Method for constructing brain function network under stimulation of right side roof inner sulcus magnetic pulse

Abstract

The invention relates to the technical field of magnetic pulse measurement, in particular to a method for constructing a brain function network under the stimulation of a right apical intranet magnetic pulse. According to the method, experimenters suffering from depression form a patient group; healthy experimenters form a control group; constructing an electrode topological graph according to the electrode distribution of the experimenter, and obtaining peripheral nodes in the electrode topological graph; according to the change of the functional connection difference between peripheral nodes at the same position between the patient group and the control group under the condition that transcranial magnetic stimulation is not applied and applied, the depression correlation degree is obtained, the peripheral nodes are divided into peripheral categories, and then the number of electrodes in the brain surface area corresponding to the peripheral categories is obtained. According to the method, the number of the electrodes corresponding to each region of the brain is accurately determined by acquiring the number of the electrodes, so that the condition of incomplete acquisition of depression related data caused by uniform distribution of the electrodes is effectively avoided, and the accuracy and reliability of depression mechanism research results and the depression treatment effect are improved.

Description

Technical Field

[0001] The present invention relates to the field of magnetic pulse measurement technology, and in particular to a method for constructing a brain functional network under magnetic pulse stimulation of the right intraparietal sulcus. Background Art

[0002] Depression is a common mental illness characterized by low mood, slowed thinking, decreased volitional activity, and cognitive impairment. It is characterized by high morbidity, relapse rates, and suicide rates, seriously endangering patients' physical and mental health. In recent years, transcranial magnetic stimulation (TMS), a non-invasive neuromodulation technology, has been widely used in depression treatment research.

[0003] Transcranial magnetic stimulation (TMS) applies magnetic pulses of specific frequency, intensity, and duration, temporarily altering the activation patterns of neurons in target areas (such as the right intraparietal sulcus). This allows for studying the impact of the target area on specific tasks and potentially aids in the treatment of depression. Existing methods for studying the mechanisms of depression typically place a large number of electrodes only in the target area (such as the right intraparietal sulcus), while distributing electrodes evenly across other areas. This uniform distribution of electrodes ignores the fact that different brain regions may have varying degrees of impact on depression, limiting the accuracy and reliability of research findings on the mechanisms of depression and, in turn, hindering the effectiveness of depression treatment. Summary of the Invention

[0004] To address the technical problem that the uniform distribution of electrodes ignores the varying degrees of impact of different brain regions on depression, thus limiting the accuracy and reliability of research results on the mechanism of depression, the present invention aims to provide a method for constructing a brain functional network under magnetic pulse stimulation of the right intraparietal sulcus. The technical solutions adopted are as follows: An embodiment of the present invention provides a method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation, the method comprising the following steps: Subjects with depression were selected as the patient group; healthy subjects were selected as the control group; the electrodes in the corresponding area of the right intraparietal sulcus of each subject were used as the target electrodes; An electrode topology map is constructed based on the electrode distribution corresponding to any experimenter, and peripheral nodes in the electrode topology map are obtained based on the positional relationship between each node in the electrode topology map and the node corresponding to the target electrode; Based on the changes in functional connectivity differences of peripheral nodes at the same locations between the patient group and the control group without and with transcranial magnetic stimulation, the depression association degree of the peripheral nodes at each location was obtained; the peripheral nodes were divided into peripheral categories based on the depression association degree; According to the magnitude of depression association of peripheral nodes in each peripheral category, the number of electrodes in the brain surface area corresponding to each peripheral category was obtained.

[0005] Furthermore, the method for constructing an electrode topology map according to the electrode distribution corresponding to any experimenter is: For any subject, each electrode in the subject's brain surface area is used as a node in the graph structure, and the distance between any two nodes is used as the edge value. The electrode topology map of the subject is obtained through the Delaunay triangulation algorithm.

[0006] Furthermore, the method for obtaining the peripheral nodes is: For any experimenter, the node corresponding to the target electrode in the experimenter's electrode topology map is used as the target node; Obtain the center point of the target node through the position coordinates of each target node as the target center point; Obtain the peripheral degree of each node based on the distance between each node and the target center point, the degree between each node and the target node, and the degree between each node and the non-target node in the electrode topology map; When the peripheral degree is greater than a preset peripheral degree threshold, the corresponding node is regarded as a peripheral node.

[0007] Furthermore, the method for obtaining the peripheral degree is: For any node in the electrode topology map, the Euclidean distance between the node and the target center point is taken as the first distance; The degree between the node and the target node is taken as the first value, and the degree between the node and the non-target node is taken as the second value; The result of negatively correlating and normalizing the ratio of the first value to the second value is used as the peripheral probability of the node; The product of the normalized result of the first distance and the peripheral probability is taken as the peripheral degree of the node.

[0008] Furthermore, the method for obtaining the degree of association with depression is: When no transcranial magnetic stimulation was applied, the initial degree of difference of peripheral nodes at each location was obtained based on the functional connectivity differences of peripheral nodes at the same location between the patient group and the control group; When transcranial magnetic stimulation was applied, the degree of stimulation difference of the peripheral nodes at each location was obtained based on the functional connectivity difference of the peripheral nodes at the same location between the patient group and the control group; According to the initial difference degree and stimulation difference degree of the peripheral nodes at each position, the depression association degree of the peripheral nodes at each position is obtained; among them, the initial difference degree is positively correlated with the depression association degree, and the stimulation difference degree is negatively correlated with the depression association degree.

[0009] Furthermore, the method for obtaining the initial difference degree is: When transcranial magnetic stimulation was not applied, a reference electrode topology map of each subject was constructed based on the EEG signals of each electrode of each subject, and an embedding vector of each peripheral node in the reference electrode topology map of each subject was obtained; For any peripheral node at any position, the representative embedding vector of the peripheral node at this position in the patient group is obtained according to the embedding vector of the peripheral node at this position in the reference electrode topology map of each experimenter in the patient group; According to the embedding vector of the peripheral node at the position in the reference electrode topology map of each experimenter in the control group, the representative embedding vector of the peripheral node at the position in the control group is obtained; The result of negative correlation between the cosine similarity of the representative embedding vector of the peripheral node at this position in the patient group and the representative embedding vector in the control group is used as the initial difference degree of the peripheral node at this position.

[0010] Furthermore, the method for obtaining the representative embedding vector is: For any group in the patient group or the control group, the embedding vector of the peripheral node at that position in the reference electrode topology map of each subject in the group is used as the reference embedding vector; For any experimenter in the group, obtain the sum of the cosine similarities between the experimenter's reference embedding vector and each other reference embedding vector as the representative reference value of the experimenter; The representative reference value of each experimenter in the group is obtained, and the reference embedding vector of the experimenter corresponding to the largest representative reference value is used as the representative embedding vector of the peripheral node at that position in the group.

[0011] Furthermore, the method for obtaining the degree of stimulus difference is: When transcranial magnetic stimulation is applied, the frequency of the magnetic pulse is adjusted once every preset time interval, and a target electrode topology map of each subject at each frequency is constructed based on the EEG signal of each electrode of each subject at each frequency; and an embedding vector of each peripheral node in the target electrode topology map of each subject at each frequency is obtained; For a peripheral node at any frequency and any position, according to the embedding vector of the peripheral node at this position in the target electrode topology map of each experimenter in the patient group at this frequency, obtain the representative embedding vector of the peripheral node at this position in the patient group at this frequency as the first vector; According to the embedding vector of the peripheral node at the position in the target electrode topology map of each experimenter in the control group at the frequency, the representative embedding vector of the peripheral node at the position in the control group at the frequency is obtained as the second vector; The result of negative correlation between the cosine similarity of the first vector and the second vector is used as the reference stimulus difference value of the peripheral node at the position at the frequency; The result of normalizing the sum of the reference stimulus difference values of the peripheral nodes at this position at all frequencies is used as the stimulus difference degree of the peripheral nodes at this position.

[0012] Furthermore, the method for obtaining the peripheral category is: According to the depression association degree of the peripheral nodes at each location, the peripheral nodes were divided into peripheral categories using the Laplace clustering algorithm.

[0013] Furthermore, the method for obtaining the number of electrodes is: For any peripheral category and any peripheral node in the peripheral category, obtain the difference in the degree of association between the peripheral node and each other peripheral node in the peripheral category with depression, all as the first difference; The sum of all first differences is used as the representative analysis value of the peripheral node; The depression association degree of the peripheral node corresponding to the minimum representative analysis value is taken as the representative depression association degree of the peripheral category; The ratio of the representative depression association level of each peripheral category to the minimum representative depression association level was used as the adjustment weight of each peripheral category; The number of peripheral nodes in the peripheral category corresponding to the smallest degree of association representing depression is taken as the benchmark number; The product of the baseline number and the adjusted weight of each peripheral category is rounded up to an integer, which is used as the number of electrodes in the brain surface area corresponding to each peripheral category.

[0014] The present invention has the following beneficial effects: The present invention first constructs an electrode topology map according to the electrode distribution corresponding to any experimenter, which is conducive to the subsequent accurate and efficient analysis of the electrode distribution of the brain area, and then obtains the peripheral nodes in the electrode topology map according to the positional relationship between each node and the node corresponding to the target electrode, accurately determines the nodes corresponding to the brain area outside the right intraparietal sulcus, which is conducive to the subsequent accurate and efficient acquisition of the number of electrodes corresponding to each brain area outside the right intraparietal sulcus; in order to obtain the number of electrodes corresponding to each brain area outside the right intraparietal sulcus, the depression correlation degree of the peripheral nodes at each position is obtained according to the changes in the functional connectivity differences of the peripheral nodes at the same position between the patient group and the control group under no transcranial magnetic stimulation and under transcranial magnetic stimulation. degree, accurately reflecting the degree of association between the peripheral nodes at each position and depression; in order to determine the number of electrodes corresponding to each brain area, the peripheral nodes are divided into peripheral categories based on the degree of association with depression, and each brain area is determined, which is conducive to subsequent efficient electrode density adjustment; then, according to the size of the depression association degree of the peripheral nodes in each peripheral category, the number of electrodes in the brain surface area corresponding to each peripheral category is obtained, and the number of electrodes corresponding to the brain area is accurately determined, which improves the completeness and accuracy of the acquisition of depression-related data, effectively avoids the situation where the acquisition of depression-related data is incomplete due to uniform distribution of electrodes, and improves the accuracy and reliability of the research results on the mechanism of depression, as well as the treatment effect of depression. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a schematic flow chart of a method for constructing a brain functional network under magnetic pulse stimulation of the right intraparietal sulcus provided by one embodiment of the present invention; Figure 2 A flow chart of a method for obtaining the degree of association with depression provided by one embodiment of the present invention; Figure 3 This is a structural diagram of a brain function network construction system under right intraparietal sulcus magnetic pulse stimulation provided by one embodiment of the present invention; Figure 4 A schematic diagram of a computer device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation method, structure, features and effects of a brain functional network construction method under right intraparietal sulcus magnetic pulse stimulation proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following describes in detail a specific scheme of a method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation provided by the present invention with reference to the accompanying drawings. Example 1:

[0020] The specific implementation scenario of this embodiment is as follows: In the existing method, the brain surface area corresponding to the right intraparietal sulcus of the brain is used as the stimulation target area for depression research. A large number of electrodes are set in the stimulation target area, while the electrodes in other areas are evenly distributed to study the mechanism of depression. However, in actual situations, different brain regions have different degrees of influence on depression. The uniform distribution of electrodes cannot obtain more accurate data, and thus cannot accurately study the mechanism of depression. In order to reasonably set the number of electrodes corresponding to each brain region other than the stimulation target area, so as to accurately study the mechanism of depression, this embodiment first determines the peripheral electrodes that do not belong to the stimulation target area, and then analyzes the signal differences between depression patients and normal people under the two conditions of no transcranial magnetic stimulation and with transcranial magnetic stimulation. The peripheral electrodes with the greater correlation with depression are obtained, and then the brain regions with the greater impact on depression are determined. Increasing the number of electrodes in the brain regions with the greater impact on depression helps to accurately study the mechanism of depression and is also conducive to better treatment of depression. Among them, transcranial magnetic stimulation is a well-known technology and will not be described in detail.

[0021] This paper proposes a method for constructing brain functional network under magnetic pulse stimulation of the right intraparietal sulcus. Figure 1 , which shows a schematic flow chart of a method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation provided by one embodiment of the present invention, the method comprising the following steps: Step S1: Subjects with depression constitute the patient group; healthy subjects constitute the control group; the electrode in the corresponding area of the right intraparietal sulcus of each subject is used as the target electrode.

[0022] Specifically, this embodiment uses the right intraparietal sulcus as the stimulation target area. In order to determine the degree of influence of various brain areas other than the stimulation target area on depression, and then reasonably set the electrode distribution, so as to accurately study the mechanism of depression and more effectively treat depression, this embodiment uses a first preset number of subjects with depression to form a patient group, and a second preset number of healthy subjects to form a control group. In this embodiment, the first preset number and the second preset number are both set to 100. The implementer can set the size of the first preset number and the second preset number according to actual conditions, and there is no limitation here. It should be noted that the subjects in the control group have no history of neurological diseases or mental illnesses, and the subjects in the patient group have not received transcranial magnetic stimulation treatment.

[0023] In order to reasonably determine the electrode distribution of the brain surface area during the treatment of depression, it is necessary to first install the electrodes combined with transcranial magnetic stimulation. The specific installation process is as follows: Step 1: Determine the target area for stimulation. To precisely locate the target area for stimulation, individualized brain imaging (such as structural magnetic resonance imaging) can be used to determine the subject's brain structure and the specific location of the target area for stimulation. Step 2: Calibrate the scalp position corresponding to the electrodes. Before starting to install the electrodes, this embodiment uses the standard 10-20 system for scalp calibration. By measuring the marker points on the subject's scalp, it ensures that the electrodes can be accurately placed near the target stimulation area. Step 3: Place EEG electrodes. The EEG electrodes need to cover the subject's entire scalp, with multiple electrodes placed near the target stimulation area to record changes in brain electrical activity. Step 4: Calibrate the transcranial magnetic stimulation device. Before stimulation, the transcranial magnetic stimulation device needs to set the stimulation parameters according to the experimental design, including stimulation intensity (usually based on the activation threshold), frequency (high or low frequency), and duration.

[0024] Step 5: Place the transcranial magnetic stimulation coil. The transcranial magnetic stimulation coil is usually placed on the top of the subject's head, above the brain area. For the right intraparietal sulcus area, the transcranial magnetic stimulation coil is usually placed near the right side of the skull and adjusted according to individual positioning to ensure that the stimulation reaches the target area accurately; Step 6: Apply magnetic pulses. Once the transcranial magnetic stimulation coil is correctly placed and the electrodes are installed, magnetic pulses can be applied. By adjusting the frequency and intensity of the magnetic pulses, the intervention on neural activity can be adjusted. Step 7: Record the EEG signal from each electrode in real time. When transcranial magnetic stimulation is applied, the EEG electrodes record the brain's electrical activity in real time, capturing the neural responses of various brain regions.

[0025] At this point, the electrode installation on each subject's head was determined.

[0026] It is known that the number of electrodes corresponding to the target stimulation area is relatively large, and the electrodes corresponding to brain areas other than the target stimulation area are evenly distributed. However, in actual situations, the degree of influence of each brain area other than the target stimulation area on depression may be different. In order to more accurately study the mechanism of depression and treat depression more effectively, it is necessary to set more electrodes for brain areas other than the target stimulation area that have a greater impact on depression, so as to ensure that the data related to depression is acquired more accurately and completely. In order to improve the efficiency of subsequent analysis of the degree of influence of other brain areas other than the target stimulation area on depression, this embodiment uses the electrodes in the area corresponding to the right intraparietal sulcus of each experimenter as the target electrode, that is, the electrodes corresponding to the target stimulation area are calibrated in advance.

[0027] Step S2: construct an electrode topology map according to the electrode distribution corresponding to any experimenter, and obtain peripheral nodes in the electrode topology map according to the positional relationship between each node in the electrode topology map and the node corresponding to the target electrode.

[0028] Specifically, it can be seen from step S1 that the electrode distribution of the head surface area of each experimenter is the same. Therefore, this embodiment constructs an electrode topology map based on the electrode distribution corresponding to any experimenter. It should be noted that in this embodiment, the overall distribution of the electrode topology map of each experimenter is the same, that is, the position of the node corresponding to the target electrode in the electrode topology map corresponding to each experimenter is the same. Among them, the method of constructing an electrode topology map based on the electrode distribution corresponding to any experimenter is: for any experimenter, each electrode in the brain surface area of the experimenter is used as a node in the graph structure, and the distance between any two nodes is used as the edge value, and the electrode topology map of the experimenter is obtained through the Delaunay triangulation algorithm. Among them, the Delaunay triangulation algorithm is a well-known technology and will not be described in detail.

[0029] Before adjusting the number of electrodes in the peripheral area of the stimulation target area, it is necessary to first find the electrodes in the non-stimulation target area, taking into account that in actual situations, there are deviations in the distribution of brain electrodes. For example, the electrodes corresponding to the stimulation target area may all be the electrodes corresponding to the right intraparietal sulcus, or they may not all be. Therefore, this embodiment combines the position distribution of the nodes corresponding to the electrodes in the electrode topology map to determine the peripheral nodes, that is, the electrodes in the non-stimulation target area. In the electrode topology map, the closer a node is to the center position of the node corresponding to the target electrode and the more connections the node has with the node corresponding to the target electrode, the less likely the node is to be a peripheral node, and vice versa. Therefore, this embodiment obtains the peripheral nodes in the electrode topology map based on the positional relationship between each node in the electrode topology map and the node corresponding to the target electrode.

[0030] Preferably, in one possible implementation of this embodiment, the method for obtaining peripheral nodes is as follows: for any experimenter, the node corresponding to the target electrode in the experimenter's electrode topology map is used as the target node; the center point of the target node is obtained through the position coordinates of each target node as the target center point; wherein, the method for obtaining the center point is a well-known technology and will not be described in detail. The greater the distance between a certain node and the target center point, and the fewer connections the node has with the target node and the more connections it has with non-target nodes, the more likely the node is to be located on the periphery of the right intraparietal sulcus. Then, this embodiment obtains the peripheral degree of each node based on the distance between each node and the target center point, the degree between each node and the target node, and the degree between each node and the non-target node in the electrode topology map; wherein, the method for obtaining the degree is a well-known technology and will not be described in detail; the greater the peripheral degree, the more likely the corresponding node is to be a peripheral node; Among them, the method for obtaining the peripheral degree is: for any node in the electrode topology map, the Euclidean distance between the node and the target center point is used as the first distance; the larger the first distance, the smaller the probability that the node is located in the corresponding area of the right intraparietal sulcus; among them, the method for obtaining the Euclidean distance is a well-known technology and will not be repeated here. In order to more accurately analyze the possibility that the node is located outside the area corresponding to the right intraparietal sulcus, the degree between the node and the target node is further obtained as the first value. The smaller the first value, the fewer connections between the node and the target node, and the more likely the node is to be located outside the area corresponding to the right intraparietal sulcus; the degree between the node and the non-target node is obtained as the second value. The larger the second value, the more connections between the node and the non-target node, which also indicates that the node is more likely to be located outside the area corresponding to the right intraparietal sulcus. The ratio of the first value to the second value is negatively correlated and normalized, and the result is taken as the peripheral probability of the node; the larger the peripheral probability, the smaller the first value and the larger the second value, and the more likely the node is to be located outside the area corresponding to the right intraparietal sulcus; in order to accurately indicate the degree to which the node is a peripheral node, the product of the normalized result of the first distance and the peripheral probability is taken as the peripheral degree of the node; The calculation formula of the peripheral degree is: Where, is the peripheral degree of the i-th node; is the first distance between the i-th node and the target center point; norm is the normalization function; a is the first value; b is the second value; is the peripheral probability of the i-th node; exp is an exponential function with a natural constant as the base; It is known that the greater the degree of peripherality, the more likely the corresponding node is a peripheral node located outside the corresponding region of the right intraparietal sulcus. Therefore, this embodiment sets the preset peripheral degree threshold to 0.5. Implementers can set the preset peripheral degree threshold based on actual conditions and are not limited here. When the peripheral degree is greater than the preset peripheral degree threshold, the corresponding node is considered a peripheral node.

[0031] At this point, the peripheral nodes in the electrode topology map of each experimenter are obtained. It should be noted that the position distribution of the peripheral nodes in the electrode topology map of each experimenter is the same. In this embodiment, the peripheral nodes can be obtained for analysis of any experimenter.

[0032] Step S3: Based on the changes in the functional connectivity differences of the peripheral nodes at the same location between the patient group and the control group without and with transcranial magnetic stimulation, the depression association degree of the peripheral nodes at each location is obtained; and the peripheral nodes are divided into peripheral categories based on the depression association degree.

[0033] It is known that when no transcranial magnetic stimulation is applied, the functional connectivity difference of the peripheral node at a certain location between the patient group and the control group is large, and when transcranial magnetic stimulation is applied, the functional connectivity difference of the peripheral node at that location between the patient group and the control group is small. This indicates that the peripheral node at that location has a significant response to the treatment of depression, that is, the degree of association with depression is greater. Furthermore, this embodiment obtains the degree of depression association of the peripheral node at each location based on the change in the functional connectivity difference of the peripheral node at the same location between the patient group and the control group without and with transcranial magnetic stimulation. The greater the degree of depression association, the more effective the electrode corresponding to the peripheral node at the corresponding location is in the treatment of depression. In order to determine the impact of various brain regions outside the right intraparietal sulcus on depression, this embodiment further divides the peripheral nodes into peripheral categories based on the degree of depression association, wherein each peripheral category corresponds to a brain region with the same impact on depression, which facilitates the subsequent adjustment of the number of electrodes in the brain region corresponding to each peripheral category.

[0034] Preferably, in one possible implementation of this embodiment, the method for obtaining the degree of association with depression can be found in Figure 2 , which shows a flow chart of a method for obtaining the degree of association with depression provided in this embodiment, the method comprising the following steps: Step S201: When transcranial magnetic stimulation is not applied, the initial difference degree of the peripheral nodes at each location is obtained based on the functional connectivity difference of the peripheral nodes at the same location between the patient group and the control group.

[0035] The greater the initial difference, the more obvious the difference in the EEG signals of the electrodes corresponding to the peripheral nodes at the corresponding positions between patients with depression and normal people.

[0036] In one possible implementation of this embodiment, the method for obtaining the initial degree of difference is as follows: when transcranial magnetic stimulation is not applied, a reference electrode topology map of each subject is constructed based on the EEG signal of each electrode of each subject, and an embedding vector of each peripheral node in the reference electrode topology map of each subject is obtained through the node2vec graph embedding algorithm, and the specific function of each peripheral node is represented by the embedding vector; wherein, the node2vec graph embedding algorithm is a well-known technology and will not be described in detail. For a peripheral node at any position, based on the embedding vector of the peripheral node at that position in the reference electrode topology map of each subject in the patient group, a representative embedding vector of the peripheral node at that position in the patient group is obtained; based on the embedding vector of the peripheral node at that position in the reference electrode topology map of each subject in the control group, a representative embedding vector of the peripheral node at that position in the control group is obtained; Among them, the method for obtaining the representative embedding vector is: for any group in the patient group or the control group, the embedding vectors of the peripheral nodes at that position in the reference electrode topology map of each experimenter in the group are all used as reference embedding vectors; for any experimenter in the group, the sum of the cosine similarities of the experimenter's reference embedding vector and each other reference embedding vector is obtained as the representative reference value of the experimenter; the larger the representative reference value, the more similar the experimenter's reference embedding vector is to the reference embedding vectors of other experimenters in the group, and the more representative the experimenter's reference embedding vector is. Furthermore, this embodiment obtains the representative reference value of each experimenter in the group, and uses the reference embedding vector of the experimenter corresponding to the largest representative reference value as the representative embedding vector of the peripheral nodes at that position in the group. Among them, the method for obtaining cosine similarity is a well-known technology and will not be described in detail; The result of negatively correlating the cosine similarity between the representative embedding vector of the peripheral node at that location in the patient group and the representative embedding vector in the control group is used as the initial difference degree of the peripheral node at that location. In this embodiment, the cosine similarity between the representative embedding vector of the peripheral node at that location in the patient group and the representative embedding vector in the control group is negatively correlated by (1 - cosine similarity between the representative embedding vector of the peripheral node at that location in the patient group and the representative embedding vector in the control group).

[0037] At this point, the initial difference degree of the peripheral nodes at each position is obtained.

[0038] Step S202: When transcranial magnetic stimulation is applied, the degree of stimulation difference of the peripheral nodes at each location is obtained based on the functional connectivity difference of the peripheral nodes at the same location between the patient group and the control group.

[0039] The smaller the degree of stimulation difference, the more similar the EEG signal of the electrode corresponding to the peripheral node at the corresponding position is to that of a normal person after transcranial magnetic stimulation treatment, which means that the correlation between the electrode corresponding to the peripheral node at the corresponding position and depression is greater.

[0040] In one possible implementation of this embodiment, the degree of stimulation difference is obtained by adjusting the frequency of the magnetic pulses at predetermined intervals during transcranial magnetic stimulation. In this embodiment, the predetermined interval is set to 5 minutes, and the frequency of the magnetic pulses is gradually increased from 1 Hz to 10 Hz. The low frequency of the magnetic pulses is typically 1 Hz, and the high frequency is typically 10 Hz. Implementers may adjust the predetermined interval and the frequency adjustment of the magnetic pulses based on actual circumstances, and these are not limited herein. Based on the EEG signals of each electrode of each subject at each frequency, a target electrode topology map of each subject at each frequency is constructed; an embedding vector of each peripheral node in the target electrode topology map of each subject at each frequency is obtained using the node2vec graph embedding algorithm; for peripheral nodes at any frequency and any position, according to the method for obtaining a representative embedding vector in step S201, a representative embedding vector of the peripheral node at that position in the patient group at that frequency is obtained as a first vector based on the embedding vector of the peripheral node at that position in the target electrode topology map of each subject at that frequency; and a representative embedding vector of the peripheral node at that position in the control group at that frequency is obtained as a second vector based on the embedding vector of the peripheral node at that position in the target electrode topology map of each subject at that frequency; then, a result of negatively correlating the cosine similarity between the first vector and the second vector is used as a reference stimulus difference value for the peripheral node at that position at that frequency; wherein, in this embodiment, the cosine similarity between the first vector and the second vector is negatively correlated by (1-cosine similarity between the first vector and the second vector); In order to comprehensively analyze the response of the peripheral nodes at that location to magnetic pulses at different frequencies of transcranial magnetic stimulation, this embodiment normalizes the sum of the reference stimulation difference values of the peripheral nodes at that location at all frequencies to obtain the stimulation difference degree of the peripheral nodes at that location. It should be noted that this embodiment uses the norm normalization function to perform the normalization processing on the sum of the reference stimulation difference values of the peripheral nodes at that location at all frequencies.

[0041] At this point, the degree of stimulus difference of the peripheral nodes at each location is obtained.

[0042] Step S203: Obtain the depression association degree of the peripheral nodes at each position based on the initial difference degree and the stimulation difference degree of the peripheral nodes at each position; wherein the initial difference degree is positively correlated with the depression association degree, and the stimulation difference degree is negatively correlated with the depression association degree.

[0043] It is known that when the initial difference degree of a peripheral node at a certain location is greater and the stimulation difference degree is smaller, it indicates that after transcranial magnetic stimulation treatment, the EEG signal of the electrode corresponding to the peripheral node at that location is closer to that of a normal person and has undergone significant changes before and after treatment, indicating that the peripheral node at that location has a greater correlation with depression. Therefore, this embodiment obtains the depression correlation degree of each peripheral node at each location based on the initial difference degree and stimulation difference degree of the peripheral node at each location; the initial difference degree is positively correlated with the depression correlation degree, while the stimulation difference degree is negatively correlated with the depression correlation degree.

[0044] The calculation formula for the degree of association with depression is: Where, is the depression association degree of the peripheral node at the jth position; is the initial difference degree of the peripheral node at the jth position; is the degree of stimulus difference of the peripheral node at the jth position; e is a natural constant.

[0045] At this point, the depression correlation degree of the peripheral nodes at each location is obtained.

[0046] To rationally adjust the electrode distribution corresponding to other brain regions outside the right intraparietal sulcus, the Laplace clustering algorithm was used to classify peripheral nodes into peripheral categories based on their association with depression. Peripheral nodes within the same peripheral category had similar associations with depression. The Laplace clustering algorithm is well-known and will not be described in detail here.

[0047] Step S4: According to the degree of depression association of the peripheral nodes in each peripheral category, the number of electrodes in the brain surface area corresponding to each peripheral category is obtained.

[0048] Specifically, when the depression-related degree of a peripheral node in a peripheral category is greater, the correlation degree between the electrodes corresponding to the peripheral node in the peripheral category and depression is greater. In order to accurately analyze the mechanism of depression and effectively treat depression, the number of electrodes in the brain surface area corresponding to the peripheral category should be larger. Furthermore, this embodiment obtains the number of electrodes in the brain surface area corresponding to each peripheral category based on the degree of depression-related degree of the peripheral node in each peripheral category.

[0049] Preferably, in one possible implementation of this embodiment, the method for obtaining the number of electrodes is as follows: for any peripheral category and any peripheral node in the peripheral category, obtaining the absolute value of the difference between the depression association degree of the peripheral node and each other peripheral node in the peripheral category, each of which is used as a first difference; adding up the results of all the first differences as the representative analysis value of the peripheral node; the smaller the representative analysis value, the more equal the depression association degree of the peripheral node is to the depression association degrees of other peripheral nodes in the peripheral category, and the more representative the depression association degree of the peripheral node is. Therefore, in this embodiment, the depression association degree of the peripheral node corresponding to the smallest representative analysis value is used as the representative depression association degree of the peripheral category; When the degree of association representing depression is greater, the number of electrodes in the brain surface area corresponding to the corresponding peripheral category should be greater to ensure more complete acquisition of depression-related data and improve the treatment effect on depression; further, this embodiment uses the ratio of the degree of association representing depression of each peripheral category to the minimum degree of association representing depression as the adjustment weight of each peripheral category; the number of peripheral nodes in the peripheral category corresponding to the minimum degree of association representing depression is used as the benchmark number; then the product of the benchmark number and the adjustment weight of each peripheral category is obtained and rounded up as the number of electrodes in the brain surface area corresponding to each peripheral category.

[0050] At this point, the number of electrodes corresponding to each brain area outside the right intraparietal sulcus has been accurately determined, effectively avoiding the situation where the even distribution of electrodes ignores the possible different degrees of influence of different brain areas on depression. This facilitates more detailed collection of depression data and obtains more information related to depression, effectively improving the accuracy and reliability of the research results on the mechanism of depression, and at the same time improving the effective treatment of depression.

[0051] After the study, the EEG electrodes and transcranial magnetic stimulation coil are carefully removed from the subject's head, and the subject is then allowed to rest to avoid further stimulation or interference with neural activity. Throughout the study, the subject's reactions are closely monitored to ensure that transcranial magnetic stimulation remains within a safe range and avoids any adverse reactions or discomfort.

[0052] In summary, this embodiment comprises subjects with depression as a patient group and healthy subjects as a control group. An electrode topology map is constructed based on the electrode distribution of the subjects, and peripheral nodes in the electrode topology map are obtained. Based on the changes in functional connectivity differences between peripheral nodes at the same location in the patient and control groups, the degree of depression association is obtained, and the peripheral nodes are divided into peripheral categories, and the number of electrodes in the brain surface area corresponding to the peripheral category is obtained. By obtaining the number of electrodes, the present invention accurately determines the number of electrodes corresponding to each brain area, effectively avoiding the situation where uniform electrode distribution leads to incomplete acquisition of depression-related data, thereby improving the accuracy and reliability of research results on the mechanism of depression and the treatment effect of depression. Example 2:

[0053] The present invention also proposes a brain function network construction system under the right intraparietal sulcus magnetic pulse stimulation, please refer to Figure 3 , which shows a structural diagram of a brain functional network construction system under magnetic pulse stimulation of the right intraparietal sulcus provided by an embodiment of the present invention. The system includes: a parameter acquisition module 10, a peripheral node acquisition module 20, a peripheral category acquisition module 30 and an electrode quantity acquisition module 40.

[0054] The parameter acquisition module 10 is used to form the subjects with depression into a patient group; form the healthy subjects into a control group; and use the electrode in the corresponding area of the right intraparietal sulcus of each subject as the target electrode.

[0055] The peripheral node acquisition module 20 is used to construct an electrode topology map according to the electrode distribution corresponding to any experimenter, and acquire peripheral nodes in the electrode topology map according to the positional relationship between each node in the electrode topology map and the node corresponding to the target electrode.

[0056] The peripheral category acquisition module 30 is used to obtain the depression association degree of the peripheral nodes at each location between the patient group and the control group based on the changes in the functional connectivity differences of the peripheral nodes at the same location between the patient group and the control group under conditions without transcranial magnetic stimulation and with transcranial magnetic stimulation; and divide the peripheral nodes into peripheral categories based on the depression association degree.

[0057] The electrode quantity acquisition module 40 is configured to acquire the number of electrodes in the brain surface area corresponding to each peripheral category according to the depression association degree of the peripheral nodes in each peripheral category.

[0058] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the brain function network construction system under the right intraparietal sulcus magnetic pulse stimulation provided in the above embodiment and the brain function network construction method embodiment under the right intraparietal sulcus magnetic pulse stimulation are of the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here. Example 3:

[0059] The present invention also proposes a device for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation. The device includes a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation provided in an embodiment of the present application. The device can specifically be a chip, component, or module, and the chip may include a connected processor and memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation provided in the above embodiment.

[0060] In addition, the present application also protects a computer device, see Figure 4 The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402, wherein when the processor 402 executes the computer program 403, the computer device can execute any of the brain functional network construction methods under right intraparietal sulcus magnetic pulse stimulation introduced above. Example 4:

[0061] This embodiment also provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes the above-mentioned related method steps to implement a brain functional network construction method under right intraparietal sulcus magnetic pulse stimulation provided by the above embodiment. Example 5:

[0062] This embodiment also provides a computer program product. When the computer program product is run on a computer, it enables the computer to execute the above-mentioned related steps to implement a method for constructing a brain functional network under magnetic pulse stimulation of the right intraparietal sulcus provided in the above embodiment.

[0063] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved are the beneficial effects of the corresponding methods provided above, which will not be repeated here.

[0064] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation, characterized in that: The method comprises the following steps: Subjects with depression were selected as the patient group; healthy subjects were selected as the control group; the electrodes in the corresponding area of the right intraparietal sulcus of each subject were used as the target electrodes; An electrode topology map is constructed based on the electrode distribution corresponding to any experimenter, and peripheral nodes in the electrode topology map are obtained based on the positional relationship between each node in the electrode topology map and the node corresponding to the target electrode; Based on the changes in functional connectivity differences of peripheral nodes at the same locations between the patient group and the control group without and with transcranial magnetic stimulation, the depression association degree of the peripheral nodes at each location was obtained; the peripheral nodes were divided into peripheral categories based on the depression association degree; According to the magnitude of depression association of peripheral nodes in each peripheral category, the number of electrodes in the brain surface area corresponding to each peripheral category was obtained.

2. The method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation according to claim 1, characterized in that: The method for constructing an electrode topology map according to the electrode distribution corresponding to any experimenter is: For any subject, each electrode in the subject's brain surface area is used as a node in the graph structure, and the distance between any two nodes is used as the edge value. The electrode topology map of the subject is obtained through the Delaunay triangulation algorithm.

3. The method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation according to claim 1, characterized in that: The method for obtaining the peripheral nodes is: For any experimenter, the node corresponding to the target electrode in the experimenter's electrode topology map is used as the target node; Obtain the center point of the target node through the position coordinates of each target node as the target center point; Obtain the peripheral degree of each node based on the distance between each node and the target center point, the degree between each node and the target node, and the degree between each node and the non-target node in the electrode topology map; When the peripheral degree is greater than a preset peripheral degree threshold, the corresponding node is regarded as a peripheral node.

4. The method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation according to claim 3, characterized in that: The method for obtaining the peripheral degree is: For any node in the electrode topology map, the Euclidean distance between the node and the target center point is taken as the first distance; The degree between the node and the target node is taken as the first value, and the degree between the node and the non-target node is taken as the second value; The result of negatively correlating and normalizing the ratio of the first value to the second value is used as the peripheral probability of the node; The product of the normalized result of the first distance and the peripheral probability is taken as the peripheral degree of the node.

5. The method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation according to claim 1, characterized in that: The method for obtaining the depression association degree is: When no transcranial magnetic stimulation was applied, the initial degree of difference of peripheral nodes at each location was obtained based on the functional connectivity differences of peripheral nodes at the same location between the patient group and the control group; When transcranial magnetic stimulation was applied, the degree of stimulation difference of the peripheral nodes at each location was obtained based on the functional connectivity difference of the peripheral nodes at the same location between the patient group and the control group; According to the initial difference degree and stimulation difference degree of the peripheral nodes at each position, the depression association degree of the peripheral nodes at each position is obtained; among them, the initial difference degree is positively correlated with the depression association degree, and the stimulation difference degree is negatively correlated with the depression association degree.

6. The method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation according to claim 5, characterized in that: The method for obtaining the initial difference degree is: When transcranial magnetic stimulation was not applied, a reference electrode topology map of each subject was constructed based on the EEG signals of each electrode of each subject, and an embedding vector of each peripheral node in the reference electrode topology map of each subject was obtained; For any peripheral node at any position, the representative embedding vector of the peripheral node at this position in the patient group is obtained according to the embedding vector of the peripheral node at this position in the reference electrode topology map of each experimenter in the patient group; According to the embedding vector of the peripheral node at the position in the reference electrode topology map of each experimenter in the control group, the representative embedding vector of the peripheral node at the position in the control group is obtained; The result of negative correlation between the cosine similarity of the representative embedding vector of the peripheral node at this position in the patient group and the representative embedding vector in the control group is used as the initial difference degree of the peripheral node at this position.

7. The method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation according to claim 6, characterized in that: The method for obtaining the representative embedding vector is: For any group in the patient group or the control group, the embedding vector of the peripheral node at that position in the reference electrode topology map of each subject in the group is used as the reference embedding vector; For any experimenter in the group, obtain the sum of the cosine similarities between the experimenter's reference embedding vector and each other reference embedding vector as the representative reference value of the experimenter; The representative reference value of each experimenter in the group is obtained, and the reference embedding vector of the experimenter corresponding to the largest representative reference value is used as the representative embedding vector of the peripheral node at that position in the group.

8. The method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation according to claim 7, characterized in that: The method for obtaining the stimulus difference degree is: When transcranial magnetic stimulation was applied, the frequency of the magnetic pulse was adjusted once every preset time interval, and the target electrode topology map of each subject at each frequency was constructed based on the EEG signal of each electrode at each frequency for each subject. Obtain the embedding vector of each peripheral node in the target electrode topology for each experimenter at each frequency; For a peripheral node at any frequency and any position, according to the embedding vector of the peripheral node at this position in the target electrode topology map of each experimenter in the patient group at this frequency, obtain the representative embedding vector of the peripheral node at this position in the patient group at this frequency as the first vector; According to the embedding vector of the peripheral node at the position in the target electrode topology map of each experimenter in the control group at the frequency, the representative embedding vector of the peripheral node at the position in the control group at the frequency is obtained as the second vector; The result of negative correlation between the cosine similarity of the first vector and the second vector is used as the reference stimulus difference value of the peripheral node at the position at the frequency; The result of normalizing the sum of the reference stimulus difference values of the peripheral nodes at this position at all frequencies is used as the stimulus difference degree of the peripheral nodes at this position.

9. The method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation according to claim 1, characterized in that: The method for obtaining the peripheral category is: According to the depression association degree of the peripheral nodes at each location, the peripheral nodes were divided into peripheral categories using the Laplace clustering algorithm.

10. The method for constructing a brain functional network under right intraparietal sulcus magnetic pulse stimulation according to claim 1, characterized in that: The method for obtaining the number of electrodes is: For any peripheral category and any peripheral node in the peripheral category, obtain the difference in the degree of association between the peripheral node and each other peripheral node in the peripheral category with depression, all as the first difference; The sum of all first differences is used as the representative analysis value of the peripheral node; The depression association degree of the peripheral node corresponding to the minimum representative analysis value is taken as the representative depression association degree of the peripheral category; The ratio of the representative depression association level of each peripheral category to the minimum representative depression association level was used as the adjustment weight of each peripheral category; The number of peripheral nodes in the peripheral category corresponding to the smallest degree of association representing depression is taken as the benchmark number; The product of the baseline number and the adjusted weight of each peripheral category is rounded up to an integer, which is used as the number of electrodes in the brain surface area corresponding to each peripheral category.

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