Depression patient brain nerve activity difference acquisition method, device and equipment and medium

Abstract

The invention is applicable to the technical field of neuroscience, and provides a depression patient brain nerve activity difference acquisition method, which comprises the following steps: constructing an experimental normal form containing two emotion stimulations including physiological and social pain stimulations, collecting first EEG signals of a plurality of depression patients in a first test group and a second test group in an experiment of the experimental normal form, and calculating the depression patient brain nerve activity difference. Preprocessing each first EEG signal to obtain a second EEG signal of each subject depression patient in an emotional stimulation stage of an experiment, and processing the second EEG signals in combination with a micro-state analysis strategy and an electroencephalogram traceability analysis strategy to obtain a first EEG signal; brain nerve activity differences of the depression patients in the first test group and the second test group are obtained when the depression patients face physiological and social pain stimulation, so that a brain function network of the depression patients is comprehensively understood, a bottom layer mechanism of brain functions and activity modes is disclosed, and the method has obvious advantages in research of abnormal nerve mechanisms of the depression patients and has good application prospects. And a new way is provided for early diagnosis and treatment of NSSI behaviors of depression patients.

Description

Technical Field

[0001] The present invention belongs to the field of neuroscience technology, and in particular relates to a method, device, equipment and medium for obtaining differences in brain neural activity in patients with depression. Background Art

[0002] Electroencephalogram (EEG) signals are generated by synaptic transmission of neurons and changes in the membrane potential of neurons themselves. They propagate through the skull and surrounding tissues and eventually reach the surface of the scalp. They are measured and recorded by electrodes placed on the surface of the scalp. EEG microstates are an effective analysis method for studying the dynamic changes of EEG signals in time and space. By capturing the changes of multi-channel EEG topography over time in the whole brain, the dynamic changes of brain activity states under specific tasks can be described. In the process of EEG signal analysis, the brain dynamics detected by EEG can be generally divided into a series of "microstates". These microstates are usually short-lived, lasting about 40 to 100 milliseconds. The spatial configuration of the scalp global field power (GFP) remains stable before rapidly changing to another configuration. This stable representation of the potential topology can be obtained from the original EEG signal using a spatial clustering algorithm in a predefined number or data-driven manner, such as the K-means clustering algorithm and the hierarchical clustering algorithm.

[0003] As the basic component or "atom of thought" reflecting consciousness, many previous studies have explored the physiological functional significance behind EEG microstates through EEG source analysis or functional magnetic resonance imaging (fMRI) data, so as to understand the neural mechanism reflected by brain activity. As in the studies of some researchers, the spatial clustering templates were marked as MSA, MSB, MSC and MSD, respectively. Through independent component analysis of fMRI, it was concluded that MSA was related to the Auditory Network (AN), MSB was related to the Visual Network (VN), MSC was related to the Default Mode Network (DMN), and MSD was related to the Dorsal Attention Network (DAN). In addition, some other researchers used a source localization method to explore the source activation of EEG microstates and found the relationship between consistent EEG microstates and functional brain networks, providing further evidence for these associations. Given the association between EEG microstates and functional brain networks, by observing the spatiotemporal changes of microstates, the activation of different functional networks and the dynamic information interaction process between them can be revealed. At present, the EEG microstate analysis method has been widely used in many fields such as clinical neuroscience and brain disease research. For example, in some studies, microstate analysis was performed on the closed-eye resting EEG data of patients with bipolar disease (BD), schizophrenia (SCH) and healthy controls, and the brain dysfunction of patients with schizophrenia and bipolar disorder was explained. The results showed that four cluster templates (AD) were obtained from the analysis, among which MSA and MSB were abnormally represented in BD patients, while the abnormal representation of SCH patients was reflected in all four microstates. In addition, compared with SCH patients, BD patients had more frequent conversions between MSA and MSB, while the frequency of conversions between MSC and MSD was reduced. Given that MSB is more closely related to the visual function network, and MSC is more involved in the posterior part of the anterior cingulate cortex, the study believes that these characteristics may indicate that SCH patients and BD patients have potential abnormalities in the allocation of neural resources and the conversion between brain activities. At the same time, microstates can not only understand the brain activity state of individuals under different cognitive tasks and reflect the cognitive state of individuals, but also assist in the diagnosis and treatment monitoring of mental illness.Existing studies have explored the potential of EEG microstates as biomarkers of schizophrenia and predicting patients who may develop schizophrenia in the future (ultra-high risk group, UHR). The results show that MSA and MSB can be used as state markers of general psychopathology and psychotic symptoms, respectively. The proportion and frequency of MSA can distinguish the first-episode psychosis group (FEP), ultra-high risk of mental disorders (UHR) from healthy subjects, and the proportion and frequency of MSB can further distinguish FEP from UHR, while MSD can selectively identify UHR patients who will develop psychosis in the future. In general, the physiological functional significance of EEG microstates covers reflecting the activity pattern of brain functional networks, indicating cognitive states and emotions, evaluating neural plasticity and developmental states, and assisting in the diagnosis and treatment monitoring of neurological diseases. Therefore, EEG microstate analysis is of great significance in revealing the spatiotemporal dynamic characteristics of brain activity, exploring the neural basis of cognitive processes, and studying the pathogenesis of neurological diseases. Through the application of EEG microstates, we can have a deeper understanding of the complexity and diversity of brain functions, providing useful inspiration for neuroscience research and clinical practice.

[0004] In the study of EEG signals, there are some defects in the current research on abnormal neural mechanisms of patients with depression (especially adolescent patients with depression). First, the sample size is usually small, which limits the general applicability and reliability of the research results; second, some studies lack patients with clinically diagnosed depression, and the degree of depression is only evaluated by the scale, which may not accurately reflect the characteristics of the EEG microstate of depression; in addition, the research paradigm may be too limited, for example, only the resting EEG signals with eyes open were collected, and cognitive tasks such as calculation and working memory were not involved, and no comparison was made with the data collected with eyes closed, which may limit the comprehensive understanding of the brain mechanism of depression; finally, most studies have not explored the study of abnormal neural mechanisms of adolescent depression under different emotional stimuli. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device, equipment and medium for obtaining differences in brain neural activity in patients with depression, aiming to solve the problem that the existing technology has defects in the study of abnormal neural mechanisms of patients with depression.

[0006] In one aspect, the present invention provides a method for obtaining differences in brain neural activity in patients with depression, the method comprising the following steps: An experimental paradigm including two emotional stimuli, including physiological pain stimuli and social pain stimuli, is constructed to induce specific emotional responses; Collecting first EEG signals of a number of depressed subjects in two test groups in an experiment of the experimental paradigm, wherein the test groups include a first test group and a second test group; Preprocessing each of the first EEG signals to obtain a second EEG signal of each of the tested depressed patients during the emotional stimulation phase of the experiment; The second EEG signal is processed in combination with a preset microstate analysis strategy and an electroencephalogram (EEG) source tracing analysis strategy to obtain differences in brain neural activity of the depressed subjects in the first and second test groups when facing the physiological pain stimulus and the social pain stimulus.

[0007] Preferably, the step of processing the second EEG signal in combination with a preset microstate analysis strategy and an EEG source tracing analysis strategy includes: Performing microstate analysis on the second EEG signal using the microstate analysis strategy to obtain a first analysis result; Based on the first analysis result, the EEG source tracing analysis strategy is used to perform EEG source tracing analysis to obtain a second analysis result; Based on the first and second analysis results, the differences in brain neural activities of the depressed subjects in the first and second test groups when facing the physiological pain stimulation and the social pain stimulation are determined.

[0008] Preferably, the step of performing microstate analysis on the second EEG signal using the microstate analysis strategy comprises: Performing spatial clustering on the second EEG signal to obtain a global optimal microstate template; Based on the global optimal microstate template, a two-factor mixed effects analysis of variance was used to perform statistical analysis and obtain microstate characteristics with significant results.

[0009] Preferably, based on the first analysis result, the step of performing EEG source tracing analysis using the EEG source tracing analysis strategy includes: Using a spatiotemporal source estimation algorithm to obtain the β activation value of the global optimal microstate template in the source space; Performing statistical tests on the beta activation values ​​to determine regions of interest with significant activation; The significantly activated regions of interest are allocated to each functional brain network divided by a preset template, and the proportion of the activated regions of interest in each brain network in all significantly activated regions of interest and the average activation of the regions of interest on each brain network are calculated.

[0010] Preferably, the step of obtaining the β activation value of the global optimal microstate template in the source space using a spatiotemporal source estimation algorithm comprises: Performing a generalized linear model regression fit on the global optimal microstate template and each of the second EEG signals to obtain a microstate time series; Using a standardized low-resolution electromagnetic tomography algorithm, the data of each of the second EEG signals at each time point are traced to the source, and the time course of the current density at each time point in the source space is estimated; Extracting the current density time course of the region of interest from the current density time course to obtain a source activity time series; The microstate time series and the source activity time series are subjected to generalized linear model regression fitting to obtain the β activation value.

[0011] Preferably, the experimental paradigm includes a first resting state of a first duration threshold and a task state of a second duration threshold, the task state includes two first task blocks and second task blocks in a random and balanced order, the first and second task blocks have the same number of trials, and each trial includes four stages: a second resting state, emotional stimulation, emotional assessment, and a black screen, wherein the emotional stimulation in the first task block is the social pain stimulation, and the emotional stimulation in the second task block is the physiological pain stimulation.

[0012] In another aspect, the present invention provides a device for acquiring differences in brain neural activity of a depressed patient, the device comprising: An experimental paradigm construction unit, used to construct an experimental paradigm including two emotional stimuli, wherein the emotional stimuli include physiological pain stimuli and social pain stimuli, so as to induce specific emotional responses; An EEG signal acquisition unit is used to acquire first EEG signals of a number of depressed subjects in two test groups in an experiment of the experimental paradigm, wherein the test groups include a first test group and a second test group; A first signal processing unit is used to pre-process each of the first EEG signals to obtain a second EEG signal of each of the tested depressed patients during the emotional stimulation phase of the experiment; The second signal processing unit is used to process the second EEG signal in combination with a preset microstate analysis strategy and an EEG source tracing analysis strategy to obtain the difference in brain neural activity of the depressed subjects in the first and second test groups when facing the physiological pain stimulation and the social pain stimulation.

[0013] Preferably, the second signal processing unit includes: a microstate analysis unit, configured to perform microstate analysis on the second EEG signal using the microstate analysis strategy to obtain a first analysis result; An EEG source tracing analysis unit, configured to perform EEG source tracing analysis based on the first analysis result and adopt the EEG source tracing analysis strategy to obtain a second analysis result; The neural difference determination unit is used to determine the difference in brain neural activity of the depressed subjects in the first and second test groups when facing the physiological pain stimulation and the social pain stimulation based on the first and second analysis results.

[0014] On the other hand, the present invention also provides a computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps described in the above-mentioned method for obtaining differences in brain neural activity in depressed patients are implemented.

[0015] On the other hand, the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps described in the above-mentioned method for obtaining differences in brain neural activity of depressed patients.

[0016] The present invention constructs an experimental paradigm including two emotional stimuli, wherein the emotional stimuli include physiological pain stimuli and social pain stimuli for inducing specific emotional responses, collects first EEG signals of several depressed patients in two test groups in the experiment of the experimental paradigm, wherein the test groups include a first test group and a second test group, preprocesses each first EEG signal to obtain a second EEG signal of each depressed patient in the emotional stimulation stage of the experiment, processes the second EEG signal in combination with a microstate analysis strategy and an electroencephalogram tracing analysis strategy, and obtains differences in brain neural activity of depressed patients in the first and second test groups when facing physiological and social pain stimuli, thereby achieving a comprehensive understanding of the brain functional network of depressed patients and revealing the underlying mechanisms of brain functions and activity patterns, which has obvious advantages in the study of abnormal neural mechanisms of depressed patients and provides a new approach for the early diagnosis and treatment of NSSI behaviors in depressed patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of the method for obtaining differences in brain neural activity of depressed patients provided in the first embodiment of the present invention; Figure 2 It is a schematic diagram of the experimental paradigm provided in the first embodiment of the present invention; Figure 3 is a schematic diagram of a process of spatially clustering a second EEG signal provided in the first embodiment of the present invention; Figure 4 is a schematic diagram of a template of a spatial clustering result provided in the first embodiment of the present invention; Figure 5 is a schematic diagram of analysis results of statistical analysis of the global optimal micro-state template provided in the first embodiment of the present invention; Figure 6This is a flowchart of the implementation of obtaining the β activation value of the global optimal microstate template in the source space using the spatiotemporal source estimation algorithm provided by the first embodiment of the present invention; Figure 7 The distribution and activation of the eight functional brain networks provided in the first embodiment of the present invention; Figure 8 The distribution and activation of 18 functional brain networks provided in Example 1 of the present invention; Fig. 9 is a schematic diagram of the structure of a device for acquiring differences in brain neural activity of a depressed patient provided in Embodiment 2 of the present invention; Fig.10 This is a schematic diagram of a preferred structure of a device for acquiring differences in brain neural activity of a depressed patient provided in Embodiment 2 of the present invention; Fig.11 It is a schematic diagram of the structure of a computing device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] It should be understood that the terms "including" and "having" and any variations thereof in the present invention are intended to cover but not exclude inclusion. For example, a product or device comprising a series of components is not necessarily limited to those components explicitly listed, but may include other components not explicitly listed or inherent to these products or devices.

[0020] In the present invention, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0021] Unless otherwise specified, the term "plurality" in the present invention refers to two or more than two, and other quantifiers are similar to them.

[0022] The terms "first", "second", "third", etc. in the present invention are used to distinguish similar or similar objects or entities, and do not necessarily mean to limit a specific order or sequence, unless otherwise noted. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, for example, they can be implemented in an order other than those given in the diagrams or descriptions of the embodiments of the present disclosure.

[0023] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards.

[0024] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments: Embodiment 1: Figure 1 The implementation process of the method for obtaining the difference in brain neural activity of a depressed patient provided by the first embodiment of the present invention is shown. For the sake of convenience, only the part related to the embodiment of the present invention is shown, which is described in detail as follows: In step S101, an experimental paradigm including two emotional stimuli is constructed, wherein the emotional stimuli include physiological pain stimuli and social pain stimuli, so as to induce specific emotional responses.

[0025] The embodiment of the present invention is applicable to computing devices, such as personal computers, servers, etc. In the embodiment of the present invention, for patients with depression, an experimental paradigm including two emotional stimuli, namely physiological pain stimulation and social pain stimulation, is constructed to induce specific emotional responses, so as to comprehensively evaluate the neural response patterns of patients with depression under different emotional stimuli, which is helpful to explore the abnormal neural mechanisms of patients with depression.

[0026] In a feasible embodiment, physical pain pictures are used to induce emotional responses to physical pain stimuli, and social rejection pictures are used to induce emotional responses to social pain stimuli. The physical pain pictures and social rejection pictures can be selected from the Internet, and their sources are not specifically limited here.

[0027] In another feasible embodiment, the experimental paradigm includes a first resting state of a first duration threshold and a task state of a second duration threshold, the task state includes two first task blocks and second task blocks in a random and balanced order, the first and second task blocks have the same number of trials, and each trial includes four stages: a second resting state, emotional stimulation, emotional evaluation, and a black screen, wherein the emotional stimulation in the first task block is a social pain stimulation, and the emotional stimulation in the second task block is a physiological pain stimulation.

[0028] As an example, in a specific embodiment, the experimental procedure is presented by E-prime2.0 presentation software. The entire experimental paradigm includes a five-minute resting state and two task states. In the task state, there are two task blocks (Block 1 and Block 2). The order of Block 1 and Block 2 is random and balanced between subjects. Block 1 and Block 2 each contain 35 trials. A single trial 4 includes four parts: resting state, emotional stimulation, emotional evaluation and black screen. Figure 2 As shown: (a) Resting state: The resting state lasts for 2 seconds. The subjects are asked to look at the cross cursor in the center of the screen, keep their eyes open, relax, and not think. (b) Emotional stimulation: This part lasted for 5 seconds and presented social rejection pictures or physical pain pictures. During this period, the subjects were asked to passively watch the pictures. (c) Emotional assessment: The subjects were asked to rate the intensity of their negative emotions using a keyboard, with scores ranging from 1 to 9, with 1 indicating no negative emotions and 9 indicating strong negative emotions. The higher the score, the stronger the negative emotions. This part lasted 5 seconds. When the subjects completed the rating, the emotional assessment part ended immediately. (d) Black screen: The duration is 1 second, and the screen will appear completely black; Among them, Block 1 presented social exclusion pictures in a random order. In this part, the subjects were required to passively watch the pictures and imagine that the person being excluded in the picture was themselves. Block 2 presented physical pain pictures in a random order. In this part, the subjects were also required to passively watch the pictures and were instructed to imagine that the injured person in the picture was themselves.

[0029] In step S102, first EEG signals of several depressed patients in two test groups in an experimental paradigm are collected, and the test groups include a first test group and a second test group.

[0030] In an embodiment of the present invention, a number of adolescent depression patients (age: 13-18 years old; education years ≥ 5 years) are selected as depression test patients. All patients are recommended to different test groups by psychiatrists according to the diagnostic criteria of major depressive disorder, anxiety disorder or post-traumatic stress disorder in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and other mental disorders have been excluded through the Mini-International Neuropsychiatric Interview (MINI) screening. Here, the depression test patients are divided into a first test group and a second test group. Specifically, the first test group is a depression with self-injury group (Depression with NSSI, DN+), and the second test group is a depression without self-injury group (Depression without NSSI, DN-). The EEG activity of each depression test patient in DN+ and DN- in a resting state or a task state is collected using a multi-channel EEG signal acquisition device to obtain an EEG signal (i.e., a first EEG signal) of each depression test patient.

[0031] In step S103, each first EEG signal is preprocessed to obtain a second EEG signal of each depressed patient during the emotional stimulation phase of the experiment.

[0032] In an embodiment of the present invention, the first EEG signal of each depressed patient is preprocessed. The preprocessing process is specifically as follows: according to the marked time point in the first EEG signal, the EEG signal corresponding to the first task block (referred to as Block1 signal) and the EEG signal corresponding to the second task block (referred to as Block2 signal) are segmented from the first EEG signal, the eye movement channel (IO) of the Block1 signal and the Block2 signal is deleted to reduce the influence of eye movement on the EEG signal, and the damaged channel signal is interpolated to maintain the integrity of the data, and then all the interpolated data are subjected to a common average reference to remove the random noise of the zero mean distribution and improve the signal-to-noise ratio of the signal, and then, a 1-45 Hz bandpass filter and a 50 Hz notch filter are used for filtering to reduce the interference of low-frequency noise, and then, an independent component analysis (ICA) is used to analyze the signal. After removing artifacts using the Inductively Coupled Component Analysis (ICA) algorithm, the data segments (0-5s) of the emotional stimulation stage in all trials were extracted to obtain the second EEG signal of each depressed patient in the emotional stimulation stage. In addition, the preprocessing of the first EEG signal can be performed on the Letswave toolkit of MATLAB.

[0033] In step S104, the second EEG signal is processed in combination with the preset microstate analysis strategy and the EEG tracing analysis strategy to obtain the difference in brain neural activity of the depressed subjects in the first and second test groups when facing physiological pain stimulation and social pain stimulation.

[0034] In an embodiment of the present invention, a microstate analysis strategy is first used to identify different EEG topographic map patterns from the second EEG signal, and then EEG tracing analysis is used to determine the neural activity areas corresponding to these patterns. Next, the differences between the two groups of depressed patients in these areas and patterns are compared to reveal the different responses of their brain neural activities when facing physical pain stimuli and social pain stimuli.

[0035] In a feasible embodiment, the second EEG signal is processed by combining a preset microstate analysis strategy and an EEG source tracing analysis strategy through the following steps: (1) performing microstate analysis on the second EEG signal using a microstate analysis strategy to obtain a first analysis result; In an embodiment of the present invention, a microstate analysis strategy is used to perform microstate analysis on the second EEG signal to reveal a brief and stable electrophysiological state of the brain in a resting state or a task state, thereby obtaining a first analysis result.

[0036] In a feasible embodiment, the microstate analysis of the second EEG signal is implemented by the following steps: ① Perform spatial clustering on the second EEG signal to obtain the global optimal microstate template; In an embodiment of the present invention, first, the second EEG signal of each depressed patient subject is spatially clustered to obtain the individual optimal microstate template of each depressed patient subject. Then, based on the individual optimal microstate template of each depressed patient subject, spatial clustering across subjects is performed to obtain the global optimal microstate template.

[0037] Specifically, Figure 3As shown in the figure, first, the first step of clustering is performed: the second EEG signal of each depressed patient is spatially clustered using the K-means algorithm considering polarity, and the number of clustering templates is defined as K (K=2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), the number of random initializations is 2000, the number of iterations is 5000, and in the iterative process of clustering, the samples with high global explained variance (Global Explained Variance, GEV) and low contrast value (Contrast The cluster centroid of the optimal microstate template is identified as the most representative template, and the number of centroids is the optimal number of cluster templates. After the first step of spatial clustering, all trials of all depressed patients will eventually obtain a set of microstate templates, that is, the individual optimal microstate template. Then, the second step of clustering is performed: all individual optimal microstate templates obtained in the first step of clustering are subjected to a second K-time spatial clustering, and the number of clusters defined is still K. After the second step of spatial clustering, K microstate templates across subjects and trials for neural dynamics analysis are obtained. Finally, according to the corresponding microstate templates obtained in the second step of clustering The two indicators of GEV and CV are used to determine the number of final optimal templates and the corresponding optimal microstate template, that is, the global optimal microstate template. Among them, GEV is used to measure the degree to which the microstate template explains the overall variability of EEG data. The higher the GEV value, the better the template fits the data. CV is used to reflect the stability or consistency of the microstate topographic map. The CV value is usually negative. The larger its absolute value, the more stable the topographic map. The ratio of GEV to CV is used to evaluate the quality of the microstate template. When the first-order difference of this ratio (that is, the difference between adjacent ratios) is the largest, it indicates that the corresponding microstate template is the best microstate template.

[0038] As an example, in a specific embodiment, according to the clustering results of the second step, it is found that when clustered into 8 microstate templates, they have high GEV (76.598%) and low CV (-75.617%), and the maximum first-order difference shown on these two indicators, then the corresponding 8 microstate templates at this time are used to determine the final global optimal microstate template. Each global optimal microstate template has a unique topographic map topological structure. Here, according to the topological structure of the microstate topographic map, these 8 global optimal microstate templates are recorded as MS1, MS2, MS3, MS4, MS5, MS6, MS7, and MS8 in sequence, as shown in FIG. Figure 4 shown.

[0039] ②Based on the global optimal microstate template, a two-factor mixed effects analysis of variance was used to perform statistical analysis and obtain microstate characteristics with significant results.

[0040] In an embodiment of the present invention, the global optimal microstate template is back-fitted to the second EEG signal to obtain a series of EEG microstate time series, and then the microstate features are extracted from the EEG microstate time series to quantify the time domain information of each microstate. After that, according to the preset statistical analysis conditions, a two-way mixed effects analysis of variance (Two-way ME-ANOVA) is used to perform statistical analysis on the extracted microstate features to obtain microstate features with significant results, wherein the statistical analysis conditions include taking the subject group (DN- or DN+) as the between-subject factor, taking the pain picture category (physiological pain pictures or social exclusion pictures) as the within-subject factor, taking the gender and age of the depressed patients as covariates, setting the alpha significance level to p<0.05, and using a false positive test (False Discovery Test) with a significance level of 5% for all P values. The FDR was corrected for multiple comparisons (p<0.05). The characteristics of microstates include coverage (Cov), duration (Dur), occurrence frequency (Occ), and transition probability (TP). Coverage refers to the ratio of the total duration of a specific microstate remaining in the dominant state to the total duration of the microstate sequence. Duration refers to the average duration of a specific microstate remaining in the dominant state. Occurrence refers to the number of times a specific microstate appears continuously in one second. Transition probability refers to the probability of a specific microstate transitioning to another microstate.

[0041] As an example, in one embodiment, the microstate characteristics that present significant results are as follows Figure 5 As shown in (a), the features with significant results are mainly concentrated on the transition probability (TP) of the microstate. Figure 5 As can be seen from (a) in the figure, the microstate transition pairs with significant interaction effects are as follows: the probability of transition from MS1 to MS5, MS2 to MS6, MS3 to MS5, MS4 to MS7, and MS7 to MS1; the transition pairs with significant group main effects include: the transition from MS2 to MS8, MS4 to MS1, MS4 to MS8, and MS8 to MS1; the transition from MS1 to MS5 and the transition from MS3 to MS5 have significant Block main effects. Subsequently, further simple effects were performed on the microstate transition pairs with significant interaction effects, and the results are as follows: Figure 5 As shown in (b), the differences in microstate activities under the two emotional stimuli in different test groups are as follows Figure 5 As shown in (c) and (d), red represents an increase in probability and blue represents a decrease in probability. Figure 5The results of (b), (c), and (d) in the figure show that in the DN+ group, the conversion probabilities of MS7 to MS1, MS1 to MS5, and MS3 to MS5 under physiological pain stimulation are significantly greater than those under social pain stimulation, and the conversion probability of MS2 to MS6 is significantly lower; in the DN- group, the conversion probability of MS3 to MS5 under physiological pain stimulation is significantly lower than that under social pain stimulation, and the conversion probability of MS4 to MS7 is significantly higher. It can be seen that the significant difference between the DN- and DN+ groups of patients is reflected in the MS3->MS5 conversion pair, which can be used as a representation of the neural differences between the two groups of patients.

[0042] Through the above steps ①②, the microstate analysis of the second EEG signal is realized. The first analysis results include but are not limited to the global optimal microstate template and the microstate characteristics with significant results. The first analysis results can accurately evaluate the differences in microstate characteristics of depressed patients in different test groups under different experimental conditions, providing important clues for understanding the neural mechanisms of adolescent depression patients with non-suicidal self-injury (NSSI).

[0043] (2) Based on the first analysis result, the EEG source tracing analysis strategy is used to perform EEG source tracing analysis to obtain the second analysis result; In an embodiment of the present invention, based on the first analysis result, an EEG source tracing analysis strategy is adopted to perform EEG source tracing analysis, so as to establish a connection between the EEG topography map corresponding to the global optimal microstate template in the first analysis result and the source space (i.e., the actual position or area of ​​the brain), and obtain the source space distribution under different EEG microstates, so as to deeply understand the origin and distribution of the global optimal microstate template in the brain, and further reveal the neural activity patterns of the brain in different states.

[0044] In a feasible embodiment, the following steps are used to implement EEG source tracing analysis using an EEG source tracing analysis strategy: ( ) Use the spatiotemporal source estimation algorithm to obtain the β activation value of the global optimal microstate template in the source space; In an embodiment of the present invention, a Topographic Electro-physiological State Source-imaging (TESS) algorithm is used to obtain the β activation values ​​of the global optimal microstate template in the source space, which reflect the intensity of neural activity of the brain in the microstate.

[0045] In a possible embodiment, if Figure 6 As shown, the β activation value of the global optimal microstate template in the source space is obtained through the following steps: In step S601, a generalized linear model regression fit is performed on the global optimal microstate template and each second EEG signal to obtain a microstate time series; In the embodiment of the present invention, TESS includes a two-stage generalized linear model (Generalized Linear Model, GLM). First, through the first stage GLM, the temporal dynamics of the microstate on each data segment (i.e., the microstate time series) is obtained. Specifically, the numerical features of the microstate topographic map corresponding to the global optimal microstate template obtained by spatial clustering are used as the design matrix, and the second EEG signal is used as the dependent variable. Through the first GLM regression formula Fit back the data segments of each depressed patient in each trial to obtain the time course of each microstate in each data segment (i.e., microstate time series), where: Indicates the electrode position and the current sampling time point The EEG signal on Indicates The numerical characteristics of the microstate topographic map corresponding to the global optimal microstate template, , is the number of global optimal microstate templates, represents the residual noise, is the coefficient to be estimated, that is, each microstate at time The time process on.

[0046] In step S602, a standardized low-resolution electromagnetic tomography algorithm is used to trace the data of each second EEG signal at each time point, and estimate the time course of current density at each time point in the source space; In an embodiment of the present invention, a standardized low-resolution electromagnetic tomography (sLORETA) algorithm is used to perform source localization on each data segment in each second EEG signal to reconstruct the spatial distribution of the EEG signal at each time point, and obtain the current density time course at each time point in the source space. Specifically, this process produces a current density time course of 6239 solution points with a spatial resolution of 5 mm. These solution points cover the entire cerebral cortex and represent the locations where current activity may occur in the brain. Here, the sLORETA algorithm is used to standardize the current density estimate given by the minimum norm solution, thereby improving quantitative comparability through the standardization process, and improving the stability of the solution in the case of noise through regularization technology, and making the solution have better spatial resolution.

[0047] In step S603, the current density time course of the region of interest is extracted from the current density time course to obtain a source activity time series; In the embodiment of the present invention, the cortical template (Cortical Template) of Schaefer400 and the subcortical template (Subcortical Templates) containing 16 regions of interest (ROIs) are used to allocate voxels (i.e., the solution points obtained in step S602) to each ROI, and the current density time course of the region of interest is obtained. Specifically, the current density time course of 416 ROIs is obtained, which is recorded as ,in, Indicates time, Indicates the ROI position. Here, As source activity time series.

[0048] In step S604, the microstate time series and the source activity time series are subjected to generalized linear model regression fitting to obtain a β activation value.

[0049] In the embodiment of the present invention, in the second stage GLM included in TESS, the second GLM regression formula is The microstate time series (Represents microstate In time The activation level) and source activity time series Fitting is performed, and finally the microstates of all ROIs are obtained in each data segment. Variable estimates, i.e., β activation values ​​( ), these β activation values ​​provide the location and amplitude information of the generator in the source space, where represents the residual noise.

[0050] The above steps S601 to S604 are used to obtain the β activation value of the global optimal microstate template in the source space.

[0051] ( ) Perform statistical tests on the beta activation values ​​to identify regions of interest with significant activation; In the embodiment of the present invention, a single-sample t-test was performed on the activation distribution of the microstates in the source space (i.e., the β activation value) to test the significantly activated ROI on each microstate, and all P values ​​were corrected for multiple comparisons (p<0.0001) using a FDR with a significance level of 0.1% to reduce the impact of type I errors (i.e., the probability of incorrectly rejecting the true null hypothesis), and finally the ROI with significant activation was obtained; ( ) The significantly activated regions of interest were assigned to each functional brain network divided by the preset template, and the proportion of activated regions of interest in each brain network in all significantly activated regions of interest, as well as the average activation of the regions of interest on each brain network were calculated.

[0052] In an embodiment of the present invention, the significantly activated ROIs are allocated to each functional brain network divided by a preset template, and the proportion of the activated ROIs in each brain network in all significantly activated ROIs and the average activation of the ROIs on each brain network (presented as a t-value in statistical analysis) are calculated, so that the activation of the functional brain network under each microstate can be viewed, and the abnormalities in microstate transitions in patients with depression and self-injury (i.e., patients in the DN+ group) can be explained.

[0053] As an example, in a specific embodiment, the β activation values ​​of 8 global optimal microstate templates in the source space were obtained by using the TESS method, and then the β activation values ​​on 416 ROIs were subjected to a single-sample t-test and FDR correction to obtain ROIs with significant activation. The significantly activated ROIs were assigned to the 8 functional brain networks to which they belonged according to the cortical template and the subcortical template. Here, the transformation pairs with significant statistical results in the microstate analysis (i.e., the MS3 and MS5 transformation pairs) were taken separately, and the activation of the 8 functional brain networks corresponding to them were shown as follows: Figure 7 As shown in the figure, the activation distribution of the source space can be seen from the radar chart. MS3 and MS5 are similar in the number of significantly activated ROIs contained in the brain network, which are mainly distributed in SMN, DAN and DMN. However, in MS3, the number of significantly activated ROIs on SMN and DAN is greater than that on MS5, while the number of ROIs on VN and DMN is less than that on MS5. In addition, there are some differences in the strength of ROI activation contained in the brain network. MS3 has greater activation on SAN and SMN than MS5, while its activation on VN is weaker than that on MS5. That is, in the process of transformation from MS3 to MS5, the activation of SAN and SMN weakens, while the activation on VN strengthens. In addition, Figure 7The eight functional brain networks shown are Dorsal Attention Network (DAN), Default Mode Network (DMN), Cognitive Network (CN), Salience Network (SN), Visual Network (VN), Sensorimotor Network (SMN), Subcortical Affective Network (SAN), and Limbic Network (LN).

[0054] As an example, in another specific embodiment, in order to further explore the differences between the two microstate templates MS3 and MS5, the activation of the functional subnetworks can be divided more finely, and the significant activation ROIs of these two microstates can be redistributed to 18 functional subnetworks, such as Figure 8 As shown in the figure, from the new activation results, the activation distribution of the two microstates on the 18 functional subnetworks is still very similar. The most prominent result is that the number of ROIs of MS3 on SomMot A is greater than that of MS5, and the number of ROIs on Peripheral Visual is less than that of MS5. There are also some differences in the strength of activation. Figure 8 It can be seen that, compared with MS5, MS3 has stronger activation on SalVentAttn A, SomMot A, DorsAttn B, and DorsAttn A, but weaker activation on Default C, Default B, Peripheral Visual, Central Visual, SomMot B, and SalVentAttn B. In addition, Figure 8The 18 functional subnetworks shown are default network A (Default A), default network B (Default B), default network C (Default C), control network A (Control A), control network B (Control B), control network C (Control C), subcortical network (Subcortical Network), peripheral visual network (Peripheral Visual), central visual network (Central Visual), temporal parietal network (TemporalPartietal), sensorimotor network A (SomMot A), sensorimotor network B (SomMot B), salient attention network A (or salient-ventral attention network A, SalVentAttn A), salient attention network B (or salient-ventral attention network B, SalVentAttn B), limbic network A (Limbic A), limbic network B (Limbic B), dorsal attention network A (DorsAttnA), dorsal attention network B (DorsAttn B).

[0055] Through the above steps ( )-( ) can realize the traceability analysis of EEG signals, which helps to reveal the underlying mechanisms of brain functions and activity patterns, help reveal the brain activation patterns under specific tasks, stimuli or disease states, and how the brain transmits and integrates information when performing different tasks or receiving different stimuli.

[0056] (3) Based on the results of the first and second analyses, determine the differences in brain neural activity between the first and second test groups when depressed patients are faced with physical pain stimuli and social pain stimuli.

[0057] In the embodiment of the present invention, based on the first and second analysis results, the difference in brain neural activity of the depressed patients in the first and second test groups when facing physiological pain stimulation and social pain stimulation is determined. Specifically, the difference in brain neural activity of the depressed patients in the DN- and DN+ groups is described in detail as follows: (a) In addition to differences in processing different emotional stimuli, the two groups of depressed patients also had different microstate activities under the same emotional stimulus. When facing social pain stimuli, the probability of MS4 switching to MS7 in the DN+ group was significantly greater than that in the DN- group, while when facing physiological pain stimuli, the probability of MS3 switching to MS5 in the DN+ group was significantly greater than that in the DN- group. (b) The significant differences in brain activity between DN- and DN+ under negative emotional stimulation are mainly reflected in the probability of microstate switching. The changes in brain activity of DN- and DN+ in processing the two painful emotions show opposite trends. Under the stimulation of physiological pain, this microstate switching pattern can significantly distinguish DN- and DN+. (c) Combining the activation of the eight functional brain networks with the results of microstate analysis, the activation of SAN and SMN weakened during the transition from MS3 to MS5, while the activation of VN increased. Among them, SAN covers the affective network (AN) and the ventral attention network (VAN), which is composed of the insula, parietal operculum, precentral gyrus and ventral prefrontal cortex, and plays an important role in bottom-up attention orientation and emotion processing. The VAN contained in SAN has a key emotion regulation function and is prone to cause attention bias to emotional stimuli. SMN involves the coordinated work of multiple brain regions, responsible for controlling, regulating and executing activities related to body movement, and is mainly involved in perceiving and processing information from body sensation and movement execution. VN and SMN belong to the primary sensorimotor network, which is mainly involved in processing and processing cognitive information, including the visual cortex, vision-related association cortex and other cognitive function-related areas; (d) After expanding brain network activation to 18 functional brain networks, it was found that different areas within the SAN and SMN also showed different trends under the change of brain state. For example, within the SAN, the activation of SAN A weakened, while the activation of SAN B strengthened. The same changes occurred in the SMN, with the activation of SMN A weakened and the activation of SAN B strengthened. In the VN network, Central Visual and Peripheral Visual showed the same degree of enhancement, which indicates that the abnormality of depression with self-injury in the brain network may also be related to the internal connection of SAN and SMN; (e) Combined with the results of microstate analysis, in DN+ patients, this conversion occurs more frequently when stimulated by physical pain. On the one hand, from the perspective of information processing, the visual network is involved in the activation of functions related to visual formation and visual perception, while the SAN is responsible for processing external stimuli and guiding the allocation of attention. This shows that DN+ patients are more sensitive to physical pain than social pain when facing physical pain stimuli, resulting in enhanced visual perception of the VN. From the perspective of emotion regulation, the key areas in the SAN that play an important role in emotion regulation function, such as the right lower frontal cortex (rlFC) and the right temporal parietal junction (rTPJ), then the reduced activation of the SAN also means a weakening of the ability to regulate emotions. Therefore, in DN+ patients, the regulation of physical pain stimuli is relatively weak compared to social pain stimuli. In DN- patients, this conversion presents the opposite phenomenon to DN+, which shows that in DN- patients, the visual attention to social pain may be stronger. The significant difference between the two groups of patients was mainly reflected in physical pain. When faced with physical pain, this activation conversion occurred more frequently in DN+ than in DN-, which means that DN+ has a different inhibition of emotional response when stimulated by physical pain than DN-. (f) Combining the above analysis, it can be seen that the significant differences between DN- and DN+ in processing different pain emotions are mainly reflected in the brain area functions of VN, SAN and SMN. This finding is similar to previous studies. After further research, it was found that there are differences between the two at multiple levels of brain activity, including visual information processing, emotion perception and emotional information integration. In summary, by observing the activation responses of microstates MS3 and MS5 in the source space, we can obtain the activation changes of brain functional networks and different brain regions when MS3 is converted to MS5. These activation changes are similar to the positions of scalp electrode clusters in event-related potential (ERP) analysis. Through these results, it can be found that when DN+ and DN- are faced with two kinds of pain emotions, the brain activities on SAN, VN and SMN are opposite, and this difference is particularly reflected under physiological pain emotional stimulation.

[0058] In an embodiment of the present invention, an experimental paradigm including two emotional stimuli is constructed, wherein the emotional stimuli include physiological pain stimuli and social pain stimuli to induce specific emotional responses, and first EEG signals of several depressed subjects in two test groups in the experiment of the experimental paradigm are collected, wherein the test groups include a first test group and a second test group, and each first EEG signal is preprocessed to obtain a second EEG signal of each depressed subject in the emotional stimulation stage of the experiment, and the second EEG signal is processed in combination with a microstate analysis strategy and an electroencephalogram (EEG) tracing analysis strategy to obtain differences in brain neural activity of the depressed subjects in the first and second test groups when facing physiological and social pain stimuli, thereby providing high-temporal-resolution data through microstate analysis, capturing transient changes in brain activity, and providing certain spatial information, which is helpful to explore the functional mapping of macro-scale brain networks, so that the brain functional networks of patients with depression can be understood more comprehensively, and specific brain regions can be precisely stimulated through electroencephalogram (EEG) tracing analysis, thereby intervening in brain neural activity to form a closed loop, which has potential application value for the treatment of refractory depression. The analysis method of the present application can also reveal abnormal changes in the microstate of the electroencephalogram of patients with depression and explore its complexity through nonlinear analysis methods, providing a new approach for the early diagnosis and treatment of depression.

[0059] Embodiment 2: Fig. 9 The structure of the apparatus for obtaining the difference in brain neural activity of a depressed patient provided by the second embodiment of the present invention is shown. For the sake of convenience, only the part related to the embodiment of the present invention is shown, including: The experimental paradigm construction unit 91 is used to construct an experimental paradigm including two emotional stimuli, where the emotional stimuli include physiological pain stimuli and social pain stimuli, so as to induce specific emotional responses.

[0060] The EEG signal acquisition unit 92 is used to acquire first EEG signals of a number of depressed patients in two test groups in an experiment of an experimental paradigm, wherein the test groups include a first test group and a second test group.

[0061] The first signal processing unit 93 is used to pre-process each first EEG signal to obtain a second EEG signal of each depressed patient during the emotional stimulation phase of the experiment.

[0062] The second signal processing unit 94 is used to process the second EEG signal in combination with a preset microstate analysis strategy and an EEG source tracing analysis strategy to obtain differences in brain neural activities of the depressed subjects in the first and second test groups when facing physiological pain stimulation and social pain stimulation.

[0063] Preferably, if Fig.10 As shown, the second signal processing unit 94 includes: A microstate analysis unit 941 is used to perform microstate analysis on the second EEG signal using a microstate analysis strategy to obtain a first analysis result; The EEG source tracing analysis unit 942 is used to perform EEG source tracing analysis based on the first analysis result by adopting the EEG source tracing analysis strategy to obtain a second analysis result; The neural difference determination unit 943 is used to determine the difference in brain neural activity of the depressed subjects in the first and second test groups when facing physiological pain stimulation and social pain stimulation based on the first and second analysis results.

[0064] In the embodiment of the present invention, each unit of the device for obtaining the difference in brain neural activity of a depressed patient can be implemented by a corresponding hardware or software unit, and each unit can be an independent software or hardware unit, or can be integrated into a software or hardware unit, which is not intended to limit the present invention. Specifically, the implementation of each unit can refer to the description of the aforementioned embodiment 1, which will not be repeated here.

[0065] Embodiment three: Fig.11 The structure of a computing device provided in the third embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown.

[0066] The computing device 11 of the embodiment of the present invention includes a processor 110, a memory 111, and a computer program 112 stored in the memory 111 and executable on the processor 110. When the processor 110 executes the computer program 112, the steps in the above-mentioned method for obtaining the difference in brain neural activity of a depressed patient are implemented, for example: Figure 1 Alternatively, when the processor 110 executes the computer program 112, the functions of each unit in the above-mentioned device embodiments are implemented, for example Fig. 9 Function of the unit shown.

[0067] In an embodiment of the present invention, an experimental paradigm including two emotional stimuli is constructed, wherein the emotional stimuli include physiological pain stimuli and social pain stimuli to induce specific emotional responses, and first EEG signals of several depressed subjects in two test groups in the experiment of the experimental paradigm are collected, wherein the test groups include a first test group and a second test group, and each first EEG signal is preprocessed to obtain a second EEG signal of each depressed subject in the emotional stimulation stage of the experiment, and the second EEG signal is processed in combination with a microstate analysis strategy and an electroencephalogram (EEG) tracing analysis strategy to obtain differences in brain neural activity of the depressed subjects in the first and second test groups when facing physiological and social pain stimuli, thereby providing high-temporal-resolution data through microstate analysis, capturing transient changes in brain activity, and providing certain spatial information, which is helpful to explore the functional mapping of macro-scale brain networks, so that the brain functional networks of patients with depression can be understood more comprehensively, and specific brain regions can be precisely stimulated through electroencephalogram (EEG) tracing analysis, thereby intervening in brain neural activity to form a closed loop, which has potential application value for the treatment of refractory depression. The analysis method of the present application can also reveal abnormal changes in the microstate of the electroencephalogram of patients with depression and explore its complexity through nonlinear analysis methods, providing a new approach for the early diagnosis and treatment of depression.

[0068] The computing device of the embodiment of the present invention may be a personal computer or a server. The steps implemented when the processor 110 in the computing device 11 executes the computer program 112 to implement the method for obtaining the difference in brain neural activity of patients with depression can refer to the description of the aforementioned method embodiment, which will not be repeated here.

[0069] Embodiment 4: In an embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method for obtaining differences in brain neural activity of depressed patients are implemented. For example, Figure 1 Alternatively, when the computer program is executed by a processor, the functions of each unit in the above-mentioned device embodiments are realized, for example Fig. 9 Function of the unit shown.

[0070] In an embodiment of the present invention, an experimental paradigm including two emotional stimuli is constructed, wherein the emotional stimuli include physiological pain stimuli and social pain stimuli to induce specific emotional responses, and first EEG signals of several depressed subjects in two test groups in the experiment of the experimental paradigm are collected, wherein the test groups include a first test group and a second test group, and each first EEG signal is preprocessed to obtain a second EEG signal of each depressed subject in the emotional stimulation stage of the experiment, and the second EEG signal is processed in combination with a microstate analysis strategy and an electroencephalogram (EEG) tracing analysis strategy to obtain differences in brain neural activity of the depressed subjects in the first and second test groups when facing physiological and social pain stimuli, thereby providing high-temporal-resolution data through microstate analysis, capturing transient changes in brain activity, and providing certain spatial information, which is helpful to explore the functional mapping of macro-scale brain networks, so that the brain functional networks of patients with depression can be understood more comprehensively, and specific brain regions can be precisely stimulated through electroencephalogram (EEG) tracing analysis, thereby intervening in brain neural activity to form a closed loop, which has potential application value for the treatment of refractory depression. The analysis method of the present application can also reveal abnormal changes in the microstate of the electroencephalogram of patients with depression and explore its complexity through nonlinear analysis methods, providing a new approach for the early diagnosis and treatment of depression.

[0071] The computer-readable storage medium of the embodiment of the present invention may include any entity or device or recording medium capable of carrying computer program code, for example, ROM / RAM, magnetic disk, optical disk, flash memory and other memories.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for obtaining differences in brain neural activity in patients with depression, characterized in that: The method comprises the following steps: An experimental paradigm including two emotional stimuli, including physiological pain stimuli and social pain stimuli, is constructed to induce specific emotional responses; Collecting first EEG signals of a number of depressed subjects in two test groups in an experiment of the experimental paradigm, wherein the test groups include a first test group and a second test group; Preprocessing each of the first EEG signals to obtain a second EEG signal of each of the tested depressed patients during the emotional stimulation phase of the experiment; The second EEG signal is processed in combination with a preset microstate analysis strategy and an electroencephalogram (EEG) source tracing analysis strategy to obtain differences in brain neural activity of the depressed subjects in the first and second test groups when facing the physiological pain stimulus and the social pain stimulus.

2. The method according to claim 1, characterized in that The step of processing the second EEG signal in combination with a preset microstate analysis strategy and an electroencephalogram source tracing analysis strategy includes: Performing microstate analysis on the second EEG signal using the microstate analysis strategy to obtain a first analysis result; Based on the first analysis result, the EEG source tracing analysis strategy is used to perform EEG source tracing analysis to obtain a second analysis result; Based on the first and second analysis results, the differences in brain neural activities of the depressed subjects in the first and second test groups when facing the physiological pain stimulation and the social pain stimulation are determined.

3. The method according to claim 2, characterized in that The step of performing microstate analysis on the second EEG signal using the microstate analysis strategy comprises: Performing spatial clustering on the second EEG signal to obtain a global optimal microstate template; Based on the global optimal microstate template, a two-factor mixed effects analysis of variance was used to perform statistical analysis and obtain microstate characteristics with significant results.

4. The method according to claim 3, characterized in that Based on the first analysis result, the step of performing EEG source tracing analysis using the EEG source tracing analysis strategy includes: Using a spatiotemporal source estimation algorithm to obtain the β activation value of the global optimal microstate template in the source space; Performing statistical tests on the beta activation values ​​to determine regions of interest with significant activation; The significantly activated regions of interest are allocated to each functional brain network divided by a preset template, and the proportion of the activated regions of interest in each brain network in all significantly activated regions of interest and the average activation of the regions of interest on each brain network are calculated.

5. The method according to claim 4, characterized in that The step of using a spatiotemporal source estimation algorithm to obtain the beta activation value of the global optimal microstate template in the source space comprises: Performing a generalized linear model regression fit on the global optimal microstate template and each of the second EEG signals to obtain a microstate time series; Using a standardized low-resolution electromagnetic tomography algorithm, the data of each of the second EEG signals at each time point are traced to the source, and the time course of the current density at each time point in the source space is estimated; Extracting the current density time course of the region of interest from the current density time course to obtain a source activity time series; The microstate time series and the source activity time series are subjected to generalized linear model regression fitting to obtain the β activation value.

6. The method according to claim 1, characterized in that The experimental paradigm includes a first resting state of a first duration threshold and a task state of a second duration threshold, wherein the task state includes two first task blocks and second task blocks in a random and balanced order, wherein the first and second task blocks have the same number of trials, and each trial includes four stages: a second resting state, emotional stimulation, emotional evaluation, and a black screen, wherein the emotional stimulation in the first task block is the social pain stimulation, and the emotional stimulation in the second task block is the physiological pain stimulation.

7. A device for acquiring differences in brain neural activity of patients with depression, characterized in that: The device comprises: An experimental paradigm construction unit, used to construct an experimental paradigm including two emotional stimuli, wherein the emotional stimuli include physiological pain stimuli and social pain stimuli, so as to induce specific emotional responses; An EEG signal acquisition unit is used to acquire first EEG signals of a number of depressed subjects in two test groups in an experiment of the experimental paradigm, wherein the test groups include a first test group and a second test group; A first signal processing unit is used to pre-process each of the first EEG signals to obtain a second EEG signal of each of the tested depressed patients during the emotional stimulation phase of the experiment; The second signal processing unit is used to process the second EEG signal in combination with a preset microstate analysis strategy and an EEG source tracing analysis strategy to obtain differences in brain neural activities of the depressed subjects in the first and second test groups when facing physiological pain stimulation and social pain stimulation.

8. The device according to claim 7, characterized in that The second signal processing unit comprises: a microstate analysis unit, configured to perform microstate analysis on the second EEG signal using the microstate analysis strategy to obtain a first analysis result; An EEG source tracing analysis unit, configured to perform EEG source tracing analysis based on the first analysis result and adopt the EEG source tracing analysis strategy to obtain a second analysis result; The neural difference determination unit is used to determine the difference in brain neural activity of the depressed subjects in the first and second test groups when facing the physiological pain stimulation and the social pain stimulation based on the first and second analysis results.

9. A computing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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