Nerve regulation and control method, device and equipment based on electrical stimulation and storage medium

Abstract

The invention discloses a nerve regulation and control method, device and equipment based on electrical stimulation and a storage medium. The method comprises the steps that metabolism image data, functional image data and anatomical image data of a detected person are acquired; based on the metabolism image data and the functional image data, respectively calculating the standardized uptake value ratio of each brain region of the detected person and the functional connection strength between each brain region and all other brain regions; on the basis of the standardized uptake value ratio of each brain region of the detected person and the functional connection strength between each brain region and all other brain regions, determining the abnormal characteristics of the brain of the detected person and the position data of a target spot; and time domain interference electrical stimulation parameters are set based on the position data of the target spot, the brain abnormal characteristics of the detected person and the anatomical image data. According to the method, the PET image data and the rs-fMRI image data can be combined and analyzed, the metabolism and function state of the brain of the detected person can be evaluated more comprehensively, and corresponding nerve regulation and control can be carried out based on the time domain interference electrical stimulation parameters.

Description

Technical Field

[0001] This application relates to the technical field of medical data processing, and particularly relates to a nerve regulation method, device, equipment and storage medium based on electrical stimulation. Background Art

[0002] Positron Emission Tomography (PET) is a nuclear medicine imaging technology that can display the metabolic process in the body of the person being examined. The basis of PET imaging is the use of radioactive nuclides that emit positrons (also known as radioactive drugs or tracers), and these nuclides can be labeled onto compounds that participate in the blood flow or metabolic process of human tissues. By injecting these compounds labeled with positron-emitting radioactive nuclides into the subject's body, the PET system can sensitively capture the gamma-ray radiation inside the body and use software to draw triangulation emission sources to create a three-dimensional computed tomography image of the tracer concentration in the body. PET imaging can be used to study human physiology, biochemistry, chemical transmitters, receptors and even gene changes, and it shows unique advantages especially in diagnosing and guiding the treatment of tumors, coronary heart disease and brain diseases.

[0003] Resting-state functional magnetic resonance imaging (rs-fMRI) is a method of brain functional magnetic resonance imaging. In this technology, the subject is in a resting state, and a brain activity functional map is obtained through blood oxygenation level-dependent brain functional imaging. Rs-fMRI can observe that there is still a regular functional activity network in the normal human brain in the resting state, and there are differences and remodeling in the brain functional activity network in the pathological state compared with the normal human brain. This technology does not require complex task design, has good operability, and can avoid the incomparability of experimental results caused by different task designs and the differences in the execution of the subjects in task-based research.

[0004] The above PET technology and rs-fMRI technology can both obtain the brain information of the subject, and these brain information can characterize whether the brain region function of the subject is normal. However, the image data of a single modality often has limitations and it is difficult to comprehensively evaluate the metabolic and functional states of the subject's brain at the same time.

[0005] In the field of neuromodulation, neuroelectrical stimulation techniques, such as transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), have been widely applied. However, these existing electrical stimulation techniques usually use fixed stimulation parameters, lack precise regulation of the individual characteristics of the subjects being examined, have large individual differences in the regulation effect, and lack pertinence. Time-domain interference electrical stimulation (TIS), as an emerging neuromodulation technique, utilizes the interference effect of currents with different frequencies to achieve precise stimulation of deep brain regions and has significant neuromodulation effects. Compared with traditional electrical stimulation techniques, TIS can focus on deep brain regions without damaging the cortex and is applicable to the field of neuromodulation. However, the existing applications of TIS still lack precise guidance based on individualized imaging data, which limits its application scope and effect.

[0006] Therefore, how to combine and analyze PET imaging data and rs-fMRI imaging data to more comprehensively evaluate the metabolic and functional status of the brain of the subject being examined, and how to perform corresponding neuromodulation based on the time-domain interference electrical stimulation parameters are problems that need to be solved urgently at present.

[0007] It should be noted that the above statements are only used to provide background technical information related to this application and do not necessarily constitute prior art. Summary of the Invention

[0008] In view of the above problems, embodiments of the present application provide a neuromodulation method, device, electronic device, and storage medium based on electrical stimulation, which can combine and analyze PET imaging data and rs-fMRI imaging data, so as to more comprehensively evaluate the metabolic and functional status of the brain of the subject being examined, and perform corresponding neuromodulation based on the determined time-domain interference electrical stimulation parameters.

[0009] In a first aspect, embodiments of the present application provide a neuromodulation method based on electrical stimulation, and the method includes:

[0010] Obtain the metabolic imaging data, functional imaging data, and anatomical imaging data of the subject being examined; the metabolic imaging data is used to reflect the cerebral glucose metabolism level, the functional imaging data is used to reflect the cerebral functional connection strength, and the anatomical imaging data is used to reflect the internal structure of the brain;

[0011] Based on the metabolic imaging data and the functional imaging data, calculate the standardized uptake value ratio of each brain region of the subject being examined, and the functional connection strength between each brain region and all other brain regions respectively;

[0012] Based on the standardized uptake value ratio of each brain region of the subject being examined and the functional connection strength between each brain region and all other brain regions, determine the abnormal characteristics of the brain of the subject being examined and the position data of the target target;

[0013] Set the time-domain interference electrical stimulation parameters based on the brain abnormality characteristics of the subject, the position data of the target target, and the anatomical image data.

[0014] In some alternative embodiments, calculating the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region and all other brain regions based on the metabolic image data and the functional image data respectively includes:

[0015] Perform standardized preprocessing and multimodal registration on the metabolic image data and the functional image data;

[0016] Based on the metabolic image data and the functional image data after the standardized preprocessing and multimodal registration, calculate the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region of the subject and all other brain regions respectively.

[0017] In some alternative embodiments, performing standardized preprocessing and multimodal registration on the metabolic image data, the functional image data, and the anatomical image data to generate multimodal fusion image data includes:

[0018] Perform motion correction on the metabolic image data, register the corrected metabolic image data to the anatomical image data, and standardize it to the MNI space;

[0019] Perform temporal correction on the functional image data, register the corrected metabolic image data to the anatomical image data, and standardize it to the MNI space.

[0020] In some alternative embodiments, calculating the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region of the subject and all other brain regions based on the metabolic image data and the functional image data after the standardized preprocessing and multimodal registration respectively includes:

[0021] Based on the metabolic image data after the standardized preprocessing and multimodal registration and a preset brain partition template, calculate the standardized uptake value ratio of each brain region of the subject;

[0022] Based on the functional image data after the standardized preprocessing and multimodal registration and the preset brain partition template, calculate the functional connection strength between each brain region of the subject and all other brain regions.

[0023] In some alternative embodiments, determining the brain abnormality characteristics and the position data of the target target based on the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region and all other brain regions includes:

[0024] Calculating the metabolic abnormality index data of each brain region of the subject based on the standardized uptake value ratio within each brain partition and the mean and standard deviation of the standardized uptake value ratio in the standard data of the healthy control group;

[0025] Calculating the functional abnormality index data of each brain region of the subject based on the functional characteristic data within each brain partition and the functional connection strength between each brain region and all other brain regions in the standard data of the healthy control group;

[0026] Determining the risk index data of each brain region of the subject according to a preset risk assessment model based on the metabolic abnormality index data of each brain region of the subject and the functional abnormality index data of each brain region of the subject;

[0027] Determining the position data of the target target based on the metabolic abnormality index data of each brain region of the subject, the functional abnormality index data of each brain region, and the risk index data.

[0028] In some alternative embodiments, determining the risk index data of each brain region of the subject according to a preset risk assessment model based on the metabolic abnormality index data of each brain region of the subject and the functional abnormality index data between every two brain regions of the subject includes:

[0029] Determining the risk index data of each brain region of the subject according to the following formula based on the metabolic abnormality index data of each brain region of the subject and the functional abnormality index data between every two brain regions of the subject:

[0030] S(i) = ω1·Z SUVR (i) + ω2·Z FC (i)

[0031] Wherein, Z SUVR (i) represents the standardized metabolic abnormality index data, Z FC (i) represents the standardized functional abnormality index data, and ω1 and ω2 are the weight coefficients of the metabolic level and the functional connection respectively, reflecting the relative importance of the metabolic level and the functional connection characteristics.

[0032] In some alternative embodiments, setting the time-domain interference electrical stimulation parameters based on the brain abnormality characteristics of the subject, the position data of the target target, and the anatomical image data includes:

[0033] Set the frequency, intensity, time, and period of the time-domain interference electrical stimulation based on the position data of the target target, the brain abnormality characteristics, and the physiological characteristics of the subject.

[0034] Construct an individualized head model based on the anatomical image data of the subject, and preset the electrode positions based on the constructed head model.

[0035] In a second aspect, an embodiment of the present application provides a neural regulation device based on electrical stimulation, where the neural regulation device based on electrical stimulation includes:

[0036] An image data acquisition module, configured to acquire metabolic image data, functional image data, and anatomical image data of a subject; the metabolic image data is used to reflect the cerebral glucose metabolism level, the functional image data is used to reflect the cerebral functional connection strength, and the anatomical image data is used to reflect the internal brain structure;

[0037] An image data processing module, configured to calculate the standardized uptake value ratio of each brain region of the subject, and the functional connection strength between each brain region and all other brain regions respectively based on the metabolic image data and the functional image data;

[0038] A combined risk identification module, configured to determine the brain abnormality characteristics of the subject and the position data of the target target based on the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region and all other brain regions;

[0039] A stimulation parameter setting module, configured to set time-domain interference electrical stimulation parameters based on the brain abnormality characteristics of the subject, the position data of the target target, and the anatomical image data.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor runs the computer program to implement the method as described in the first aspect.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to implement the method as described in the first aspect.

[0042] In this application, after obtaining the metabolic imaging data, functional imaging data, and anatomical imaging data of the subject, based on the metabolic imaging data and functional imaging data, the standardized uptake value ratio of each brain region of the subject can be calculated respectively, as well as the functional connection strength between each brain region and all other brain regions. Based on the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region and all other brain regions, the position data of the target target and the brain abnormalities of the subject can be determined, and based on the determined target target position data, the brain abnormalities of the subject, and the anatomical imaging data of the subject, the time-domain interference electrical stimulation parameters can be set. In this way, the PET and rs-fMRI imaging data are combined and analyzed to further determine the target target and the time-domain interference electrical stimulation parameters, so as to more comprehensively reveal the pathological mechanism of Alzheimer's disease (AD), and perform corresponding neuromodulation based on the calculated time-domain interference electrical stimulation parameters, providing more accurate data support for subsequent condition analysis and medical treatment.

[0043] The above description is only an overview of the technical solutions of the embodiments of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features, and advantages of the embodiments of this application more obvious and understandable, the specific embodiments of this application are specifically given below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of this application. And in all the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0045] Figure 1 It is a schematic flowchart of a neuromodulation method based on electrical stimulation provided by some embodiments of this application;

[0046] Figure 2 It is a specific flowchart of step S2 provided by some embodiments of this application;

[0047] Figure 3 It is a specific flowchart of step S3 provided by some embodiments of this application;

[0048] Figure 4 It is a schematic diagram of the framework result of a neuromodulation device based on electrical stimulation provided by some embodiments of this application;

[0049] Figure 5 It shows a schematic diagram of the structure of an electronic device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The embodiments of the technical solution of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and thus are only examples and cannot be used to limit the protection scope of the present application.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the embodiments of the present application belong; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusion.

[0052] In the description of the embodiments of the present application, "a plurality" means more than two, unless otherwise specifically defined.

[0053] Referring to "embodiment" herein means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0054] In the description of the embodiments of the present application, the term "and / or" is only a relationship describing the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0055] In the related art, although both PET technology and rs-fMRI technology can obtain the brain information of the subject, and this brain information can characterize whether the brain region function of the subject is normal. However, the single-modal image data often has limitations and it is difficult to comprehensively evaluate the metabolic and functional states of the subject's brain at the same time.

[0056] For the above reasons, the embodiments of the present application propose a nerve regulation method based on electrical stimulation. This method combines and analyzes PET and rs-fMRI image data, and further determines, so as to be able to more comprehensively reveal the pathological mechanism of Alzheimer's disease (AD) and provide more accurate data support for subsequent condition analysis and medical treatment.

[0057] The following will describe in detail the nerve regulation method based on electrical stimulation provided by the embodiments of the present application with reference to the accompanying drawings. Please refer to Figure 1 , Figure 1Schematic flowchart of a neural regulation method based on electrical stimulation provided by an embodiment of the present application, as shown in Figure 1 As shown, the neural regulation method based on electrical stimulation may include the following steps:

[0058] Step S1, obtaining metabolic image data, functional image data, and anatomical image data of the subject;

[0059] Step S2, based on the metabolic image data and the functional image data, calculating the standardized uptake value ratio of each brain region of the subject, and the functional connection strength between each brain region and all other brain regions;

[0060] Step S3, based on the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region and all other brain regions, determining the abnormal characteristics of the subject's brain and the position data of the target target;

[0061] Step S4, setting time-domain interference electrical stimulation parameters based on the abnormal characteristics of the subject's brain, the position data of the target target, and the anatomical image data.

[0062] Among them, the metabolic image data is used to reflect the brain glucose metabolism level, the functional image data is used to reflect the brain functional connection strength, and the anatomical image data is used to reflect the internal structure of the brain. Time-domain interference electrical stimulation (TIS), as an emerging neural regulation technology, utilizes the current interference effect of different frequencies to achieve precise stimulation of deep brain regions and has a significant neural regulation effect. By adjusting the time-domain interference electrical stimulation parameters, it is ensured that the stimulation effectively intervenes in the abnormal brain regions while reducing the interference to the normal brain regions.

[0063] In the above step S1, a standardized image acquisition protocol can be adopted to obtain multi-modal image data of the subject, including FDG PET (fluorodeoxyglucose positron emission tomography) metabolic images, rs-fMRI (resting-state functional magnetic resonance imaging) functional images, and T1 MRI (refers to using T1-weighted imaging in magnetic resonance imaging) anatomical images.

[0064] When collecting FDG PET metabolic images, by injecting the 18F-FDG (fluorodeoxyglucose) tracer and using Positron Emission Tomography (PET) technology, the brain metabolic levels in each brain region of the subject can be accurately identified. Before injecting the radioactive drug, the subject needs to fast for at least 6 hours to ensure that their blood glucose level is appropriate, thereby reducing potential interference with the metabolic image results. During the rs-fMRI data collection process, the subject is instructed to keep their eyes closed and awake and complete at least 5 minutes of resting-state scanning to obtain functional connectivity information between brain regions. For the acquisition of T1 MRI images, high-resolution T1-weighted imaging technology is used to capture the subtle features of the brain structure, providing a basis for subsequent anatomical registration and brain segmentation, and assisting in constructing an accurate individualized head anatomical model.

[0065] The data collected above can be stored in DICOM format and transmitted to subsequent modules through encryption to ensure data security and integrity and prevent data loss or leakage.

[0066] In some alternative embodiments, as Figure 2 shown, the above step S2 may include the following specific steps: Step S21, perform standard preprocessing and multimodal registration on the metabolic image data and functional image data; Step S22, based on the metabolic image data and functional image data after standard preprocessing and multimodal registration, calculate the standardized uptake value ratio of each brain region of the subject, and the functional connectivity strength between each brain region of the subject and all other brain regions.

[0067] In this embodiment, the metabolic image data and functional image data can be uniformly preprocessed and fused in the same space and coordinate system to form multimodal fused image data. Based on this multimodal fused image data, data quality and cross-individual consistency can be ensured, thereby providing a solid data foundation for subsequent analysis and processing.

[0068] Specifically, the above step S21 may include the following processing: perform motion correction on the metabolic image data, register the corrected metabolic image data to the anatomical image data, and standardize it to the MNI space; perform temporal correction on the functional image data, register the corrected metabolic image data to the anatomical image data, and standardize it to the MNI space.

[0069] In this embodiment, when performing standardized preprocessing and multimodal registration on metabolic imaging data, functional imaging data, and anatomical imaging data, first, motion correction is performed on the FDG PET image, which is registered to the T1 MRI image, standardized to the MNI space, and Gaussian smoothing is applied to reduce noise. For rs-fMRI images, temporal correction is first performed, followed by motion correction, then registration to the T1 MRI and MNI spaces, and finally spatial smoothing is applied to improve the signal-to-noise ratio.

[0070] In addition, various brain tissue structure data can be obtained based on the anatomical imaging data, and the obtained brain tissue structure data is standardized to the MNI space. That is, after skull stripping of the T1 MRI image, 11 types of brain tissues such as gray matter, white matter, cerebrospinal fluid, and muscle tissue are identified and separated through a brain tissue segmentation algorithm and standardized to the MNI space, providing a reliable basis for subsequent electrical stimulation simulation. Finally, by fusing metabolic, functional, and anatomical information, high-quality multimodal fusion images are generated based on the registered FDG PET, rs-fMRI, and T1 MRI images, providing a solid data foundation for subsequent analysis and processing.

[0071] Furthermore, the above step S22 may include the following processing: calculating the standardized uptake value ratio of each brain region of the subject based on the metabolically imaged data after standardized preprocessing and multimodal registration, and a preset brain parcellation template; calculating the functional connectivity strength between each brain region of the subject and all other brain regions based on the functionally imaged data after standardized preprocessing and multimodal registration, and a preset brain parcellation template.

[0072] In this embodiment, after the multimodal image preprocessing is completed, in order to accurately evaluate the metabolic and functional abnormalities of brain regions, the Schaefer 400 brain parcellation template is further introduced for brain parcellation. The Schaefer 400 template divides the whole brain into 400 regions of interest (ROIs) with good anatomical and functional consistency based on the functional connectivity map. Specifically, first, the FDG PET, rs-fMRI, and T1 MRI data standardized to the MNI space are spatially aligned with the Schaefer 400 template respectively to ensure the accurate mapping of the template parcellation to the individual imaging data. Subsequently, the template is used to perform brain parcellation on the imaging data to facilitate the extraction of metabolic indicators and functional characteristics within each ROI. Based on the extracted metabolic indicators and functional characteristics, the standardized uptake value ratio of each brain region and the functional connectivity strength between each brain region and all other brain regions can be accurately calculated. This parcellation method based on the Schaefer 400 template can not only provide refined metabolic and functional evaluations but also ensure the consistency and comparability of cross-individual analyses, providing a reliable basis for subsequent disease analysis and medical treatment.

[0073] The standardized uptake value ratio (SUVR) is a method for quantifying metabolic activity in PET data. Its principle is based on the fact that the uptake of the FDG tracer reflects the glucose metabolism level in brain regions. By calculating the ratio of the average radioactive activity value in the target region to the average value of the reference region (cerebellar cortex or whole-brain white matter), the influence of inter-individual dose and body weight differences is eliminated.

[0074] Based on rs-fMRI data, an ROI-based whole-brain functional connectivity matrix is constructed. For each pair of ROIs, the correlation of time series between brain regions is measured by calculating the Pearson correlation coefficient, and the functional connectivity strength FC patients (i, j) between brain region i and every other brain region j is calculated.

[0075] After calculating the SUVR value of each brain region of the subject, calculate its metabolic abnormality score S SUVR (i), which is used to quantify the degree of decline in the metabolic level of this brain region relative to the healthy baseline. The calculation formula is as follows:

[0076]

[0077] Among them, μ HC (i) and σ HC (i) are the mean and standard deviation of the SUVR values of brain region i in the healthy control group respectively. SUVR patient (i) is the actual SUVR value of brain region i of the subject. S SUVR (i)>0 indicates a decline in the metabolic level, suggesting a possible lesion; a negative value indicates that the metabolic level is close to or higher than the healthy baseline.

[0078] To ensure the comparability and consistency of the scores, all metabolic abnormality scores are further standardized by Z-score to obtain Z SUVR (i), to unify the dimension and provide a unified standard for the calculation of subsequent combined risk scores.

[0079] After calculating the functional connectivity strength FC patients (i, j) between brain region i and brain region j, the functional connectivity abnormality score S FC (i) of each brain region can be calculated, which is used to measure the degree of deviation of the functional connectivity of brain region i from the healthy baseline. The calculation formula is as follows:

[0080]

[0081] Among them, and are the functional connection strengths of brain regions i and j in the subject and the healthy control group, respectively, and N refers to the total number of brain regions connected to brain region i. To reduce noise interference, the definition of functional connection uses a globally unified threshold determined by the functional connection distribution of the healthy control group data (for example, retaining the top 10% of strong connections). Ensure the consistency and comparability of the functional connection matrices of all subjects.

[0082] On this basis, to further reduce noise interference and avoid the loss of key connections due to sparse networks, a regularization method (such as Lasso or graph regularization) is introduced to balance network sparsity and key information retention. This method can not only effectively remove the interference of low-intensity connections but also ensure the biological credibility of strong connections, thus providing a more robust functional connection matrix for subsequent analysis. After the calculation is completed, all functional connection abnormality scores are standardized by Z-score to obtain Z FC (i).

[0083] In some other alternative embodiments, such as Figure 3 shown, the above step S3 may include the following specific steps: Step S31, calculate the metabolic abnormality index data of each brain region of the subject based on the standardized uptake value ratio within each brain partition, as well as the mean and standard deviation of the standardized uptake value ratio in the standard data of the healthy control group; Step S32, calculate the functional abnormality index data between each brain region of the subject and all other brain regions based on the functional characteristic data within each brain partition and the functional connection strength of each brain region in the standard data of the healthy control group; Step S33, determine the risk index data of each brain region of the subject according to a preset risk assessment model based on the metabolic abnormality index data of each brain region of the subject and the functional abnormality index data between any two brain regions of the subject; Step S34, determine the location data of the target target based on the metabolic abnormality index data of each brain region of the subject, the functional abnormality index data of each brain region, and the risk index data.

[0084] Specifically, the above step S33 may include the following processing: Based on the metabolic abnormality index data of each brain region of the subject and the functional abnormality index data between any two brain regions of the subject, determine the risk index data of each brain region of the subject according to the following formula:

[0085] S(i) = ω1·Z SUVR (i) + ω2·Z FC (i)

[0086] where, Z SUVR (i) represents the standardized metabolic abnormality index data, Z FC (i) represents the standardized functional abnormality index data, and ω1 and ω2 are the weight coefficients of the metabolic level and functional connection respectively, reflecting the relative importance of the metabolic level and functional connection characteristics.

[0087] In this embodiment, after calculating the standardized metabolic abnormality score Z SUVR (i) and the functional connectivity abnormality score Z FC (i), in order to comprehensively evaluate the risk level of each brain region and accurately identify high-risk lesions, and provide key targets for subsequent personalized treatment, this module proposes the above-mentioned risk assessment index data, that is, the function-metabolism combined risk score S(i). To ensure the scientificity of score calculation and the best performance of the model, the weights ω1 and ω2 will be determined on the public dataset ADNI through the following methods:

[0088] 1. Initial weight setting:

[0089] The initial weights are set through the characteristic statistics of the healthy control group, specifically initialized based on the variance contribution of the metabolic and functional connectivity scores. Specifically, the larger the variance of a characteristic, the more obvious its distribution difference in the healthy baseline, the higher its sensitivity to abnormal characteristics, and thus it is given a higher weight. The formula for setting the initial weights is:

[0090]

[0091] where σ k represents the standard deviation of feature k (of the metabolic or functional connectivity score).

[0092] 2. Bayesian optimization process:

[0093] Bayesian optimization approximates the distribution of the objective function by constructing a probability model (such as a Gaussian process, GP), gradually narrowing the search range and improving the optimization efficiency. First, set the search range of the weight parameters ω1 and ω2 (for example, [0,1]), and impose the constraint condition ω1 + ω2 = 1 to ensure the rationality of weight allocation. GP, as a surrogate model of the objective function, fits the distribution of the objective function according to the evaluated weight combinations and their corresponding classification performance indicators (such as AUC value, sensitivity, and specificity), and at the same time provides predictions of the objective function value and its uncertainty. On this basis, an acquisition function (such as expected improvement EI or upper confidence bound UCB) is used to select the next set of weight combinations for evaluation, balancing exploration and exploitation, and preferentially selecting the most potential combinations. Add the newly evaluated results to the dataset and update the surrogate model to gradually improve its prediction accuracy and the fitting ability of the objective function distribution. When the improvement amplitude of the acquisition function is lower than the preset threshold or reaches the maximum number of iterations, the optimization process stops and outputs the optimal weight combination and

[0094] Before setting the time-domain interference electrical stimulation parameters, based on the calculation result of the combined risk score S(i), candidate high-risk targets can be determined by setting a screening threshold T, and the threshold T is determined in the following way: 1. Initially set the screening threshold THC :

[0095] Calculate the preliminary screening threshold based on the combined score distribution of the healthy control group in the ADNI dataset. The formula is as follows:

[0096] T HC = μ HC + k·σ HC

[0097] Where μ HC and σ HC are the mean and standard deviation of the combined scores of the healthy control group, respectively. Initially set k = 1.96 (corresponding to the 95% percentile) to ensure that the selected brain regions are significantly deviated in the healthy control group.

[0098] 2. Dynamically correct the screening threshold:

[0099] Based on the preliminary screening threshold T HC , combine the data of the subject group and dynamically correct the screening threshold T to comprehensively consider the healthy baseline and pathological characteristics. The formula is as follows:

[0100] T = α·T HC + (1 - α)·T Patient

[0101] Where T Patient is the upper quartile of the combined score distribution of the subject group, and α is the weight coefficient to control the influence ratio of the healthy control group and the subject group.

[0102] By adjusting α, make the screening threshold T achieve the optimal classification performance (such as AUC value, sensitivity, and specificity) in the validation set, and finally output the optimal threshold T * , as the global standard for target screening, and is used for subsequent screening and identification of targets for the subjects.

[0103] For each brain region in the subject's data, after calculating the combined risk score, screen the brain regions that satisfy S(i) > T * , and mark them as candidate high-risk targets. For the candidate targets, sort them in descending order according to the combined score S(i), and preferentially recommend the brain regions with high scores as the targets for therapeutic intervention. If some targets are only significantly abnormal in a certain characteristic (such as only metabolic abnormality or only functional connectivity abnormality), they are marked as low-priority targets.

[0104] In some alternative embodiments, the individual characteristics of the subject include the physiological characteristics and anatomical characteristics of the subject. The physiological characteristics of the subject may include parameters such as the age, gender, and weight of the subject. The anatomical characteristics of the subject may include tissue segmentation characteristics and brain region segmentation characteristics. The subject anatomy includes tissue segmentation and brain region segmentation.

[0105] Accordingly, the step S4 of setting the time-domain interference electrical stimulation parameters based on the brain abnormality characteristics of the subject and the position data of the target can include the following specific processes: setting the frequency, intensity, time, and period of the time-domain interference electrical stimulation based on the position data of the target, the brain abnormality characteristics, and the physiological characteristics of the subject; constructing an individualized head model based on the anatomical image data of the subject, and presetting the electrode positions based on the constructed head model.

[0106] Specifically, the characteristics of the subject include the multi-level individualized characteristics of the subject, mainly including: (1) Target characteristics: Obtain the specific position (anatomical coordinates) of the high-risk target and the degree of metabolic abnormality and functional connectivity abnormality (combined score S(i) and characteristic components Z SUVR (i), Z FC (i)); (2) Subject anatomical and physiological characteristics (i.e., anatomical image data): including tissue segmentation and brain region segmentation, as well as parameters such as age, gender, and weight.

[0107] The stimulation parameters of TIS can be optimized from the following aspects: (1) Stimulation frequency setting: Use the interference wave of two groups of high-frequency currents (such as 2 kHz and 2.01 kHz) to form a focused low-frequency (such as 10 Hz) stimulation at the target through phase interference; (2) Stimulation intensity calculation: Combine the combined score of the target as the adjustment coefficient, and the higher the abnormality degree of the target, the greater the stimulation intensity; (3) Electrode position optimization: Combine T1 MRI data and brain tissue segmentation results to construct an individualized head model through MRI or CT navigation, and accurately preset the electrode positions. Ensure that the electrode placement can effectively generate the interference focusing effect of two groups of high-frequency currents. Use the finite element method (FEM) or other bioelectric simulation technologies to simulate the current distribution and the focusing effect of the stimulation field based on the segmented brain tissue information, and verify the effectiveness of the electrode arrangement. In this way, the accuracy and safety of electrode stimulation can be ensured, and the treatment effect can be further optimized; (4) Stimulation time and period setting: Optimize the stimulation time (such as 20 minutes per time) and period (such as 3 times a week for 4 weeks) according to the treatment tolerance of the subject to achieve the best treatment effect.

[0108] In addition, the treatment effect can be monitored in real time through imaging and behavioral evaluation methods. For example: (1) Metabolic monitoring: Use PET imaging to monitor the metabolic activity level of the target and its surrounding brain regions, and evaluate the metabolic recovery situation by quantifying the change in metabolic rate; (2) Network function monitoring: Combine EEG and fMRI data to evaluate the enhancement degree of the synergistic effect between the neural activity of the target and the whole-brain network. (3) Behavioral function evaluation: Quantify the changes in the cognitive, emotional, and motor functions of the subject through neuropsychological tests (such as MMSE, WAIS) to provide an intuitive reference for the treatment effect.

[0109] In summary, for the neuroregulation method based on electrical stimulation provided in this embodiment, after obtaining the metabolic imaging data, functional imaging data, and anatomical imaging data of the subject, based on the metabolic imaging data and functional imaging data, the standardized uptake value ratio of each brain region of the subject is calculated respectively, as well as the functional connection strength between each brain region and all other brain regions, and based on the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region and all other brain regions, the position data of the target target and the brain abnormality characteristics of the subject are determined, and based on the determined target target position data, the brain abnormality characteristics of the subject, and the anatomical imaging data of the subject, the time-domain interference electrical stimulation parameters are set. In this way, the PET and rs-fMRI imaging data are combined and analyzed to further determine the target target and the time-domain interference electrical stimulation parameters, so as to more comprehensively reveal the pathological mechanism of Alzheimer's disease (AD), and perform corresponding neuroregulation based on the calculated time-domain interference electrical stimulation parameters, providing more accurate data support for subsequent disease analysis and medical treatment.

[0110] Based on the same concept as the above-mentioned neuroregulation method based on electrical stimulation, an embodiment of the present application also provides a neuroregulation device based on electrical stimulation for implementing the above-mentioned neuroregulation method based on electrical stimulation, as Figure 4 shown, the neuroregulation device based on electrical stimulation includes:

[0111] An imaging data acquisition module, configured to acquire the metabolic imaging data, functional imaging data, and anatomical imaging data of the subject; the metabolic imaging data is used to reflect the brain glucose metabolism level, the functional imaging data is used to reflect the brain functional connection strength, and the anatomical imaging data is used to reflect the internal brain structure;

[0112] An imaging data processing module, configured to calculate the standardized uptake value ratio of each brain region of the subject respectively, as well as the functional connection strength between each brain region and all other brain regions based on the metabolic imaging data and functional imaging data;

[0113] A combined risk identification module, configured to determine the brain abnormality characteristics of the subject and the position data of the target target based on the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region and all other brain regions;

[0114] A stimulation parameter setting module, configured to set the time-domain interference electrical stimulation parameters based on the brain abnormality characteristics of the subject, the position data of the target target, and the anatomical imaging data.

[0115] It is understandable that the neural regulation device based on electrical stimulation provided in this embodiment is used to execute the above-mentioned neural regulation method based on electrical stimulation. Therefore, it can at least achieve the beneficial effects that the above-mentioned neural regulation method based on electrical stimulation can achieve. Moreover, the various embodiments of the above-mentioned neural regulation method based on electrical stimulation are equally applicable to this neural regulation device based on electrical stimulation, and will not be elaborated here.

[0116] Based on the same concept as the above-mentioned neural regulation method based on electrical stimulation, an embodiment of the present application also provides an electronic device to execute the above-mentioned neural regulation method based on electrical stimulation. Please refer to Figure 5 , which shows a schematic diagram of an electronic device provided by some embodiments of the present application. As Figure 5 shown, the electronic device 5 includes: a processor 501, a memory 502, a bus 503, and a communication interface 504. The processor 501, the communication interface 504, and the memory 502 are connected through the bus 503; a computer program that can run on the processor 501 is stored in the memory 502, and when the processor 501 runs the computer program, it executes the neural regulation method based on electrical stimulation provided by any one of the foregoing embodiments of the present application.

[0117] Among them, the memory 502 may include a high-speed random access memory (RAM: Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 504 (which can be wired or wireless), a communication connection is established between this device network element and at least one other network element, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0118] The bus 503 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 502 is used to store the program. After receiving the execution instruction, the processor 501 executes the program. The neural regulation method based on electrical stimulation disclosed in any one of the foregoing embodiments of the present application can be applied to the processor 501 or implemented by the processor 501.

[0119] The processor 501 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 501 or the instructions in the form of software. The above-mentioned processor 501 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register, etc. This storage medium is located in the memory 502, and the processor 501 reads the information in the memory 502 and combines its hardware to complete the steps of the above method.

[0120] The electronic device provided by the embodiment of the present application and the method for neuromodulation based on electrical stimulation provided by the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by it.

[0121] The embodiment of the present application also provides a computer-readable storage medium corresponding to the method for neuromodulation based on electrical stimulation provided by the foregoing embodiment, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the method for neuromodulation based on electrical stimulation provided by any of the foregoing embodiments.

[0122] It should be noted that the computer-readable storage medium may include, but is not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, optical disc or other optical and magnetic storage media, etc., which will not be elaborated here one by one.

[0123] The computer-readable storage medium provided by the embodiment of the present application and the method for neuromodulation based on electrical stimulation provided by the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored in it.

[0124] An embodiment of the present application also provides a computer program product corresponding to the neuroregulation method based on electrical stimulation provided in the foregoing embodiment. The computer program product includes a computer program, and the computer program is executed by a processor to implement the above-mentioned neuroregulation method based on electrical stimulation.

[0125] The computer program product provided in the embodiment of the present application and the neuroregulation method based on electrical stimulation provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method implemented when the computer program is executed by a processor.

[0126] It can be understood that the descriptions of the foregoing embodiments tend to emphasize the differences between the embodiments. Their similarities or similarities can be referred to each other. For the sake of brevity, they will not be repeated herein.

[0127] Those skilled in the art can understand that in the above method of the specific embodiment, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be covered by the scope of the claims and the description of the present application. In particular, as long as there is no structural conflict, the technical features mentioned in each embodiment can be combined in any way. The present application is not limited to the specific embodiments disclosed in the text, but includes all technical solutions falling within the scope of the claims.

Claims

1. A neural regulation method based on electrical stimulation, characterized in that, The method includes: Obtaining metabolic imaging data, functional imaging data, and anatomical imaging data of the subject; the metabolic imaging data is used to reflect the cerebral glucose metabolism level, the functional imaging data is used to reflect the cerebral functional connectivity strength, and the anatomical imaging data is used to reflect the internal structure of the brain; Based on the metabolic imaging data and the functional imaging data, calculating the standardized uptake value ratio of each brain region of the subject, and the functional connectivity strength between each brain region and all other brain regions respectively; Based on the standardized uptake value ratio of each brain region of the subject and the functional connectivity strength between each brain region and all other brain regions, determining the brain abnormality characteristics of the subject and the position data of the target target; Based on the brain abnormality characteristics of the subject, the position data of the target target, and the anatomical imaging data, setting the time-domain interference electrical stimulation parameters.

2. The method according to claim 1, characterized in that, The calculating the standardized uptake value ratio of each brain region of the subject, and the functional connectivity strength between each brain region and all other brain regions respectively based on the metabolic imaging data and the functional imaging data includes: Performing standardized preprocessing and multimodal registration on the metabolic imaging data and the functional imaging data; Based on the metabolic imaging data and the functional imaging data after the standardized preprocessing and multimodal registration, calculating the standardized uptake value ratio of each brain region of the subject, and the functional connectivity strength between each brain region of the subject and all other brain regions respectively.

3. The method according to claim 2, wherein The performing standardized preprocessing and multimodal registration on the metabolic imaging data and the functional imaging data includes: Performing motion correction on the metabolic imaging data, registering the corrected metabolic imaging data to the anatomical imaging data, and standardizing it to the MNI space; Performing time correction on the functional imaging data, registering the corrected metabolic imaging data to the anatomical imaging data, and standardizing it to the MNI space.

4. The method according to claim 2, characterized in that The calculating the standardized uptake value ratio of each brain region of the subject, and the functional connectivity strength between each brain region of the subject and all other brain regions respectively based on the metabolic imaging data and the functional imaging data after the standardized preprocessing and multimodal registration includes: Based on the metabolic imaging data after the standardized preprocessing and multimodal registration, and a preset brain partition template, calculating the standardized uptake value ratio of each brain region of the subject; Based on the functional imaging data after the standardized preprocessing and multimodal registration, and the preset brain partition template, calculating the functional connectivity strength between each brain region of the subject and all other brain regions.

5. The method according to claim 1, characterized in that, The determining the brain abnormality characteristics of the subject and the position data of the target target based on the standardized uptake value ratio of each brain region of the subject and the functional connectivity strength between each brain region and all other brain regions includes: Based on the standardized uptake value ratio within each brain partition, and the mean and standard deviation of the standardized uptake value ratio in the standard data of the healthy control group, calculating the metabolic abnormality index data of each brain region of the subject; Based on the functional characteristic data within each brain partition and the functional connection strength between each brain region and all other brain regions in the standard data of the healthy control group, calculate the functional abnormality index data of each brain region of the subject; Based on the metabolic abnormality index data of each brain region of the subject and the functional abnormality index data of each brain region of the subject, determine the risk index data of each brain region of the subject according to a preset risk assessment model; Based on the metabolic abnormality index data of each brain region of the subject, the functional abnormality index data of each brain region, and the risk index data, determine the position data of the target target; 6. The method according to claim 5, wherein The determining the risk index data of each brain region of the subject according to a preset risk assessment model based on the metabolic abnormality index data of each brain region of the subject and the functional abnormality index data between every two brain regions of the subject includes: Based on the metabolic abnormality index data of each brain region of the subject and the functional abnormality index data between every two brain regions of the subject, determine the risk index data of each brain region of the subject according to the following formula: S(i) = ω1·Z SUVR (i) + ω2·Z FC (i) Among them, Z SUVR (i) represents the standardized metabolic abnormality index data, Z FC (i) represents the standardized functional abnormality index data, and ω1 and ω2 are the weight coefficients of the metabolic level and functional connectivity, respectively, reflecting the relative importance of the metabolic level and functional connectivity characteristics.

7. The method according to any one of claims 1-6, characterized in that, The setting of the time-domain interference electrical stimulation parameters based on the brain abnormality characteristics of the subject, the position data of the target target, and the anatomical image data includes: Based on the position data of the target target, the brain abnormality characteristics, and the physiological characteristics of the subject, set the frequency, intensity, time, and period of the time-domain interference electrical stimulation; Construct an individualized head model based on the anatomical image data of the subject, and preset the electrode positions based on the constructed head model.

8. A nerve regulation device based on electrical stimulation, characterized in that, The neurostimulation device based on electrical stimulation includes: An image data acquisition module for acquiring the metabolic image data, functional image data, and anatomical image data of the subject; the metabolic image data is used to reflect the brain glucose metabolism level, the functional image data is used to reflect the brain functional connection strength, and the anatomical image data is used to reflect the internal brain structure; An image data processing module for calculating the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region and all other brain regions respectively based on the metabolic image data and the functional image data; A combined risk identification module for determining the brain abnormality characteristics of the subject and the position data of the target target based on the standardized uptake value ratio of each brain region of the subject and the functional connection strength between each brain region and all other brain regions; A stimulation parameter setting module for setting the time-domain interference electrical stimulation parameters based on the brain abnormality characteristics of the subject, the position data of the target target, and the anatomical image data.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor runs the computer program to implement the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method according to any one of claims 1-7.

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