Depression patient pleasant sensation loss treatment monitoring system

Abstract

The invention discloses a depression patient pleasant sensation loss treatment monitoring system, and belongs to the field of depression patient pleasant sensation loss treatment monitoring, and the depression patient pleasant sensation loss treatment monitoring system comprises an acquisition module, a test module, a source positioning module, an analysis module and a treatment module; the acquisition module is used for acquiring patient information and an electrophysiological signal of a patient brain; the test module is used for testing the patient by applying the door gambling task to obtain a door gambling task analysis result; the source positioning module is used for positioning an abnormal brain region related to pleasant sensation loss according to the electrophysiological signal and gambling task analysis result, and sorting and marking the abnormal brain region as source positioning data; the analysis module is used for monitoring and analyzing the source positioning data and generating a dynamic brain region map; determining each abnormal brain region, identifying brain region features of each abnormal brain region, and merging the abnormal brain regions to obtain a monitored brain region; integrating the corresponding data into a monitoring result; the treatment module is used for formulating and adjusting a treatment scheme according to the monitoring result and treating the patient based on the treatment scheme.

Description

Technical Field

[0001] The present invention belongs to the field of anhedonia treatment monitoring for depressed patients, and in particular is a anhedonia treatment monitoring system for depressed patients. Background Art

[0002] Anhedonia is one of the most devastating symptoms of depression, manifested as a loss of interest or pleasure in activities that are usually pleasurable. Not only is it a core symptom of depression, it also often responds very poorly to traditional antidepressant treatments, becoming a major problem in treatment. Although anhedonia seriously affects patients' quality of life, existing clinical assessment methods have significant deficiencies in identifying and quantifying this symptom. Especially during individual treatment, dynamic and accurate monitoring of changes in anhedonia is crucial to optimizing treatment strategies. Anhedonia is closely related to dysfunctional brain reward systems. In order to effectively treat depression, there is an urgent need for a method to accurately locate specific brain areas in the brains of depressed patients that are associated with anhedonia and to dynamically monitor them. By accurately locating and real-time monitoring of functional changes in these brain areas, clinicians can better evaluate treatment effects, develop individualized treatment plans, and significantly improve the accuracy and effectiveness of treatment.

[0003] Based on this, in order to solve the above problems, the present invention provides a monitoring system for the treatment of anhedonia in patients with depression. Summary of the invention

[0004] In order to solve the problems existing in the above-mentioned solutions, the present invention provides a monitoring system for the treatment of anhedonia in patients with depression.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A treatment monitoring system for anhedonia in patients with depression, comprising an acquisition module, a test module, a source location module, an analysis module and a treatment module; The acquisition module is used to acquire patient information and electrophysiological signals of the patient's brain.

[0006] Furthermore, the electrophysiological signal is collected in real time by using an EEG array of 128 electrodes to obtain the corresponding electrophysiological signal.

[0007] The test module is used to test the patient using a door gambling task to obtain a door gambling task analysis result; the door gambling task is a decision-making task.

[0008] Furthermore, the test method includes: The subjects faced two doors, each with a hidden reward or loss behind it; the reward was a virtual reward brought by the patient's choice in the corresponding round, which was used to evaluate the patient's brain's response to pleasure; the punishment was a virtual loss caused by the patient's choice in the corresponding round, which was used to detect the neural response to punishment or pain; The patient is required to choose a door, and then the reward result behind the door chosen by the patient will be presented; the test task includes a certain number of trials, and the probability and amount of the reward vary randomly between trials.

[0009] Furthermore, during the patient's door gambling task, the test module records the patient's behavioral data in real time, and the behavioral data includes the door selection, reaction time, and reward result of each trial.

[0010] The source localization module is used to locate abnormal brain areas related to anhedonia based on electrophysiological signals and gated gambling task analysis results, and organize and mark them as source localization data.

[0011] The analysis module is used to monitor and analyze the source localization data, perform data preprocessing on the source localization data, and generate a dynamic brain area map based on the preprocessed source localization data; the dynamic brain area map should show the activity pattern of each key brain area at different time points; determine each abnormal brain area, and identify the brain area characteristics of each abnormal brain area, including spatial position, activity intensity, and functional connection; merge each abnormal brain area to obtain a monitored brain area; and integrate each corresponding data into a monitoring result.

[0012] Further, the method for determining the monitored brain area includes: Monitor and evaluate each abnormal brain region according to the source localization data to obtain corresponding monitoring and evaluation results, including lack of corresponding electrophysiological characteristics and normal monitoring; Adjacent abnormal brain regions that lack corresponding electrophysiological characteristics in monitoring and evaluation results are merged to obtain the monitored brain region.

[0013] Furthermore, the method for monitoring and evaluating each abnormal brain region based on the source localization data includes: A monitoring and evaluation model is established, and the expression of the monitoring and evaluation model is: ; Where: q is the input data, the input data is the corresponding source location data, and the output data is the monitoring evaluation value YG(q); The source localization data is analyzed through the monitoring and evaluation model to obtain the monitoring and evaluation values ​​corresponding to the location of each brain region; The monitoring and evaluation results are determined based on the various monitoring and evaluation values.

[0014] The treatment module is used to formulate and adjust a treatment plan in combination with monitoring results, and treat the patient based on the treatment plan.

[0015] Furthermore, methods for formulating and adjusting treatment plans include: Set up an initial treatment plan based on the monitoring results; treat the patient according to the initial treatment plan; during the treatment process, monitor the patient's brain activity changes and task performance in real time, integrate them into monitoring data, compare the monitoring data with the preset treatment goals, and evaluate the immediate effect of the treatment; After each treatment cycle, the treatment effect of the patient is evaluated to obtain treatment evaluation data; The initial treatment plan is dynamically adjusted based on the treatment evaluation data, including stimulation parameters, treatment cycle, and frequency.

[0016] Furthermore, the method for evaluating the treatment effect on the patient includes: Obtain treatment expectations and determine various treatment indicators based on treatment expectations; obtain patient change data after the treatment cycle; Extracting features of the change data according to each of the treatment indicators to obtain a completion indicator corresponding to each of the treatment indicators; Evaluate each of the treatment indicators according to each of the completion indicators to determine the degree of completion of each of the treatment indicators; According to the formula Calculate the corresponding comprehensive evaluation value; Where: PW is the comprehensive evaluation value; i represents the corresponding treatment index, i=1, 2, ..., n, n is a positive integer; βi is the proportional coefficient of the corresponding treatment index, and the value range is 0<βi<1; WDi is the completion degree of the corresponding treatment index; The comprehensive evaluation value and the completion degree of each treatment indicator are integrated into treatment evaluation data.

[0017] Furthermore, the treatment evaluation data also includes supplementary evaluation data, and the method for determining the supplementary evaluation data includes: Determine each change item, and establish a change evaluation model based on each change item. The expression of the change evaluation model is: ; Where: cj is the input data, j represents the corresponding change term, j=1, 2, ..., m, m is a positive integer; the input data is the change data; the output data is the change evaluation value PB(cj) of the corresponding change term; Identify each treatment indicator and screen each change item according to each treatment indicator; Analyzing the change data through a change evaluation model to obtain a change evaluation value of each change item after screening; Mark each change item whose change evaluation value is not 0 as an indicator supplement item; determine the supplementary result of each indicator supplement item according to the change evaluation value; integrate each indicator supplement item and the corresponding supplementary result into supplementary evaluation data.

[0018] Compared with the prior art, the present invention has the following beneficial effects: Through the mutual cooperation among the acquisition module, test module, source location module, analysis module and treatment module, intelligent monitoring of the treatment of anhedonia in depressive patients can be achieved.

[0019] EEG source localization technology has been used in research, but most of them are offline analysis rather than real-time feedback to the treatment system. Most related patents and research focus on the monitoring of overall brain wave activity and do not involve real-time application of source localization. The present invention uses high-density EEG and source localization algorithms to accurately locate brain area activities related to depressive anhedonia symptoms, providing higher spatial resolution than traditional methods; real-time capture of neural electrical activity related to reward mechanisms, and use of ERP components in gated gambling experiments to quantify and monitor anhedonia, providing direct biomarkers for individualized treatment; by analyzing changes in the patient's EEG activity during treatment, dynamic adjustment of treatment plans can be achieved to improve treatment outcomes.

[0020] Although EEG is used to evaluate the effectiveness of treatment, most existing systems are based on fixed monitoring modes and cannot adjust the treatment plan in real time according to the brain activity of individual patients. This system adjusts the treatment plan according to the changes in brain activity and the ERP signals of the gate gambling task, especially for physical therapies such as rTMS. This personalized feedback mechanism allows the treatment process of each patient to be dynamically adjusted to maximize the efficacy, which is especially suitable for individualized treatment optimization for patients with anhedonia. After understanding the specific areas of abnormal neural activity, neuromodulation technology can be applied more effectively to specifically regulate the abnormally active brain areas, restore normal reward processing functions, and relieve depression symptoms. It can not only improve the treatment effect, but also reduce unnecessary drug use, and has broad market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] like Figure 1 As shown, a treatment monitoring system for anhedonia in patients with depression includes an acquisition module, a test module, a source location module, an analysis module and a treatment module; The acquisition module is used to collect patient information and electrophysiological signals of the patient's brain; Collect patient information: Basic personal information: Collect the patient's name, gender, age, height, weight, occupation and other basic information to ensure adaptive adjustments based on individual differences in personalized treatment.

[0025] Privacy protection mechanism: The system uses encryption technology to ensure the secure storage and transmission of patients' privacy data and strictly abides by data protection regulations.

[0026] Medical history and medical background: The system collects the patient's complete medical history, including previous mental health conditions, duration of anhedonia, course of illness, previous treatment history (such as medication, electrical stimulation therapy, etc.), and previous EEG or brain imaging data (such as MRI, fMRI).

[0027] Family medical history: Record the patient's family history of mental illness and related chronic diseases (such as depression, anxiety, etc.) to understand whether the patient has genetic factors.

[0028] Psychological assessment tools and scales: The psychological assessment collection unit integrates a variety of standardized psychological assessment tools to comprehensively assess the patient's mental health and the severity of anhedonia. These assessment tools can be used regularly during the collection process to dynamically track changes in the patient's mental state.

[0029] Anhedonia assessment: Snaith-Hamilton Pleasurable Experience Scale (SHAPS): used to assess the degree of loss of daily pleasant experiences in patients. This scale can be used to quantitatively assess the severity of anhedonia and serve as a reference point for treatment effectiveness. Anhedonia Symptoms Checklist (ASC): used to further refine the assessment of anhedonia symptoms and distinguish between social anhedonia and physical anhedonia. Depression and anxiety assessment: Hamilton Depression Rating Scale (HAM-D), Beck Depression Inventory (BDI). Generalized Anxiety Scale (GAD-7): assess whether the patient has anxiety symptoms and their severity, and understand the potential impact of anxiety on EEG activity and anhedonia. Other psychological status assessments: Montreal Cognitive Assessment (MoCA): screen cognitive function and assess the possible impact of depression and anhedonia on cognitive abilities, such as memory, attention, and executive function. Quality of life assessment: The World Health Organization Quality of Life Assessment Scale (WHOQOL-BREF) is used to understand the impact of anhedonia on the patient's overall quality of life.

[0030] Electrophysiological signals: 128 electrodes are used for acquisition, ensuring high spatial resolution to capture complex activity patterns of the brain.

[0031] During the acquisition process, filtering and denoising are performed to eliminate interference from electrocardiogram, electromyography and environmental noise, thereby extracting clear EEG signals.

[0032] The acquisition module should also integrate signal preprocessing functions, including time-frequency analysis, artifact detection and correction, to ensure data accuracy.

[0033] The test module is used to test the patient using a gate gambling task; 1. The door gambling task is a classic decision-making task that can effectively activate brain areas related to pleasure and reward, such as the ventral striatum and orbitofrontal cortex. In the task, the subject faces two doors, each of which hides a reward (positive feedback) or a loss (negative feedback). The subject needs to choose a door, and then the system will present the reward result behind the door. The task contains a certain number of trials (such as 100 trials), and the probability and amount of rewards vary randomly between trials.

[0034] Reward and penalty settings: Rewards: In some rounds, the patient's choices will result in virtual rewards (such as monetary rewards or points rewards), and this positive feedback is used to assess the patient's brain's response to pleasure.

[0035] Punishment: In other rounds, the patient’s choices may result in a virtual loss, and this negative feedback is used to detect neural responses to punishment or pain.

[0036] Feedback randomness: The occurrence of rewards and punishments is randomly distributed. This uncertainty is intended to mimic real-life decision-making situations and make patients’ neural responses more natural.

[0037] 2. Neural response and ERP signal capture: The test module quantifies the patient's neural responses to pleasure or aversion by capturing ERP components related to task feedback, specifically the feedback negativity (FRN) and P300 waveform.

[0038] Feedback Negativity (FRN): When patients receive negative feedback (i.e., loss or punishment), FRN usually occurs within 200-300 milliseconds after the feedback appears. It mainly reflects the brain's response when it realizes an error or negative result, and is an important signal for evaluating anhedonia.

[0039] Abnormality of FRN: Depressed patients tend to be slow to respond to negative feedback or lack corresponding neural activity, so the degree of anhedonia in patients can be assessed by capturing FRN.

[0040] P300 wave: P300 wave usually occurs within 300-600 milliseconds after the reward or positive feedback appears. It reflects the brain's attention and positive emotional response to novel events and is an indicator of the neural response to pleasure.

[0041] Abnormality of P300: Patients with anhedonia often lack a strong response to rewards, and the P300 amplitude is reduced or delayed, which is a key signal for the system to evaluate the patient's response to positive feedback.

[0042] 3. Data collection and real-time synchronization: Signal acquisition process: EEG acquisition: During the gate gambling task, the system collects EEG data in real time through a high-density EEG array of 128 electrodes to ensure the capture of ERP signals related to task feedback.

[0043] Trigger synchronization: The system uses a timestamp synchronization mechanism to ensure that the presentation of each feedback matches the acquisition of the EEG signal. Whenever the task provides positive or negative feedback, the trigger signal is immediately marked, which facilitates the precise positioning of the starting point of the ERP waveform in subsequent data analysis.

[0044] Data annotation: After each round of tasks, the system annotates the feedback (reward / penalty) and corresponding EEG response of each task round. These data are used for source localization and input of subsequent analysis modules.

[0045] 4. Anhedonia Assessment: Evaluation Mechanism: ERP analysis: The test module preprocesses the recorded EEG data (such as filtering, artifact removal, etc.), and then performs ERP analysis. ERP reflects the brain's electrophysiological response to specific events or stimuli and is closely related to cognitive processing. The key events in the gate gambling task include the presentation of the gate, the selection of the gate, the presentation of the reward, etc. Analyzing the ERP components related to these events (such as P300, FRN, etc.) can evaluate the patient's cognitive functions such as attention allocation, decision making, and pleasure processing.

[0046] ERP data collection: After completing the clinical data collection, participants were asked to complete the door gambling experiment in a dimly lit, soundproof, and electrically shielded room. The specific experimental process is as follows: Formal experiment: (1) The subjects sat in a quiet shielded room, wore an electrode cap, adjusted the seat height and screen distance, and maintained a comfortable posture.

[0047] (2) Start the gambling task program, and the patient to be tested completes the gambling task according to the instructions: The door task consisted of three blocks of 20 trials (60 trials in total), with two identical door images presented at the beginning of each trial. Participants were instructed to select the left or right door by clicking the left or right button. Participants were told that they could win 1 yuan or lose 0.5 yuan per trial and that they would receive the total prize at the end of the task. The door images were displayed until the participant selected a door. Subsequently, a fixation cross was displayed for 1000 ms, followed by a feedback stimulus for 2000 ms. The feedback stimulus indicated a win with a green arrow pointing up and a loss with a red arrow pointing down. Next, another fixation cross was displayed for 1500 ms, followed by a prompt to "click to proceed to the next round" until the participant clicked a button to start the next trial. Winning and losing trials had equal probability and were presented in a pseudo-random manner.

[0048] (3) During the gambling task, the ERP instrument continuously records the subjects’ EEG activity, marking the time points of stimulus presentation and response.

[0049] (4) After the task is completed, the subjects are asked about their subjective feelings to understand whether they know the probability of winning and to evaluate the difficulty of the task and the attractiveness of the reward.

[0050] Data processing and analysis: (1) Use ERP analysis software to preprocess the EEG data, including filtering, segmentation, baseline correction, superposition and averaging, to obtain the ERP waveform under each condition.

[0051] (2) Extract the amplitude and latency indicators of ERP components, such as FRN and P300, based on the appropriate time window and electrode points.

[0052] (3) Statistical analysis was performed on the behavioral data and ERP data to compare the differences in behavioral performance and EEG activity between the depression group and the control group, and to explore the relationship between depressive symptoms and reward processing.

[0053] The system assesses the intensity and timing of a patient's response to rewards and punishment by comparing the patient's ERP waveform (particularly FRN and P300) with the typical neural responses of a normal group.

[0054] Quantitative evaluation indicators: The system calculates the amplitude, latency and frequency changes of FRN and P300, and quantitatively evaluates the patient's degree of anhedonia by comparing them with the patient's previous data or the control group data.

[0055] Identification of neural abnormalities: When the system detects that the patient's P300 wave is weakened when facing rewards, or the FRN wave is weakened when facing punishment, combined with the activation of the brain area located at the source, it can identify the abnormal areas of neural activity related to anhedonia.

[0056] Monitoring of changes in mental health: Through long-term task testing, the system can dynamically track changes in patients' anhedonia, provide doctors with a basis for follow-up, and determine whether the treatment effect is significant.

[0057] Behavioral data recording: During the door gambling task, the test module records the patient's behavioral data in real time, including the door selection, reaction time, reward results, etc. of each trial. These behavioral data can be used to evaluate the patient's decision-making ability, reward sensitivity and pleasure experience, and provide a reference for subsequent treatment effect evaluation.

[0058] Data quality control: To ensure the quality of test data, the test module has set up a series of quality control measures. For example, the EEG electrode impedance is checked before the task begins to ensure that the impedance of each electrode is within the appropriate range; the quality of the EEG signal is monitored in real time during the task, and if there is a large artifact or signal loss, prompt the user to re-collect the data in a timely manner; the patient's behavioral performance is monitored, such as if the reaction time is too long or too short, prompting the patient to stay focused and cooperate, etc.

[0059] This module supports longitudinal analysis of multiple test results.

[0060] The source localization module is the core component of the present invention, which is mainly used to locate abnormal brain areas related to anhedonia, and provide key neurophysiological basis for subsequent treatment monitoring and effect evaluation. The source localization module is based on high-density EEG data and ERP analysis results of the gated gambling task, and adopts source localization algorithm and brain area merging technology.

[0061] The source localization module uses a standardized template and the low-resolution electromagnetic tomography (LORETA) function of Brain Vision Analyzer 2.2 to perform source tracing analysis on the pre-processed EEG data.

[0062] The steps of source analysis are as follows: 1) Establish regions of interest (ROI): The establishment of ROI depends on the source analysis file provided by Brain Vision Analyzer 2.2. 2) Calculate the current source density of each ROI: Select current density in the LORETA interface and calculate the current density value in each ROI; 3) Output: Select the general data export module on the LORETA node to export the current density value of each ROI at each time point; 4) Import into MATLAB: The source data after the above steps are imported into MATLAB using the EEGLAB toolbox based on MATLAB R2023a for subsequent ERP and functional connectivity analysis.

[0063] The analysis module is the core component of the system. Its main function is to determine the brain areas related to anhedonia and their dynamic changes through long-term monitoring and comparative analysis of source location data, and to help doctors make personalized adjustments to treatment plans. The following is a detailed description of the analysis module: 1. Data preprocessing: Perform quality control on the data output by the source localization module to remove artifacts and noise data.

[0064] Data standardization was performed to ensure comparability between different patients.

[0065] Align data in time and space to allow long-term monitoring and comparative analysis.

[0066] 2. Dynamic brain area map generation: Generate individualized dynamic brain area maps based on the preprocessed source localization data.

[0067] The atlas should include key brain regions associated with anhedonia, such as the ventromedial prefrontal cortex (BA11) and the insula (BA13).

[0068] Dynamic brain area maps should show the activity patterns of each key brain area at different time points (such as different stages of the gambling task).

[0069] Provides intuitive visual reports to help doctors quickly understand changes in patients' brain activity.

[0070] 3. Statistical analysis: The source localization results were statistically analyzed to identify abnormal brain regions associated with anhedonia. Nonparametric statistical methods based on permutation tests, such as the threshold-free clustering enhancement (TFCE) algorithm, were used to compare the source space activity maps of the patient group and the normal control group voxel by voxel to obtain brain regions with significant differences. These brain regions may show increased or decreased activity, reflecting the neural basis of anhedonia in patients with depression.

[0071] Feature extraction: The brain region features related to anhedonia are extracted from the source localization results, such as the spatial location, activity intensity, and functional connectivity of abnormal brain regions. The graph theory analysis method is used to calculate the network properties such as the centrality, clustering coefficient, and efficiency of each brain region to characterize the importance and interconnection pattern of the brain region in the pleasure processing network. In addition, the time domain and frequency domain features of brain region activity, such as amplitude, latency, power spectrum, etc., can be extracted to comprehensively characterize the spatiotemporal dynamics of brain region activity.

[0072] Feature Selection: Machine learning algorithms, such as recursive feature elimination (RFE) or regularization methods (such as Lasso, Ridge), are used to select the most discriminative and stable feature subsets from high-dimensional brain region features. These features show significant differences between the patient group and the control group, and have good repeatability under different data sets and test conditions, and can serve as potential biomarkers of anhedonia.

[0073] Identification of abnormal brain regions: Based on the selected feature subset, machine learning models, such as support vector machines (SVM) or random forests (RF), are trained to automatically identify brain regions associated with anhedonia. Cross-validation and other methods are used to evaluate the generalization performance of the model to ensure that abnormal brain regions can be accurately identified on new patient data. The identification results of abnormal brain regions can be used to guide subsequent treatment monitoring and effect evaluation.

[0074] 4. Merging and classification of monitored brain regions: Based on the activity characteristics of brain regions, adjacent brain regions with similar functions were merged to simplify the analysis.

[0075] Definitive methods for monitoring brain areas include: Monitor and evaluate each abnormal brain region based on the source localization data, that is, evaluate whether the corresponding brain region lacks the electrophysiological characteristics related to pleasure, that is, determine whether it is lacking based on the changes in ERP signals during the gambling task; obtain the corresponding monitoring and evaluation results, which include the lack of corresponding electrophysiological characteristics and normal monitoring; Adjacent abnormal brain regions that lack corresponding electrophysiological characteristics in monitoring and evaluation results are merged to obtain the monitored brain region.

[0076] The above data are organized into monitoring results, which are different from monitoring evaluation results. They mainly include relevant data required in the subsequent treatment module.

[0077] In one embodiment, a method for monitoring and evaluating each abnormal brain region based on source localization data includes: A monitoring and evaluation model was established. The monitoring and evaluation model was used to evaluate each abnormal brain region to determine whether it lacked the corresponding electrophysiological characteristics. If not, it was considered to meet the monitoring requirements. The expression of the monitoring and evaluation model is: ; In the formula: q is the input data, the input data is the corresponding source positioning data, and the output data is the monitoring evaluation value YG(q); The source localization data is analyzed through the monitoring and evaluation model to obtain the monitoring and evaluation values ​​corresponding to the location of each brain region; The monitoring and evaluation results are determined based on the various monitoring and evaluation values.

[0078] The treatment module is an important part of the system. Its main function is to formulate and adjust the treatment plan based on the monitoring results, especially the personalized treatment plan of physical therapy (such as rTMS). The following is a detailed description of the treatment module: Treatment plan formulation: An initial treatment plan is developed for the patient based on the individualized brain area activity patterns and comprehensive assessment results provided by the analysis module.

[0079] Treatment options include drugs, rTMS, etc. rTMS stimulation parameters (such as stimulation location, frequency, intensity and duration, etc.), as well as treatment cycles and frequency 2. Real-time monitoring: During the treatment, the patient's brain activity changes and task performance are monitored in real time.

[0080] Compare monitoring data with preset treatment goals to evaluate the immediate effects of treatment.

[0081] 3. Evaluation of treatment effect: After each treatment cycle, the patient's treatment effect was comprehensively evaluated.

[0082] Evaluation indicators should include changes in brain area activity, improvements in task performance, and the degree of relief of clinical symptoms.

[0083] A multi-dimensional evaluation model is used to comprehensively consider objective electroencephalographic physiological indicators and subjective clinical scores to generate a quantitative treatment effect evaluation report.

[0084] Compare the patient's treatment effect with that of the normal population or other patients to determine the relative effectiveness of the treatment.

[0085] Methods for evaluating the effectiveness of treatment for patients include: Obtain treatment expectations, that is, the treatment goals corresponding to the corresponding treatment plan, which are set by the doctor; determine each treatment indicator based on the treatment expectations; obtain the change data after the patient's treatment cycle, which is the change data related to the treatment before and after, and is mainly collected based on each treatment indicator; extract features from the change data based on each treatment indicator, and obtain the completion indicator corresponding to each treatment indicator, which is the completion status corresponding to the treatment indicator; evaluate the corresponding treatment indicator based on each completion indicator, determine the degree of completion of each treatment indicator, and mark it as completion. If there is no change or no effect, the completion degree is 0; if the completion degree is 90%, the completion degree is 0.9, and so on; According to the formula Calculate the corresponding comprehensive evaluation value; Where: PW is the comprehensive evaluation value; i represents the corresponding treatment index, i=1, 2, ..., n, n is a positive integer; βi is the proportional coefficient of the corresponding treatment index, and the value range is 0<βi<1; WDi is the completion degree of the corresponding treatment index; The comprehensive evaluation value and the completion degree of each treatment indicator are integrated into treatment evaluation data.

[0086] In one embodiment, during the actual treatment process, the change data may have unexpected treatment effects. If no analysis is performed, the treatment evaluation will be incomplete, especially for treatment involving brain regions, which has more uncertainties. Therefore, the treatment evaluation data also includes supplementary evaluation data, and the method for determining the supplementary evaluation data is as follows: According to the treatment records in the current field, various changes in the monitored brain areas that may occur after the patient's treatment are determined, and statistical analysis is performed to determine various change items, that is, indicator items that have changed compared with before treatment, that is, statistical analysis is performed using existing historical data, or professionals can directly set each change item; according to each change item, the historical change data is summarized to form the material data of each change item, and the identification and judgment methods corresponding to the change item are determined according to the material data, and then a change evaluation model is established. The change evaluation model is used to evaluate each change item according to the change data to obtain whether the change item is in the state of no change, malignant change or benign change; the expression of the change evaluation model is ; Wherein: cj is the input data, j represents the corresponding change item, j=1, 2, ..., m, m is a positive integer; used to indicate which change item is evaluated, the input data is the change data; the output data is the change evaluation value PB(cj) of the corresponding change item; Identify each treatment indicator, and screen each change item according to each treatment indicator, that is, eliminate the change item corresponding to the treatment indicator; reduce the amount of data analysis; Analyze the change data through the change assessment model to obtain the change assessment value corresponding to each change item after screening; Each change item whose change evaluation value is not 0 is marked as an indicator supplement item; the supplementary result of each indicator supplement item is determined according to the change evaluation value, that is, a good change or a bad change; each indicator supplement item and the corresponding supplementary result are integrated into supplementary evaluation data.

[0087] 4. Adjustment of treatment plan: The patient's treatment plan is dynamically adjusted based on treatment evaluation data.

[0088] Adjustments may include rTMS stimulation parameters, treatment cycle and frequency, etc.

[0089] Doctors can modify and confirm the automatically adjusted plan based on their own experience and patient feedback.

[0090] The analysis module conducts a comprehensive analysis of the patient's long-term monitoring data to evaluate the changes in anhedonia symptoms and the treatment effect during the treatment process. Using methods such as mixed effect models, a quantitative relationship between brain area activity and clinical symptoms is established to characterize the dynamic trend of brain area activity during treatment. At the same time, combined with the patient's subjective report and behavioral performance, a multi-dimensional evaluation of the treatment effect is conducted, such as the degree of symptom relief and improvement in quality of life.

[0091] Prognosis prediction: Survival analysis or time series prediction algorithms, such as Cox regression or long short-term memory network (LSTM), are used to predict patients' long-term prognosis and treatment response based on their baseline characteristics and early treatment response. High-risk patients with poor prognosis or no response to treatment can be identified to develop more aggressive and personalized treatment plans for them. At the same time, protective factors associated with good prognosis can be discovered to guide clinicians in optimizing treatment strategies.

[0092] In one embodiment, the system can be used as a standalone medical device in psychiatric hospitals, psychological clinics or research institutions. In addition, the system can also be integrated with existing EEG equipment in the form of a software platform to achieve a wider range of applications.

[0093] Application scenarios: When the patient is first diagnosed, baseline high-density EEG data are collected, and the patient is asked to perform a door gambling experiment to activate the brain areas related to anhedonia; EEG data refers to electrophysiological signals; The source of the ERP signal was determined using a source localization method. According to the corresponding preset map, the source of the ERP signal was localized as 88 ROIs; the preset map was an existing brain region map; During the experiment, ERP components related to reward and punishment were extracted and time-frequency analysis was performed to identify electrophysiological features related to anhedonia and to find the most relevant areas; Based on source localization and ERP analysis results, an individualized treatment monitoring report is generated for each patient, providing objective evaluation indicators of treatment effects; During treatment, the above tests and analyses are repeated regularly to monitor the effectiveness of treatment and adjust the treatment plan based on the results.

[0094] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0095] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A monitoring system for the treatment of anhedonia in patients with depression, characterized in that: It includes an acquisition module, a test module, a source location module, an analysis module and a treatment module; The acquisition module is used to collect patient information and electrophysiological signals of the patient's brain; The test module is used to test the patient using a door gambling task to obtain a door gambling task analysis result; the door gambling task is a decision-making task; The source localization module is used to locate abnormal brain areas related to anhedonia based on electrophysiological signals and gated gambling task analysis results, and organize and mark them as source localization data; The analysis module is used to monitor and analyze the source localization data, perform data preprocessing on the source localization data, and generate a dynamic brain region map based on the preprocessed source localization data; the dynamic brain region map should show the activity pattern of each key brain region at different time points; Determine each abnormal brain region, identify the brain region characteristics of each abnormal brain region, including spatial location, activity intensity, and functional connectivity; merge each abnormal brain region to obtain a monitored brain region; integrate each corresponding data into a monitoring result; The treatment module is used to formulate and adjust a treatment plan in combination with monitoring results, and treat the patient based on the treatment plan.

2. A depression patient anhedonia treatment monitoring system according to claim 1, characterized in that: The electrophysiological signals were collected using 128 electrodes.

3. The anhedonia treatment monitoring system for depression patients according to claim 1, characterized in that: The testing methods for applying the gambling task include: The subjects faced two doors, each with a hidden reward or loss behind it; the reward was a virtual reward brought by the patient's choice in the corresponding round, which was used to evaluate the patient's brain's response to pleasure; the punishment was a virtual loss caused by the patient's choice in the corresponding round, which was used to detect the neural response to punishment or pain; The patient is required to choose a door, and then the reward result behind the door chosen by the patient will be presented; the test task includes a certain number of trials, and the probability and amount of the reward vary randomly between trials.

4. A depression patient anhedonia treatment monitoring system according to claim 3, characterized in that: While the patient is performing the door gambling task, the test module records the patient's behavioral data in real time. The behavioral data includes the door selection, reaction time, and reward results of each trial.

5. The anhedonia treatment monitoring system for depression patients according to claim 1, characterized in that: Definitive methods for monitoring brain areas include: Monitor and evaluate each abnormal brain region according to the source localization data to obtain corresponding monitoring and evaluation results, including lack of corresponding electrophysiological characteristics and normal monitoring; Adjacent abnormal brain regions that lack corresponding electrophysiological characteristics in monitoring and evaluation results are merged to obtain the monitored brain region.

6. The anhedonia treatment monitoring system for depression patients according to claim 5, characterized in that: Methods for monitoring and evaluating abnormal brain regions based on source localization data include: A monitoring and evaluation model is established, and the expression of the monitoring and evaluation model is: ; Where: q is the input data, the input data is the corresponding source location data, and the output data is the monitoring evaluation value YG(q); The source localization data is analyzed through the monitoring and evaluation model to obtain the monitoring and evaluation values ​​corresponding to the location of each brain region; The monitoring and evaluation results are determined based on the various monitoring and evaluation values.

7. The anhedonia treatment monitoring system for depression patients according to claim 1, characterized in that: Methods for developing and adjusting treatment plans include: Set up an initial treatment plan based on the monitoring results; treat the patient according to the initial treatment plan; during the treatment process, monitor the patient's brain activity changes and task performance in real time, integrate them into monitoring data, compare the monitoring data with the preset treatment goals, and evaluate the immediate effect of the treatment; After each treatment cycle, the treatment effect of the patient is evaluated to obtain treatment evaluation data; The initial treatment plan is dynamically adjusted based on the treatment evaluation data, including stimulation parameters, treatment cycle, and frequency.

8. The anhedonia treatment monitoring system for depression patients according to claim 7, characterized in that: Methods for evaluating the effectiveness of treatment for patients include: Obtain treatment expectations and determine various treatment indicators based on treatment expectations; obtain patient change data after the treatment cycle; Extracting features of the change data according to each of the treatment indicators to obtain a completion indicator corresponding to each of the treatment indicators; Evaluate each of the treatment indicators according to each of the completion indicators to determine the degree of completion of each of the treatment indicators; According to the formula Calculate the corresponding comprehensive evaluation value; Where: PW is the comprehensive evaluation value; i represents the corresponding treatment index, i=1, 2, ..., n, n is a positive integer; βi is the proportional coefficient of the corresponding treatment index, and the value range is 0<βi<1; WDi is the completion degree of the corresponding treatment index; The comprehensive evaluation value and the completion degree of each treatment indicator are integrated into treatment evaluation data.

9. The anhedonia treatment monitoring system for depression patients according to claim 8, characterized in that: Treatment evaluation data also includes supplementary evaluation data. The methods for determining supplementary evaluation data include: Determine each change item, and establish a change evaluation model based on each change item. The expression of the change evaluation model is: ; Where: cj is the input data, j represents the corresponding change term, j=1, 2, ..., m, m is a positive integer; the input data is the change data; the output data is the change evaluation value PB(cj) of the corresponding change term; Identify each treatment indicator and screen each change item according to each treatment indicator; Analyzing the change data through a change evaluation model to obtain a change evaluation value of each change item after screening; Mark each change item whose change evaluation value is not 0 as an indicator supplement item; determine the supplementary result of each indicator supplement item according to the change evaluation value; integrate each indicator supplement item and the corresponding supplementary result into supplementary evaluation data.

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