Control treatment method for mood disorder and anxiety disorder in TMS treatment

Abstract

The invention relates to a control processing method for mood disorder and anxiety disorder based on emotion induction in TMS treatment, which comprises the following steps of: performing clinical assessment on mental and psychological states of a patient before treatment; performing brain magnetic resonance examination processing on the patient based on the emotion induction state; performing priority ranking processing on the selected individualized target spots according to the clinical evaluation of the patient and the abnormal degree of the brain function activity under emotion induction; a TMS treatment scheme under the emotion induction scene is formulated according to the target priority arrangement sequence; performing treatment evaluation in a short-term time period, and performing long-term prediction of a future residual treatment trajectory by using the curative effect prediction model; after treatment is finished, a treatment track is fed back to the local database, and clinical evaluation after treatment is carried out. The invention further relates to a corresponding device, a processor and a storage medium. By adopting the method, the device, the processor and the storage medium, the contrast difference of the patient in different situations can be effectively displayed through the situational stimulation library and the situational brain function image.

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 refers to a control 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 response 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. At present, there is a lack of an objective, accurate and self-feedback system to perform individualized TMS treatment in an emotion-induced state for patients with mood disorders and anxiety disorders.

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

[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 implementing TMS treatment for mood disorders and anxiety disorders based on emotion induction.

[0006] In order to achieve the above objectives, the method, device, processor and computer-readable storage medium thereof for TMS treatment of mood disorders and anxiety disorders based on emotion induction of the present invention are as follows: The method for controlling mood disorders and anxiety disorders based on emotion induction in TMS treatment has the following main features: (1) Before treatment, use basic emotional element materials and complex emotional stimulation element materials to conduct a clinical assessment of the patient's mental health; (2) Perform brain MRI examinations on patients in an emotion-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 induction; (4) Formulate TMS treatment plans for emotion-inducing situations based on the priority order of targets; (5) Conduct treatment evaluation in the short term and use the efficacy prediction model to make long-term predictions of the remaining treatment trajectory in the future; (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.

[0007] 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: (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 brain functional images under emotion-induced states to obtain brain activation maps under various emotional situations; (3.2) Extract the activation map data of the subjects’ emotion processing-related brain regions through standard brain partitioning maps, including at least the following brain regions: subgenual anterior cingulate gyrus, medial prefrontal lobe, precuneus, dorsolateral prefrontal lobe, temporoparietal junction, and supplementary motor area; (3.3) Obtain activation map data of the above brain regions of the healthy reference group according to the above process, establish a normative model for the average activation level of each brain region in each situation, and fit its probability distribution parameters, including but not limited to the center position and standard deviation; (3.4) For the average activation level of each brain region of the individual patient in each situation, compare it with the distribution of the healthy reference group obtained in step (3.3), and calculate the deviation degree Z as follows: Z = [x-mu] / std; Among them, x is the activation value of the patient's brain area, mu is the mean activation value of the healthy control brain area, and std is the standard deviation of the activation value of the healthy control brain area; (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.

[0008] Preferably, the step (3) further comprises calculating and processing individualized brain function regulation targets in the following manner: a. Based on the patient's brain functional magnetic resonance imaging (fMRI) scan results, obtain the contextual time series data of the target brain area, the functional connection matrix data of the target brain area, and the contextual activation map data of the target brain area; b. Performing spatial linear transformation on the context time series data and the functional connection matrix data of the target brain area respectively to obtain the context time series template and the functional connection matrix template of the reference group, and subjecting the two templates and the context activation map data of the target brain area to 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, and sort the target brain areas from large to small according to the deviation, and record the situations corresponding to the corresponding deviations; d. Select 1 to 3 target brain regions as targets and determine whether the selected targets are more than 3 cm away from the scalp or are not suitable for direct TMS stimulation. If so, calculate the functional connection between the target and the cortical area, and select the stimulatable position with the maximum positive functional connection value as the alternative target; otherwise, directly record the target position, the direction of deviation from the reference group, and the contextual attributes where the maximum deviation value occurs.

[0009] Preferably, the step (3.3) establishes the norm model in the following manner: (3.3.1) Extract 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 procruste transformation 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 model to calculate the brain activation map related to each emotional situation; (3.3.7) Perform procruste transformation on the correlation matrix to obtain the average correlation matrix; (3.3.8) Obtain the spatiotemporal signal template of the target brain region and the functional connection matrix template of the target brain region respectively, and calculate the individual transformation matrix for the time series, the individual transformation matrix for the functional connection, and the corresponding weighted projection transformation; (3.3.9) Performing activation map projection processing based on the obtained brain activation map, so as to obtain a normative model of the activation degree of the target brain area.

[0010] Preferably, the step (4) specifically includes the following steps: (4.1) Select at least 3 stimulus materials with the same situation attribute labels from the emotional situation material library for the current patient 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 regulation treatment; (4.3) After a stage of stimulation task is completed, the distraction task test is performed by pressing a button to promptly judge the correctness of the mathematical calculation formula. After the distraction task is completed, the next situational stimulation material is immediately viewed and the above operation is repeated; (4.4) Based on the number of selected targets, brain regulation treatments for all stimulation targets are arranged within the same day and performed at intervals so that the emotional stimulation received before each TMS regulation has the same nature, so as to provide an accurate TMS treatment plan.

[0011] Preferably, the step (5) specifically comprises the following steps: (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) With the treatment baseline as the origin and the 1 / 4 and 1 / 2 mid-term assessments as the midpoints of the path, the expected range of efficacy was drawn based on the improvement rate of the mid-term efficacy estimated by the linear mixed model, and projected to the expected efficacy according to the 95% CI range; (5.3) If the predicted range of the final efficacy based on the HAMD score reduction rate projected from the treatment start point, 1 / 4 and 1 / 2 includes a 50% score reduction rate of less than 5%, the treatment benefit is judged to be poor, and the first-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 estimation of the linear mixed model for estimating the mid-term efficacy is optimized.

[0012] The device for implementing control treatment of mood disorders and anxiety disorders based on emotion induction in TMS treatment has the following main features: the device comprises: a processor configured to execute computer-executable instructions; A memory stores one or more computer executable instructions. 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.

[0013] The processor for implementing control processing for mood disorders and anxiety disorders based on emotion induction in TMS treatment has the main feature that 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.

[0014] The main feature of the computer-readable storage medium is that a computer program is stored thereon, and the computer program can be executed by a processor to implement the steps of the control processing method for mood disorders and anxiety disorders based on emotion induction in the above-mentioned TMS treatment.

[0015] The method, device, processor and computer-readable storage medium thereof for TMS treatment of mood disorders and anxiety disorders based on emotion induction of the present invention can effectively display, through the situational stimulation library and situational brain function imaging, the significant differences between the activities of different brain regions of patients in different situations and those of the healthy control group can be intuitively seen, 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

[0016] Figure 1 The present invention is a flow chart of the method for implementing TMS treatment for mood disorders and anxiety disorders based on emotion induction.

[0017] Figure 2 The present invention is a flow chart of the individualized brain function regulation target processing.

[0018] Figure 3 A flow chart of a norm-referenced model of brain function activation in emotional situations is constructed for the present invention.

[0019] Figure 4 The figure is a flow chart of the present invention for performing individualized TMS treatment in emotional context.

[0020] 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 according to the present invention.

[0021] Figure 6 It is a schematic diagram of the comparative effect of the present invention in a negative emotional situation and a positive emotional situation. DETAILED DESCRIPTION

[0022] In order to more clearly describe the technical content of the present invention, further description is given below in conjunction with specific embodiments.

[0023] Before describing in detail embodiments according to the present invention, it should be noted that, hereinafter, the terms "comprises", "includes" or any other variations are intended to cover non-exclusive inclusion, whereby a process, method, article or apparatus comprising a series of elements includes not only these elements, but also other elements not explicitly listed or inherent to such process, method, article or apparatus.

[0024] See also Figure 1 As shown, the TMS treatment is based on emotion induction to achieve a control treatment method for mood disorders and anxiety disorders, wherein the method comprises the following steps: (1) Before treatment, use basic emotional element materials and complex emotional stimulation element materials to conduct a clinical assessment of the patient's mental health; (2) Perform brain MRI examinations on patients in an emotion-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 induction; (4) Formulate TMS treatment plans for emotion-inducing situations based on the priority order of targets; (5) Conduct treatment evaluation in the short term and use the efficacy prediction model to make long-term predictions of the remaining treatment trajectory in the future; (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.

[0025] 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: (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 brain functional images under emotion-induced states to obtain brain activation maps under various emotional situations; (3.2) Extract the activation map data of the subjects’ emotion processing-related brain regions through standard brain partitioning maps, including at least the following brain regions: subgenual anterior cingulate gyrus, medial prefrontal lobe, precuneus, dorsolateral prefrontal lobe, temporoparietal junction, and supplementary motor area; (3.3) Obtain activation map data of the above brain regions of the healthy reference group according to the above process, establish a normative model for the average activation level of each brain region in each situation, and fit its probability distribution parameters, including but not limited to the center position and standard deviation; (3.4) For the average activation level of each brain region of the individual patient in each situation, compare it with the distribution of the healthy reference group obtained in step (3.3), and calculate the deviation degree Z as follows: Z = [x-mu] / std; Among them, x is the activation value of the patient's brain area, mu is the mean activation value of the healthy control brain area, and std is the standard deviation of the activation value of the healthy control brain area; (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.

[0026] As a preferred embodiment of the present invention, the step (3) further comprises calculating and processing individualized brain function regulation targets in the following manner: a. Based on the patient's brain functional magnetic resonance imaging (fMRI) scan results, obtain the contextual time series data of the target brain area, the functional connection matrix data of the target brain area, and the contextual activation map data of the target brain area; b. Performing spatial linear transformation on the context time series data and the functional connection matrix data of the target brain area respectively to obtain the context time series template and the functional connection matrix template of the reference group, and subjecting the two templates and the context activation map data of the target brain area to 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, and sort the target brain areas from large to small according to the deviation, and record the situations corresponding to the corresponding deviations; d. Select 1 to 3 target brain regions as targets and determine whether the selected targets are more than 3 cm away from the scalp or are not suitable for direct TMS stimulation. If so, calculate the functional connection between the target and the cortical area, and select the stimulatable position with the maximum positive functional connection value as the alternative target; otherwise, directly record the target position, the direction of deviation from the reference group, and the contextual attributes where the maximum deviation value occurs.

[0027] As a preferred embodiment of the present invention, the step (3.3) establishes the norm model in the following manner: (3.3.1) Extract 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 procruste transformation 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 model to calculate the brain activation map related to each emotional situation; (3.3.7) Perform procruste transformation on the correlation matrix to obtain the average correlation matrix; (3.3.8) Obtain the spatiotemporal signal template of the target brain region and the functional connection matrix template of the target brain region respectively, and calculate the individual transformation matrix for the time series, the individual transformation matrix for the functional connection, and the corresponding weighted projection transformation; (3.3.9) Performing activation map projection processing based on the obtained brain activation map, so as to obtain a normative model of the activation degree of the target brain area.

[0028] As a preferred embodiment of the present invention, the step (4) specifically includes the following steps: (4.1) Select at least 3 stimulus materials with the same situation attribute labels from the emotional situation material library for the current patient 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 regulation treatment; (4.3) After a stage of stimulation task is completed, the distraction task test is performed by pressing a button to promptly judge the correctness of the mathematical calculation formula. After the distraction task is completed, the next situational stimulation material is immediately viewed and the above operation is repeated; (4.4) Based on the number of selected targets, brain regulation treatments for all stimulation targets are arranged within the same day and performed at intervals so that the emotional stimulation received before each TMS regulation has the same nature, so as to provide an accurate TMS treatment plan.

[0029] As a preferred embodiment of the present invention, the step (5) specifically includes the following steps: (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) With the treatment baseline as the origin and the 1 / 4 and 1 / 2 mid-term assessments as the midpoints of the path, the expected range of efficacy was drawn based on the improvement rate of the mid-term efficacy estimated by the linear mixed model, and projected to the expected efficacy according to the 95% CI range; (5.3) If the predicted range of the final efficacy based on the HAMD score reduction rate projected from the treatment start point, 1 / 4 and 1 / 2 includes a 50% score reduction rate of less than 5%, the treatment benefit is judged to be poor, and the first-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 estimation of the linear mixed model for estimating the mid-term efficacy is optimized.

[0030] 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: 1. Conduct clinical assessment of the patient's mental health; 2. Perform a brain MRI, including structural, resting function, and functional scans in a variety of emotional situations; 3. The data were processed through functional data under emotion induction, and individualized targets in the frontal lobe (sgACC, MPFC, DLPFC, SMA), parietal lobe (Precuneus, IPL) and temporoparietal junction (TPJ) under negative and positive emotions were selected. The above targets were prioritized according to the patient's clinical assessment and the degree of abnormal brain functional activity under emotion induction.

[0031] 3a. The PhiPipe multimodal neural imaging processing system (Hu et al., 2022 Human Brain Mapping) that the inventor team has publicly published is used to process the brain functional images induced by emotions. The main steps include head movement detection and removal, noise signal removal, time series high-pass filtering, image spatial registration, linear regression analysis, etc., to obtain brain activation maps of various emotional situations.

[0032] 3b. Using standard brain partitioning maps, extract the activation map data of the subjects' emotion processing-related brain regions, including at least the subgenual anterior cingulate gyrus, medial prefrontal lobe, precuneus, dorsolateral prefrontal lobe, temporoparietal junction, and supplementary motor area.

[0033] 3c. Obtain activation map data of the above brain regions of a healthy reference group (at least 50 people) according to the above steps 2, 3a, and 3b, establish a normative model for the average activation level of each brain region in each situation, and fit its probability distribution parameters, including but not limited to the center position and standard deviation.

[0034] 3d. For each brain region of the individual patient and in each situation, the average activation level was compared with the distribution of the healthy reference group obtained in 3c, and the degree of deviation was calculated: 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.

[0035] 3e Summarize the activation level deviation Z of all brain regions and all situations of the individual patient, and sort the absolute values ​​of Z from large to small. Record the sorting and the corresponding brain regions and activation situations.

[0036] 4. Formulate TMS treatment plans for emotion-induced scenarios based on the priority ranking of targets, including screening and matching the most optimized plan from the treatment plan library. In TMS treatment, select the top several brain regions and corresponding situations in the ranking table obtained in 3e as intervention targets and corresponding situations. In the intervention, first select materials with the same situational attributes as the corresponding situation of the current target from the emotional situation material library, let the patient watch, hear or experience, and then stimulate the corresponding target with the TMS stimulation system. The above process can be repeated multiple times, with a rest time between each time, and before the next situational stimulation begins, use distraction tasks 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.

[0037] 5. Conduct a psychiatric assessment after 1 / 4 and 1 / 2 of the treatment. Based on the feedback data, use the efficacy prediction model to predict the remaining treatment trajectory in the future. If the predicted improvement is less than 50%, adjust the treatment plan according to the target priority ranking.

[0038] 6. After the treatment, the treatment trajectory will be fed back to the system database to iteratively optimize the individualized target priority sorting algorithm.

[0039] In actual application, this technical solution divides the situational material design into two categories: basic emotion and complex emotion stimulation. The specific processing flow is as follows: 1. The elemental materials of basic emotions include three types, namely no obvious emotion, sad emotion, and happy emotion. The length of each material is about 30 seconds. The screened materials are sorted and numbered and the basic data is stored. In order to simulate the psychological process of emotional fluctuations and emotional conversions in real life, based on the elemental materials of basic emotions, according to the average value and variance of each dimension evaluation of different emotional materials, social attributes, scope of application, etc., the elemental materials are combined into overall stimulus 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, 4-distraction task. The distraction task material is to judge whether a calculation equation is correct within a limited time.

[0040] 2. Complex emotional materials use multiple videos with a duration of 30 seconds to 2 minutes and 30 seconds. Each video (element material) tells a social plot with emotional fluctuations and plot reversals. Unlike basic emotional element materials, complex emotional materials are composed of different character stories. Each material has relative integrity and coherence, and has advantages in emotional arousal and content involvement. It also involves more complex emotional content such as sympathy, emotion, anxiety, and hatred. The emotional material library consists of multiple versions of the above situational materials. Each element material is associated with emotional valence and arousal scores, and event attribute scores. Establish a database to manage material fragments and their corresponding scores and attributes.

[0041] Regarding the computational methods of individualized brain function regulation targets in emotional situations, such as Figure 2 As shown, this technical solution specifically includes the following processing steps: 1. Based on the emotion-inducing video materials in the video library and their second-by-second negative emotion-neutral emotion-positive emotion evaluation labels, a general linear model Y=BX+A+E at the individual level was established, where Y is the real signal of each brain region, X is the vector generated by the convolution of the three emotion coordinates of negative-neutral-positive defined by the emotion response curve and the blood oxygen dynamic function, E is the calculation error, A is the intercept, which represents the baseline activation level, and B is the regression coefficient vector, which represents the linear relationship between X and Y, and represents the brain activation level under each emotional context, which can be expressed as [b1, b2, b3, b4…]. Among them, b1 is the negative emotion response parameter, b2 is the neutral emotion response parameter, b3 is the positive emotion response parameter, and b4 and subsequent coefficients are the contributions of the control variables. Let b1-b2 be the negative emotion brain activation pattern, and b3-b2 be the positive emotion brain activation pattern. The brain activation standard is judged by the single-sample T test that b1-b2 or b3-b2 is significantly not 0.

[0042] 2. Construct a normative reference model of brain function activation in emotional situations for more than 50 healthy people to characterize the brain function activation characteristics of the healthy group and its population variation level. The target brain areas involved in the following steps include but are not limited to: precuneus, subgenual anterior cingulate gyrus, medial prefrontal lobe, supplementary motor area, motor area, visual cortex, and right temporoparietal junction.

[0043] In practical applications, such as Figure 3 As shown, the specific method of constructing the norm reference model of brain function activation in emotional situations in this technical solution is as follows: 1.1. Extract masks for marking target brain regions from standard brain atlases.

[0044] 1.2 On this mask, dilate 5 voxels in each direction.

[0045] 1.3. For each subject's fMRI data, after preprocessing steps (including at least head motion correction, noise signal removal, and alignment with the standard brain map), the mask generated in step 1.2 is used to extract the time series signal of the target brain area, represented by the matrix Di, which contains T rows (number of time points) and V columns (number of voxels), where i represents the subject's serial number.

[0046] 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 an integer of 1 / 2), do the following calculations (where steps ad are part of the procruste transformation): 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.

[0047] 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.

[0048] c) Calculate R = UV T ; R represents the rotation transformation of the data from subject i to subject j.

[0049] 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.

[0050] e) Calculate Dij = cRDj'+1 Tby1 mean(di) T , where 1 Tby1 It is a T-dimensional column vector with all elements being 1; Dij represents the data after the procruste space transformation of the data matrix of subject i to the data matrix of subject j.

[0051] f) Exchange Di and Dj, repeat steps ae to obtain Dji.

[0052] g) Average Dij and Dji to obtain Di+j.

[0053] 1.5. For every two subjects Di, Dj, calculate the multiple Di+j obtained, 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 the Procrustes transformation on each other.

[0054] 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 of the data matrix of subject i after performing the Procrustes transformation towards Dtemplate.

[0055] 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 other than the target brain region, and V is the number of voxels in the target brain region.

[0056] 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.

[0057] 1.9. For every two subjects Ei, Ej, calculate the multiple Di+j obtained, 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 the Procrustes transformation on each other.

[0058] 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 of the functional connection matrix of subject i after performing the Procrustes transformation towards Etemplate.

[0059] 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 of the scaling and rotation transformation of subject i towards Dtemplate and Etempalte.

[0060] 1.12. For each subject’s data, a linear regression model was used to calculate the brain activation map associated with each emotional situation 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 situations and V is the number of voxels in the target brain area.

[0061] 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 between scenarios, and the mean of each column is 0.

[0062] 1.14. For each subject’s data, calculate ” ACTi” is the activation map matrix of subject i after procruste transformation by RRi and cci.

[0063] 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 parameters of the distribution curve Beta_v (mu, sigma, skew, kurtosis), where v represents the voxel number in the target brain area, v = 1…V, that is, the parameter group is fitted for each voxel v separately.

[0064] 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 value. The specific steps are as follows:

[0065] 2.1 After the patient's target brain area has undergone preprocessing steps (including at least head motion correction, noise signal removal, and alignment with the standard brain map), the mask generated in step 1.2 is used to extract the time series signal of the target brain area, which is represented by a matrix D containing T rows (number of time points) and V columns (number of voxels) as input variables. The calculation step 1.6 is performed to obtain c and R, which respectively represent the scaling and rotation transformation of the patient's data D to the data template Dtemplate of the reference group by procruste transformation.

[0066] 2.2 Calculate the correlation matrix between the average time series of the target brain region and other brain regions to obtain the correlation coefficient matrix E, which contains P rows and V columns, where P is the number of other brain regions other than the target brain region, and V is the number of voxels in the target brain region. Take E as the input variable and perform calculation step 1.10 to obtain c', R', which respectively represent the scaling and rotation transformation of the patient's data E to the data template Etemplate of the reference group by procruste transformation.

[0067] 2.3 Using c, R, c', R' obtained in calculation steps 1.6 and 1.10 as input, perform calculation step 1.11 to obtain cc, RR, which are the scaling and rotation transformations after the procruste transformation of D and E, respectively.

[0068] 2.4 The linear regression model is used to calculate the brain activation map related to each emotional situation condition of the patient, denoted as ACT, which 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 area. As the input variable, perform calculation steps 1.13 and 1.14 to obtain ACT.

[0069] 2.5 Combined with 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|.

[0070] 2.6 Sort the deviation values ​​of the target brain regions obtained by the above calculations in multiple situations, select the top 1-3 target brain regions as individualized stimulation targets, and store the direction of deviation from the reference group (higher or lower) and the situational attributes that cause the maximum deviation values ​​in these brain regions.

[0071] 2.7 If the stimulation target is located more than 3 cm away from the scalp or is not suitable for TMS stimulation, it is identified as a non-cortical stimulation target. The contextual stimulation time series data are used to calculate the functional connection between the original target and the cerebral cortical area, and the stimulatable position with the maximum positive functional connectivity value is selected as the indirect stimulation target corresponding to the original target.

[0072] like Figure 4 As shown in Figure 2, the individualized TMS treatment plan in emotional situations is as follows: 1. According to the individualized brain regulation targets and their corresponding situational attribute labels determined by the above method, select at least 3 stimulus materials with the same situational attribute labels from the above emotional material library.

[0073] 2. In the implementation phase of brain regulation, the patient is first asked to watch the situational video material for 1 minute. After watching, repetitive transcranial magnetic stimulation is immediately started, and the target is the target associated with the stimulation material and determined by the above calculation. The stimulation modes include iTBS mode (3 50Hz pulses in a group, stimulating at a frequency of 5Hz for 2 minutes), 10Hz rTMS mode and 1Hz rTMS mode. The specific stimulation mode is determined based on whether the activity of the patient's stimulation target is lower or higher than the reference as a whole. The duration and timing of the stimulation can be customized according to the requirements of different stimulation modes.

[0074] 3. After the stimulation of a stage is over (or the subject has rested), the distraction task is performed for 2 minutes. The distraction task is to judge the correctness of mathematical calculations in a timely manner by pressing keys. After the distraction task is completed, the next situational stimulus material is immediately viewed and the above operation is repeated.

[0075] 4. According to the number of selected targets, brain regulation of all stimulation targets is arranged on the same day and carried out at intervals, and the situational video of the corresponding situation is given before the stimulation as described in step 2. The situational material library ensures that the emotional stimulation received before each TMS regulation has the same nature, but is not simply repeated.

[0076] like Figure 5 As shown, the present technical solution is a method for optimizing individualized treatment plans based on short-term evaluation of treatment response and treatment response trajectory, which specifically includes the following steps: 1. After 1 / 4 and 1 / 2 of the treatment is completed, the mid-term HAMD score or PHQ9 self-assessment score will be conducted.

[0077] 2. Taking the treatment baseline as the origin and the 1 / 4 and 1 / 2 mid-term assessments as the midpoints of the path, the expected range of efficacy was drawn 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.

[0078] 3. If the predicted range of the final therapeutic effect based on the HAMD reduction rate projected from the starting point of treatment, 1 / 4 and 1 / 2 includes a range of 50% reduction rate <5%, it is judged that the treatment benefit is poor. In this case, the first-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 1st, 2nd and 3rd-ranked targets, then the 2nd, 3rd and 4th-ranked targets are used when the treatment benefit is poor.

[0079] 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.

[0080] The device for implementing control treatment of mood disorders and anxiety disorders based on emotion induction in TMS treatment, wherein the device comprises: a processor configured to execute computer-executable instructions; A memory stores one or more computer executable instructions. 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.

[0081] 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, and 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.

[0082] 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 processing method for mood disorders and anxiety disorders based on emotion induction in the above-mentioned TMS treatment.

[0083] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes 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 not be performed in 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 belong.

[0084] 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.

[0085] A person of ordinary skill in the art may understand that all or part of the steps of the method for implementing the above-mentioned embodiment may be completed by instructing the relevant hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0086] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0087] In the description of this specification, the description with reference to the terms "an embodiment", "some embodiments", "example", "specific example", or "embodiment" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0088] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

[0089] The method, device, processor and computer-readable storage medium thereof for TMS treatment of mood disorders and anxiety disorders based on emotion induction of the present invention can effectively display, through the situational stimulation library and situational brain function imaging, the significant differences between the activities of different brain regions of patients in different situations and those of the healthy control group can be intuitively seen, 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.

[0090] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it is apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.

Claims

1. A method for controlling mood disorders and anxiety disorders based on emotion induction in TMS treatment, characterized in that: The method comprises the following steps: (1) Before treatment, use basic emotional element materials and complex emotional stimulation element materials to conduct a clinical assessment of the patient's mental health; (2) Perform brain MRI examinations on patients in an emotion-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 induction; (4) Formulate TMS treatment plans for emotion-inducing situations based on the priority order of targets; (5) Conduct treatment evaluation in the short term and use the efficacy prediction model to make long-term predictions of the remaining treatment trajectory in the future; (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.

2. The method for controlling mood disorders and anxiety disorders based on emotion induction in TMS therapy according to claim 1, characterized in that: 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 brain functional images under emotion-induced states to obtain brain activation maps under various emotional situations; (3.2) Extract the activation map data of the subjects’ emotion processing-related brain regions through standard brain partitioning maps, including at least the following brain regions: subgenual anterior cingulate gyrus, medial prefrontal lobe, precuneus, dorsolateral prefrontal lobe, temporoparietal junction, and supplementary motor area; (3.3) Obtain activation map data of the above brain regions of the healthy reference group according to the above process, establish a normative model for the average activation level of each brain region in each situation, and fit its probability distribution parameters, including but not limited to the center position and standard deviation; (3.4) For the average activation level of each brain region of the individual patient in each situation, compare it with the distribution of the healthy reference group obtained in step (3.3), and calculate the deviation degree Z as follows: Z = [x-mu] / std; Among them, x is the activation value of the patient's brain area, mu is the mean activation value of the healthy control brain area, and std is the standard deviation of the activation value of the healthy control brain area; (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.

3. The method for controlling mood disorders and anxiety disorders based on emotion induction in TMS therapy according to claim 2, characterized in that: The step (3) further includes calculating and processing individualized brain function regulation targets in the following manner: a. Based on the patient's brain functional magnetic resonance imaging (fMRI) scan results, obtain the contextual time series data of the target brain area, the functional connection matrix data of the target brain area, and the contextual activation map data of the target brain area; b. Performing spatial linear transformation on the context time series data and the functional connection matrix data of the target brain area respectively to obtain the context time series template and the functional connection matrix template of the reference group, and subjecting the two templates and the context activation map data of the target brain area to 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, and sort the target brain areas from large to small according to the deviation, and record the situations corresponding to the corresponding deviations; d. Select 1 to 3 target brain areas as targets and determine whether the selected target is more than 3 cm away from the scalp or is not suitable for direct TMS stimulation. If so, calculate the functional connection between the target and the cortical area and select the stimulatable position with the maximum positive functional connectivity value as the alternative target; Otherwise, directly record the target location, the direction of deviation from the reference group, and the context attribute where the maximum deviation occurs.

4. The method for controlling mood disorders and anxiety disorders based on emotion induction in TMS therapy according to claim 2, characterized in that: The step (3.3) establishes the norm model in the following manner: (3.3.1) Extract 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 procruste transformation 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 model to calculate the brain activation map related to each emotional situation; (3.3.7) Perform procruste transformation on the correlation matrix to obtain the average correlation matrix; (3.3.8) Obtain the spatiotemporal signal template of the target brain region and the functional connection matrix template of the target brain region respectively, and calculate the individual transformation matrix for the time series, the individual transformation matrix for the functional connection, and the corresponding weighted projection transformation; (3.3.9) Performing activation map projection processing based on the obtained brain activation map, so as to obtain a normative model of the activation degree of the target brain area.

5. The method for controlling mood disorders and anxiety disorders based on emotion induction in TMS therapy according to claim 3, characterized in that: The step (4) specifically includes the following steps: (4.1) Select at least 3 stimulus materials with the same situation attribute labels from the emotional situation material library for the current patient 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 regulation treatment; (4.3) After a stage of stimulation task is completed, the distraction task test is performed by pressing a button to promptly judge the correctness of the mathematical calculation formula. After the distraction task is completed, the next situational stimulation material is immediately viewed and the above operation is repeated; (4.4) Based on the number of selected targets, brain regulation treatments for all stimulation targets are arranged within the same day and performed at intervals so that the emotional stimulation received before each TMS regulation has the same nature, so as to provide an accurate TMS treatment plan.

6. The method for controlling mood disorders and anxiety disorders based on emotion induction in TMS therapy according to claim 5, characterized in that: The step (5) specifically includes the following steps: (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) With the treatment baseline as the origin and the 1 / 4 and 1 / 2 mid-term assessments as the midpoints of the path, the expected range of efficacy was drawn based on the improvement rate of the mid-term efficacy estimated by the linear mixed model, and projected to the expected efficacy according to the 95% CI range; (5.3) If the predicted range of the final efficacy based on the HAMD score reduction rate projected from the treatment start point, 1 / 4 and 1 / 2 includes a 50% score reduction rate of less than 5%, the treatment benefit is judged to be poor, and the first-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 estimation of the linear mixed model for estimating the mid-term efficacy is optimized.

7. A device for implementing control treatment of mood disorders and anxiety disorders based on emotion induction in TMS treatment, 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 steps of the control treatment method for mood disorders and anxiety disorders based on emotion induction in TMS treatment according to any one of claims 1 to 6 are implemented.

8. 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 control treatment method for mood disorders and anxiety disorders based on emotion induction in TMS treatment according to any one of claims 1 to 6 are implemented.

9. 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 control treatment method for mood disorders and anxiety disorders based on emotion induction in TMS treatment according to any one of claims 1 to 6.

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