Hand motion hot spot rapid positioning method and device

Abstract

The invention provides a hand motion hot spot rapid positioning method and device, and the method comprises the steps: obtaining magnetic resonance image data and MEP signal data of an experiment subject, and carrying out the preprocessing of the magnetic resonance image data, and obtaining an electric field simulation result; further constructing a group hand motion hotspot map, and dividing experimental subjects into positive samples and negative samples for training to obtain an outlier classification model; dividing the to-be-positioned testees into an outlier group and a non-outlier group through an outlier classification model; when the to-be-positioned testee is an outlier, performing correlation analysis on an electric field simulation result corresponding to the to-be-positioned testee and the MEP signal data to obtain a target hand motion hotspot; and when the group is a non-outlier group, performing individual hand motion hot spot registration through the group hand motion hot spot map to obtain a target hand motion hot spot. By means of the method and device, the defects that in the prior art, time and labor are wasted in the hand movement hot spot positioning process, positioning accuracy cannot be guaranteed, and consequently clinical treatment efficiency is low are overcome.

Description

Technical Field

[0001] The present invention relates to the field of transcranial magnetic stimulation medical technology, and in particular to a method and device for quickly locating a hand movement hotspot. Background Art

[0002] Transcranial Magnetic Stimulation (TMS) is a non-invasive neuromodulation technique used to study human neurophysiology and treat neurological diseases. The hand motor hotspot (hMHS) refers to the part of the cerebral cortex where motor evoked potentials (MEPs) are most easily elicited under TMS, and is used to determine the resting motor threshold (MT) of the subject. The stimulation intensity that induces MEPs is often used as a reference value for determining individual-specific stimulation intensity. Therefore, determining the hand motor hotspot is a common operation in TMS treatment.

[0003] The existing clinical hand movement hotspot location method requires multiple stimulations at multiple locations around the patient's brain motor area to collect MEP signal data, and then perform surface fitting on the collected MEP signal data, so as to determine the location of the maximum MEP signal data on the surface as the hand movement hotspot. However, the entire hand movement hotspot location process is time-consuming and labor-intensive, and the accuracy of the location cannot be guaranteed, resulting in low treatment efficiency. Summary of the invention

[0004] The present invention provides a method and device for quickly locating a hand motion hotspot, which is used to solve the technical problems in the prior art that the hand motion hotspot locating process is time-consuming and labor-intensive, and the accuracy of the locating cannot be guaranteed, resulting in low clinical treatment efficiency.

[0005] The present invention provides a method for quickly locating a hand motion hotspot, comprising the following steps: Acquire magnetic resonance imaging data of the experimental subject and MEP signal data corresponding to different locations of the stimulated cerebral cortex, and pre-process the magnetic resonance imaging data to obtain electric field simulation results; According to the MEP signal data and the electric field simulation results, a group hand motion hotspot map is constructed, and the experimental subjects are divided into positive samples and negative samples by using the group hand motion hotspot map; An outlier classification model is obtained by training the positive samples and the negative samples; For the subjects to be located, the subjects to be located are divided into an outlier group and a non-outlier group by using the outlier classification model; When the subject to be located is an outlier, a correlation analysis is performed between the electric field simulation result corresponding to the subject to be located and the MEP signal data to obtain a target hand movement hotspot; When the subject to be located is a non-outlier group, individual hand motion hotspot registration is performed through the group hand motion hotspot map to obtain the target hand motion hotspot.

[0006] In some embodiments, constructing a group hand motion heat map according to the MEP signal data and the electric field simulation results includes: Performing correlation analysis on the electric field simulation results and the MEP signal data to obtain individual hand movement hot spots of the experimental subjects; Based on the individual hand movement hot spots, a Gaussian window function is used to construct a corresponding sample probability map; Each sample probability map is accumulated to construct a group hand movement hotspot map.

[0007] In some embodiments, the performing correlation analysis on the electric field simulation results and the MEP signal data to obtain individual hand motion hotspots of the experimental subjects includes: Determine the vertices corresponding to the stimulation positions of the cerebral cortex of the experimental subject, and determine the electric field values ​​of each vertex at different stimulation positions from the electric field simulation results; For each vertex, determining a positive correlation value between the electric field value and the MEP signal data; An individual hand motion hotspot probability map is determined according to the positive correlation value, and a vertex with the highest probability in the individual hand motion hotspot probability map is determined as the individual hand motion hotspot of the experimental subject.

[0008] In some embodiments, dividing the experimental subjects into positive samples and negative samples by the group hand movement hot spot map includes: From the group hand motion hotspot map, determine the vertex corresponding to the maximum probability as the group hand motion hotspot; Determining the cortical distance between the individual hand movement hotspot of each experimental subject and the group hand movement hotspot; When the cortical distance is greater than the distance threshold, the experimental subject is determined to be a negative sample; When the cortical distance is less than or equal to the distance threshold, the experimental subject is determined to be a positive sample.

[0009] In some embodiments, the training of the outlier classification model based on the positive samples and the negative samples includes: From the group hand motion hotspot map, determine the vertex corresponding to the maximum probability as the group hand motion hotspot; Taking the group hand motion hotspot as the center, determining a region of interest from the cerebral cortex of the training sample, wherein the training sample includes the positive sample and the negative sample; Extracting the cerebral cortex structural information in the region of interest from the magnetic resonance imaging data corresponding to the training sample, wherein the cerebral cortex structural information includes cortical thickness, cortical curvature, and cortical sulcus index; The cerebral cortex structural information is used as training data to train an initial logistic regression model to obtain an outlier classification model.

[0010] In some embodiments, performing individual hand motion hotspot registration through the group hand motion hotspot map to obtain a target hand motion hotspot includes: From the group hand motion hotspot map, determine the vertex corresponding to the maximum probability as the group hand motion hotspot; Determine the registration mapping relationship from group to individual; According to the registration mapping relationship, the group hand motion hotspots are mapped to target hand motion hotspots of the subject to be located.

[0011] The present invention also provides a device for quickly locating a hand motion hotspot, the device comprising the following modules: A preprocessing module is used to obtain the magnetic resonance imaging data of the experimental subjects and the MEP signal data corresponding to different locations of the brain motor area stimulated, and preprocess the magnetic resonance imaging data to obtain electric field simulation results; A construction module, used to construct a group hand motion hotspot map according to the MEP signal data and the electric field simulation result, and divide the experimental subjects into positive samples and negative samples through the group hand motion hotspot map; A training module, used for training an outlier classification model according to the positive samples and the negative samples; A division module, for dividing the subjects to be located into an outlier group and a non-outlier group by using the outlier classification model; An analysis module, for performing correlation analysis on the electric field simulation result corresponding to the subject to be located and the MEP signal data to obtain a target hand movement hotspot when the subject to be located is out of the group; The registration module is used to perform individual hand motion hotspot registration through the group hand motion hotspot map to obtain the target hand motion hotspot when the subject to be located is a non-outlier group.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for quickly locating a hand motion hotspot as described above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for quickly locating hand motion hotspots.

[0014] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for quickly locating hand motion hotspots.

[0015] The method and device for quickly locating hand motion hotspots provided by the present invention first collect the magnetic resonance imaging data and MEP signal data of the experimental subjects to construct a group hand motion hotspot map, and divide the experimental subjects into negative samples and positive samples to train the outlier classification model. The outlier nature of the subject to be located is classified. When the subject to be located is an outlier, the target hand motion hotspot is determined by the MEP signal data. When the subject to be located is not an outlier, the group hand motion hotspot map is directly used for alignment to determine the target hand motion hotspot. In this way, the hand motion hotspot of the subject to be located is located by combining the outlier classification model and the group hand motion hotspot map, which not only realizes the rapid positioning of the hand motion hotspot, but also effectively ensures the accuracy of the hand motion hotspot, thereby improving the clinical treatment efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced one by one below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of the flow of the method for quickly locating the hand movement hotspot provided by the present invention.

[0018] Figure 2 It is a visualization diagram of the cerebral cortex search grid provided by the present invention.

[0019] Figure 3 It is a MEP signal distribution diagram of 25 stimulation points provided by the present invention.

[0020] Figure 4 It is a visualization diagram of the visualized simulation electric field results of 25 stimulation points provided by the present invention.

[0021] Figure 5 It is a visualization diagram of cortical structural information provided by the present invention.

[0022] Figure 6 It is a positive correlation diagram between the electric field value and the MEP signal data provided by the present invention.

[0023] Figure 7 It is a visualization diagram of the hand movement hotspot probability map provided by the present invention.

[0024] Figure 8 It is a schematic diagram of the region of interest of the cerebral cortex provided by the present invention.

[0025] Fig. 9 It is a structural schematic diagram of the hand movement hotspot rapid positioning device provided by the present invention.

[0026] Fig.10 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Combine the following Figure 1-Figure 8 The present invention describes a method for quickly locating hand motion hotspots. Figure 1 FIG. 1 is a flow chart of a method for quickly locating a hand motion hotspot provided by the present invention. Figure 1 As shown, the method includes the following steps 101 to 106.

[0029] Step 101: Acquire the magnetic resonance imaging data of the experimental subject and the MEP signal data corresponding to the stimulation of different positions of the cerebral cortex, and pre-process the magnetic resonance imaging data to obtain the electric field simulation results.

[0030] The embodiment of the present invention takes into account that the situations of each subject to be located are different. The hand motion hotspots of some groups of people are relatively similar, and can be summarized and determined as a group hand motion hotspot. The hand motion hotspots of another group of people are relatively special, and their hand motion hotspots are outliers. Therefore, when locating hand motion hotspots, the embodiment of the present invention needs to construct a group hand motion hotspot map as prior knowledge, which can not only divide samples to train the outlier classification model, but also quickly locate the hand motion hotspots of the subjects to be located.

[0031] Therefore, before locating the hand movement hotspot of the subject to be positioned, it is necessary to obtain the magnetic resonance imaging data of the experimental subject and the MEP signal data corresponding to different locations of the cerebral cortex (motor area) stimulated. Here, in the process of collecting MEP signal data, such as Figure 2 As shown, the neuroregulatory robot can be used to firmly fix the TMS coil, traverse the predefined search grid corresponding to the cerebral cortex of the experimental subject in a random order, apply single pulse stimulation at a certain intensity, and record the corresponding MEP signal. The search grid is a 5×5 square matrix with a spacing of 1 cm between each stimulation point, a total of 25 stimulation points, and the center of the matrix falls on the central stimulation point. In the process of searching for hand movement hotspots, the TMS coil will traverse the 25 stimulation points in a random order. Each stimulation point is stimulated using a single pulse transcranial magnetic stimulation (spTMS) method with an interval of 5s. Each stimulation point is stimulated 3 times, and the corresponding MEP signal is collected. From the three collected MEP signals, the maximum value of the MEP signal is selected as the MEP signal data induced by the TMS stimulation at that point. Among them, the MEP signal distribution of the 25 stimulation points of the experimental subject can be shown as follows Figure 3 shown.

[0032] Magnetic resonance imaging data (MRI data) can be extracted from experimental subjects through relevant magnetic resonance instruments and equipment, which will not be elaborated here. After the data acquisition is completed, the magnetic resonance imaging data is preprocessed to obtain the electric field simulation results. Here, a series of preprocessing processes such as cortical reconstruction, electric field simulation, and calculation of cortical structure information are performed on the collected MRI data. For example, the brain of the experimental subject is first extracted, the brain tissue is segmented, and the T1w image of the individual space of the experimental subject is aligned with the MNI standard template. The cerebral cortex is also segmented and reconstructed to accurately divide the brain sulcus structure. Then, the electric field simulation is performed at the corresponding stimulation points on the cerebral cortex, and the cortical structure information is extracted. In this embodiment of the present invention, the visual simulation electric field results of 25 stimulation points are as follows. Figure 4 As shown, the visualization results of the extracted cortical structure information can be seen in Figure 5 As shown, the cortical structural information specifically includes cortical thickness, cortical curvature, and cortical sulcus index.

[0033] Step 102: construct a group hand motion hotspot map based on the MEP signal data and the electric field simulation results, and divide the experimental subjects into positive samples and negative samples through the group hand motion hotspot map.

[0034] After obtaining the MEP signal data and electric field simulation results of the experimental subjects, the group hand movement hotspot map can be constructed based on the MEP signal data and electric field simulation results.

[0035] First, it is necessary to determine the individual hand motion hotspot of each experimental subject. In this embodiment of the present invention, the electric field simulation results of the experimental subject and the MEP signal data are subjected to correlation analysis to obtain the individual hand motion hotspot. The specific process of the correlation analysis is described below.

[0036] First, the vertices corresponding to different positions of the cerebral cortex (motor area) of the experimental subjects are determined (for example, the 25 stimulation points described above), and the electric field values ​​of each vertex at different stimulation positions are determined from the electric field simulation results.

[0037] Then, for each vertex, the positive correlation value between the electric field value and the MEP signal data is determined. Figure 6 As shown, Figure 6 The positive correlation between the electric field value (E Filed) and the MEP signal data is shown in Figure 1. Based on this, the corresponding positive correlation value can be calculated. The positive correlation value can be mapped to the corresponding hand movement hotspot probability. The higher the positive correlation value, the higher the positive correlation between the electric field value and the MEP signal, and the higher the hand movement hotspot probability. The hand movement hotspot probability of each vertex is combined to form a hand movement hotspot probability map. Figure 7 As shown, Figure 7 The visualization results of the hand motion hotspot probability map of some vertices are shown in Figure 1. In the hand motion hotspot probability map, the vertex corresponding to the maximum probability is determined as the individual hand motion hotspot.

[0038] After determining the individual hand motion hotspots based on the electric field simulation results and the MEP signal data, each experimental subject can determine the corresponding individual hand motion hotspot, and then use the Gaussian window function to construct the corresponding sample probability map based on the individual hand motion hotspot. Here, the corresponding sample probability map g(x) is constructed based on the true value of the individual hand motion hotspot (that is, the coordinates of the individual hand motion hotspot on the cerebral cortex (motor area)). For each true value of the individual hand motion hotspot, the Gaussian window function is used to construct the corresponding sample probability model, which is expressed as the following formula (1): (1)

[0039] In the above formula (1), represents the true value of any individual hand movement hotspot, x represents any vertex on the cerebral cortex (motor area), is a hyperparameter that represents the effective stimulation range of TMS, which is generally determined by the interval between 25 stimulation points. By performing the calculation process of formula (1) on any vertex on the cerebral cortex (motor area), the probability corresponding to the vertex can be obtained. In this way, multiple different probabilities can be obtained, thus forming a sample probability map.

[0040] Finally, the sample probability maps of each experimental subject are accumulated to construct the group hand movement hotspot map. Here, the sample probability maps corresponding to each individual hand movement hotspot are uniformly accumulated to obtain the group hand movement hotspot map p(x) corresponding to the population. The accumulation process is to add the Gaussian window functions corresponding to each hand movement hotspot, which is expressed as the following formula (2): (2)

[0041] In the above formula (2), It represents the sample probability map corresponding to the individual hand movement hotspot of the ith experimental subject. The meanings of the other parameters are the same as those in formula (1), and they can be referenced to each other and will not be repeated here.

[0042] Next, the experimental subjects are divided into positive samples and negative samples through the group hand movement hotspot map. The division process is as follows: first, the vertex corresponding to the maximum probability is determined as the group hand movement hotspot from the group hand movement hotspot map, and then the cortical distance between the individual hand movement hotspot of each experimental subject and the group hand movement hotspot is determined. This distance is generally obtained by coordinate calculation of the true value of the individual hand movement hotspot (coordinates on the cerebral cortex (motor area)) and the true value of the group hand movement hotspot.

[0043] When the cortical distance is greater than the distance threshold, it means that the individual hand motion hotspot of the experimental subject is far away from the group hand motion hotspot, and belongs to the outlier group, and the experimental subject in the outlier group is determined to be a negative sample. When the cortical distance is less than or equal to the distance threshold, it means that the individual hand motion hotspot of the experimental subject is close to the group hand motion hotspot, and belongs to the non-outlier group, and the experimental subject in the non-outlier group is determined to be a positive sample.

[0044] Step 103: Obtain an outlier classification model based on the positive samples and the negative samples.

[0045] After the experimental subjects are divided into positive samples and negative samples through the group hand movement hot spot map according to step 102, an outlier classification model can be obtained by training the positive samples and the negative samples.

[0046] When training an outlier classification model for distinguishing outliers, negative samples and positive samples are mixed to obtain training samples for the outlier classification model. First, training data needs to be obtained from the training samples. Here, the vertex corresponding to the maximum probability is first determined from the group hand motion hotspot map as the group hand motion hotspot. Because on the group hand motion hotspot map, the probability corresponding to each vertex represents the possibility of being determined as a group hand motion hotspot, so the vertex corresponding to the maximum probability can be used as the group hand motion hotspot to characterize the hand motion hotspot of this group of people.

[0047] Next, the region of interest (ROI) is determined from the cerebral cortex (motor area) of the training samples (including negative and positive samples) with the group hand movement hotspot as the center. Figure 8 As shown in Figure 2, a 20 mm area can be defined in the cerebral cortex (motor area) with the group hand movement hotspot as the center. Figure 8 The yellow area in the middle is taken as the region of interest, and then the cerebral cortical structure information in the region of interest is extracted from the magnetic resonance imaging data of the training samples (i.e., the experimental subjects corresponding to the negative samples and the positive samples). The cerebral cortical structure information includes cortical thickness, cortical curvature and cortical sulcus index.

[0048] Finally, the cerebral cortex structural information (i.e., cortical thickness, cortical curvature, and cortical sulcus index) is used as training data to train the initial logistic regression model and obtain an outlier classification model. During the training process, the information features can be extracted based on the cerebral cortex structural information, and the logistic regression model can learn the differences in information features between negative samples and positive samples, so that the model can subsequently identify the information features of the subjects to be located, that is, identify yin or yang, thereby distinguishing whether the hand movement hotspot of the subjects to be located is outlier. The logistic regression model can adopt a classification model in machine learning, such as a random forest, a decision tree, etc., which is not limited in the embodiments of the present application.

[0049] Step 104: For the subjects to be located, the subjects to be located are divided into an outlier group and a non-outlier group by using an outlier classification model.

[0050] In some embodiments, after the outlier classification model is trained, it can be used to distinguish the subjects to be located (i.e., new subjects). For the subjects to be located, magnetic resonance imaging data can be obtained first, and then the cerebral cortex structure information can be extracted from it, and then the information features can be extracted. Then the outlier classification model can identify the information features and predict whether the subject to be located is an outlier group or a non-outlier group, thereby dividing the subjects to be located into outlier groups and non-outlier groups.

[0051] Step 105: When the subject to be located is out of the group, correlation analysis is performed between the electric field simulation result corresponding to the subject to be located and the MEP signal data to obtain the target hand movement hotspot.

[0052] When it is determined that the subject to be located is out of the group, it means that the hand motion hotspot of the subject to be located is far away from the group hand motion hotspot, and the target hand motion hotspot cannot be determined through the group hand motion hotspot map. Therefore, the correlation analysis method is used to locate the target hand motion hotspot. First, the magnetic resonance imaging data of the subject to be located and the MEP signal data corresponding to different positions of the cerebral cortex are obtained, and the magnetic resonance imaging data are preprocessed to obtain the electric field simulation results, and then the vertices corresponding to the stimulation positions of the cerebral cortex of the subject to be located are determined, and the electric field values ​​of each vertex at different stimulation positions are determined from the electric field simulation results.

[0053] Next, for each vertex, the positive correlation value between the electric field value and the MEP signal data is determined, and the individual hand motion hotspot probability map is determined based on the positive correlation value, and the vertex with the highest probability in the individual hand motion hotspot probability map is determined as the target hand motion hotspot of the subject to be located. The process of this correlation analysis is similar to step 102, and the specific implementation details are not repeated here.

[0054] Step 106: When the subject to be located is not out of the group, the individual hand motion hotspots are registered through the group hand motion hotspot map to obtain the target hand motion hotspot.

[0055] When it is determined that the subject to be located is not out of the group, it means that the hand motion hotspot of the subject to be located is close to the group hand motion hotspot, and the target hand motion hotspot can be determined through the group hand motion hotspot map. Then, the individual hand motion hotspot is registered through the group hand motion hotspot map to obtain the target hand motion hotspot.

[0056] Specifically, the vertex corresponding to the maximum probability is determined from the group hand motion hotspot map as the group hand motion hotspot, and then the registration mapping relationship from the group to the individual is determined. Here, the open source registration tool Advanced Normalization Tools (ANTs) can be used to register the T1w image corresponding to the magnetic resonance imaging data of the subject to be located to the standard MNI152 image, and the mapping relationship obtained by the registration is used as the registration mapping relationship. Finally, the group hand motion hotspot is mapped to the individual according to the registration mapping relationship to obtain the target hand motion hotspot of the subject to be located.

[0057] The method and device for quickly locating hand motion hotspots provided by the present invention first collect the magnetic resonance imaging data and MEP signal data of the experimental subjects to construct a group hand motion hotspot map, and divide the experimental subjects into negative samples and positive samples to train the outlier classification model. The outlier nature of the subject to be located is classified. When the subject to be located is an outlier, the target hand motion hotspot is determined by the MEP signal data. When the subject to be located is not an outlier, the group hand motion hotspot map is directly used for alignment to determine the target hand motion hotspot. In this way, the hand motion hotspot of the subject to be located is located by combining the outlier classification model and the group hand motion hotspot map, which not only realizes the rapid positioning of the hand motion hotspot, but also effectively ensures the accuracy of the hand motion hotspot, thereby improving the clinical treatment efficiency.

[0058] The hand motion hotspot rapid positioning device provided by the present invention is described below. The hand motion hotspot rapid positioning device described below and the hand motion hotspot rapid positioning method described above can be referenced to each other.

[0059] See also Fig. 9 , Fig. 9 : is a schematic diagram of the structure of the hand movement hotspot rapid positioning device provided by the present invention, such as Fig. 9 As shown, the hand motion hotspot rapid positioning device includes a preprocessing module 901, a construction module 902, a training module 903, a division module 904, an analysis module 905, and a registration module 906. Among them, the preprocessing module 901 is used to obtain the magnetic resonance imaging data of the experimental subjects and the MEP signal data corresponding to different positions of the brain motor area, and preprocess the magnetic resonance imaging data to obtain the electric field simulation results; the construction module 902 is used to construct a group hand motion hotspot map according to the MEP signal data and the electric field simulation results, and divide the experimental subjects into positive samples and negative samples through the group hand motion hotspot map; the training module 903 is used to train the positive samples and the negative samples to obtain outliers A classification model; a division module 904, for dividing the subjects to be located into an outlier group and a non-outlier group by using the outlier classification model; an analysis module 905, for performing correlation analysis on the electric field simulation results corresponding to the subjects to be located and the MEP signal data when the subjects to be located are outliers, so as to obtain target hand motion hotspots; a registration module 906, for performing individual hand motion hotspot registration by using the group hand motion hotspot map when the subjects to be located are non-outliers, so as to obtain target hand motion hotspots.

[0060] It should be noted that the beneficial effects of the hand motion hotspot rapid positioning device here and the hand motion hotspot rapid positioning method mentioned above can correspond to each other, so the beneficial effects of the hand motion hotspot rapid positioning device are not repeated here.

[0061] Fig.10 is a schematic diagram of the physical structure of an electronic device provided by the present invention, such as Fig.10 As shown, the electronic device may include: a processor (processor) 910 , a communication interface (Communications Interface) 920 , a memory (memory) 930 and a communication bus 940 , wherein the processor 910 , the communication interface 920 , and the memory 930 communicate with each other through the communication bus 940 . The processor 910 can call the logic instructions in the memory 930 to execute a method for quickly locating hand motion hotspots, which method includes: obtaining magnetic resonance image data of the experimental subject and MEP signal data corresponding to different locations of the cerebral cortex, and preprocessing the magnetic resonance image data to obtain electric field simulation results; constructing a group hand motion hotspot map based on the MEP signal data and the electric field simulation results, and dividing the experimental subjects into positive samples and negative samples through the group hand motion hotspot map; training an outlier classification model based on the positive samples and the negative samples; for the subject to be located, dividing the subject to be located into an outlier group and a non-outlier group through the outlier classification model; when the subject to be located is an outlier group, performing a correlation analysis between the electric field simulation result corresponding to the subject to be located and the MEP signal data to obtain a target hand motion hotspot; when the subject to be located is a non-outlier group, performing individual hand motion hotspot registration through the group hand motion hotspot map to obtain a target hand motion hotspot.

[0062] In addition, the logic instructions in the above-mentioned memory 930 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0063] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the hand motion hotspot rapid positioning method provided by the above methods, the method comprising: obtaining magnetic resonance imaging data of the experimental subject and MEP signal data corresponding to different locations of the cerebral cortex, and preprocessing the magnetic resonance imaging data to obtain electric field simulation results; constructing a group hand motion hotspot map based on the MEP signal data and the electric field simulation results, and The experimental subjects are divided into positive samples and negative samples through the group hand motion hotspot map; an outlier classification model is obtained according to the positive samples and the negative samples; for the subjects to be located, the subjects to be located are divided into an outlier group and a non-outlier group through the outlier classification model; when the subjects to be located are outliers, the electric field simulation results corresponding to the subjects to be located are correlated with the MEP signal data to obtain target hand motion hotspots; when the subjects to be located are non-outliers, individual hand motion hotspots are aligned through the group hand motion hotspot map to obtain target hand motion hotspots.

[0064] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the hand motion hotspot rapid location method provided by the above-mentioned methods, the method comprising: obtaining magnetic resonance imaging data of the experimental subject and MEP signal data corresponding to different locations of the cerebral cortex stimulated, and preprocessing the magnetic resonance imaging data to obtain an electric field simulation result; constructing a group hand motion hotspot map according to the MEP signal data and the electric field simulation result, and using the group hand motion hotspot map to locate the hand motion hotspot quickly; The experimental subjects are divided into positive samples and negative samples; an outlier classification model is obtained by training the positive samples and the negative samples; for the subjects to be located, the subjects to be located are divided into an outlier group and a non-outlier group through the outlier classification model; when the subjects to be located are the outlier group, the electric field simulation results corresponding to the subjects to be located are correlated with the MEP signal data to obtain the target hand motion hotspot; when the subjects to be located are the non-outlier group, the individual hand motion hotspots are aligned through the group hand motion hotspot map to obtain the target hand motion hotspot.

[0065] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0066] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quickly locating hand motion hotspots, characterized in that: The method comprises: Acquire magnetic resonance imaging data of the experimental subject and MEP signal data corresponding to different locations of the stimulated cerebral cortex, and pre-process the magnetic resonance imaging data to obtain electric field simulation results; According to the MEP signal data and the electric field simulation results, a group hand motion hotspot map is constructed, and the experimental subjects are divided into positive samples and negative samples by using the group hand motion hotspot map; An outlier classification model is obtained by training the positive samples and the negative samples; For the subjects to be located, the subjects to be located are divided into an outlier group and a non-outlier group by using the outlier classification model; When the subject to be located is an outlier, a correlation analysis is performed between the electric field simulation result corresponding to the subject to be located and the MEP signal data to obtain a target hand movement hotspot; When the subject to be located is a non-outlier group, individual hand motion hotspot registration is performed through the group hand motion hotspot map to obtain the target hand motion hotspot.

2. The method for quickly locating hand motion hotspots according to claim 1, characterized in that: The constructing a group hand motion heat map according to the MEP signal data and the electric field simulation result includes: Performing correlation analysis on the electric field simulation results and the MEP signal data to obtain individual hand movement hot spots of the experimental subjects; Based on the individual hand movement hot spots, a Gaussian window function is used to construct a corresponding sample probability map; Each sample probability map is accumulated to construct a group hand movement hotspot map.

3. The method for quickly locating hand motion hotspots according to claim 2, characterized in that: The step of performing correlation analysis on the electric field simulation result and the MEP signal data to obtain individual hand motion hot spots of the experimental subject includes: Determine the vertices corresponding to the stimulation positions of the cerebral cortex of the experimental subject, and determine the electric field values ​​of each vertex at different stimulation positions from the electric field simulation results; For each vertex, determining a positive correlation value between the electric field value and the MEP signal data; An individual hand motion hotspot probability map is determined according to the positive correlation value, and a vertex with the highest probability in the individual hand motion hotspot probability map is determined as the individual hand motion hotspot of the experimental subject.

4. The method for quickly locating hand motion hotspots according to claim 1, characterized in that: The experimental subjects are divided into positive samples and negative samples through the group hand movement hot spot map, including: From the group hand motion hotspot map, determine the vertex corresponding to the maximum probability as the group hand motion hotspot; Determining the cortical distance between the individual hand movement hotspot of each experimental subject and the group hand movement hotspot; When the cortical distance is greater than the distance threshold, the experimental subject is determined to be a negative sample; When the cortical distance is less than or equal to the distance threshold, the experimental subject is determined to be a positive sample.

5. The method for quickly locating hand motion hotspots according to claim 1, characterized in that: The outlier classification model is obtained by training the positive samples and the negative samples, comprising: From the group hand motion hotspot map, determine the vertex corresponding to the maximum probability as the group hand motion hotspot; Taking the group hand motion hotspot as the center, determining a region of interest from the cerebral cortex of a training sample, wherein the training sample includes the positive sample and the negative sample; Extracting the cerebral cortex structural information in the region of interest from the magnetic resonance imaging data corresponding to the training sample, wherein the cerebral cortex structural information includes cortical thickness, cortical curvature, and cortical sulcus index; The cerebral cortex structural information is used as training data to train an initial logistic regression model to obtain an outlier classification model.

6. The method for quickly locating hand motion hotspots according to claim 1, characterized in that: The registering of individual hand motion hotspots through the group hand motion hotspot map to obtain a target hand motion hotspot includes: From the group hand motion hotspot map, determine the vertex corresponding to the maximum probability as the group hand motion hotspot; Determine the registration mapping relationship from group to individual; According to the registration mapping relationship, the group hand motion hotspots are mapped to the target hand motion hotspots of the subject to be located.

7. A device for quickly locating hot spots of hand movements, characterized in that: The device comprises: A preprocessing module is used to obtain the magnetic resonance imaging data of the experimental subjects and the MEP signal data corresponding to different locations of the brain motor area stimulated, and preprocess the magnetic resonance imaging data to obtain electric field simulation results; A construction module, used to construct a group hand motion hotspot map according to the MEP signal data and the electric field simulation result, and divide the experimental subjects into positive samples and negative samples through the group hand motion hotspot map; A training module, used for training an outlier classification model according to the positive samples and the negative samples; A division module, for dividing the subjects to be located into an outlier group and a non-outlier group by using the outlier classification model; An analysis module, for performing correlation analysis on the electric field simulation result corresponding to the subject to be located and the MEP signal data to obtain a target hand movement hotspot when the subject to be located is out of the group; The registration module is used to perform individual hand motion hotspot registration through the group hand motion hotspot map to obtain the target hand motion hotspot when the subject to be located is a non-outlier group.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for quickly locating the hand motion hotspot as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for quickly locating the hand motion hotspot as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for quickly locating the hand motion hotspot as described in any one of claims 1 to 6 is implemented.

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