Controlled treatment methods for mood and anxiety disorders in TMS therapy

Abstract

The present invention provides a method for controlling mood and anxiety disorders in TMS therapy based on emotion induction, including the following steps: conducting a clinical assessment of the patient's mental health before treatment; performing a brain magnetic resonance imaging (MRI) examination of the patient under emotion induction; prioritizing selected individualized targets based on the patient's clinical assessment and the degree of abnormal brain function activity under emotion induction; formulating a TMS treatment plan under emotion induction based on the target priority ranking; conducting a short-term treatment evaluation and utilizing an efficacy prediction model to perform a long-term prediction of the remaining treatment trajectory; and after completion of treatment, feeding the treatment trajectory back to a local database and conducting a post-treatment clinical evaluation. The present invention also relates to a corresponding device, processor, and storage medium. Using the method, device, processor, and storage medium of the present invention, the control differences of patients under different scenarios can be effectively displayed using a scenario stimulus library and scenario brain function imaging.

Description

Technical Field

[0001] The present invention relates to the field of smart medical technology, in particular to the field of transcranial magnetic stimulation, and specifically to a control and processing method, device, processor and computer-readable storage medium thereof for mood disorders and anxiety disorders based on emotion induction in TMS treatment. Background Art

[0002] Transcranial magnetic stimulation (TMS) is a common neuromodulatory method for treating mood disorders and anxiety disorders, but its efficacy is average. Mood disorders and anxiety disorders are emotion-dependent mood disorders, and conventional TMS treatment modes are all performed in a resting state, ignoring the patient's neural responses under different emotions. The way to improve the efficacy of TMS is to treat the patient in a state of emotional induction through external stimulation. Currently, there is a lack of an objective, accurate and self-feedback system for individualized TMS treatment of patients with mood disorders and anxiety disorders under emotion-induced conditions.

[0003] Currently, the existing personalized TMS treatment method uses magnetic resonance imaging to extract the patient's resting brain structure and functional characteristics for target calculation. The most common target calculation method uses functional connectivity and structural connectivity using the subgenual anterior cingulate gyrus as a seed point to identify coupling targets located in the dorsolateral prefrontal cortex of the left hemisphere and then uses high-frequency transcranial magnetic stimulation for treatment. The main problem with this method is that it cannot effectively distinguish between patients with MDD (major depressive disorder) and healthy controls in the resting state. The core symptom of MDD is mood disturbance. This clinical characteristic suggests that identifying personalized treatment targets and performing TMS treatment in the context of specific emotions (such as sadness, happiness) or transitions between emotions can achieve better results.

[0004] Based on this, it is necessary to propose an improvement plan to overcome the defects and shortcomings of the existing technology. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, device, processor and computer-readable storage medium thereof for TMS treatment of mood disorders and anxiety disorders based on emotion induction.

[0006] To achieve the above objectives, the present invention provides a method, apparatus, processor, and computer-readable storage medium for TMS treatment of mood disorders and anxiety disorders based on emotion induction as follows:

[0007] The method for controlling mood disorders and anxiety disorders based on emotion induction in TMS therapy is characterized in that the method comprises the following steps:

[0008] (1) Before treatment, conduct a clinical assessment of the patient's mental state using basic emotional element materials and complex emotional stimulation element materials;

[0009] (2) Conduct brain MRI examinations on patients in an emotionally induced state;

[0010] (3) Prioritize the selected individualized targets based on the patient's clinical assessment and the degree of abnormal brain functional activity under emotion-induced stimulation;

[0011] (4) Develop a TMS treatment plan for emotion-induced situations based on the order of target priority;

[0012] (5) Conduct short-term treatment evaluation and use efficacy prediction models to make long-term predictions of the remaining treatment trajectory;

[0013] (6) After the treatment is completed, the treatment trajectory will be fed back to the local database and a post-treatment clinical evaluation will be conducted.

[0014] Preferably, the individualized targets in step (3) include: the frontal lobe under negative emotions, the frontal lobe under positive emotions, the parietal lobe, and the temporoparietal junction, and are prioritized according to the following method:

[0015] (3.1) Perform head motion detection and removal, noise signal removal, time series high-pass filtering, image spatial registration, and linear regression analysis on the acquired emotion-induced brain functional images to obtain brain activation maps under various emotional situations;

[0016] (3.2) Using standard brain mapping, extract activation map data of the subjects' emotion processing-related brain regions, including at least the subgenual anterior cingulate gyrus, medial prefrontal cortex, precuneus, dorsolateral prefrontal cortex, temporoparietal junction, and supplementary motor area;

[0017] (3.3) Following the above process, obtain activation map data for the above brain regions from a healthy reference group, establish a normative model for the average activation level of each brain region in each context, and fit its probability distribution parameters, including but not limited to center location and standard deviation;

[0018] (3.4) For each patient's average activation level in each brain region in each situation, compare it with the distribution of the healthy reference group obtained in step (3.3), and calculate the deviation Z as follows:

[0019] Z=[x-mu] / std;

[0020] Where x is the activation value of the patient's brain region, mu is the mean activation value of the healthy control brain region, and std is the standard deviation of the activation value of the healthy control brain region;

[0021] (3.5) Summarize the activation level deviations Z of all brain regions of the individual patient in all situations, and sort the absolute values ​​of each deviation level Z from large to small. Record the sorting and the corresponding brain regions and activation situations to complete the priority sorting process.

[0022] Preferably, the step (3) further comprises calculating and processing individualized brain function regulation targets in the following manner:

[0023] a. Based on the patient's functional magnetic resonance imaging (fMRI) scan results, obtain contextual time series data, functional connectivity matrix data, and contextual activation map data of the target brain region;

[0024] b. Performing spatial linear transformation on the context time series data and the functional connectivity matrix data of the target brain region to obtain a context time series template and a functional connectivity matrix template of the reference group, and subjecting these two templates and the context activation map data of the target brain region to a comprehensive spatial linear transformation to obtain a context activation map aligned with the reference group;

[0025] c. Refer to the group norm model to obtain the activation deviation of the target brain area in various situations, sort the target brain areas from large to small according to the deviation, and record the situations corresponding to the corresponding deviations;

[0026] d. Select one to three target brain regions and determine whether the selected target is more than 3 cm from the scalp or is unsuitable for direct TMS stimulation. If so, calculate the functional connectivity between the target and the cortical area, and select the stimulatable location with the largest positive functional connectivity value as an alternative target. Otherwise, directly record the target location, the direction of deviation from the reference group, and the contextual attributes where the largest deviation value occurs.

[0027] Preferably, the step (3.3) establishes the norm model in the following manner:

[0028] (3.3.1) Extracting the mask of the target brain region from the standard atlas;

[0029] (3.3.2) On this mask, dilate the edges by 5 voxels in all directions;

[0030] (3.3.3) Applied to each patient's fMRI data to obtain individual data of the target brain region;

[0031] (3.3.4) Performing a procruste transform on the time series of the target brain region to obtain the average spatiotemporal signal;

[0032] (3.3.5) Calculate the correlation coefficient of the time series between each voxel in the target brain region and other voxels outside the target brain region, and use this to obtain the correlation matrix;

[0033] (3.3.6) Use linear regression models to calculate brain activation maps associated with each emotional situation;

[0034] (3.3.7) Perform a procruste transformation on the correlation matrix to obtain the average correlation matrix;

[0035] (3.3.8) Obtain the spatiotemporal signal template and the functional connectivity matrix template of the target brain region, and calculate the individual transformation matrix for the time series, the individual transformation matrix for the functional connectivity, and the corresponding weighted projection transformation;

[0036] (3.3.9) Performing activation map projection processing based on the obtained brain activation map to obtain a normative model of the activation level of the target brain region.

[0037] Preferably, the step (4) specifically includes the following steps:

[0038] (4.1) Select at least three stimulus materials with the same situational attribute labels for the current patient from the emotional situational material library to induce emotions;

[0039] (4.2) Select the stimulation target associated with the current stimulation material and choose the corresponding iTBS stimulation mode, 10 Hz rTMS stimulation mode, or 1 Hz rTMS mode for TMS modulation treatment;

[0040] (4.3) After the stimulation task is completed, the participants are tested for distraction by pressing a button to determine the correctness of a mathematical equation. After the distraction task is completed, the participants are tested for the next stimulus and the above steps are repeated.

[0041] (4.4) Based on the number of selected targets, arrange brain regulation treatment for all stimulation targets on the same day and perform them at intervals so that the emotional stimulation received before each TMS regulation has the same nature, so as to provide a precise TMS treatment plan.

[0042] Preferably, the step (5) specifically includes the following steps:

[0043] (5.1) When 1 / 4 and 1 / 2 of the treatment is completed, the mid-term HAMD score or PHQ9 self-assessment score will be performed respectively;

[0044] (5.2) Using the treatment baseline as the origin and the 1 / 4 and 1 / 2 mid-term assessments as the midpoints of the pathway, the expected range of efficacy was plotted based on the improvement rate of the mid-term efficacy estimated by the linear mixed model, and the expected efficacy was projected onto the expected efficacy according to the 95% CI range;

[0045] (5.3) If the predicted range of the final therapeutic effect based on the HAMD score reduction rate at the treatment start point, 1 / 4, and 1 / 2 projections contains a 50% score reduction rate of less than 5%, the treatment benefit is judged to be poor, and the top-ranked target is removed from the treatment plan and replaced with the highest-ranked target among the alternative targets;

[0046] (5.4) After the treatment is completed, the treatment trajectory is archived and the parameter estimates of the linear mixed model for estimating the mid-term efficacy are optimized.

[0047] The device for implementing control treatment of mood disorders and anxiety disorders based on emotion induction in TMS treatment has the following main features:

[0048] a processor configured to execute computer-executable instructions;

[0049] A memory storing one or more computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the steps of the control treatment method for mood disorders and anxiety disorders based on emotion induction in the above-mentioned TMS treatment are implemented.

[0050] The main feature of the processor for implementing control processing for mood disorders and anxiety disorders based on emotion induction in TMS treatment is that the processor is configured to execute computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above-mentioned control processing method for mood disorders and anxiety disorders based on emotion induction in TMS treatment are implemented.

[0051] The main feature of this computer-readable storage medium is that it stores a computer program, which can be executed by a processor to implement the steps of the control treatment method for mood disorders and anxiety disorders based on emotion induction in the above-mentioned TMS treatment.

[0052] The method, device, processor and computer-readable storage medium of the present invention for TMS treatment of mood disorders and anxiety disorders based on emotion induction can effectively show, through the situational stimulation library and situational brain function imaging, that significant differences can be intuitively seen between the activities of different brain regions of patients and the healthy control group in different situations, and the direction of the difference is related to the situation. It also effectively illustrates the effectiveness of situational stimulation materials and the effectiveness of situational brain function imaging in the selection of individualized target regulation targets, and has relatively significant application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Flowchart of the method for implementing TMS treatment for mood disorders and anxiety disorders based on emotion induction of the present invention.

[0054] Figure 2 This is a flow chart of the present invention for performing individualized brain function regulation target processing.

[0055] Figure 3 A flow chart of the norm-referenced model of brain function activation in emotional situations constructed for the present invention.

[0056] Figure 4 Flowchart of the present invention for performing individualized TMS treatment in emotional context.

[0057] Figure 5 This is a diagram showing the effect of individualized treatment based on short-term evaluation of treatment response and optimization of treatment response trajectory in the present invention.

[0058] Figure 6 Schematic diagram of the comparison effect of the present invention in a negative emotional situation and a positive emotional situation. DETAILED DESCRIPTION

[0059] In order to more clearly describe the technical content of the present invention, further description is given below in conjunction with specific embodiments.

[0060] Before describing in detail embodiments according to the present invention, it should be noted that, hereinafter, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, whereby a process, method, article, or apparatus comprising a list of elements includes not only those elements, but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0061] See also Figure 1 As shown, the TMS treatment method for controlling mood disorders and anxiety disorders based on emotion induction includes the following steps:

[0062] (1) Before treatment, conduct a clinical assessment of the patient's mental state using basic emotional element materials and complex emotional stimulation element materials;

[0063] (2) Conduct brain MRI examinations on patients in an emotionally induced state;

[0064] (3) Prioritize the selected individualized targets based on the patient's clinical assessment and the degree of abnormal brain functional activity under emotion-induced stimulation;

[0065] (4) Develop a TMS treatment plan for emotion-induced situations based on the order of target priority;

[0066] (5) Conduct short-term treatment evaluation and use efficacy prediction models to make long-term predictions of the remaining treatment trajectory;

[0067] (6) After the treatment is completed, the treatment trajectory will be fed back to the local database and a post-treatment clinical evaluation will be conducted.

[0068] As a preferred embodiment of the present invention, the individualized targets in step (3) include: the frontal lobe under negative emotions, the frontal lobe under positive emotions, the parietal lobe, and the temporoparietal junction, and are prioritized according to the following method:

[0069] (3.1) Perform head motion detection and removal, noise signal removal, time series high-pass filtering, image spatial registration, and linear regression analysis on the acquired emotion-induced brain functional images to obtain brain activation maps under various emotional situations;

[0070] (3.2) Using standard brain mapping, extract activation map data of the subjects' emotion processing-related brain regions, including at least the subgenual anterior cingulate gyrus, medial prefrontal cortex, precuneus, dorsolateral prefrontal cortex, temporoparietal junction, and supplementary motor area;

[0071] (3.3) Following the above process, obtain activation map data for the above brain regions from a healthy reference group, establish a normative model for the average activation level of each brain region in each context, and fit its probability distribution parameters, including but not limited to center location and standard deviation;

[0072] (3.4) For each patient's average activation level in each brain region in each situation, compare it with the distribution of the healthy reference group obtained in step (3.3), and calculate the deviation Z as follows:

[0073] Z=[x-mu] / std;

[0074] Where x is the activation value of the patient's brain region, mu is the mean activation value of the healthy control brain region, and std is the standard deviation of the activation value of the healthy control brain region;

[0075] (3.5) Summarize the activation level deviations Z of all brain regions of the individual patient in all situations, and sort the absolute values ​​of each deviation level Z from large to small. Record the sorting and the corresponding brain regions and activation situations to complete the priority sorting process.

[0076] As a preferred embodiment of the present invention, the step (3) further includes performing calculation processing of individualized brain function regulation targets in the following manner:

[0077] a. Based on the patient's functional magnetic resonance imaging (fMRI) scan results, obtain contextual time series data, functional connectivity matrix data, and contextual activation map data of the target brain region;

[0078] b. Performing spatial linear transformation on the context time series data and the functional connectivity matrix data of the target brain region to obtain a context time series template and a functional connectivity matrix template of the reference group, and subjecting these two templates and the context activation map data of the target brain region to a comprehensive spatial linear transformation to obtain a context activation map aligned with the reference group;

[0079] c. Refer to the group norm model to obtain the activation deviation of the target brain area in various situations, sort the target brain areas from large to small according to the deviation, and record the situations corresponding to the corresponding deviations;

[0080] d. Select one to three target brain regions and determine whether the selected target is more than 3 cm from the scalp or is unsuitable for direct TMS stimulation. If so, calculate the functional connectivity between the target and the cortical area, and select the stimulatable location with the largest positive functional connectivity value as an alternative target. Otherwise, directly record the target location, the direction of deviation from the reference group, and the contextual attributes where the largest deviation value occurs.

[0081] As a preferred embodiment of the present invention, the step (3.3) establishes the norm model in the following manner:

[0082] (3.3.1) Extracting the mask of the target brain region from the standard atlas;

[0083] (3.3.2) On this mask, dilate the edges by 5 voxels in all directions;

[0084] (3.3.3) Applied to each patient's fMRI data to obtain individual data of the target brain region;

[0085] (3.3.4) Performing a procruste transform on the time series of the target brain region to obtain the average spatiotemporal signal;

[0086] (3.3.5) Calculate the correlation coefficient of the time series between each voxel in the target brain region and other voxels outside the target brain region, and use this to obtain the correlation matrix;

[0087] (3.3.6) Use linear regression models to calculate brain activation maps associated with each emotional situation;

[0088] (3.3.7) Perform a procruste transformation on the correlation matrix to obtain the average correlation matrix;

[0089] (3.3.8) Obtain the spatiotemporal signal template and the functional connectivity matrix template of the target brain region, and calculate the individual transformation matrix for the time series, the individual transformation matrix for the functional connectivity, and the corresponding weighted projection transformation;

[0090] (3.3.9) Performing activation map projection processing based on the obtained brain activation map to obtain a normative model of the activation level of the target brain region.

[0091] As a preferred embodiment of the present invention, the step (4) specifically includes the following steps:

[0092] (4.1) Select at least three stimulus materials with the same situational attribute labels for the current patient from the emotional situational material library to induce emotions;

[0093] (4.2) Select the stimulation target associated with the current stimulation material and choose the corresponding iTBS stimulation mode, 10 Hz rTMS stimulation mode, or 1 Hz rTMS mode for TMS modulation treatment;

[0094] (4.3) After the stimulation task is completed, the participants are tested for distraction by pressing a button to determine the correctness of a mathematical equation. After the distraction task is completed, the participants are tested for the next stimulus and the above steps are repeated.

[0095] (4.4) Based on the number of selected targets, arrange brain regulation treatment for all stimulation targets on the same day and perform them at intervals so that the emotional stimulation received before each TMS regulation has the same nature, so as to provide a precise TMS treatment plan.

[0096] As a preferred embodiment of the present invention, the step (5) specifically includes the following steps:

[0097] (5.1) When 1 / 4 and 1 / 2 of the treatment is completed, the mid-term HAMD score or PHQ9 self-assessment score will be performed respectively;

[0098] (5.2) Using the treatment baseline as the origin and the 1 / 4 and 1 / 2 mid-term assessments as the midpoints of the pathway, the expected range of efficacy was plotted based on the improvement rate of the mid-term efficacy estimated by the linear mixed model, and the expected efficacy was projected onto the expected efficacy according to the 95% CI range;

[0099] (5.3) If the predicted range of the final therapeutic effect based on the HAMD score reduction rate at the treatment start point, 1 / 4, and 1 / 2 projections contains a 50% score reduction rate of less than 5%, the treatment benefit is judged to be poor, and the top-ranked target is removed from the treatment plan and replaced with the highest-ranked target among the alternative targets;

[0100] (5.4) After the treatment is completed, the treatment trajectory is archived and the parameter estimates of the linear mixed model for estimating the mid-term efficacy are optimized.

[0101] In a specific embodiment of the present invention, the overall processing flow of the method for TMS treatment of mood disorders and anxiety disorders based on emotion induction is as follows:

[0102] 1. Conduct clinical assessment of the patient's mental health;

[0103] 2. A brain MRI scan, including structural, resting function, and functional scans under various emotional conditions;

[0104] 3. Data were processed using emotion-induced functional data to identify individualized targets in the frontal lobe (sgACC, MPFC, DLPFC, SMA), parietal lobe (Precuneus, IPL), and temporoparietal junction (TPJ) in response to negative and positive emotions. These targets were prioritized based on the patient's clinical assessment and the degree of abnormal brain activity during emotion-induced responses.

[0105] 3a. We used the PhiPipe multimodal neural imaging processing system (Hu et al., 2022 Human Brain Mapping), previously published by our team, to process emotion-induced brain functional images. This process involved head motion detection and removal, noise signal removal, time series high-pass filtering, image spatial registration, and linear regression analysis, generating brain activation maps for a variety of emotional situations.

[0106] 3b. Using standard brain mapping, extract activation map data for the subjects' emotion processing-related brain regions, including at least the subgenual anterior cingulate gyrus, medial prefrontal cortex, precuneus, dorsolateral prefrontal cortex, temporoparietal junction, and supplementary motor area.

[0107] 3c. Obtain activation map data for the aforementioned brain regions from a healthy reference group (at least 50 individuals) following steps 2, 3a, and 3b above. Establish a normative model for the mean activation level for each brain region in each context, fitting its probability distribution parameters, including but not limited to the center position and standard deviation.

[0108] 3d. For each patient's average brain region activation level in each situation, compare it with the distribution of the healthy control group obtained in 3c, and calculate the degree of deviation: Z = [x - mu] / std, where x is the patient's brain region activation value, mu is the mean activation value of the healthy control brain region, and std is the standard deviation of the healthy control brain region activation value.

[0109] 3e Summarize the deviations in activation levels (Z) across all brain regions and situations for each patient, and sort the absolute values ​​of Z from largest to smallest. Record the sorting and the corresponding brain regions and activation situations.

[0110] 4. Develop a TMS treatment plan for emotion-induced scenarios based on the priority ranking of targets, including screening the optimal matching plan from the treatment plan library. In TMS treatment, select the top several brain regions and corresponding scenarios in the ranking table obtained in 3e as intervention targets and corresponding scenarios. In the intervention, first select materials with the same situational attributes as the corresponding scenario of the current target from the emotional situation material library, let the patient watch, hear or experience it, and then use the TMS stimulation system to stimulate the corresponding target. The above process can be repeated multiple times, with a rest period between each time, and before the next situational stimulation begins, use a distraction task to reduce the previous emotional experience. Different brain regions and their corresponding situational attributes can be selected from the ranking table obtained in 3e for each repetition. The course of treatment is 10 days or less, and a brief symptom assessment is performed every day.

[0111] 5. Conduct a psychological assessment after one-quarter and one-half of the treatment course. Based on this feedback, use the efficacy prediction model to predict the remaining treatment trajectory. If the predicted improvement is less than 50%, adjust the treatment plan based on the target priority ranking.

[0112] 6. After the treatment is completed, the treatment trajectory will be fed back to the system database to iteratively optimize the individualized target priority sorting algorithm.

[0113] In practical application, this technical solution divides the design of situational materials into two categories: basic emotion and complex emotion stimulation. The specific processing flow is as follows:

[0114] 1. The basic emotion materials consist of three types: no clear emotion, sadness, and happiness. Each segment is approximately 30 seconds long. The selected materials are organized and numbered, and the basic data is stored. To simulate the psychological process of emotional fluctuations and transitions in real life, based on the basic emotion materials, the elements are combined into overall stimulus materials according to the mean and variance of each dimension, social attributes, and scope of application of the different emotion materials in the following order: 1 → 2 → 3 → 4 → 1 → 2 → 3 → 4 → 1 → 2 → 3 → 4, where the numbers represent: 1 - no clear emotion, 2 - negative emotion, 3 - positive emotion, and 4 - distraction task. The distraction task involves determining whether a calculation equation is correct within a limited time.

[0115] 2. Complex emotion materials utilize multiple videos ranging in length from 30 seconds to 2 minutes and 30 seconds. Each video (elementary material) depicts a social scenario characterized by emotional fluctuations and plot twists. Unlike basic emotion elemental materials, complex emotion materials are composed of distinct character stories. Each elemental material exhibits relative integrity and coherence, offering advantages in terms of emotional arousal and content engagement. These materials also include more complex emotions such as sympathy, emotion, anxiety, and resentment. The emotional material library consists of multiple versions of the aforementioned situational materials, with each elemental material associated with an emotional valence and arousal score, as well as an event attribute score. A database is established to manage these material segments and their corresponding scores and attributes.

[0116] Regarding the calculation method of individualized brain function regulation targets in emotional situations, such as Figure 2 As shown, this technical solution specifically includes the following processing steps:

[0117] 1. Based on the emotion-evoking video material in the video library and its second-by-second negative, neutral, and positive emotion evaluation labels, we established an individual-level general linear model (Y = BX + A + E). Here, Y represents the true signal of each brain region, X represents the vector resulting from the convolution of the negative, neutral, and positive emotion coordinates defined by the emotion response curve with the blood oxygen dynamics function, E represents the computational error, A represents the intercept, representing the baseline activation level, and B represents the regression coefficient vector, indicating the linear relationship between X and Y. This represents the brain activation level in each emotional context, which can be expressed as [b1, b2, b3, b4…]. Here, b1 represents the negative emotion response parameter, b2 represents the neutral emotion response parameter, b3 represents the positive emotion response parameter, and b4 and subsequent coefficients represent the contribution of the control variables. Let b1-b2 represent the negative emotion brain activation pattern, and b3-b2 represent the positive emotion brain activation pattern. A one-sample T-test was used to determine whether b1-b2 or b3-b2 was significantly different from zero, defining the brain activation criterion.

[0118] 2. Construct a norm-referenced model of brain function activation in emotional situations for at least 50 healthy individuals to characterize the activation characteristics and population variation within this population. Target brain regions involved in the following steps include, but are not limited to, the precuneus, subgenual anterior cingulate gyrus, medial prefrontal cortex, supplementary motor area, motor area, visual cortex, and right temporoparietal junction.

[0119] In practical applications, such as Figure 3 As shown, the specific method of constructing the norm-referenced model of brain function activation in emotional situations in this technical solution is as follows:

[0120] 1.1. Extract masks for marking target brain regions from standard brain atlases.

[0121] 1.2 On the mask, dilate 5 voxels in each direction.

[0122] 1.3. For each subject's fMRI data, after preprocessing (including at least motion correction, noise removal, and registration with a standard brain atlas), use the mask generated in Step 1.2 to extract the time series signal of the target brain region. This is represented by the matrix Di, which consists of T rows (number of time points) and V columns (number of voxels), where i represents the subject's sequence number.

[0123] 1.4. For Di and Dj (i = 2n, j = 2n+1, n = 0…floor(nsubj / 2), where floor(nsubj / 2) is the number of subjects rounded down to 1 / 2), perform the following calculations (steps ad are part of the procruste transformation):

[0124] a) Calculate Di', where each column is: dik'= dik – mean(dik); Dj', where each column is: djk'= djk –mean(djk), k = 1,2, …,V; each column of Di' represents the normalized representation of the time series of each voxel of subject i, and the mean of the time series is 0.

[0125] b) Calculate the singular value decomposition: USV T = Di'(Dj') T , where U and V are orthogonal matrices, S is a diagonal matrix; S is the eigenvalue of the projection matrix of the data from subject i to j.

[0126] c) Calculate R = UV T ; R represents the rotation transformation of the data from subject i to subject j.

[0127] d) Calculate c = tr(S) / tr((Di') T Di'), where tr represents the trace of the matrix; c represents the scaling coefficient in the spatial transformation from subject i to subject j.

[0128] e) Calculate Dij = cRDj'+1 Tby1 mean(di) T , of which 1 Tby1 is a T-dimensional column vector with all elements set to 1; Dij represents the data after the data matrix of subject i is transformed into the data matrix of subject j through procruste space transformation.

[0129] f) Exchange Di and Dj, repeat steps ae to obtain Dji.

[0130] g) Average Dij and Dji to obtain Di+j.

[0131] 1.5. For every two subjects Di, Dj, calculate the obtained multiple Di+j, and iteratively execute step 1.4 until a unique Di+j is obtained. Denote the finally obtained unique Di+j as Dtemplate. Dtempalte is the average data matrix of all subjects after performing procruste transformation on each other.

[0132] 1.6. Take the time series signal Di of the target brain region of each subject and Dtemplate as the input of step 1.4, and perform the calculations of 1.4a to 1.4d to obtain ci and Ri; ci and Ri respectively represent the scaling and selection transformation after subject i's data matrix performs procruste transformation towards Dtemplate.

[0133] 1.7. For subject i, calculate the correlation matrix of the average time series between the target brain region and other brain regions to obtain the correlation coefficient matrix Ei, which contains P rows and V columns, where P is the number of other brain regions except the target brain region, and V is the number of voxels in the target brain region.

[0134] 1.8. Replace Di and Dj in step 1.4 with Ei and Ej (i = 2n, j = 2n+1, n = 0……floor(nsubj / 2), where floor(nsubj / 2) is the downward integer of half of the number of subjects), and execute all calculation steps of step 1.4.

[0135] 1.9. For every two subjects Ei, Ej, calculate the obtained multiple Di+j, and iteratively execute step 1.4 until a unique Ei+j is obtained. Denote the finally obtained unique Ei+j as Etemplate; Etemplate represents the average data matrix of the correlation matrices of all subjects after performing procruste transformation on each other.

[0136] 1.10. Take the time series signal Ei of the target brain region of each subject and Etemplate as the input of step 1.4, and perform the calculations of 1.4a to 1.4d to obtain ci’ and Ri’; ci and Ri respectively represent the scaling and selection transformation after subject i's functional connection matrix performs procruste transformation towards Etemplate.

[0137] 1.11. For the data of each subject, calculate ; , where i represents the subject number, lamda is a constant, 0 < lamda < 1; cci and RRi respectively represent the weighted average scaling and rotation transformation of subject i's transformation towards Dtemplate and Etempalte.

[0138] 1.12. For each subject's data, a linear regression model was used to calculate the brain activation map associated with each emotional context condition, denoted as ACTi, where i represents the subject number and ACTi is a matrix with m rows and V columns, where m is the number of contexts and V is the number of voxels in the target brain region.

[0139] 1.13. For each subject's data, calculate ACTi', where each column is: ACTik' = ACTik – mean(ACTik), k = 1, 2, …, V. ACTi' is the activation map data of subject i after normalization across contexts, and each column has a mean of 0.

[0140] 1.14. For each subject's data, calculate ACTi” is the activation map matrix of subject i after procruste transformation by RRi and cci.

[0141] 1.15. For each row of ACTi”, calculate the mean and standard deviation of the corresponding column of ACTi” for all subjects and fit the distribution curve parameters Beta_v (mu, sigma, skew, kurtosis), where v represents the voxel number in the target brain region, v = 1…V, that is, this parameter set is fitted for each voxel v separately.

[0142] 2. Compare the activation levels of the patient's target brain area in various situations with the healthy population distribution curve of the corresponding target brain area obtained in step 1 to obtain a low-level value. The specific steps are as follows:

[0143] 2.1 After preprocessing the patient's target brain region (including at least head motion correction, noise signal removal, and alignment with a standard brain atlas), use the mask generated in step 1.2 to extract the time series signal of the target brain region. This signal is represented by a matrix D, consisting of T rows (number of time points) and V columns (number of voxels). This matrix D is used as the input variable. Execute the calculation in step 1.6 to obtain c and R, where c and R represent the scaling and rotation transformations of the patient's data D onto the reference group's data template D template, respectively.

[0144] 2.2 Calculate the correlation matrix between the average time series of the target brain region and other brain regions, obtaining a correlation coefficient matrix E, consisting of P rows and V columns, where P is the number of other brain regions in addition to the target brain region, and V is the number of voxels in the target brain region. Using E as the input variable, perform the calculation in step 1.10 to obtain c' and R', where they represent the scaling and rotation transformations of the patient's data E onto the reference group's data template Etemplate using the procruste transformation, respectively.

[0145] 2.3 Using c, R, c', R' obtained in steps 1.6 and 1.10 as input, perform step 1.11 to obtain cc, RR, which are the scale and rotation transformations after the procruste transformation of D and E, respectively.

[0146] 2.4 Use a linear regression model to calculate the patient's brain activation map associated with each emotional situation. This map, denoted as ACT, is a matrix with m rows and V columns, where m is the number of situations and V is the number of voxels in the target brain region. Perform steps 1.13 and 1.14 as input variables to obtain the ACT.

[0147] 2.5 Based on the distribution parameters Beta_v (mu, sigma, skew, kurtosis) obtained in 1.15, calculate the percentile of each voxel in ACT' relative to Beta_v (v refers to the voxel corresponding to ACT'), and calculate: deviation value = |50-percentile|.

[0148] 2.6 Rank the deviation values ​​of each target brain region obtained from the above calculations under various scenarios, select the top 1-3 target brain regions as individual stimulation targets, and store the direction of deviation from the reference group (higher, lower) and the scenario attributes that cause the maximum deviation values ​​in these brain regions.

[0149] 2.7 If the stimulation target is located more than 3 cm away from the scalp or is unsuitable for TMS stimulation, it is considered a non-cortical stimulation target. In this case, the functional connectivity between the original target and the cerebral cortical region is calculated using the contextual stimulation time series data. The stimulatable location with the maximum positive functional connectivity value is selected as the indirect stimulation target corresponding to the original target.

[0150] like Figure 4 As shown in Figure 2, the individualized TMS treatment plan in emotional situations is as follows:

[0151] 1. Based on the individualized brain regulation targets and their corresponding situational attribute labels determined by the above method, select at least three stimulus materials with the same situational attribute labels from the above emotional material library.

[0152] 2. During the brain manipulation implementation phase, patients first watched a one-minute video of the contextual material. Immediately afterward, repetitive transcranial magnetic stimulation (RTMS) was initiated, targeting the target associated with the stimulus material and determined by the aforementioned calculations. Stimulation modes included iTBS (three 50Hz pulses per group, 2 minutes at 5Hz), 10Hz rTMS, and 1Hz rTMS. The specific stimulation mode was determined based on whether the patient's stimulation target activity was lower or higher than the reference population. The duration and timing of the stimulation could be customized to suit the specific stimulation mode.

[0153] 3. After a stimulation period ends (or after the subject rests), perform a 2-minute distraction task. This task involves pressing a button to determine the correctness of a mathematical equation. Immediately after the distraction task, the subject will be presented with the next stimulus and the above procedure repeated.

[0154] 4. Based on the number of selected targets, schedule brain manipulation sessions for all stimulation targets on the same day, spaced out, and pre-stimulated with contextual videos of the corresponding situations as described in step 2. This contextual library ensures that the emotional stimulation received before each TMS session is consistent, but not simply repeated.

[0155] like Figure 5 As shown, this technical solution is based on a method for optimizing individualized treatment plans based on short-term treatment response evaluation and treatment response trajectory, specifically comprising the following steps:

[0156] 1. Perform mid-term HAMD scores or PHQ9 self-assessment scores after 1 / 4 and 1 / 2 of treatment completion.

[0157] 2. With the treatment baseline as the origin and the 1 / 4 and 1 / 2 mid-term assessments as the midpoints of the pathway, the expected range of efficacy was plotted based on the improvement rate of the mid-term efficacy estimated by the linear mixed model, and the expected efficacy was projected according to the 95% CI range.

[0158] 3. If the predicted range of the final therapeutic effect based on the HAMD score reduction rate projected from the starting point, 1 / 4, and 1 / 2 of the treatment is less than 5% and includes a 50% score reduction rate, the treatment benefit is judged to be poor. In this case, the top-ranked target is removed from the treatment plan and replaced with the highest-ranked target among the alternative targets. For example, if the initial treatment uses the targets ranked 1, 2, and 3, then the targets ranked 2, 3, and 4 are used when the treatment benefit is poor.

[0159] 4. After treatment, the treatment trajectory was archived and the parameter estimates of the linear mixed model for estimating the mid-term efficacy were optimized.

[0160] The device for implementing control treatment of mood disorders and anxiety disorders based on emotion induction in TMS treatment, wherein the device comprises:

[0161] a processor configured to execute computer-executable instructions;

[0162] A memory storing one or more computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the steps of the control treatment method for mood disorders and anxiety disorders based on emotion induction in the above-mentioned TMS treatment are implemented.

[0163] The processor is used to implement control processing for mood disorders and anxiety disorders based on emotion induction in TMS treatment, wherein the processor is configured to execute computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the control processing method for mood disorders and anxiety disorders based on emotion induction in TMS treatment are implemented.

[0164] The computer-readable storage medium stores a computer program thereon, and the computer program can be executed by a processor to implement the steps of the control treatment method for mood disorders and anxiety disorders based on emotion induction in the above-mentioned TMS treatment.

[0165] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0166] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device.

[0167] Those skilled in the art will understand that all or part of the steps of the method for implementing the above-mentioned embodiment can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0168] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0169] Throughout this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "embodiment" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0170] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

[0171] The method, device, processor and computer-readable storage medium of the present invention for TMS treatment of mood disorders and anxiety disorders based on emotion induction can effectively show, through the situational stimulation library and situational brain function imaging, that significant differences can be intuitively seen between the activities of different brain regions of patients and the healthy control group in different situations, and the direction of the difference is related to the situation. It also effectively illustrates the effectiveness of situational stimulation materials and the effectiveness of situational brain function imaging in the selection of individualized target regulation targets, and has relatively significant application value.

[0172] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it will be apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Accordingly, the specification and drawings are to be regarded as illustrative rather than restrictive.

Claims

1. A device for implementing TMS therapy based on emotion induction to control mood disorders and anxiety disorders, characterized in that: The device comprises: a processor configured to execute computer-executable instructions; A memory storing one or more computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the processor implements the steps of a control treatment method for mood disorders and anxiety disorders based on emotion induction in TMS treatment, wherein the treatment method includes the following steps: (1) Before treatment, conduct a clinical assessment of the patient's mental state using basic emotional element materials and complex emotional stimulation element materials; (2) Conduct brain MRI examinations on patients in an emotionally induced state; (3) Prioritize the selected individualized targets based on the patient's clinical assessment and the degree of abnormal brain functional activity under emotion-induced stimulation; (4) Develop a TMS treatment plan for emotion-induced situations based on the order of target priority; (5) Conduct short-term treatment evaluation and use efficacy prediction models to make long-term predictions of the remaining treatment trajectory in the future; (5.1) When 1 / 4 and 1 / 2 of the treatment is completed, the mid-term HAMD score or PHQ9 self-assessment score will be performed respectively; (5.2) Using the treatment baseline as the origin and the 1 / 4 and 1 / 2 mid-term assessments as the midpoints of the pathway, the expected range of efficacy was plotted based on the improvement rate of the mid-term efficacy estimated by the linear mixed model, and the expected efficacy was projected onto the expected efficacy according to the 95% CI range; (5.3) If the predicted range of the final therapeutic effect based on the HAMD score reduction rate at the treatment start point, 1 / 4, and 1 / 2 projections contains a 50% score reduction rate of less than 5%, the treatment benefit is judged to be poor, and the top-ranked target is removed from the treatment plan and replaced with the highest-ranked target among the alternative targets; (5.4) After the treatment is completed, the treatment trajectory is archived and the parameter estimates of the linear mixed model estimating the mid-term efficacy are optimized; (6) After the treatment is completed, the treatment trajectory will be fed back to the local database and a post-treatment clinical evaluation will be conducted; The device further comprises the following steps: The individualized targets in step (3) include the frontal lobe under negative emotions, the frontal lobe under positive emotions, the parietal lobe, and the temporoparietal junction, and are prioritized according to the following method: (3.1) Perform head motion detection and removal, noise signal removal, time series high-pass filtering, image spatial registration, and linear regression analysis on the acquired emotion-induced brain functional images to obtain brain activation maps under various emotional situations; (3.2) Using standard brain mapping, extract activation map data of the subjects' emotion processing-related brain regions, including at least the subgenual anterior cingulate gyrus, medial prefrontal cortex, precuneus, dorsolateral prefrontal cortex, temporoparietal junction, and supplementary motor area; (3.3) Following the above process, obtain activation map data for the above brain regions from a healthy reference group, establish a normative model for the average activation level of each brain region in each context, and fit its probability distribution parameters, including but not limited to center location and standard deviation; (3.4) For each patient's average activation level in each brain region in each situation, compare it with the distribution of the healthy reference group obtained in step (3.3), and calculate the deviation Z as follows: Z=[x-mu] / std; Where x is the activation value of the patient's brain region, mu is the mean activation value of the healthy control brain region of the healthy reference group, and std is the standard deviation of the activation value of the healthy control brain region of the healthy reference group; (3.5) Summarize the activation level deviations Z of all brain regions of the individual patient in all situations, and sort the absolute values ​​of each deviation level Z from large to small. Record the sorting and the corresponding brain regions and activation situations to complete the priority sorting process.

2. The device for implementing control treatment of mood disorders and anxiety disorders based on emotion induction in TMS therapy according to claim 1, characterized in that The device further comprises the following steps: Step (3) also includes computational processing of individualized brain function regulation targets in the following manner: a. Based on the patient's functional magnetic resonance imaging (fMRI) scan results, obtain contextual time series data, functional connectivity matrix data, and contextual activation map data of the target brain region; b. Performing spatial linear transformation on the context time series data and the functional connectivity matrix data of the target brain region to obtain a context time series template and a functional connectivity matrix template of the reference group, and subjecting these two templates and the context activation map data of the target brain region to a comprehensive spatial linear transformation to obtain a context activation map aligned with the reference group; c. Refer to the group norm model to obtain the activation deviation of the target brain area in various situations, sort the target brain areas from large to small according to the deviation, and record the situations corresponding to the corresponding deviations; d. Select one to three target brain regions and determine whether the selected target is more than 3 cm from the scalp or is unsuitable for direct TMS stimulation. If so, calculate the functional connectivity between the target and the cortical area, and select the stimulatable location with the largest positive functional connectivity value as an alternative target; Otherwise, directly record the target location, the degree of deviation from the reference group, and the context attribute where the maximum deviation occurs.

3. The device for implementing control treatment of mood disorders and anxiety disorders based on emotion induction in TMS therapy according to claim 2, characterized in that: The device further comprises the following steps: Step (3.3) establishes the normative model as follows: (3.3.1) Extracting the mask of the target brain region from the standard atlas; (3.3.2) On this mask, dilate the edges by 5 voxels in all directions; (3.3.3) Applied to each patient's fMRI data to obtain individual data of the target brain region; (3.3.4) Performing a procruste transform on the time series of the target brain region to obtain the average spatiotemporal signal; (3.3.5) Calculate the correlation coefficient of the time series between each voxel in the target brain region and other voxels outside the target brain region, and use this to obtain the correlation matrix; (3.3.6) Use linear regression models to calculate brain activation maps associated with each emotional situation; (3.3.7) Perform a procruste transformation on the correlation matrix to obtain the average correlation matrix; (3.3.8) Obtain the spatiotemporal signal template and the functional connectivity matrix template of the target brain region, and calculate the individual transformation matrix for the time series, the individual transformation matrix for the functional connectivity, and the corresponding weighted projection transformation; (3.3.9) Performing activation map projection processing based on the obtained brain activation map to obtain a normative model of the activation level of the target brain region.

4. The device for implementing control treatment of mood disorders and anxiety disorders based on emotion induction in TMS therapy according to claim 3, characterized in that: The device further comprises the following steps: Step (4) specifically includes the following steps: (4.1) Select at least three stimulus materials with the same situational attribute labels for the current patient from the emotional situational material library to induce emotions; (4.2) Select the stimulation target associated with the current stimulation material and choose the corresponding iTBS stimulation mode, 10 Hz rTMS stimulation mode, or 1 Hz rTMS mode for TMS modulation treatment; (4.3) After the stimulation task is completed, the participants are tested for distraction by pressing a button to determine the correctness of a mathematical equation. After the distraction task is completed, the participants are tested for the next stimulus and the above steps are repeated. (4.4) Based on the number of selected targets, arrange brain regulation treatment for all stimulation targets on the same day and perform them at intervals so that the emotional stimulation received before each TMS regulation has the same nature, so as to provide a precise TMS treatment plan.

5. A processor for implementing control processing for mood disorders and anxiety disorders based on emotion induction in TMS treatment, characterized in that: The processor is configured to execute computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the processing method of the device for controlling mood disorders and anxiety disorders based on emotion induction in TMS treatment are implemented as described in claims 1 to 4.

6. A computer-readable storage medium, characterized in that A computer program is stored thereon, and the computer program can be executed by a processor to implement the steps of the processing method of the device for controlling mood disorders and anxiety disorders based on emotion induction in TMS treatment according to any one of claims 1 to 4.

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