasive deep brain electrical stimulation device of the embodiment of the present application, or to perform various steps in the personalized non-invasive deep brain electrical stimulation method of the method embodiment of the present application.
The communication interface 603 enables communication between the electronic device 600 and other devices or communication networks using transceiving means such as, but not limited to, transceivers, input-output devices, etc. For example, a plurality of candidate electrode sets corresponding to a target group may be acquired through the communication interface 603. Specifically, when the communication interface 603 is an output device, it may be a stimulation module for electrically stimulating the target group.
A bus 604 may include a path to transfer information between components of the device electronics 600 (e.g., the memory 601, the processor 602, the communication interface 603).
It should be noted that while the electronic device 600 shown in fig. 6 shows only a memory, a processor, and a communication interface, those skilled in the art will appreciate that in a particular implementation, the electronic device 600 also includes other components necessary to achieve proper operation. Also, those skilled in the art will appreciate that the electronic device 600 may also include hardware devices that implement other additional functions, as desired. Furthermore, it will be appreciated by those skilled in the art that the electronic device 600 may also include only the components necessary to implement embodiments of the present application, and not necessarily all of the components shown in FIG. 6.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present application. 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.
The foregoing is merely illustrative embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present application, and the application should be covered. Therefore, the protection scope of the application is subject to the protection scope of the claims.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize part or all of the steps of any one of the individual noninvasive deep brain electrical stimulation methods described in the embodiment of the method.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any of the personalized non-invasive deep brain electrical stimulation methods described in the method embodiments above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units described above may be implemented either in hardware or in software program modules.
The integrated units, if implemented in the form of software program modules and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned memory includes: a usb disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the idea of the present application, the present disclosure should not be construed as limiting the present application in summary.
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