Supplementary motor area imaging diagnosis and positioning method based on resting state

Abstract

The present invention relates to the field of image processing technology, and in particular to a method for image diagnosis and positioning of a supplementary motor area based on a resting state, comprising: acquiring a user's brain magnetic resonance image based on an MRI scanner, creating a database, and storing the acquired user's brain magnetic resonance image; acquiring the user's brain magnetic resonance image, and removing high-frequency physiological noise and low-frequency drift in the user's brain magnetic resonance image; acquiring the supplementary motor area, and performing low-frequency pulsation amplitude analysis, local consistency analysis, and functional connection abnormality analysis; the present invention utilizes brain maps for preliminary positioning and functional connection mutual verification, can more accurately determine the position of the supplementary motor area, reduce positioning errors, and evaluate the state of the supplementary motor area from two aspects of local brain activity and functional connection abnormality analysis, and based on this method, reduces the interference of subjective factors, realizes the quantification of diagnostic indicators, and makes the results more objective and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a resting-state-based auxiliary motor area image diagnosis and positioning method. Background Art

[0002] Resting-state supplementary motor area imaging is a technique for studying the supplementary motor area of ​​the brain in a task-free state. Through functional magnetic resonance imaging and other means, the changes in blood oxygen level-dependent signals in this area are detected. It can show the spontaneous neural activity, local consistency and functional connectivity of the supplementary motor area, which helps to understand its normal function and diagnose related lesions.

[0003] The invention patent with application number 201610998725.3 discloses a method for localizing the auxiliary motor area function based on the resting state, which is characterized by comprising: using a machine learning algorithm and preprocessing the resting-state functional images and high-precision structural images of patients with glioma in the brain functional area, including: scanning layer time correction, head motion correction, mapping to standardized space, detrending items, bandpass filtering and scrubbing. At the same time as the registration, the tumor MASK is used to remove its influence on standardization, and the healthy side of the brain that is not affected by the tumor image is mapped to the standard space. Then, for each target area on the affected side, voxels, and calculate their correlation coefficients with the signals of multiple characteristic regions on the opposite side, which are used as the input of the support vector machine SVM classifier, and the output is whether each voxel in the target area belongs to SMA; finally, the results of the three positioning are spliced ​​and mapped back to the individual space to form the positioning result of the entire SMA; the target area is defined in the method: the SMA position located by the AAL template is taken as the center, and the area expanded by 2 voxels is used as the target area; the target area is defined in the method: the area expanded by 2 voxels is 6mm, and the target area has a total of 1472 voxels, of which 666 voxels are in the SMA.

[0004] This application aims to solve the problem that "practice shows that a single task paradigm has limited effects on the activation of brain functional areas. Taking the motor area as an example, it is difficult to activate the supplementary motor area by rhythmic tapping of fingers. Some patients are already in a state of severe motor or cognitive impairment and are unable to cooperate with the task paradigm at all. Therefore, the positioning of brain functional areas based on task states is limited by incomplete activation of functional areas by tasks and the degree of task execution, and cannot fully meet clinical needs."

[0005] However, most of the existing technologies have the problems of inaccurate positioning and single diagnostic information.

[0006] Therefore, a resting-state based supplementary motor area imaging diagnosis and positioning method was proposed. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides a resting-state-based supplementary motor area imaging diagnosis and positioning method, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] The resting state-based supplementary motor area imaging diagnosis and positioning method includes:

[0010] Based on the MRI scanner, the user's brain magnetic resonance images are collected, a database is created, and the collected user's brain magnetic resonance images are stored; the user's brain magnetic resonance images are obtained, and the high-frequency physiological noise and low-frequency drift in the user's brain magnetic resonance images are removed; the auxiliary motor area is obtained, and low-frequency beat amplitude analysis, local consistency analysis and functional connection abnormality analysis are performed; the auxiliary motor area motor dysfunction judgment index is set, and the set judgment index is compared with the decomposition result to determine whether the user has motor dysfunction; a diagnosis report is generated, and the diagnosis report is fed back to the user.

[0011] Furthermore, the MRI scanner collects the user's brain magnetic resonance image at a frequency of 500 to 3000 milliseconds per time. During the stage of collecting the user's brain magnetic resonance image, the MRI scanner simultaneously performs an environmental analysis on the scene in which the MRI scanner collects the user's brain magnetic resonance image, and selects the user's brain magnetic resonance image as the output result of the MRI scanner based on the environmental analysis result.

[0012] Furthermore, an environmental sensing device is provided in the scene where the MRI scanner collects magnetic resonance images of the user's brain. The frequency of environmental state parameters sensed by the environmental sensing device is consistent with the frequency at which the MRI scanner collects magnetic resonance images of the user's brain. The environmental state parameters sensed by the environmental sensing device include: temperature, humidity, and noise level. In the scene where the MRI scanner collects magnetic resonance images of the user's brain, the user wears dry electrode electromyography sensors on his limbs, and senses muscle tissue state information of the user's body based on the dry electrode electromyography sensors. The frequency at which the dry electrode electromyography sensors sense the muscle tissue state information of the user's body is consistent with the frequency at which the MRI scanner collects magnetic resonance images of the user's brain.

[0013] Furthermore, the operation of selecting the user's brain magnetic resonance image based on the environmental analysis result is:

[0014]

[0015] Where: f is the selection tendency value of the user's brain magnetic resonance image; τ is the user's comfort level reflected by the user's body muscle tissue state information corresponding to the user's brain magnetic resonance image; c i RH i、F i The temperature, humidity, and noise level perceived by the environment sensing device during the user's brain magnetic resonance image acquisition phase; c i-1 RH i-1 、F i-1 The temperature, humidity, and noise levels perceived by the environmental sensing device during the brain magnetic resonance image acquisition phase for the previous group of users;

[0016] The larger the selection tendency value f of the user's brain magnetic resonance image, the higher the accuracy of the corresponding user's brain magnetic resonance image. Conversely, the lower the accuracy, the brain magnetic resonance images of each user are arranged in descending order based on the selection tendency value f of the user's brain magnetic resonance image. The user terminal customizes the number of user brain magnetic resonance images to be selected, and selects the corresponding number of user brain magnetic resonance images at the front position in the descending queue. Only the selected user brain magnetic resonance images are stored in the database.

[0017] The user comfort level τ reflected by the user's brain magnetic resonance image corresponding to the user's body muscle tissue state information is obtained by the following formula:

[0018]

[0019] Where: ω1, ω2 are weights; RMS norr is the reference RMS value determined based on prior data; T is the number per unit time; EMG(t) is the electromyographic signal sensed by the dry electrode electromyographic sensor per unit time t; σ f,norr is the reference standard deviation determined based on prior data; σ f is the standard deviation of the main frequency components in the spectrum corresponding to the electromyographic signal; υ and δ are compensation;

[0020] The sum of weights ω1 and ω2 is 1. The values ​​of weights ω1 and ω2 are user-defined, and ω1 ≥ ω2. RMS norr , σ f,norr It is known that the compensation υ and δ are positive numbers and less than 0.1, and the spectrum corresponding to the electromyographic signal is converted from the time domain to the frequency domain by fast Fourier transform.

[0021] Furthermore, the user's brain magnetic resonance image is obtained from a database, and the operation of removing high-frequency physiological noise and low-frequency drift in the user's brain magnetic resonance image is:

[0022] A bandpass filter is configured for the user's brain magnetic resonance image, a lower cutoff frequency and an upper cutoff frequency of the bandpass filter are set, a bandpass frequency range is set based on the lower cutoff frequency and the upper cutoff frequency, and the bandpass filter is applied to remove high-frequency physiological noise and low-frequency drift of the user's brain magnetic resonance image according to the bandpass frequency range.

[0023] Furthermore, after completing the positioning, the functional connection network further loads the positioning result into the three-dimensional brain network model, and the positioning result is represented by the three-dimensional brain network model.

[0024] Furthermore, the low-frequency pulsation amplitude analysis logic is expressed as:

[0025]

[0026] Where: ALFF voxel is the low frequency band power value; w is the frequency; m is the time series set; x(t) is the user's brain magnetic resonance image data at time t; i is the imaginary unit;

[0027] The local consistency analysis logic is expressed as:

[0028]

[0029] Where: ReHo i is the local consistency performance value; m is the time series set; KCC i (t) is the Kendall harmony coefficient centered on voxel i at time t;

[0030] Based on the above formula, the local consistency performance value is calculated for each voxel in the auxiliary motion area;

[0031] The analysis of the functional connectivity abnormality is the calculation of the Pearson correlation coefficient. The functional connectivity abnormality analysis logic is expressed as:

[0032]

[0033] Where: r Sv is the seed point time series S(t) and the voxel time series x v (t); m is the time series set; S(t) is the seed point time series; is the mean of the seed point time series; x v (t) is the voxel time series; is the mean of the time series of voxel v.

[0034] Furthermore, the content of the diagnosis report includes: basic information of the user, low-frequency pulsation amplitude analysis, local consistency analysis and functional connectivity abnormality analysis results, and the result of the user's motor dysfunction determination, i.e., the user's brain magnetic resonance image.

[0035] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0036] The present invention provides a method for image diagnosis and positioning of the supplementary motor area based on the resting state. During the execution of the method, the brain map is used for preliminary positioning and the functional connections are mutually verified, so the position of the supplementary motor area can be determined more accurately and the positioning error can be reduced. The state of the supplementary motor area can be evaluated from two aspects: local brain activity and functional connection abnormality analysis. In addition, based on this method, the interference of subjective factors is reduced, the diagnostic index is quantified, and the results are more objective and reliable. The resting state scan does not require the patient to perform specific tasks, the operation is relatively simple, the patient's cooperation requirement is low, and it is a non-invasive examination with high patient acceptance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 The figure is a flowchart of the supplementary motor area imaging diagnosis and positioning method based on resting state. DETAILED DESCRIPTION

[0039] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0040] The present invention will be further described below in conjunction with the embodiments.

[0041] Embodiment 1:

[0042] The auxiliary motor area imaging diagnosis and positioning method based on the resting state of this embodiment is as follows: Figure 1 As shown, including:

[0043] Collecting a user's brain magnetic resonance image based on an MRI scanner, creating a database, and storing the collected user's brain magnetic resonance image;

[0044] Acquire a brain magnetic resonance image of a user, and remove high-frequency physiological noise and low-frequency drift in the brain magnetic resonance image of the user;

[0045] Acquire supplementary motor areas and perform low-frequency beat amplitude analysis, regional consistency analysis, and functional connectivity abnormality analysis;

[0046] The logic of low-frequency beat amplitude analysis is expressed as:

[0047]

[0048] Where: ALFFvoxel is the low frequency band power value; w is the frequency; m is the time series set; x(t) is the user's brain magnetic resonance image data at time t; i is the imaginary unit;

[0049] The local consistency analysis logic is expressed as:

[0050]

[0051] Where: ReHo i is the local consistency performance value; m is the time series set; KCC i (t) is the Kendall harmony coefficient centered on voxel i at time t;

[0052] Based on the above formula, the local consistency performance value is calculated for each voxel in the auxiliary motion area;

[0053] The analysis of functional connectivity abnormalities is the calculation of the Pearson correlation coefficient. The logic of functional connectivity abnormality analysis is expressed as follows:

[0054]

[0055] Where: r Sv is the seed point time series S(t) and the voxel time series x v (t); m is the time series set; S(t) is the seed point time series; is the mean of the seed point time series; x v (t) is the voxel time series; is the mean of the time series of voxel v;

[0056] Setting a motor dysfunction determination index for the auxiliary motor area, comparing the set determination index with the decomposition result, and determining whether the user has motor dysfunction;

[0057] Generate a diagnostic report and feed it back to the user;

[0058] The diagnostic report includes: basic information of the user, low-frequency pulsation amplitude analysis, local consistency analysis and functional connectivity abnormality analysis results, user motor dysfunction determination results and user brain magnetic resonance imaging.

[0059] In this embodiment, by collecting and analyzing the user's brain magnetic resonance image, the user's brain magnetic resonance image is cleaned, thereby effectively improving the accuracy of the auxiliary motor area imaging diagnosis and positioning process;

[0060] The following is a further explanation of low-frequency beat amplitude analysis, local consistency analysis, and functional connectivity abnormality analysis:

[0061] Low-frequency beat amplitude analysis: Calculate the ALFF value of each voxel in the supplementary motor area in the low-frequency band (0.01-0.1Hz). Under normal circumstances, the supplementary motor area has a relatively stable ALFF range. If there is a lesion, such as a tumor, cerebrovascular disease, or neurodegenerative disease that affects the supplementary motor area, its ALFF value may change. For example, a lesion may cause abnormal neuronal activity, which manifests as an increase or decrease in the ALFF value. By comparing with the ALFF database of the normal control group, it can assist in the diagnosis of abnormalities in the supplementary motor area;

[0062] Local consistency analysis: Calculate the Kendall's coefficient of concordance between each voxel in the supplementary motor area and its surrounding neighboring voxels to evaluate the consistency of local brain activity. The normal supplementary motor area has a specific ReHo pattern. When there is a lesion in this area, such as local tissue damage or functional abnormality, the ReHo value may change. For example, a lesion may destroy the coordinated activity between local neurons, resulting in a decrease in the ReHo value, or an increase in the ReHo value due to abnormal neuronal excitation. Comparing the changes in ReHo values ​​with the normal database can help diagnose lesions in the supplementary motor area;

[0063] Analysis of abnormal functional connectivity: Based on the functional connection network between the supplementary motor area and other brain areas, observe whether there are changes in connection strength and connection pattern in the patient data. For example, in some patients with epilepsy, the supplementary motor area may form abnormal functional connections with the brain area where the epileptic lesion is located, which is manifested as an abnormal increase in connection strength or the emergence of new connections. In patients with cerebrovascular disease, changes in local blood supply affect neural activity, which may lead to weakened functional connections between the supplementary motor area and related brain areas. By comparing the functional connection patterns of the normal control group, these abnormalities can be discovered, providing a basis for diagnosis.

[0064] Further expansion of the diagnostic report content may also include:

[0065] The diagnostic report should include the scanning equipment and parameters, the localization results of the supplementary motor area, the results of the resting-state functional analysis (including local brain activity and functional connectivity) and the comprehensive diagnostic conclusion, as well as a detailed description of whether there are abnormalities in the supplementary motor area and the manifestations of the abnormalities, such as whether there is local activity enhancement or weakening, functional connectivity destruction or abnormal connectivity formation, etc.

[0066] If multimodal analysis is performed, findings related to structure and white matter fiber bundles should also be included. Provide a preliminary diagnostic opinion, such as whether there is a suspected lesion related to the supplementary motor area (such as tumors, cerebrovascular diseases, neurodegenerative lesions, epilepsy, etc.), and recommend further clinical examinations or treatment directions;

[0067] Regarding functional connectivity positioning:

[0068] Seed point selection: Based on existing anatomical and functional knowledge, one or more seed points are selected within the initially determined supplementary motor area. These seed points can be based on average coordinates or representative voxels.

[0069] Calculate functional connectivity: Use a voxel-based whole-brain functional connectivity analysis method to calculate the Pearson's correlation coefficient between the seed point and other voxels in the whole brain. By setting an appropriate threshold (such as r>0.6), the brain areas with significant functional connectivity with the seed point are determined. The network formed by these related brain areas can further accurately locate the supplementary motor area, because the supplementary motor area has stable functional connections with specific brain areas in the resting state, such as the primary motor area, premotor cortex, basal ganglia and other areas.

[0070] Embodiment 2:

[0071] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The resting state-based supplementary motor area imaging diagnosis and positioning method in Example 1 is further specifically described as follows:

[0072] The frequency of the MRI scanner collecting the user's brain magnetic resonance image is 500 to 3000 milliseconds per time. During the stage of collecting the user's brain magnetic resonance image, the MRI scanner simultaneously performs an environmental analysis on the scene in which the MRI scanner collects the user's brain magnetic resonance image. Based on the environmental analysis result, the user's brain magnetic resonance image is selected as the output result of the MRI scanner:

[0073] An environment sensing device is provided in a scene where an MRI scanner collects a magnetic resonance image of a user's brain. The frequency of environment state parameters sensed by the environment sensing device is consistent with the frequency at which the MRI scanner collects magnetic resonance images of the user's brain. The environment state parameters sensed by the environment sensing device include: temperature, humidity, and noise level. In a scene where an MRI scanner collects magnetic resonance images of a user's brain, a user wears dry electrode electromyography sensors on his or her limbs. The dry electrode electromyography sensors sense the state information of the user's body muscle tissue. The frequency at which the dry electrode electromyography sensors sense the state information of the user's body muscle tissue is consistent with the frequency at which the MRI scanner collects magnetic resonance images of the user's brain.

[0074] Through the above settings, the acquisition logic of the user's brain magnetic resonance image and related data is further limited.

[0075] The operation of selecting the user's brain magnetic resonance image based on the environmental analysis result is:

[0076]

[0077] Where: f is the selection tendency value of the user's brain magnetic resonance image; τ is the user's comfort level reflected by the user's body muscle tissue state information corresponding to the user's brain magnetic resonance image; c i RH i 、F i The temperature, humidity, and noise level perceived by the environment sensing device during the user's brain magnetic resonance image acquisition phase; c i-1 RH i-1 、F i-1 The temperature, humidity, and noise levels perceived by the environmental sensing device during the brain magnetic resonance image acquisition phase for the previous group of users;

[0078] The larger the selection tendency value f of the user's brain magnetic resonance image, the higher the accuracy of the corresponding user's brain magnetic resonance image. Conversely, the lower the accuracy, the brain magnetic resonance images of each user are arranged in descending order based on the selection tendency value f of the user's brain magnetic resonance image. The user terminal customizes the number of user brain magnetic resonance images to be selected, and selects the corresponding number of user brain magnetic resonance images at the front position in the descending queue. Only the selected user brain magnetic resonance images are stored in the database.

[0079] The user comfort τ reflected by the user's brain magnetic resonance image corresponding to the user's body muscle tissue state information is obtained by the following formula:

[0080]

[0081] Where: ω1, ω2 are weights; RMS norr is the reference RMS value determined based on prior data; T is the number per unit time; EMG(t) is the electromyographic signal sensed by the dry electrode electromyographic sensor per unit time t; σ f,norr is the reference standard deviation determined based on prior data; σ f is the standard deviation of the main frequency components in the spectrum corresponding to the electromyographic signal; υ and δ are compensation;

[0082] The sum of weights ω1 and ω2 is 1. The values ​​of weights ω1 and ω2 are user-defined, and ω1 ≥ ω2. RMS norr , σ f,norr It is known that the compensation υ and δ are positive numbers and less than 0.1, and the spectrum corresponding to the electromyographic signal is converted from the time domain to the frequency domain by fast Fourier transform.

[0083] Through the above logic formula, a specified selection reference logic is provided for the selection of the user's brain magnetic resonance image, so as to ensure that the selected user's brain magnetic resonance image can more accurately reflect the actual brain state of the source user.

[0084] like Figure 1As shown, the user's brain magnetic resonance image is obtained from the database, and the operation of removing high-frequency physiological noise and low-frequency drift in the user's brain magnetic resonance image is:

[0085] A bandpass filter is configured for the user's brain magnetic resonance image, a lower cutoff frequency and an upper cutoff frequency of the bandpass filter are set, a bandpass frequency range is set based on the lower cutoff frequency and the upper cutoff frequency, and the bandpass filter is applied to remove high-frequency physiological noise and low-frequency drift of the user's brain magnetic resonance image according to the bandpass frequency range.

[0086] The above settings, as a preprocessing operation of the user's brain magnetic resonance image, further improve the accuracy of the user's brain magnetic resonance image, and provide further refinement results for the accuracy of the diagnosis report finally output by the method.

[0087] like Figure 1 As shown, after completing the positioning, the functional connection network further loads the positioning results into the three-dimensional brain network model, and the positioning results are represented by the three-dimensional brain network model.

[0088] In summary, during the execution of the method in the above embodiment, the brain map is used for preliminary positioning and the functional connections are mutually verified, which can more accurately determine the position of the supplementary motor area, reduce positioning errors, and evaluate the state of the supplementary motor area from two aspects: local brain activity and functional connection abnormality analysis. Based on this method, the interference of subjective factors is reduced, and the diagnostic indicators are quantified, making the results more objective and reliable. Resting-state scanning does not require patients to perform specific tasks, the operation is relatively simple, and the requirements for patient cooperation are low. It is a non-invasive examination and has a high degree of patient acceptance.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A resting state-based supplementary motor area imaging diagnosis and positioning method, characterized in that: include: Collecting a user's brain magnetic resonance image based on an MRI scanner, creating a database, and storing the collected user's brain magnetic resonance image; Acquire a brain magnetic resonance image of a user, and remove high-frequency physiological noise and low-frequency drift in the brain magnetic resonance image of the user; Acquire supplementary motor areas and perform low-frequency beat amplitude analysis, regional consistency analysis, and functional connectivity abnormality analysis; Setting a motor dysfunction determination index for the auxiliary motor area, comparing the set determination index with the decomposition result, and determining whether the user has motor dysfunction; Generate a diagnostic report and feed it back to the user.

2. The resting state-based supplementary motor area imaging diagnosis and positioning method according to claim 1, characterized in that: The MRI scanner collects the user's brain magnetic resonance image at a frequency of 500 to 3000 milliseconds per time. During the stage of collecting the user's brain magnetic resonance image, the MRI scanner simultaneously performs an environmental analysis on the scene in which the MRI scanner collects the user's brain magnetic resonance image, and selects the user's brain magnetic resonance image as the output result of the MRI scanner based on the environmental analysis result: An environment sensing device is provided in the scene where the MRI scanner collects magnetic resonance images of the user's brain. The frequency of environment state parameters sensed by the environment sensing device is consistent with the frequency at which the MRI scanner collects magnetic resonance images of the user's brain. The environment state parameters sensed by the environment sensing device include: temperature, humidity, and noise level. In the scene where the MRI scanner collects magnetic resonance images of the user's brain, the user wears dry electrode electromyography sensors on his limbs, and the dry electrode electromyography sensors sense the state information of the user's body muscle tissue. The frequency at which the dry electrode electromyography sensors sense the state information of the user's body muscle tissue is consistent with the frequency at which the MRI scanner collects magnetic resonance images of the user's brain.

3. The resting state-based supplementary motor area imaging diagnosis and positioning method according to claim 2, characterized in that: The operation of selecting the user's brain magnetic resonance image based on the environmental analysis result is: Where: f is the selection tendency value of the user's brain magnetic resonance image; τ is the user's comfort level reflected by the user's body muscle tissue state information corresponding to the user's brain magnetic resonance image; c i RH i 、F i The temperature, humidity, and noise level perceived by the environment sensing device during the user's brain magnetic resonance image acquisition phase; c i-1 RH i-1 、F i-1 The temperature, humidity, and noise levels perceived by the environmental sensing device during the brain magnetic resonance image acquisition phase for the previous group of users; Among them, the larger the selection tendency value f of the user's brain MRI image, the higher the accuracy of the corresponding user's brain MRI image, and vice versa. Based on the selection tendency value f of the user's brain MRI image, each user's brain MRI image is sorted in descending order, and the user end customizes the number of user brain MRI images to be selected, and selects the corresponding number of user brain MRI images at the front position in the descending queue. Only the selected user brain MRI images are stored in the database.

4. The resting state-based supplementary motor area imaging diagnosis and positioning method according to claim 1, characterized in that: The user comfort level τ reflected by the user's brain magnetic resonance image corresponding to the user's body muscle tissue state information is obtained by the following formula: Where: ω1, ω2 are weights; RMS norr is the reference RMS value determined based on a priori data; T is the number per unit time; EMG(t) is the electromyographic signal sensed by the dry electrode electromyographic sensor per unit time t; σ f,norr is the reference standard deviation determined based on prior data; σ f is the standard deviation of the main frequency components in the spectrum corresponding to the electromyographic signal; υ and δ are compensation; The sum of weights ω1 and ω2 is 1. The values ​​of weights ω1 and ω2 are user-defined, and ω1 ≥ ω2. RMS norr , σ f,norr It is known that the compensation υ and δ are positive numbers and less than 0.1, and the spectrum corresponding to the electromyographic signal is converted from the time domain to the frequency domain by fast Fourier transform.

5. The resting state-based supplementary motor area imaging diagnosis and positioning method according to claim 1, characterized in that: The user's brain magnetic resonance image is obtained from a database, and the operation of removing high-frequency physiological noise and low-frequency drift in the user's brain magnetic resonance image is: A bandpass filter is configured for the user's brain magnetic resonance image, a lower cutoff frequency and an upper cutoff frequency of the bandpass filter are set, a bandpass frequency range is set based on the lower cutoff frequency and the upper cutoff frequency, and the bandpass filter is applied to remove high-frequency physiological noise and low-frequency drift of the user's brain magnetic resonance image according to the bandpass frequency range.

6. The resting state-based supplementary motor area imaging diagnosis and positioning method according to claim 1, characterized in that: After completing the positioning, the functional connection network further loads the positioning result into the three-dimensional brain network model, and the positioning result is represented by the three-dimensional brain network model.

7. The resting state-based supplementary motor area imaging diagnosis and positioning method according to claim 1, characterized in that: The low-frequency pulsation amplitude analysis logic is expressed as: Where: ALFF voxel is the low frequency band power value; w is the frequency; m is the time series set; x(t) is the user's brain magnetic resonance image data at time t; i is the imaginary unit.

8. The resting state-based supplementary motor area imaging diagnosis and positioning method according to claim 1, characterized in that: The local consistency analysis logic is expressed as: Where: ReHo i is the local consistency performance value; m is the time series set; KCC i (t) is the Kendall harmony coefficient centered on voxel i at time t; Based on the above formula, the local consistency performance value is calculated for each voxel in the auxiliary motion area.

9. The resting state-based supplementary motor area imaging diagnosis and positioning method according to claim 1, characterized in that: The analysis of the functional connectivity abnormality is the calculation of the Pearson correlation coefficient. The functional connectivity abnormality analysis logic is expressed as: Where: r Sv is the seed point time series S(t) and the voxel time series x v (t); m is the time series set; S(t) is the seed point time series; is the mean of the seed point time series; x v (t) is the voxel time series; is the mean of the time series of voxel v.

10. The resting state-based supplementary motor area imaging diagnosis and positioning method according to claim 1, characterized in that: The diagnostic report includes: basic information of the user, low-frequency pulsation amplitude analysis, local consistency analysis and functional connectivity abnormality analysis results, user motor dysfunction determination results and user brain magnetic resonance images.

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