Neural regulation system and method for relieving MDD mouse pleasant sensation deficiency based on deep brain stimulation

Abstract

The invention relates to the technical field of medical treatment, and discloses a nerve regulation and control system and method for relieving MDD mouse pleasant sensation deficiency based on deep brain stimulation, according to the nerve regulation and control system for relieving MDD mouse pleasant sensation deficiency based on deep brain stimulation, through close cooperation of five functional modules, the defects of a traditional DBS system in parameter regulation and control and effect evaluation are overcome, and the nerve regulation and control system has the advantages that the nerve regulation and control system is simple in structure and convenient to use. Multi-dimensional physiological and behavior data are comprehensively recorded in real time through the data acquisition module, and the data preprocessing module performs cleaning and standardization processing on the data, so that high consistency of data quality is ensured; the data calculation module generates a plurality of reference coefficients through calculation, so that the effect of the stimulation source is accurately quantified; the data analysis module effectively evaluates the rationality of the stimulation source based on multi-level contrastive analysis; and the feedback module provides real-time feedback for experimenters through a visual terminal, so that stimulation parameters can be adjusted in real time.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and specifically to a neural regulation system and method for relieving anhedonia in MDD mice based on deep brain stimulation. Background Art

[0002] As an advanced neural regulation technology, deep brain stimulation (DBS) has become an important research direction in the fields of neuroscience and neurotherapy. This technology regulates neural activity by applying electrical stimulation to specific regions of the brain and is widely used in the treatment of various neurological diseases, such as Parkinson's disease, obsessive-compulsive disorder, and major depressive disorder (MDD). With the continuous development of neural regulation technology, the potential of DBS in the treatment of MDD has gradually attracted attention, especially in relieving the anhedonia symptoms of MDD patients. Anhedonia is one of the core symptoms of MDD and significantly affects patients' emotional experience and quality of life. The neural regulation system based on DBS, especially its application in MDD mouse models, has become an important exploration in the research of treatment strategies for major depressive disorder.

[0003] Although some preliminary results have been achieved in the application of DBS in the treatment of major depressive disorder, there are still certain deficiencies in the existing treatment methods. The current DBS treatment programs usually lack precise regulation of the stimulation source parameters, resulting in significant differences in the stimulation effects in different individuals or clinical settings, and even possible overstimulation or ineffective situations. Specifically, the parameter settings of the stimulation source (such as stimulation frequency, intensity, and pulse duration, etc.) lack individualized and precise optimization criteria, which may lead to different treatment effects or adverse reactions under different clinical or experimental conditions. In addition, existing research usually focuses on single-dimensional parameter analysis and fails to comprehensively consider real-time data of neural activity, behavioral responses, and physiological changes, so there are certain limitations in determining the rationality of the stimulation source and optimizing the treatment plan.

[0004] Therefore, we propose a neural regulation system for relieving anhedonia in MDD mice based on deep brain stimulation to facilitate the solution of the above-mentioned problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a neural regulation system for relieving anhedonia in MDD mice based on deep brain stimulation to solve the problem that the current DBS treatment programs usually lack precise regulation of the stimulation source parameters, resulting in significant differences in the stimulation effects in different individuals or clinical settings, and even possible overstimulation or ineffective situations as mentioned in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solution: A neuroregulation system for relieving anhedonia in MDD mice based on deep brain stimulation, comprising a data acquisition module, a data preprocessing module, a data calculation module, a data analysis module, and a feedback module;

[0007] The data acquisition module is used to record and collect the basic parameters and real-time parameters of the experiment;

[0008] The data preprocessing module is used to preprocess the collected basic parameters and real-time parameters, and reorganize them into a first data set and a second data set;

[0009] The data calculation module is used to integrally calculate the first data set and the second data set, so as to respectively generate a primary change amount reference coefficient CJC, a deep change amount reference coefficient SRC, and a verification reference coefficient YZC;

[0010] The data analysis module is used to perform data analysis on the calculated primary change amount reference coefficient CJC, deep change amount reference coefficient SRC, and verification reference coefficient YZC;

[0011] The feedback module is used to feedback various data to the visualization terminal.

[0012] Preferably, the data acquisition module includes a first acquisition unit and a second acquisition unit;

[0013] The first acquisition unit is used to record the basic parameters of the experiment, including the basic electroencephalogram frequency band, basic neuron firing rate, basic activity amount, basic reaction frequency, basic heart rate, and basic body temperature;

[0014] The second acquisition unit is used to record the real-time parameters during the experiment, including the real-time electroencephalogram frequency band, real-time neuron firing rate, real-time activity amount, real-time reaction frequency, real-time heart rate, and real-time body temperature.

[0015] Preferably, after the data preprocessing module preprocesses and dimensionlessizes the basic data and real-time data collected by the data acquisition module, it is organized into a first data group and a second data group;

[0016] The first data group includes the basic electroencephalogram frequency band JA, basic neuron firing rate JB, basic activity amount JC, basic reaction frequency JD, basic heart rate JE, and basic body temperature JF;

[0017] The second data group includes the real-time electroencephalogram frequency band, real-time neuron firing rate, real-time activity amount, real-time reaction frequency, real-time heart rate, and real-time body temperature;

[0018] The real-time electroencephalogram frequency band is respectively recorded as SA1, SA2, SA3,..., SAn according to the time stamp;

[0019] The real-time neuron firing rates are respectively recorded as SB1, SB2, SB3, ..., SBn according to the timestamps;

[0020] The real-time activity amounts are respectively recorded as SC1, SC2, SC3, ..., SCn according to the timestamps;

[0021] The real-time reaction frequencies are respectively recorded as SD1, SD2, SD3, ..., SDn according to the timestamps;

[0022] The real-time heart rates are respectively recorded as SE1, SE2, SE3, ..., SEn according to the timestamps;

[0023] The real-time body temperatures are respectively recorded as SF1, SF2, SF3, ..., SFn according to the timestamps.

[0024] Preferably, the data calculation module includes a first calculation unit, a second calculation unit, and a third calculation unit;

[0025] The first calculation unit is used to integrally calculate the parameters in the first data group and the second data group, so as to generate a primary change amount reference coefficient CJC;

[0026] The second calculation unit is used to integrally calculate the parameters in the first data group and the second data group, so as to generate an in-depth change amount reference coefficient SRC;

[0027] The third calculation unit is used to integrally calculate the parameters in the first data array and the second data group, so as to generate a verification reference coefficient YZC.

[0028] Preferably, the data analysis module includes a preliminary analysis unit, an in-depth analysis unit, and a verification unit;

[0029] The preliminary analysis unit is used to analyze the calculated result with a preset first threshold Y, so as to generate a first comparison result. According to the first comparison result, the preliminary influence of the stimulus source on the neural activities, behaviors, and physiological parameters of the mice is found;

[0030] The in-depth analysis unit is used to analyze the calculated result with a preset second threshold R, so as to generate a second comparison result to describe and predict the specific influence of the stimulus source on the behaviors, neural activities, and physiological states of the mice;

[0031] The verification unit is used to analyze the calculated result with a preset third threshold S, so as to generate a third comparison result. According to the third comparison result, it is judged whether the above parameters are effective.

[0032] Preferably, the first calculation unit obtains the primary change amount reference coefficient CJC by integrating and calculating the first data group and the second data group. First, define the differences between the basic parameters and the real-time parameters, and by summing up these difference values, the primary change amount reference coefficient CJC is obtained.

[0033] The difference terms are respectively:

[0034] ΔA, the difference between the real-time electroencephalogram frequency band SAn and the basic electroencephalogram frequency band JA, representing the change in the electroencephalogram frequency;

[0035] ΔB, the difference between the real-time neuron firing rate SBn and the basic neuron firing rate JB, reflecting the change in neuron firing activity;

[0036] ΔC, the difference between the real-time activity amount SCn and the basic activity amount JC, representing the change in the activity level of the mouse;

[0037] ΔD: the difference between the real-time response frequency SDn and the basic response frequency JD, reflecting the change in the response frequency of the mouse;

[0038] ΔE: the difference between the real-time heart rate SEn and the basic heart rate JE, representing the change in the heart function of the mouse;

[0039] ΔF: the difference between the real-time body temperature SFn and the basic body temperature JF, reflecting the change in the body temperature of the mouse;

[0040] The specific formula is as follows:

[0041] CJC = ΔA + ΔB + ΔC + ΔD + ΔE + ΔF;

[0042] ΔA = SAn - JA;

[0043] ΔB = SBn - JB;

[0044] ΔC = SCn - JC;

[0045] ΔD = SDn - JD;

[0046] ΔE = SEn - JE;

[0047] ΔF = SFn - JF;

[0048] In the formula: JA is the basic electroencephalogram frequency band, JB is the basic neuron firing rate, JC is the basic activity amount, JD is the basic response frequency, JE is the basic heart rate, and JF is the basic body temperature;

[0049] SAn is the real-time electroencephalogram frequency band, SBn is the real-time neuron firing rate, SCn is the real-time activity amount, SDn is the real-time response frequency, SEn is the real-time heart rate, and SFn is the real-time body temperature;

[0050] The specific analysis method of the preliminary analysis unit is as follows:

[0051] When CJC < Y, it means that the current stimulation effect is not obvious and has no use value, and the stimulation source needs to be amplified;

[0052] When CJC = Y, it means that the current stimulation effect is normal and has use value, and the stimulation solution does not need to be adjusted;

[0053] When CJC > Y, it means that the current stimulation effect is excessive and has no use value, and the stimulation solution needs to be down-regulated.

[0054] Preferably, the second calculation unit obtains the in-depth change amount reference coefficient SRC by calculating the first array and the second data group;

[0055] JA - SAn: Represents the difference between the basic electroencephalogram frequency band JA and the real-time electroencephalogram frequency band SAn, reflecting the influence of the stimulation on the electroencephalogram frequency band;

[0056] JB - SBn: Represents the difference between the basic neuron firing rate JB and the real-time neuron firing rate SBn, reflecting the influence of the stimulation on the neuron firing activity;

[0057] JC - SCn: Represents the difference between the basic activity amount JC and the real-time activity amount SCn, reflecting the influence of the stimulation on the activity level of the mouse;

[0058] JD - SDn: Represents the difference between the basic response frequency JD and the real-time response frequency SDn, reflecting the influence of the stimulation on the response frequency of the mouse;

[0059] JE - SEn: Represents the difference between the basic heart rate JE and the real-time heart rate SEn, reflecting the influence of the stimulation on the heart function of the mouse;

[0060] JF - SFn: Represents the difference between the basic body temperature JF and the real-time body temperature SFn, reflecting the influence of the stimulation on the body temperature of the mouse;

[0061] In the calculation of the in-depth change amount reference coefficient SRC, the formula combines the above difference terms according to a specific mathematical relationship to comprehensively quantify the influence of the stimulation source in each physiological dimension;

[0062] The specific calculation formula is as follows:

[0063]

[0064] In the formula: is the basic electroencephalogram frequency band, JB is the basic neuron firing rate, JC is the basic activity amount, JD is the basic response frequency, JE is the basic heart rate, and JF is the basic body temperature;

[0065] SAn is the real-time electroencephalogram frequency band, SBn is the real-time neuron discharge rate, SCn is the real-time activity level, SDn is the real-time response frequency, SEn is the real-time heart rate, and SFn is the real-time body temperature;

[0066] The specific analysis method of the in-depth analysis unit is as follows:

[0067] When SRC < R, it means that the current stimulation effect is not obvious and has no use value, and the stimulus source needs to be amplified;

[0068] When SRC = R, it means that the current stimulation effect is normal and has use value, and the stimulating solution does not need to be adjusted;

[0069] When SRC > R, it means that the current stimulation effect is excessive and has no use value, and the stimulating solution needs to be down-regulated.

[0070] Preferably, the third calculation unit obtains the verification reference coefficient YZC by integrating and calculating the first data group and the second data array;

[0071] The calculation of the first part combines the difference between the basic electroencephalogram frequency band JA and the real-time electroencephalogram frequency band SAn, and the difference between the basic neuron discharge rate JB and the real-time neuron discharge rate SBn. The comprehensive influence of the stimulus source on electroencephalogram activity and neuron discharge is reflected through their product. This part quantifies the change through squaring, reflecting the influence intensity under the interaction of the two;

[0072] The calculation of the second part combines the difference between the activity level SCn and the response frequency SDn, reflecting the influence of the stimulus source on the behavioral activities of mice. Through the exponential decay model, the mutual influence between the activity level and the response frequency is considered, and the response frequency difference ΔD is added to the denominator to further balance the influence of the stimulus source on the activity state;

[0073] The calculation of the third part combines the difference between the heart rate SEn and the basic heart rate JE, and the difference between the body temperature SFn and the basic body temperature JF, reflecting the in-depth influence of the stimulus source on the physiological state of mice. By squaring the ratio of the difference values, the effect of the changes in heart rate and body temperature on the overall physiological state is further quantified;

[0074] The specific calculation method is as follows;

[0075]

[0076] ΔA = SAn - JA;

[0077] ΔB = SBn - JB;

[0078] ΔC = SCn - JC;

[0079] ΔD = SDn - JD;

[0080] ΔE = SEn - JE;

[0081] ΔF = SFn - JF;

[0082] Where: JA is the basic electroencephalogram frequency band, JB is the basic neuron firing rate, JC is the basic activity level, JD is the basic response frequency, JE is the basic heart rate, and JF is the basic body temperature;

[0083] SAn is the real-time electroencephalogram frequency band, SBn is the real-time neuron firing rate, SCn is the real-time activity level, SDn is the real-time response frequency, SEn is the real-time heart rate, and SFn is the real-time body temperature;

[0084] The specific analysis method of the verification unit is as follows:

[0085] When YZC < S, it means that the data characteristics after the current stimulation are not obvious and have no use value, and the stimulus source needs to be amplified and re-extracted;

[0086] When YZC = S, it means that the data characteristic index after the current stimulation is obvious and has use value, and the existing stimulus source is maintained for data collection;

[0087] When YZC > S, it means that the data characteristics after the current stimulation are too high and have no use value, and the stimulus source needs to be reduced and re-extracted.

[0088] This application also includes a neuromodulation method for relieving anhedonia in MDD mice based on deep brain stimulation, and the specific steps are as follows:

[0089] S1. Record and collect the basic parameters and real-time parameters of the experiment through the data acquisition module;

[0090] S2. Preprocess the collected basic parameters and real-time parameters through the data preprocessing module, and reorganize them into a first data set and a second data set;

[0091] S3. Integrate and calculate the first data set and the second data set through the data calculation module, so as to generate a primary change amount reference coefficient CJC, an in-depth change amount reference coefficient SRC, and a verification reference coefficient YZC respectively;

[0092] S4. Analyze the data of the primary change amount reference coefficient CJC, the in-depth change amount reference coefficient SRC, and the verification reference coefficient YZC obtained through calculation through the data analysis module;

[0093] S5. Feed back each item of data to the visualization terminal through the feedback module.

[0094] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0095] 1. The neural regulation system based on deep brain stimulation to relieve anhedonia in MDD mice solves the deficiencies in parameter regulation and effect evaluation of traditional DBS systems through the close cooperation of five functional modules. The data acquisition module 1 comprehensively records multi-dimensional physiological and behavioral data in real time. The data preprocessing module 2 cleans and standardizes the data, ensuring high consistency in data quality; the data calculation module 3 generates multiple reference coefficients through calculation, achieving precise quantification of the effects of the stimulation source; the data analysis module 4 effectively evaluates the rationality of the stimulation source based on multi-level comparative analysis; the feedback module 5 provides real-time feedback to experimenters through a visualization terminal, facilitating immediate adjustment of stimulation parameters. Overall, the system can optimize the parameters of the stimulation source based on real-time data and precise algorithm analysis. Compared with traditional single data analysis methods, it can significantly improve the scientific nature of stimulation source adjustment and treatment effects, providing a more precise and personalized neural regulation scheme for treating anhedonia symptoms in MDD mice.

[0096] 2. The three subunits of the data analysis module 4 not only improve the evaluation accuracy of the system for the impact of the stimulation source through progressive comparative analysis but also ensure that the effects of the stimulation source can be effectively monitored and optimized at each experimental stage. Compared with the prior art, the method of phased and layer-by-layer analysis makes the system more comprehensive and scientific in evaluating the effects of the stimulation source, greatly improving the accuracy, stability, and controllability of application of the system. This design improvement helps to more precisely adjust the stimulation parameters when treating anhedonia symptoms in MDD mice, thereby achieving personalized and precise neural regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is a system flowchart of the present invention.

[0098] Figure 2 It is a method step diagram of the present invention.

[0099] In the figure: 1. Data acquisition module; 11. First acquisition unit; 12. Second acquisition unit; 2. Data preprocessing module; 3. Data calculation module; 31. First calculation unit; 32. Second calculation unit; 33. Third calculation unit; 4. Data analysis module; 41. Preliminary analysis unit; 42. In-depth analysis unit; 43. Verification unit; 5. Feedback module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0100] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0101] Embodiment 1: Please refer to Figure 1 , a neuromodulation system for relieving anhedonia in MDD mice based on deep brain stimulation, including a data acquisition module 1, a data preprocessing module 2, a data calculation module 3, a data analysis module 4, and a feedback module 5;

[0102] The data acquisition module 1 is used to record and collect the basic parameters and real-time parameters of the experiment;

[0103] The data preprocessing module 2 is used to preprocess the collected basic parameters and real-time parameters and reorganize them into a first data set and a second data set;

[0104] The data calculation module 3 is used to integrally calculate the first data set and the second data set, thereby respectively generating a primary change reference coefficient CJC, an in-depth change reference coefficient SRC, and a verification reference coefficient YZC;

[0105] The data analysis module 4 is used to perform data analysis on the calculated primary change reference coefficient CJC, in-depth change reference coefficient SRC, and verification reference coefficient YZC;

[0106] The feedback module 5 is used to feedback various data to the visualization terminal.

[0107] In this embodiment: The data acquisition module 1 is a basic component of this system and is responsible for real-time recording and collecting all relevant parameters during the experiment. Specifically, the data acquisition module 1 collects parameters such as the basic electroencephalogram frequency band, neuron firing rate, activity level, response frequency, heart rate, and body temperature through high-precision sensors and monitoring devices. These data reflect the neural activities, behavioral responses, and physiological states of the mice during the experiment and provide key original data for subsequent data processing and analysis. The function of this module ensures the comprehensiveness and accuracy of data acquisition and provides high-quality data support for the task execution of subsequent modules.

[0108] The data preprocessing module 2 is responsible for cleaning, denoising, and normalizing the collected basic parameters and real-time parameters, and organizing the processed data into the first dataset and the second dataset. The preprocessing process includes removing invalid data, filling in missing values, unifying data formats, and dimensionless processing to ensure the consistency and comparability of the data. The role of this module is to provide a reliable data foundation for subsequent calculations and analyses by optimizing data quality, avoiding interference from noise or inconsistent data on the results, and thus improving the analysis accuracy of the overall system.

[0109] The data calculation module 3 generates the primary change reference coefficient CJC, the in-depth change reference coefficient SRC, and the verification reference coefficient YZC by integrating and calculating the parameters in the first dataset and the second dataset. These reference coefficients provide important bases for quantitative analysis and are used to measure the changes in the neural activities, behavioral responses, and physiological states of mice caused by the stimulus source respectively. The primary change reference coefficient CJC can reflect the basic impact of the stimulus source on mice in the initial stage of the experiment; the in-depth change reference coefficient SRC provides more detailed impact data of the stimulus source on the performance of mice; the verification reference coefficient YZC is used to verify the effectiveness of the stimulus source effect. Through these calculation results, this module can provide accurate quantitative bases for subsequent adjustments of the stimulus source to ensure that each adjustment is based on scientific calculations and actual data.

[0110] The data analysis module 4 is responsible for deeply analyzing the primary change reference coefficient CJC, the in-depth change reference coefficient SRC, and the verification reference coefficient YZC generated by the calculation. This module generates multiple levels of comparison results by comparing with preset thresholds to comprehensively evaluate the effect of the stimulus source. The preliminary analysis evaluates the preliminary effect of the stimulus source by comparing CJC with the first threshold; the in-depth analysis further analyzes the specific impacts of the stimulus source on the behaviors and physiological states of mice by comparing SRC with the second threshold; the verification analysis determines whether the stimulus source achieves the ideal effect by comparing YZC with the third threshold. Through this hierarchical data analysis method, this module can ensure the scientificity and effectiveness of the stimulus source adjustment and provide a basis for system optimization.

[0111] The feedback module 5 is used to real-time feedback the data analysis results to the experimental personnel through a visualization terminal. Through graphical displays, including forms such as charts and curves, the experimental personnel can intuitively observe the changes in the neural activities, behavioral responses, and physiological states of mice under different stimulus conditions. This module enables the experimental personnel to quickly obtain feedback during the real-time experiment process and timely adjust the parameters of the stimulus source to optimize the treatment effect. The visual real-time feedback mechanism greatly improves the flexibility and accuracy of the experiment, helps the experimental personnel make rapid responses, and thus enhances the experimental efficiency and the effect of data application.

[0112] This neuroregulation system based on deep brain stimulation to relieve anhedonia in MDD mice solves the deficiencies of traditional DBS systems in parameter regulation and effect evaluation through the close cooperation of five functional modules. The data acquisition module 1 comprehensively records multi-dimensional physiological and behavioral data in real time. The data preprocessing module 2 cleans and standardizes the data, ensuring high consistency in data quality; the data calculation module 3 generates multiple reference coefficients through calculation, achieving precise quantification of the effects of the stimulation source; the data analysis module 4 effectively evaluates the rationality of the stimulation source based on multi-level comparative analysis; the feedback module 5 provides real-time feedback to experimenters through a visualization terminal, facilitating immediate adjustment of stimulation parameters. Overall, this system can optimize the parameters of the stimulation source based on real-time data and precise algorithm analysis. Compared with traditional single data analysis methods, it can significantly improve the scientific nature of stimulation source adjustment and treatment effects, providing a more precise and personalized neuroregulation plan for treating anhedonia symptoms in MDD mice.

[0113] Example 2: Please refer to Figure 1 , the data acquisition module 1 includes a first acquisition unit 11 and a second acquisition unit 12;

[0114] The first acquisition unit 11 is used to record the basic parameters of the experiment, including the basic electroencephalogram frequency band, basic neuron firing rate, basic activity level, basic response frequency, basic heart rate, and basic body temperature;

[0115] The second acquisition unit 12 is used to record the real-time parameters during the experiment, including the real-time electroencephalogram frequency band, real-time neuron firing rate, real-time activity level, real-time response frequency, real-time heart rate, and real-time body temperature.

[0116] In this embodiment: By setting the first acquisition unit 11 and the second acquisition unit 12, the data acquisition module 1 realizes the comprehensive acquisition and real-time monitoring of multi-dimensional data during the experiment. The key improvement in this design is that the first acquisition unit 11 records the basic parameters of the experiment, providing the basic state data of the mouse under the condition of not receiving any stimulation, while the second acquisition unit 12 records the dynamic change parameters of the mouse during the experiment in real time. Specifically, it includes the electroencephalogram frequency band, neuron firing rate, activity level, response frequency, heart rate, and body temperature, etc. These parameters reflect the neural activities, behavioral performances, and physiological states of the mouse in real time during the experiment.

[0117] The data acquisition module 1 can provide more complete and timely raw data for the experiment, making subsequent data processing and analysis more accurate. Compared with traditional methods, the basic data provided by the first acquisition unit 11 can be used as a control to help analyze the influence and changes of the stimulus source on the behavior, neural activities, and physiological parameters of mice, while the real-time data provided by the second acquisition unit 12 can reflect the changing trends of mice under the action of the stimulus source in real time. This real-time and comprehensive acquisition method not only enhances the integrity and timeliness of the experimental data but also provides an accurate data basis for subsequent adjustment and optimization of the stimulus source, improving the flexibility of the system and the controllability of the treatment effect. Therefore, the design of the data acquisition module 1 significantly improves the accuracy and reliability of the experimental data and provides important support for optimizing the neuromodulation scheme.

[0118] Example 3: Please refer to Figure 1 , after the data preprocessing module 2 preprocesses and dimensionlessizes the basic data and real-time data collected by the data acquisition module 1, they are sorted into a first data group and a second data group;

[0119] The first data group includes the basic electroencephalogram frequency band JA, the basic neuron firing rate JB, the basic activity level JC, the basic response frequency JD, the basic heart rate JE, and the basic body temperature JF;

[0120] The second data group includes the real-time electroencephalogram frequency band, the real-time neuron firing rate, the real-time activity level, the real-time response frequency, the real-time heart rate, and the real-time body temperature;

[0121] The real-time electroencephalogram frequency band is respectively recorded as SA1, SA2, SA3,..., SAn according to the time stamp;

[0122] The real-time neuron firing rate is respectively recorded as SB1, SB2, SB3,..., SBn according to the time stamp;

[0123] The real-time activity level is respectively recorded as SC1, SC2, SC3,..., SCn according to the time stamp;

[0124] The real-time response frequency is respectively recorded as SD1, SD2, SD3,..., SDn according to the time stamp;

[0125] The real-time heart rate is respectively recorded as SE1, SE2, SE3,..., SEn according to the time stamp;

[0126] The real-time body temperature is respectively recorded as SF1, SF2, SF3,..., SFn according to the time stamp.

[0127] In this embodiment: The data preprocessing module 2 performs dimensionless processing on the basic data and real-time data obtained by the data acquisition module 1, and organizes them into a first data group and a second data group, which reflects significant improvements in the data processing of this system. The main advantage of dimensionless processing is that it can eliminate the differences in dimensions among various experimental data, enabling data in different dimensions to be compared and analyzed under the same standard, thereby improving the accuracy and consistency of data analysis.

[0128] The first data group includes the basic electroencephalogram frequency band JA, the basic neuron firing rate JB, the basic activity JC, the basic response frequency JD, the basic heart rate JE, and the basic body temperature JF. These basic data provide a control benchmark for the standard state of the mouse before the experiment, facilitating subsequent comparison with the real-time data during the experiment. The second data group includes the real-time electroencephalogram frequency band, the real-time neuron firing rate, the real-time activity, the real-time response frequency, the real-time heart rate, and the real-time body temperature. Each real-time parameter is marked separately according to the time stamp, such as the real-time electroencephalogram frequency band, the real-time neuron firing rate, etc. This approach ensures the timeliness and traceability of real-time data by accurately recording data changes at different time points.

[0129] The real-time data marked by the time stamp, such as SA1, SB1, etc., can provide refined dynamic monitoring of the mouse's behavior and neural activities, enabling the experimental data to accurately reflect the real-time impact of the stimulus source on various parameters of the mouse. Compared with traditional static data acquisition, this method of recording and dimensionless processing in time series not only improves the consistency and comparability of data, but also makes data analysis more accurate, enabling real-time tracking of the change trend of the stimulus effect, thereby providing a more reliable basis for subsequent system adjustment and optimization. Through these improvements, this module enhances the efficiency and accuracy of the system in real-time data processing and analysis, and greatly enhances the monitoring and response capabilities of the system during the neural regulation process of the mouse.

[0130] Embodiment 4: Please refer to Figure 1 , the data calculation module 3 includes a first calculation unit 31, a second calculation unit 32, and a third calculation unit 33;

[0131] The first calculation unit 31 is used to perform integrated calculation on the parameters in the first data group and the second data group to generate a primary change amount reference coefficient CJC;

[0132] The second calculation unit 32 is used to perform integrated calculation on the parameters in the first data group and the second data group to generate an in-depth change amount reference coefficient SRC;

[0133] The third calculation unit 33 is used to perform integrated calculation on the parameters in the first data array and the second data group to generate a verification reference coefficient YZC.

[0134] In this embodiment: Through the close cooperation of the first calculation unit 31, the second calculation unit 32, and the third calculation unit 33, the data calculation module 3 realizes the comprehensive calculation of the parameters in the first data group and the second data group, and generates the primary change amount reference coefficient CJC, the in-depth change amount reference coefficient SRC, and the verification reference coefficient YZC. This design reflects a significant improvement in the system's data analysis and quantification.

[0135] First, the first calculation unit 31 generates the primary change amount reference coefficient CJC by integrating and calculating the parameters of the first data group and the second data group. As a preliminary quantification index, CJC can reflect the preliminary impact of the stimulus source on the neural activity, behavioral responses, and physiological states of mice. Through this calculation, the system can evaluate the preliminary effect of the stimulus source in real time, providing data support for subsequent in-depth analysis.

[0136] Secondly, the second calculation unit 32 generates the in-depth change amount reference coefficient SRC by further integrating and calculating these two data groups. SRC provides a detailed quantification of the impact of the stimulus source on the behavioral and physiological parameters of mice over a longer time span or under more complex conditions. The addition of this calculation unit enables the system to more comprehensively evaluate the effect of the stimulus source, especially its performance under different experimental conditions and parameter changes.

[0137] Finally, the third calculation unit 33 is used to generate the verification reference coefficient YZC to help the system verify whether the effect of the current calculation and adjustment meets the expectations. By comparing with a preset threshold, YZC can verify whether the effect of the stimulus source has achieved the ideal treatment goal, thus ensuring that the system can achieve the best treatment effect in clinical applications.

[0138] The division of labor and cooperation of the three calculation units enables the system to not only quickly evaluate the preliminary effect of the stimulus source, but also deeply analyze the long-term impact of the stimulus source on mice, and finally ensure the effectiveness of the treatment effect through verification calculations. This multi-level and multi-dimensional calculation method provides more detailed quantification and analysis capabilities compared to traditional technologies, can dynamically adjust the parameters of the stimulus source, and maximize the treatment effect. Through this precise calculation mechanism, the system can optimize the stimulus source under different experimental conditions, ensuring the greatest improvement in the effect of neuromodulation therapy, especially in the process of treating the anhedonia symptoms of MDD mice, and can achieve personalized and precise regulation.

[0139] Embodiment Five: Please refer to Figure 1 , the data analysis module 4 includes a preliminary analysis unit 41, an in-depth analysis unit 42, and a verification unit 43;

[0140] The preliminary analysis unit 41 is used to analyze the calculation results obtained in comparison with a preset first threshold Y, so as to generate a first comparison result. According to the first comparison result, the preliminary effects of the stimulus source on the neural activities, behaviors, and physiological parameters of the mice are discovered.

[0141] The in-depth analysis unit 42 is used to analyze the calculation results obtained in comparison with a preset second threshold R, so as to generate a second comparison result to describe and predict the specific effects of the stimulus source on the behaviors, neural activities, and physiological states of the mice.

[0142] The verification unit 43 is used to analyze the calculation results obtained in comparison with a preset third threshold S, so as to generate a third comparison result. According to the third comparison result, it is determined whether the above parameters are valid.

[0143] In this embodiment: The data analysis module 4 conducts multi-level and systematic analysis on the calculation results through the preliminary analysis unit 41, the in-depth analysis unit 42, and the verification unit 43, reflecting significant improvements in the system's precise evaluation of the effects of the stimulus source.

[0144] First, the preliminary analysis unit 41 generates a first comparison result by comparing the reference coefficient obtained through calculation with the preset first threshold Y. This process can reveal the preliminary effects of the stimulus source on the neural activities, behaviors, and physiological parameters of the mice. The preliminary analysis provides a preliminary assessment of the stimulus effects, helping experimenters quickly identify whether the stimulus source has produced the expected effects. Compared with traditional analysis methods, the first comparison result can more intuitively evaluate the preliminary effectiveness of the stimulus source, laying a foundation for subsequent analysis and optimization.

[0145] Second, the in-depth analysis unit 42 generates a second comparison result by comparing the calculation results with the preset second threshold R. This analysis further deepens the understanding of the effects of the stimulus source, and can describe and predict the specific effects of the stimulus source on the behaviors, neural activities, and physiological states of the mice at different time points and experimental conditions. The introduction of in-depth analysis not only enhances the system's multi-dimensional and detailed understanding of the effects of the stimulus source, but also provides a scientific basis for subsequent adjustments, ensuring the reliability and stability of the system under long-term or complex experimental conditions.

[0146] Finally, the verification unit 43 analyzes the calculation results with the third threshold S to generate a third comparison result, and judges the effectiveness of the stimulus source based on this result. This verification process ensures that the system can strictly verify the effects of the stimulus source, ensuring that the experimental parameters and stimulus adjustments meet the expected goals. The introduction of this module effectively improves the credibility of the system, ensuring that in practical applications, only truly effective stimulus sources will be adopted, thus avoiding ineffective or highly side-effect treatment plans.

[0147] Overall, through progressive comparative analysis, the three sub-units of the data analysis module 4 not only improve the evaluation accuracy of the system for the impact of the stimulus source, but also ensure that the effects of the stimulus source can be effectively monitored and optimized at each experimental stage. Compared with the prior art, the method of phased and layer-by-layer analysis makes the system more comprehensive and scientific in evaluating the effects of the stimulus source, greatly improving the accuracy, stability of the system and the controllability of applications. This design improvement helps to more precisely adjust the stimulation parameters when treating the anhedonia symptoms of MDD mice, thereby achieving personalized and precise neuromodulation.

[0148] Example Six: Please refer to Figure 1 , and the first calculation unit 31 obtains it by calculating according to the following formula;

[0149] CJC = ΔA + ΔB + ΔC + ΔD + ΔE + ΔF;

[0150] ΔA = SAn - JA;

[0151] ΔB = SBn - JB;

[0152] ΔC = SCn - JC;

[0153] ΔD = SDn - JD;

[0154] ΔE = SEn - JE;

[0155] ΔF = SFn - JF;

[0156] In the formula: JA is the basic electroencephalogram frequency band, JB is the basic neuron discharge rate, JC is the basic activity amount, JD is the basic response frequency, JE is the basic heart rate, and JF is the basic body temperature;

[0157] SAn is the real-time electroencephalogram frequency band, SBn is the real-time neuron discharge rate, SCn is the real-time activity amount, SDn is the real-time response frequency, SEn is the real-time heart rate, and SFn is the real-time body temperature;

[0158] The specific analysis method of the preliminary analysis unit 41 is as follows:

[0159] When CJC < Y, it means that the current stimulation effect is not obvious and has no use value, and the stimulus source needs to be amplified;

[0160] When CJC = Y, it means that the current stimulation effect is normal and has use value, and the stimulating solution does not need to be adjusted;

[0161] When CJC > Y, it means that the current stimulation effect is excessive and has no use value, and the stimulating solution needs to be down-regulated.

[0162] In this embodiment: The first calculation unit 31 generates a primary change amount reference coefficient CJC by precisely calculating the difference between real-time data and basic data. This calculation method enables the system to achieve a more accurate effect evaluation during the adjustment of the stimulus source. The CJC value is obtained by weighted summing the differences between real-time parameters and their basic data. The core advantage of this calculation method is that:

[0163] By performing differential calculations on six key physiological and behavioral parameters such as electroencephalogram, neuron firing rate, and activity level, CJC can comprehensively reflect the comprehensive impact of the stimulus source on the neural activity, behavioral responses, and physiological states of mice. This multi-dimensional evaluation method is more accurate and comprehensive than traditional techniques that only analyze based on a single parameter, and can better capture the multiple effects of the stimulus source.

[0164] The calculated CJC value can reflect the real-time effect of the stimulus source during the experiment. When CJC is less than the preset first threshold Y, it indicates that the current stimulation effect is insufficient, and the system will automatically identify and suggest increasing the intensity of the stimulus source; when CJC is equal to Y, it means that the current stimulation effect reaches the normal level, and the system does not need to adjust the stimulus source; when CJC is greater than Y, the system will prompt overstimulation and suggest reducing the stimulation intensity. This real-time feedback mechanism enables the system to automatically adjust the stimulus source according to experimental data, avoiding the deviation of manual intervention, ensuring the dynamic optimization of the stimulus source, and significantly improving the controllability and accuracy of the treatment effect.

[0165] The preliminary analysis unit 41 automatically determines whether the stimulus source needs to be amplified, adjusted, or reduced by comparing the CJC value with the preset threshold Y. This automated analysis not only saves the time of manual adjustment but also avoids the uncertainty of human operation, thereby improving the efficiency and accuracy of the experiment. Compared with traditional manual adjustment methods, this system can dynamically optimize the stimulation plan according to real-time data, ensuring flexibility and personalization during the treatment process.

[0166] In summary, the first calculation unit 31 realizes the precise quantification and real-time feedback of the effect of the stimulus source through the integration and differential calculation of multi-dimensional data. This design not only greatly improves the system's monitoring ability of the experimental process but also ensures the real-time optimization and adjustment of the treatment effect. Compared with traditional single and static methods, it can better meet the needs of treating diseases such as severe depression and achieve personalized and precise neuromodulation.

[0167] Embodiment Seven: Please refer to Figure 1 , the second calculation unit 32 calculates and obtains an in-depth change amount reference coefficient SRC through the following formula;

[0168]

[0169] Where: is the basic electroencephalogram frequency band, JB is the basic neuron firing rate, JC is the basic activity level, JD is the basic response frequency, JE is the basic heart rate, and JF is the basic body temperature;

[0170] SAn is the real-time electroencephalogram frequency band, SBn is the real-time neuron firing rate, SCn is the real-time activity level, SDn is the real-time response frequency, SEn is the real-time heart rate, and SFn is the real-time body temperature;

[0171] The specific analysis method of the in-depth analysis unit 42 is as follows:

[0172] When SRC < R, it means that the current stimulation effect is not obvious and has no use value, and the stimulation source needs to be amplified;

[0173] When SRC = R, it means that the current stimulation effect is normal and has use value, and the stimulating solution does not need to be adjusted;

[0174] When CJC > R, it means that the current stimulation effect is excessive and has no use value, and the stimulating solution needs to be reduced.

[0175] In this embodiment: The second calculation unit 32 calculates the in-depth change amount reference coefficient SRC to perform a more refined analysis and evaluation of the stimulation source effect. The calculation formula of SRC can more accurately reflect the influence of the stimulation source on various physiological indicators of the organism by comprehensively considering the differences between basic data and real-time data and combining the interactions between multiple physiological parameters. Specifically, SRC is jointly determined by the differences and proportional relationships of physiological parameters such as electroencephalogram frequency band, neuron firing rate, activity level, response frequency, heart rate, and body temperature. The innovation points and advantages of this method are mainly reflected in the following aspects:

[0176] Compared with the traditional single-parameter evaluation method, the SRC formula can capture the interaction relationships between various indicators by comprehensively analyzing the differences and ratios of multiple physiological parameters. For example, the changes in the electroencephalogram frequency band and neuron firing rate, the proportional relationship between activity level and response frequency, and the dynamic changes in heart rate and body temperature act together to form an all-round evaluation of the stimulation source effect. This method significantly improves the sensitivity and accuracy of stimulation effect analysis, can identify more potential physiological change patterns, and provides more accurate data support for stimulation source optimization.

[0177] The calculation result of the SRC value is compared with the preset threshold R to help the system judge the state of the stimulation effect. When the SRC value is lower than R, it indicates that the current stimulation effect is not obvious, and the system will automatically suggest increasing the intensity of the stimulation source; when the SRC value is equal to R, it means that the current stimulation effect is normal and no adjustment is required; when the SRC value is higher than R, it indicates that the stimulation effect is excessive, and the system will prompt to reduce the intensity of the stimulation source. This mechanism can achieve precise regulation of the stimulation source, avoid excessive or insufficient stimulation, and improve the safety and effectiveness during the experiment or treatment process.

[0178] The second calculation unit 32 and the first calculation unit 31 jointly construct a multi-level and mutually verified evaluation system through two reference coefficients, CJC and SRC. The first calculation unit 31 provides a preliminary screening of the stimulation effect, while the second calculation unit 32 conducts a more refined analysis of the results through the in-depth change amount reference coefficient SRC. Through the collaborative effect of these two calculation units, the system can provide more stable and reliable results, ensure more accurate dynamic adjustment of the stimulation source, and improve the repeatability and stability of the experiment.

[0179] The design of the SRC formula takes into account the differences in multiple physiological indicators and individual differences, enabling the system to flexibly adapt to the physiological baseline states of different subjects or patients. For example, individual differences in parameters such as basal heart rate, body temperature, and neural activity are all taken into consideration to ensure that the system can make precise adjustments based on the real-time data of different individuals. This self-adaptive adjustment mechanism enables the stimulation source to dynamically adjust according to the specific needs of each individual, thereby realizing personalized treatment plans, meeting the requirements of precision medicine.

[0180] By introducing the second calculation unit 32 and the in-depth change amount reference coefficient SRC, the system can conduct a more refined analysis of the effect of the stimulation source. This improvement makes the regulation of the stimulation source more precise and intelligent, and also enhances the personalized adjustment ability of the system. Compared with traditional single-parameter analysis methods, SRC realizes a comprehensive evaluation of the effect of the stimulation source through in-depth analysis of the interaction of multiple parameters, ensuring the safety, effectiveness, and stability during the experiment and treatment process, thus providing more efficient technical support for applications in the field of neuromodulation.

[0181] Example Eight: Please refer to Figure 1 , the third calculation unit 33 calculates and obtains the verification reference coefficient YZC through the following formula;

[0182]

[0183] ΔA = SAn - JA;

[0184] ΔB = SBn - JB;

[0185] ΔC = SCn - JC;

[0186] ΔD = SDn - JD;

[0187] ΔE = SEn - JE;

[0188] ΔF = SFn - JF;

[0189] Where: JA is the basic electroencephalogram frequency band, JB is the basic neuron discharge rate, JC is the basic activity level, JD is the basic response frequency, JE is the basic heart rate, and JF is the basic body temperature;

[0190] SAn is the real-time electroencephalogram frequency band, SBn is the real-time neuron discharge rate, SCn is the real-time activity level, SDn is the real-time response frequency, SEn is the real-time heart rate, and SFn is the real-time body temperature;

[0191] The specific analysis method of the verification unit 43 is as follows:

[0192] When YZC < S, it means that the data characteristics after the current stimulus are not obvious and have no use value, and the stimulus source needs to be amplified and re-extracted;

[0193] When YZC = S, it means that the data characteristic index after the current stimulus is obvious and has use value, and the existing stimulus source is maintained for data acquisition;

[0194] When YZC > S, it means that the data characteristics after the current stimulus are too high and have no use value, and the stimulus source needs to be reduced and re-extracted.

[0195] In this embodiment: The third calculation unit 33 further verifies and adjusts the effect of the stimulus source by calculating the verification reference coefficient YZC. The calculation of the YZC value combines the interaction between multiple physiological parameters, adopts a non-linear relationship and exponential correction to ensure accurate feedback on the stimulus effect. The calculation formula of this verification reference coefficient forms a more accurate and dynamic evaluation method through the differentiation, weighting of multiple parameters, and the introduction of exponential functions. The innovation points and advantages of this method are reflected in the following aspects:

[0196] The YZC formula introduces a complex non-linear model combining polynomials and exponential functions. For example, by multiplying the weighted parameter differences such as ΔA and ΔB and the exponential decay such as the ratio of ΔC and ΔD, it can more precisely describe the influence degree of the stimulus source on multiple physiological indicators. This non-linear analysis not only improves the sensitivity to small changes in the stimulus source but also more accurately reflects the complex relationship between the stimulus source and the physiological state, exceeding the limitations of traditional linear analysis methods.

[0197] By comparing YZC with the set threshold S, the verification unit 43 can accurately judge the effect of the stimulus source. When the YZC value is less than S, it indicates that the data characteristics after stimulation are not obvious and the stimulation effect is insufficient. The system will prompt to amplify the stimulus source and re-extract the data; when YZC is equal to S, it means that the effect of the stimulus source meets the expectation, the current data characteristics are obvious and valuable, and the system maintains the existing stimulus source for data acquisition; when YZC is greater than S, it means that the stimulation effect is excessive, and the system will prompt to reduce the stimulus source and re-collect the data. This mechanism can achieve precise adjustment of the stimulus source, avoid the situation of unclear or excessive stimulation effect, ensure that the efficiency of the stimulus source can be maximized in each data acquisition, and thus enhance the application value of the system.

[0198] The YZC formula forms a comprehensive multi-parameter feedback mechanism by combining the mutual influences among multiple physiological parameters. This enables the system to not only evaluate the impact of the stimulus source on a single parameter in real time, but also evaluate the comprehensive impact of the stimulus source on multiple physiological states. This intelligent optimization mechanism effectively improves the system's dynamic adjustment ability for the stimulus source, ensuring that each experimental or treatment stage can be optimized in a timely manner according to real-time data feedback, thereby enhancing the overall treatment effect and the reliability of data acquisition.

[0199] The calculation method of YZC is automatically adjusted according to the mutual differences and relationships among multiple physiological parameters, and the verification unit 43 automatically judges whether the stimulus source needs to be adjusted by comparing YZC with the threshold S. This automatic analysis mechanism not only reduces the possibility of manual intervention, but also improves the intelligence level of the system, enabling it to automatically adjust the intensity and nature of the stimulus source according to the actual needs of different subjects or patients, and further realizing personalized treatment plans.

[0200] The YZC verification reference coefficient introduced by the third calculation unit 33 provides a more accurate evaluation and feedback on the effect of the stimulus source through a complex non-linear calculation model. By comparing with the threshold S, the system can intelligently adjust the intensity of the stimulus source to ensure that the stimulation effect is in an ideal state and avoid excessive or insufficient stimulation. This mechanism not only improves the accuracy of stimulus source adjustment, but also enhances the system's automatic and personalized adjustment capabilities. Working in cooperation with the first two calculation units, YZC further improves the reliability, stability and effectiveness of the entire system during the regulation of the physiological stimulus source, meeting the requirements of modern neurostimulation technology for refined and personalized treatment.

[0201] This application also includes a neurostimulation method for relieving anhedonia in MDD mice based on deep brain stimulation, and the specific steps are as follows:

[0202] S1. Record and collect the basic parameters and real-time parameters of the experiment through the data acquisition module 1;

[0203] S2. Preprocess the collected basic parameters and real-time parameters through the data preprocessing module 2, and reorganize them into a first data set and a second data set;

[0204] S3. Integrate and calculate the first data set and the second data set through the data calculation module 3 to respectively generate a primary change amount reference coefficient CJC, a deep change amount reference coefficient SRC, and a verification reference coefficient YZC;

[0205] S4. Perform data analysis on the calculated primary change amount reference coefficient CJC, deep change amount reference coefficient SRC, and verification reference coefficient YZC through the data analysis module 4;

[0206] S5. Feed back each item of data to the visualization terminal through the feedback module 5.

[0207] The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0208] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A neuromodulation system for relieving anhedonia in MDD mice based on deep brain stimulation, characterized in that: It includes a data acquisition module (1), a data preprocessing module (2), a data calculation module (3), a data analysis module (4), and a feedback module (5); The data acquisition module (1) is used to record and collect the basic parameters and real-time parameters of the experiment; The data preprocessing module (2) is used to preprocess the collected basic parameters and real-time parameters and reorganize them into a first data set and a second data set; The data calculation module (3) is used to perform integrated calculations on the first data set and the second data set to respectively generate a primary change reference coefficient CJC, a deep change reference coefficient SRC, and a verification reference coefficient YZC; The data analysis module (4) is used to perform data analysis on the calculated primary change reference coefficient CJC, deep change reference coefficient SRC, and verification reference coefficient YZC; The feedback module (5) is used to feedback various data to the visualization terminal.

2. The neuroregulation system for relieving anhedonia in MDD mice based on deep brain stimulation according to claim 1, characterized in that: The data acquisition module (1) includes a first acquisition unit (11) and a second acquisition unit (12); The first acquisition unit (11) is used to record the basic parameters of the experiment, including the basic electroencephalogram frequency band, basic neuron firing rate, basic activity level, basic reaction frequency, basic heart rate, and basic body temperature; The second acquisition unit (12) is used to record the real-time parameters during the experiment, including the real-time electroencephalogram frequency band, real-time neuron firing rate, real-time activity level, real-time reaction frequency, real-time heart rate, and real-time body temperature.

3. A neuromodulation system for relieving anhedonia in MDD mice based on deep brain stimulation according to claim 2, characterized in that: After the data preprocessing module (2) preprocesses and dimensionlessizes the basic data and real-time data collected by the data acquisition module (1), it is organized into a first data group and a second data group; The first data group includes the basic electroencephalogram frequency band JA, basic neuron firing rate JB, basic activity level JC, basic reaction frequency JD, basic heart rate JE, and basic body temperature JF; The second data group includes the real-time electroencephalogram frequency band, real-time neuron firing rate, real-time activity level, real-time reaction frequency, real-time heart rate, and real-time body temperature; The real-time electroencephalogram frequency band is respectively recorded as SA1, SA2, SA3,..., SAn according to the time stamp; The real-time neuron firing rate is respectively recorded as SB1, SB2, SB3,..., SBn according to the time stamp; The real-time activity level is respectively recorded as SC1, SC2, SC3,..., SCn according to the time stamp; The real-time reaction frequency is respectively recorded as SD1, SD2, SD3,..., SDn according to the time stamp; The real-time heart rate is respectively recorded as SE1, SE2, SE3,..., SEn according to the time stamp; The real-time body temperature is respectively recorded as SF1, SF2, SF3,..., SFn according to the time stamp.

4. A neuromodulation system for relieving anhedonia in MDD mice based on deep brain stimulation according to claim 3, characterized in that: The data calculation module (3) includes a first calculation unit (31), a second calculation unit (32), and a third calculation unit (33); The first calculation unit (31) is used to perform integrated calculations on the parameters in the first data group and the second data group to generate a primary change reference coefficient CJC; The second calculation unit (32) is configured to perform an integrated calculation on the parameters within the first data group and the second data group, so as to generate a deep change amount reference coefficient SRC; The third calculation unit (33) is configured to perform an integrated calculation on the parameters within the first data array and the second data group, so as to generate a verification reference coefficient YZC.

5. A neuromodulation system for relieving anhedonia in MDD mice based on deep brain stimulation according to claim 4, characterized in that: The data analysis module (4) includes a preliminary analysis unit (41), a deep analysis unit (42), and a verification unit (43); The preliminary analysis unit (41) is configured to analyze the calculated value obtained by calculation with a preset first threshold Y, so as to generate a first comparison result, and according to the first comparison result, discover the preliminary influence of the stimulus source on the neural activities, behaviors, and physiological parameters of the mouse; The deep analysis unit (42) is configured to analyze the calculated value obtained by calculation with a preset second threshold R, so as to generate a second comparison result to describe and predict the specific influence of the stimulus source on the behaviors, neural activities, and physiological states of the mouse; The verification unit (43) is configured to analyze the calculated value obtained by calculation with a preset third threshold S, so as to generate a third comparison result, and according to the third comparison result, judge whether the above parameters are valid.

6. The neuroregulation system for relieving anhedonia in MDD mice based on deep brain stimulation according to claim 5, wherein: The first calculation unit (31) obtains a primary change amount reference coefficient CJC by performing an integrated calculation on the first data group and the second data group. First, the difference between the basic parameter and the real-time parameter is defined, and by summing up these difference values, the difference terms of the primary change amount reference coefficient CJC are respectively: ΔA, the difference between the real-time electroencephalogram frequency band SAn and the basic electroencephalogram frequency band JA, representing the change in the electroencephalogram frequency; ΔB, the difference between the real-time neuron firing rate SBn and the basic neuron firing rate JB, reflecting the change in the neuron firing activity; ΔC, the difference between the real-time activity amount SCn and the basic activity amount JC, representing the change in the activity level of the mouse; ΔD: the difference between the real-time response frequency SDn and the basic response frequency JD, reflecting the change in the response frequency of the mouse; ΔE: the difference between the real-time heart rate SEn and the basic heart rate JE, representing the change in the heart function of the mouse; ΔF: the difference between the real-time body temperature SFn and the basic body temperature JF, reflecting the change in the body temperature of the mouse; The specific analysis method of the preliminary analysis unit (41) is as follows: When CJC < Y, it represents that the current stimulation effect is not obvious and has no use value, and the stimulus source needs to be amplified; When CJC = Y, it represents that the current stimulation effect is normal and has use value, and the stimulating solution does not need to be adjusted; When CJC > Y, it represents that the current stimulation effect is excessive and has no use value, and the stimulating solution needs to be down-regulated.

7. A neuromodulation system for relieving anhedonia in MDD mice based on deep brain stimulation according to claim 6, wherein: The second calculation unit (32) obtains a deep change amount reference coefficient SRC by performing a calculation on the first data array and the second data group; JA - SAn: represents the difference between the basic electroencephalogram frequency band JA and the real-time electroencephalogram frequency band SAn, reflecting the influence of the stimulus on the electroencephalogram frequency band; JB - SBn: represents the difference between the basic neuron firing rate JB and the real-time neuron firing rate SBn, reflecting the influence of the stimulus on the neuron firing activity; JC-SCn: represents the difference between the basal activity level JC and the real-time activity level SCn, reflecting the impact of the stimulus on the activity level of the mouse; JD-SDn: represents the difference between the basal response frequency JD and the real-time response frequency SDn, reflecting the impact of the stimulus on the response frequency of the mouse; JE-SEn: represents the difference between the basal heart rate JE and the real-time heart rate SEn, reflecting the impact of the stimulus on the cardiac function of the mouse; JF-SFn: represents the difference between the basal body temperature JF and the real-time body temperature SFn, reflecting the impact of the stimulus on the body temperature of the mouse; In the calculation of the in-depth change amount reference coefficient SRC, the formula combines the above difference terms according to specific mathematical relationships to comprehensively quantify the impact of the stimulus source on various physiological dimensions; The specific analysis method of the in-depth analysis unit (42) is as follows: When SRC < R, it represents that the current stimulus effect is not obvious and has no use value, and the stimulus source needs to be amplified; When SRC = R, it represents that the current stimulus effect is normal and has use value, and the stimulus solution does not need to be adjusted; When SRC > R, it represents that the current stimulus effect is excessive and has no use value, and the stimulus solution needs to be down-regulated.

8. A neuromodulation system for relieving anhedonia in MDD mice based on deep brain stimulation according to claim 7, characterized in that: The third calculation unit (33) obtains the verification reference coefficient YZC through integrated calculation of the first data group and the second data array; The calculation of the first part combines the difference between the basal electroencephalogram frequency band JA and the real-time electroencephalogram frequency band SAn, and the difference between the basal neuron firing rate JB and the real-time neuron firing rate SBn. The comprehensive impact of the stimulus source on electroencephalogram activity and neuron firing is reflected through their product. This part quantifies the change through squaring, reflecting the impact intensity under the interaction of the two; The calculation of the second part combines the difference between the activity level SCn and the response frequency SDn, reflecting the impact of the stimulus source on the behavioral activities of the mouse. Through the exponential decay model, the mutual influence between the activity level and the response frequency is considered, and the response frequency difference ΔD is added to the denominator to further balance the impact of the stimulus source on the activity state; The calculation of the third part combines the difference between the heart rate SEn and the basal heart rate JE, and the difference between the body temperature SFn and the basal body temperature JF, reflecting the in-depth impact of the stimulus source on the physiological state of the mouse. By squaring the ratio of the difference values, the role of the changes in heart rate and body temperature on the overall physiological state is further quantified; The specific analysis method of the verification unit (43) is as follows: When YZC < S, it represents that the data characteristics after the current stimulus are not obvious and have no use value, and the stimulus source needs to be amplified and re-extracted; When YZC = S, it represents that the data characteristic index after the current stimulus is obvious and has use value, and the existing stimulus source is maintained for data acquisition; When YZC > S, it represents that the data characteristics after the current stimulus are too high and have no use value, and the stimulus source needs to be reduced and re-extracted.

9. A neuromodulation method for relieving anhedonia in MDD mice based on deep brain stimulation, characterized in that: The neuroregulation method for relieving anhedonia in MDD mice based on deep brain stimulation is used to execute a neuroregulation system for relieving anhedonia in MDD mice based on deep brain stimulation described in any one of the above claims 1 to 8, and the specific steps are as follows: S1. Record and collect the basic parameters and real-time parameters of the experiment through the data acquisition module (1); S2. Preprocess the collected basic parameters and real-time parameters through the data preprocessing module (2), and reorganize them into a first data set and a second data set; S3. Integrate and calculate the first data set and the second data set through the data calculation module (3) to respectively generate a primary change amount reference coefficient CJC, a deep change amount reference coefficient SRC, and a verification reference coefficient YZC; S4. Perform data analysis on the calculated primary change amount reference coefficient CJC, deep change amount reference coefficient SRC, and verification reference coefficient YZC through the data analysis module (4); S5. Feed back each item of data to the visualization terminal through the feedback module (5).

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