Individualized TMS positioning method and system for depression in targeted dorsal-lateral prefrontal lobe maximum deviation brain region

Abstract

The invention relates to a depression individualized TMS positioning method and system for a targeted dorsal-lateral prefrontal lobe maximum deviation brain region, and the method comprises the steps: calculating a brain region function connection strength value of a healthy individual based on the MRI data of the healthy individual; constructing a healthy individual brain region function connection strength value standard model based on the healthy individual brain region function connection strength value; on the basis of MRI data of the depression subject, calculating a brain region function connection strength value of the depression subject; based on the brain region function connection strength value of the depression subject, measuring a deviation value of the brain region function connection strength value of the depression subject in the healthy individual brain region function connection strength value standard model; and based on the obtained deviation value of the brain region function connection strength value of the depression subject, finding out the brain region with the maximum deviation in the left dorsal and outer prefrontal lobes, and taking the brain region as an individualized TMS target. The healthy individual standard model is used for measuring the deviation of the brain region function connection strength of the depression subject, and the targeted most abnormal brain region individualized TMS positioning method is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of TMS target location, and particularly relates to an individualized TMS location method and system for depression targeting the maximum deviation brain region of the dorsolateral prefrontal cortex. Background Art

[0002] Transcranial magnetic stimulation (TMS) is a non-invasive brain neuromodulation technique that generates electromagnetic induction through rapidly changing magnetic fields, thereby inducing the electrical activity of cortical neurons in the brain to regulate brain function. In 2008, the US Food and Drug Administration (FDA) first approved TMS for the treatment of refractory major depressive disorder. In 2022, the FDA further approved an innovative technology of TMS - Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT), also for the treatment of depression. The main target sites of these two regimens both focus on the left dorsolateral prefrontal cortex (L_DLPFC) and have shown certain therapeutic effects.

[0003] However, the L_DLPFC is a relatively large brain region, and different treatment targets may produce significantly different therapeutic effects. In recent years, some studies have proposed strategies for using functional connectivity to guide TMS for personalized localization. For example, using the most negative connection peak point between the subgenual anterior cingulate cortex (sgACC) and the L_DLPFC as the TMS target for treating depression. But this method is not applicable to all depression patients because not every patient shows functional abnormalities in the sgACC. At the same time, existing target selection methods usually do not fully consider the differences in brain region abnormalities among individuals, as well as the variability of MRI metrics in terms of age and gender.

[0004] Therefore, based on large-scale healthy population data, we constructed a functional connectivity strength normalization model, similar to the growth curve chart used in pediatrics. This model defines the normal range of functional connectivity strength in each brain region of healthy populations. We map the functional connectivity strength data of depression patients into this normalization model, quantify the degree of deviation relative to the average level of healthy populations, and find the brain region with the largest deviation value of functional connectivity strength in the L_DLPFC region as the TMS treatment target. In addition, this method fully considers the differences in functional connectivity strength in the life cycle and gender, thereby more accurately localizing individualized abnormalities.

[0005] Through this target selection strategy based on the normalization model, we can target the functional connectivity position in the L_DLPFC region of depression patients that is most significantly abnormal compared to healthy populations, thereby optimizing the therapeutic effect of TMS and improving its clinical efficacy. Summary of the Invention

[0006] The present invention aims to overcome the defect that the prior art fails to consider targeting the most abnormal brain regions of individuals with depression, and proposes an individualized TMS localization method and system for depression targeting the brain region with the largest deviation in the dorsolateral prefrontal cortex.

[0007] The present invention solves its technical problems through the following technical solutions:

[0008] An individualized TMS localization method for depression targeting the brain region with the largest deviation in the dorsolateral prefrontal cortex, the method steps are as follows:

[0009] Step 1: Based on the MRI data of a group of healthy individuals, calculate the functional connectivity strength values of the brain regions of the healthy individuals;

[0010] Step 2: Based on the functional connectivity strength values of the brain regions of the healthy individuals, construct a normative model of the functional connectivity strength values of the brain regions of the healthy individuals;

[0011] Step 3: Based on the MRI data of a group of depression subjects, calculate the functional connectivity strength values of the brain regions of the depression subjects;

[0012] Step 4: Based on the functional connectivity strength values of the brain regions of the depression subjects, calculate the deviation values of the functional connectivity strength values of the brain regions of the depression subjects in the normative model of the functional connectivity strength of the brain regions of the healthy individuals;

[0013] Step 5: Based on the obtained deviation values of the functional connectivity strength of the brain regions of the depression subjects, find the brain region with the largest deviation in the left dorsolateral prefrontal cortex and use it as the individualized TMS target.

[0014] Moreover, in Step 1, based on the MRI data of a group of healthy individuals, calculating the functional connectivity strength values of the brain regions of the healthy individuals, the specific method is:

[0015] 1.1. Collect the MRI data of a group of healthy individuals, including resting-state fMRI and T1-weighted MRI data, and preprocess these data. Among them, T1-weighted MRI is mainly used for the image segmentation step in the preprocessing of resting-state fMRI and helps for more accurate spatial registration; the fMRI preprocessing steps include the following: removing the initial time points, performing temporal slice correction, correcting head movement, segmenting the image, regressing covariates, registering to the MNI standard space, and performing frequency filtering to obtain the preprocessed resting-state fMRI data of the healthy individuals;

[0016] 1.2. Based on the preprocessed resting-state fMRI data of the healthy individuals and according to the atlas with multiple partitions in the L_DLPFC, extract the BOLD signals of the preprocessed resting-state fMRI data of the healthy individuals in each brain region of the atlas;

[0017] 1.3. Based on the BOLD signals of each brain region, use the DPABI software to calculate the correlation between the BOLD signal of each brain region and the BOLD signals of all other brain regions, and perform Fisher-z transformation to obtain the functional connectivity matrix of the healthy group;

[0018] 1.4. Based on the functional connectivity matrix of the healthy group, use the neuroCombat toolbox and apply the Combat method to control the different site effects of the functional connectivity matrix of the healthy group, reduce the interference of site effects on the modeling process, and obtain the functional connectivity matrix of the healthy group in the common imaging space;

[0019] 1.5. Based on the functional connectivity matrix of the healthy group in the common imaging space, further calculate the functional connectivity strength of each brain region, that is, obtain the sum of the functional connectivities of each brain region with all other brain regions, and get the functional connectivity strength values of each brain region of healthy individuals;

[0020] 1.6. Based on the functional connectivity strength values of each brain region of healthy individuals, extract the functional connectivity strength values of the brain regions located in the L_DLPFC. The definition of L_DLPFC is: the sum of spherical regions with a radius of 20 mm centered at the following four positions, specifically x = -36, y = 39, z = 43; x = -44, y = 40, z = 29; x = -41, y = 16, z = 54; and x = -39, y = 26, z = 49. Finally, obtain the functional connectivity strength values of each sub-region of L_DLPFC of healthy individuals.

[0021] Moreover, in step 2, based on the functional connectivity strength values of the brain regions of healthy individuals, a normative model of the functional connectivity strength of the brain regions of healthy individuals is constructed. The specific method is as follows:

[0022] 2.1. Based on the functional connectivity strength values of the L_DLPFC sub-regions, use the Gaussian process regression GPR of the PCN toolbox to construct a normative model of the functional connectivity strength values of the L_DLPFC sub-regions related to age and gender for all healthy individuals;

[0023] 2.2. Use the standardized mean squared error SMSE and the mean squared logarithmic loss MSLL to evaluate the performance of the normative model.

[0024] Moreover, in step 3, based on the MRI data of a group of depression subjects, calculate the functional connectivity strength values of the brain regions of the depression subjects. The specific method is as follows:

[0025] 3.1. Collect MRI data of a group of depression subjects, including resting-state fMRI and T1-weighted MRI, and preprocess this data. Among them, T1-weighted MRI is mainly used for the image segmentation step in the preprocessing of resting-state fMRI and helps with more accurate spatial registration. The fMRI preprocessing steps include the following: removing the initial time points, performing time slice correction, correcting head movement, segmenting images, regressing covariates, registering to the MNI standard space, and performing frequency filtering to obtain the preprocessed resting-state fMRI data of depression subjects;

[0026] 3.2. Based on the preprocessed resting-state fMRI data of depression subjects and according to an atlas with multiple partitions in the L_DLPFC, extract the BOLD signals of the preprocessed resting-state fMRI data of depression subjects in each brain region of this atlas;

[0027] 3.3. Based on the BOLD signals of each brain region, use the DPABI software to calculate the correlation between the BOLD signal of each brain region and the BOLD signals of all other brain regions, and perform Fisher-z transformation to obtain the functional connectivity matrix of depression subjects;

[0028] 3.4. Based on the functional connectivity matrix of depression subjects, use the neuroCombat toolbox and adopt the Combat method to adjust the functional connectivity matrix of depression subjects to the imaging space of the healthy group, reduce the interference of site effects on the modeling process, and obtain the functional connectivity matrix in the unified imaging space, that is, the functional connectivity matrix of depression subjects in the common imaging space;

[0029] 3.5. Based on the functional connectivity matrix of depression subjects in the common imaging space, further calculate the functional connectivity strength of each brain region, that is, obtain the sum of the functional connections of each brain region with all other brain regions, and get the functional connectivity strength values of each brain region of depression subjects;

[0030] 3.6. Based on the functional connectivity strength values of each brain region, extract the functional connectivity strength values of the brain regions located in the L_DLPFC. The definition of the L_DLPFC is: the sum of spherical regions with a radius of 20 mm centered at the following four positions, specifically x = -36, y = 39, z = 43; x = -44, y = 40, z = 29; x = -41, y = 16, z = 54; and x = -39, y = 26, z = 49. Finally, obtain the functional connectivity strength values of each sub-region of the L_DLPFC of depression subjects.

[0031] Moreover, in step 4, based on the functional connectivity strength values of the brain regions of depression subjects, calculate the deviation values of the functional connectivity strength values of the brain regions of depression subjects from the normative model of the functional connectivity strength of the brain regions of healthy individuals. The specific method is:

[0032] 4.1. Based on the functional connectivity strength values of each sub-region of the L_DLPFC in depressive subjects, the functional connectivity strength values of the L_DLPFC sub-regions of each depressive subject are compared with the normative model of healthy subjects, and the degree of deviation is quantified using the Z value:

[0033] ,

[0034] is the observed functional connectivity strength value of the L_DLPFC sub-region,

[0035] is the predicted functional connectivity strength value of the L_DLPFC sub-region,

[0036] is the prediction uncertainty,

[0037] is the variance obtained from the normal distribution;

[0038] The Z value provides a statistical estimate of the degree of deviation of the functional connectivity strength value of the L_DLPFC sub-region of each depressive subject relative to the healthy population, and the deviation value of the functional connectivity strength value of the L_DLPFC sub-region of each depressive individual is obtained.

[0039] Moreover, based on the deviation values of the functional connectivity strength of the brain regions of the obtained depressive subjects in step 5, the brain region with the largest deviation in the left dorsolateral prefrontal lobe is found and used as the individualized TMS target. The specific method is as follows:

[0040] 5.1. Based on the functional connectivity strength deviation values of each depressive subject in the L_DLPFC sub-region, find the maximum value of the functional connectivity strength deviation values of each depressive subject in the L_DLPFC sub-region;

[0041] 5.2. Determine the brain region with the largest functional connectivity strength deviation value as the TMS target in the MNI standard space;

[0042] 5.3. Transform the TMS target in the MNI standard space to the individual space through the inverse transformation matrix to obtain the individualized TMS target, and map the individualized TMS target to the T1-weighted MRI of the depressive subject, and the individualized TMS target can be located.

[0043] A positioning system formulated according to the individualized TMS positioning method for depression targeting the brain region with the largest deviation in the dorsolateral prefrontal lobe specifically includes: a computer and a data upload module, a data preprocessing module, a functional connectivity strength calculation module, a normative model construction module, a calculation module for the functional connectivity strength deviation value of depressive subjects, and an individualized target acquisition module running in the computer;

[0044] The data upload module is used to upload the MRI data of a group of healthy individuals and a group of depression subjects, including resting-state fMRI and T1-weighted MRI;

[0045] The data preprocessing module is used to preprocess the MRI data of a group of healthy individuals and a group of depression subjects collected, specifically including removing the initial time points, performing temporal slice correction, correcting head motion, segmenting images, regressing covariates, registering to the MNI standard space, and performing frequency filtering;

[0046] The functional connectivity strength calculation module, based on the resting-state fMRI of a group of healthy individuals and a group of depression subjects after preprocessing, calculates the correlation between the BOLD signals of each brain region and all other brain regions respectively, and performs Fisher-z transformation to obtain a functional connectivity matrix; applies the Combat method to control the different-site effects of the functional connectivity matrix, reduces the interference of site effects on the modeling process, and obtains a functional connectivity matrix in the common imaging space; based on the functional connectivity matrix in the common imaging space, further calculates the functional connectivity strength of each brain region, that is, obtains the sum of the functional connections between each brain region and all other brain regions, and obtains the functional connectivity strength values of each brain region; based on the functional connectivity strength values of the brain regions, extracts the functional connectivity strength values of the L_DLPFC sub-region from them, where L_DLPFC is defined as: the sum of spherical regions with a radius of 20 mm centered on the following four positions, and the specific positions are x=-36, y=39, z=43; x=-44, y=40, z=29; x=-41, y=16, z=54; and x=-39, y=26, z=49. Finally, obtains the functional connectivity strength values of each sub-region of L_DLPFC.

[0047] In the canonical model construction module, based on the functional connectivity strength values of the L_DLPFC sub-region, uses the Gaussian process regression GPR method in the PCN toolkit to construct a canonical model of the functional connectivity strength of the L_DLPFC sub-region related to age and gender for all healthy individuals; at the same time, uses the standardized mean squared error SMSE and the mean squared logarithmic loss MSLL to evaluate the performance of the canonical model;

[0048] The functional connectivity strength deviation value calculation module for depression subjects compares the functional connectivity strength values of the L_DLPFC sub-region of each depression subject with the functional connectivity strength values of the corresponding brain region in the healthy canonical model, and uses the Z value to quantify the deviation degree between the two;

[0049] The individualized target acquisition module, based on the functional connectivity strength deviation values of the L_DLPFC sub-region of each depression subject, finds the maximum value among the functional connectivity strength deviation values in this sub-region for each depression subject, and determines the corresponding brain region as the TMS target in the MNI standard space; the TMS target in the MNI standard space is transformed into the individual space through the inverse transformation matrix, that is, the individualized TMS target, and the individualized TMS target is mapped in the T1-weighted MRI of the depression subject, and then the individualized TMS target can be located. Description of the Drawings

[0050] Figure 1 It is the calculation flowchart of the individualized TMS positioning method for depression targeting the maximum deviation brain region of the dorsolateral prefrontal lobe of the present invention;

[0051] Figure 2a It is a graph of the deviation value data points of the functional connectivity strength value of the depression subject in the normative model constructed by using the functional connectivity strength data of the brain regions of a group of healthy individuals to construct the normative model of the functional connectivity strength of the brain regions of healthy individuals;

[0052] Figure 2b It is a graph of the deviation value data points of the functional connectivity strength value of the depression subject in the normative model constructed by using the functional connectivity strength data of the brain regions of a group of healthy individuals to construct the normative model of the functional connectivity strength of the brain regions of healthy individuals;

[0053] Figure 3a It is a graph of the correlation data points between the baseline deviation value of the brain region closest to the TMS target and the antidepressant efficacy; the antidepressant efficacy is measured by the reduction rate of the Hamilton Depression Scale (HAMD scale) ((baseline HAMD scale score - post-treatment HAMD scale score) / baseline HAMD scale score);

[0054] Figure 3b It is a graph of the correlation data points between the baseline deviation value of the brain region closest to the TMS target and the anti-anxiety efficacy; the anti-anxiety efficacy is measured by the reduction rate of the Hamilton Anxiety Scale (HAMA scale) ((baseline HAMA scale score - post-treatment HAMA scale score) / baseline HAMA scale score);

[0055] Figure 4a It is a graph of the correlation data points between the change of the deviation value of the brain region closest to the TMS target before and after treatment and the TMS antidepressant efficacy, and the difference between the deviation values before and after treatment is correlated with the TMS efficacy; the antidepressant efficacy is measured by the reduction rate of the Hamilton Depression Scale (HAMD scale) ((baseline HAMD scale score - post-treatment HAMD scale score) / baseline HAMD scale score);

[0056] Figure 4bIt is a scatter plot of the correlation data between the changes in the deviation values of the brain regions closest to the TMS target before and after treatment and the anti-anxiety efficacy of TMS. The difference in the deviation values before and after treatment was correlated with the TMS efficacy. The anti-anxiety efficacy was measured by the reduction rate of the Hamilton Anxiety Scale (HAMA scale) ((baseline HAMA scale score - post-treatment HAMA scale score) / baseline HAMA scale score).

[0057] Figure 5 It is a schematic diagram of the composition structure of the positioning system in the present invention. Detailed implementation manners

[0058] Next, the present invention will be described in more detail through specific embodiments. These embodiments are intended to provide a further understanding and interpretation of the present invention and are only descriptive, without constituting any limitation to the protection scope of the present invention.

[0059] As Figure 1 shown, an individualized TMS positioning method for depression targeting the brain region with the largest deviation in the dorsolateral prefrontal cortex, the method steps are as follows:

[0060] Step 1: Based on the MRI data of a group of healthy individuals, calculate the functional connectivity strength values of the brain regions of the healthy individuals;

[0061] 1.1. Collect the MRI data of a group of healthy individuals, including resting-state fMRI and T1-weighted MRI data, and preprocess these data. Among them, T1-weighted MRI is mainly used for the image segmentation step in the preprocessing of resting-state fMRI and helps for more accurate spatial registration. The fMRI preprocessing steps include the following: removing the initial time points, performing time slice correction, correcting head movement, segmenting the images, regressing covariates, registering to the MNI standard space, and performing frequency filtering to obtain the preprocessed resting-state fMRI data of the healthy individuals;

[0062] 1.2. Based on the preprocessed resting-state fMRI data of the healthy individuals and according to the atlas with multiple partitions in the L_DLPFC, extract the BOLD signals of the preprocessed resting-state fMRI data of the healthy individuals in each brain region of the atlas;

[0063] 1.3. Based on the BOLD signals of each brain region, use DPABI software to calculate the correlation between the BOLD signal of each brain region and the BOLD signals of all other brain regions, and perform Fisher-z transformation to obtain the functional connectivity matrix of the healthy group;

[0064] 1.4. Based on the functional connectivity matrix of the healthy group, using the neuroCombat toolkit, apply the Combat method to control the different site effects of the functional connectivity matrix of the healthy group, reduce the interference of site effects on the modeling process, and obtain the functional connectivity matrix of the healthy group in the common imaging space;

[0065] 1.5 Based on the functional connectivity matrix of the healthy group in the common imaging space, further calculate the functional connectivity strength of each brain region, that is, obtain the sum of the functional connectivity of each brain region with all other brain regions, and get the functional connectivity strength values of each brain region;

[0066] 1.6. Based on the functional connectivity strength values of the brain regions, extract the functional connectivity strength values of the brain regions located in the L_DLPFC. L_DLPFC is defined as: the sum of spherical regions with a radius of 20 mm centered at the following four positions, specifically x = -36, y = 39, z = 43; x = -44, y = 40, z = 29; x = -41, y = 16, z = 54 and x = -39, y = 26, z = 49. Finally, obtain the functional connectivity strength values of each sub-region of the L_DLPFC of healthy individuals.

[0067] We screened the resting-state fMRI data and T1-weighted MRI data of 897 healthy people from a Chinese public database, the REST-meta-MDD database, aged between 14 and 65 years old, including 22 sites. The exclusion criteria were: 1) The data from the 4th and 25th research centers were excluded because the data from the 4th and 25th centers were repeated with the data from the 14th center or mainly consisted of elderly subjects. 2) Subjects with substandard imaging data quality (based on visual inspection, quality control score less than 4 points), subjects with excessive head movement (average Jenkinson head movement displacement (FD) > 0.2 mm), and subjects with incomplete imaging data were excluded. 3) Subjects with missing signals in the complete region of interest (ROI) were excluded.

[0068] These data were preprocessed by researchers at each site who mastered DPABI according to the default process of DPABI, mainly including removing the initial time points, performing temporal slice correction, correcting head movement, segmenting images, regressing covariates, registering to the MNI standard space, and performing frequency filtering;

[0069] We also collected the resting-state fMRI data and T1-weighted MRI data of 46 healthy people from Tianjin Anding Hospital. The MRI data of these subjects were also preprocessed with the same process as above, mainly including removing the initial time points, performing temporal slice correction, correcting head movement, segmenting images, regressing covariates, registering to the MNI standard space, and performing frequency filtering;

[0070] A total of 943 healthy subjects' preprocessed resting-state fMRI data were obtained from two datasets.

[0071] Next, based on the preprocessed resting-state fMRI data of healthy individuals, the BOLD signals of all brain regions were extracted according to the Dosenbach atlas. The DPABI software was used to calculate the correlation between the BOLD signals of each brain region and all other brain regions, and Fisher-z transformation was performed to obtain the functional connectivity matrix. Subsequently, the Combat method was applied to control the different site effects in the functional connectivity matrix, reducing the interference of site effects on the modeling process, and finally, the functional connectivity matrix of the healthy group in the common imaging space was obtained.

[0072] Based on the functional connectivity matrix of the healthy group in the common imaging space, the functional connectivity strength was further calculated, that is, the sum of the functional connections of each brain region with all other brain regions was obtained to get the functional connectivity strength values of each brain region; based on the functional connectivity strength values in the Dosenbatch brain regions, the functional connectivity strength values of 6 L_DLPFC sub-regions located in the L_DLPFC were extracted. Among them, L_DLPFC is defined as the sum of spherical regions with a radius of 20 mm centered at the following four positions, specifically x = -36, y = 39, z = 43; x = -44, y = 40, z = 29; x = -41, y = 16, z = 54, and x = -39, y = 26, z = 49. Finally, the functional connectivity strength values of 6 L_DLPFC sub-regions were extracted.

[0073] Step 2: Based on the functional connectivity strength values of the brain regions of healthy individuals, a normative model of the functional connectivity strength values of the brain regions of healthy individuals was constructed. The specific method is as follows:

[0074] 2.1 Based on the functional connectivity strength values of the L_DLPFC sub-regions, the Gaussian process regression (GPR) of the PCN toolbox was used to construct a normative model of the functional connectivity strength values of the L_DLPFC sub-regions related to age and gender for all healthy individuals;

[0075] 2.2 The standardized mean squared error (SMSE) and mean squared logarithmic loss (MSLL) were used to evaluate the performance of the normative model.

[0076] Based on the functional connectivity strength values of 6 L_DLPFC sub-regions, the Gaussian process regression (GPR) of the PCN toolbox was used to construct a normative model of the functional connectivity strength values of 6 L_DLPFC sub-regions related to age and gender for all healthy individuals, and the standardized mean squared error (SMSE) and mean squared logarithmic loss (MSLL) were used to evaluate the performance of the normative model.

[0077] As shown in Figure 2, two examples are provided for constructing a normative model of the functional connectivity strength in the brain regions of healthy individuals using the functional connectivity strength data of a group of healthy individuals, as well as examples of the deviation values of the functional connectivity strength of depressed subjects from the normative model.

[0078] Figure 2a It shows the normative model of a sub-region (central MNI coordinates: x = -16, y = 29, z = 54) in the left dorsolateral prefrontal cortex with age in the normal male population. The blue solid line represents the normative trajectory of the functional connectivity strength of this sub-region with age, the blue dashed line is the 95% confidence interval, and the light blue dots are the functional connectivity strengths of healthy individuals. The red dots are the functional connectivity strengths of depressed subjects, and the distance from the red dots to the normative trajectory is the deviation value. Only a small number of depressed subjects have the functional connectivity strength of this sub-region exceeding the 95% confidence interval of normal people, while other depressed subjects show abnormalities in other dorsolateral prefrontal sub-regions. This highlights the inconsistency of the abnormal brain regions in the left dorsolateral prefrontal cortex of depressed subjects.

[0079] Figure 2b It shows the normative model of a sub-region (central MNI coordinates: x = -16, y = 29, z = 54) in the left dorsolateral prefrontal cortex with age in the normal female population. The blue solid line represents the normative trajectory of the functional connectivity strength of this sub-region with age, the blue dashed line is the 95% confidence interval, and the light blue dots are the functional connectivity strengths of healthy individuals.

[0080] Step 3: Based on the MRI data of a group of depressed subjects, calculate the functional connectivity strength values of the brain regions of depressed subjects. The specific method is as follows:

[0081] 3.1 Collect the MRI data of a group of depressed subjects, including resting-state fMRI and T1-weighted MRI, and preprocess these data. Among them, the T1-weighted MRI is mainly used for the image segmentation step in the preprocessing of resting-state fMRI and helps for more accurate spatial registration. The fMRI preprocessing steps include the following: removing the initial time points, performing time slice correction, correcting head motion, segmenting the images, regressing covariates, registering to the MNI standard space, and performing frequency filtering to obtain the preprocessed resting-state fMRI data of depressed subjects;

[0082] 3.2 Based on the preprocessed resting-state fMRI data of depressed subjects and according to the atlas with multiple partitions in the L_DLPFC, extract the BOLD signals of the preprocessed resting-state fMRI data of depressed subjects in each brain region of the atlas;

[0083] 3.3. Based on the BOLD signals of each brain region, use the DPABI software to calculate the correlation between the BOLD signal of each brain region and the BOLD signals of all other brain regions, and perform Fisher-z transformation to obtain the functional connectivity matrix of the depression subjects;

[0084] 3.4. Based on the functional connectivity matrix of the depression subjects, use the neuroCombat toolbox and adopt the Combat method to adjust the functional connectivity matrix of the depression subjects to the imaging space of the healthy group, reduce the interference of the site effect on the modeling process, and obtain the functional connectivity matrix in the unified imaging space, that is, the functional connectivity matrix of the depression subjects in the common imaging space.

[0085] 3.5 Based on the functional connectivity matrix of the depression subjects in the common imaging space, further calculate the functional connectivity strength of each brain region, that is, obtain the sum of the functional connections of each brain region with all other brain regions, and get the functional connectivity strength values of each brain region;

[0086] 3.6. Based on the functional connectivity strength values of the brain regions, extract the functional connectivity strength values of the brain regions located in the L_DLPFC. L_DLPFC is defined as: the sum of spherical regions with a radius of 20 mm centered on the following four positions, specifically x = -36, y = 39, z = 43; x = -44, y = 40, z = 29; x = -41, y = 16, z = 54; and x = -39, y = 26, z = 49. Finally, obtain the functional connectivity strength values of each sub-region of the L_DLPFC of the depression subjects.

[0087] We screened 971 resting-state fMRI data of depression patients from a Chinese public database, the REST-meta-MDD database, aged between 14 and 65 years old, including 22 sites. The exclusion criteria were as follows: 1) The data from the 4th and 25th research centers were excluded because the data from the 4th and 25th centers were repeated with the data from the 14th center or mainly consisted of elderly depressive patients and remitted patients. 2) Subjects with substandard imaging data quality (based on visual inspection, quality control score less than 4 points), subjects with excessive head movement (average Jenkinson head movement displacement (FD) > 0.2 mm), and subjects with incomplete imaging data were excluded. 3) Individuals in the remission period, that is, subjects with a Hamilton Depression Rating Scale (HAMD) score not higher than 7 points, were excluded. 4) Subjects lacking complete region of interest (ROI) signals were excluded.

[0088] These data were preprocessed by researchers at each site who mastered DPABI according to the default process of DPABI, mainly including removing the initial time points, performing temporal slice correction, correcting head movement, segmenting images, regressing covariates, registering to the MNI standard space, and performing frequency filtering;

[0089] We also collected resting-state fMRI data and T1-weighted MRI data of 28 depressed subjects who had undergone TMS intervention from Tianjin Anding Hospital and recorded the TMS target positions. The MRI data of these subjects also underwent the same preprocessing procedures as above, mainly including removing the initial time points, performing temporal slice correction, correcting head motion, segmenting images, regressing covariates, registering to the MNI standard space, and performing frequency filtering;

[0090] A total of 999 depressed subjects' preprocessed resting-state fMRI data were obtained from the two datasets.

[0091] Next, based on the preprocessed resting-state fMRI data of healthy individuals, BOLD signals of all brain regions were extracted according to the Dosenbach atlas. The DPABI software was used to calculate the correlation between the BOLD signals of each brain region and all other brain regions, and Fisher-z transformation was performed to obtain the functional connectivity matrix. Subsequently, the Combat method was applied to control the different site effects in the functional connectivity matrix and reduce the interference of site effects on the modeling process. Finally, the functional connectivity matrix of the healthy group in the common imaging space was obtained.

[0092] Based on the functional connectivity matrix of the depressed subjects in the common imaging space, the functional connectivity strength was further calculated, that is, the sum of the functional connectivity of each brain region with all other brain regions was obtained to get the functional connectivity strength values of each brain region; based on the functional connectivity strength values in the Dosenbatch brain regions, the functional connectivity strength values of 6 L_DLPFC subregions located in the L_DLPFC were extracted. Among them, L_DLPFC is defined as: the sum of spherical regions with a radius of 20 mm centered at the following four positions, specifically x = -36, y = 39, z = 43; x = -44, y = 40, z = 29; x = -41, y = 16, z = 54; and x = -39, y = 26, z = 49. Finally, the functional connectivity strength values of 6 L_DLPFC subregions were extracted.

[0093] Step 4: Based on the functional connectivity strength values of the brain regions of the depressed subjects, measure the deviation values of the functional connectivity strength values of the brain regions of the depressed subjects from the normative model of the functional connectivity strength of the brain regions of healthy individuals. The specific method is as follows:

[0094] 4.1. Based on the functional connectivity strength values of the L_DLPFC subregions of the depressed subjects, compare the functional connectivity strength values of the L_DLPFC subregions of each depressed subject with the normative model of the healthy subjects, and use the Z value to quantify the degree of deviation:

[0095] ,

[0096] is the observed functional connectivity strength value of the L_DLPFC sub-region,

[0097] is the predicted functional connectivity strength value of the L_DLPFC sub-region,

[0098] is the prediction uncertainty,

[0099] is the variance obtained from the canonical distribution;

[0100] The Z-value provides a statistical estimate of the deviation degree of the functional connectivity strength value of the L_DLPFC sub-region of each depression subject relative to the healthy population, and the deviation value of the functional connectivity strength value of the L_DLPFC sub-region of each individual with depression is obtained.

[0101] Based on the functional connectivity strength values of 6 L_DLPFC sub-regions, the functional connectivity strength values of the 6 L_DLPFC sub-regions of each depression subject are compared with the functional connectivity strength values of the 6 L_DLPFC sub-regions of the brain regions of the healthy canonical model, and the Z-value is used to quantify the deviation degree.

[0102] As shown in the two examples in Figure 2, they are examples of using the functional connectivity strength data of the brain regions of a group of healthy individuals to construct a canonical model of the functional connectivity strength of the brain regions of healthy individuals and the deviation values of the functional connectivity strength values of depressed subjects in the canonical model.

[0103] Figure 2a Shows the canonical model of the change of a sub-region of the left dorsolateral prefrontal cortex (central MNI coordinates: x = -16, y = 29, z = 54) with age in the normal male population. The blue solid line represents the canonical trajectory of the change of the functional connectivity strength of this sub-region with age, the blue dashed line is the 95% confidence interval, and the light blue dots are the functional connectivity strengths of healthy people; the red dots are the functional connectivity strengths of depression subjects, and the distance from the red dots to the canonical trajectory is the deviation value. Only a small part of the depression subjects have the functional connectivity strength of this sub-region exceeding the 95% confidence interval of normal people, while other depression subjects show abnormalities in other dorsolateral prefrontal sub-regions. This highlights the inconsistency of the abnormal brain regions in the left dorsolateral prefrontal cortex (L_DLPFC) of depression subjects.

[0104] Figure 2bShows a normative model of the change with age of a sub-region in the left dorsolateral prefrontal cortex (central MNI coordinates: x = -16, y = 29, z = 54) in the normal female population. The solid blue line represents the normative trajectory of the functional connectivity strength of this sub-region changing with age, the blue dashed line is the 95% confidence interval, and the light blue dots are the functional connectivity strengths of healthy individuals; the red dots are the functional connectivity strengths of depressive subjects, and the distance from the red dots to the normative trajectory is the deviation value. Only a small number of depressive subjects have the functional connectivity strength of this sub-region exceeding the 95% confidence interval of normal people, while other depressive subjects show abnormalities in other dorsolateral prefrontal sub-regions. This highlights the inconsistency of the abnormal brain regions in the left dorsolateral prefrontal cortex (L_DLPFC) of depressive subjects.

[0105] Step 5. Based on the deviation values of the functional connectivity strengths of the brain regions of the obtained depressive subjects, find the brain region with the largest deviation in the left dorsolateral prefrontal lobe as the individualized TMS target. The specific method is as follows:

[0106] 5.1. Based on the deviation values of the functional connectivity strengths of each depressive subject in the L_DLPFC sub-region, find the maximum value of the deviation values of the functional connectivity strengths of each depressive subject in the L_DLPFC sub-region.

[0107] 5.2. Determine the brain region with the largest deviation value of the functional connectivity strength as the TMS target in the MNI standard space;

[0108] 5.3. Transform the TMS target in the MNI standard space to the individual space through the inverse transformation matrix to obtain the individualized TMS target, and map this individualized TMS target to the T1-weighted MRI of the depressive subject, then the individualized TMS target can be located.

[0109] To verify the effectiveness of this TMS target localization, we used the resting-state fMRI data of 28 depressive subjects who had received TMS intervention and recorded their TMS target positions. The TMS intervention adopted the intermittent theta burst stimulation (iTBS) protocol and was carried out 20 times in total. The target position was based on one of the following two methods: 1) the peak point of the most negative functional connectivity of the ventral anterior cingulate cortex in the left dorsolateral prefrontal cortex; or 2) the target determined by the traditional 5-cm localization method (MNI coordinates: x = -41, y = 16, z = 54). The antidepressant efficacy of depressive subjects was measured by the reduction rate of the Hamilton Depression Scale (HAMD scale) ((baseline HAMD scale score - post-treatment HAMD scale score) / baseline HAMD scale score), and the anti-anxiety efficacy was measured by the reduction rate of the Hamilton Anxiety Scale (HAMA scale) ((baseline HAMA scale score - post-treatment HAMA scale score) / baseline HAMA scale score).

[0110] AsFigure 3a The correlation between the baseline deviation value of the brain region closest to the TMS target and the antidepressant efficacy; the antidepressant efficacy was measured by the reduction rate of the Hamilton Depression Scale (HAMD scale) ((baseline HAMD scale score - post-treatment HAMD scale score) / baseline HAMD scale score). This figure shows that the greater the deviation value of the target baseline level, the better the antidepressant efficacy (r = 0.39).

[0111] Figure 3b The correlation between the baseline deviation value of the brain region closest to the TMS target and the anti-anxiety efficacy; the anti-anxiety efficacy was measured by the reduction rate of the Hamilton Anxiety Scale (HAMA scale) ((baseline HAMA scale score - post-treatment HAMA scale score) / baseline HAMA scale score). This figure shows that the greater the deviation value of the target baseline level, the better the anti-anxiety efficacy (r = 0.41).

[0112] Figure 4a To analyze the correlation between the change in the deviation value of the brain region closest to the target before and after treatment and the TMS antidepressant efficacy, the difference in the deviation values before and after treatment was correlated with the TMS efficacy. The antidepressant efficacy was measured by the reduction rate of the Hamilton Depression Scale (HAMD scale) ((baseline HAMD scale score - post-treatment HAMD scale score) / baseline HAMD scale score). This figure shows that the reduction of the deviation value of the target by TMS intervention is related to the antidepressant efficacy (r = 0.48), and the more the deviation value is reduced, the better the antidepressant efficacy.

[0113] Figure 4b To analyze the correlation between the change in the deviation value of the brain region closest to the target before and after treatment and the TMS anti-anxiety efficacy, the difference in the deviation values before and after treatment was correlated with the TMS efficacy. The anti-anxiety efficacy was measured by the reduction rate of the Hamilton Anxiety Scale (HAMA scale) ((baseline HAMA scale score - post-treatment HAMA scale score) / baseline HAMA scale score). This figure shows that the reduction of the deviation value of the target by TMS intervention is related to the anti-anxiety efficacy (r = 0.37), and the more the deviation value is reduced, the better the anti-anxiety efficacy.

[0114] A positioning system formulated according to an individualized TMS positioning method for depression targeting the brain region with the largest deviation in the dorsolateral prefrontal lobe, specifically including: a computer and a data upload module, a data preprocessing module, a functional connectivity strength calculation module, a normative model construction module, a functional connectivity strength deviation value calculation module for depression subjects, and an individualized target acquisition module running in the computer.

[0115] The data upload module is used to upload the MRI data of a group of healthy individuals and a group of depression subjects, including resting-state fMRI and T1-weighted MRI;

[0116] A data preprocessing module for preprocessing the MRI data of a group of healthy individuals and a group of depression subjects collected, specifically including removing the initial time points, performing temporal slice correction, correcting head motion, segmenting images, regressing covariates, registering to the MNI standard space, and performing frequency filtering;

[0117] A functional connectivity strength calculation module, based on the resting-state fMRI of a group of healthy individuals and a group of depression subjects after preprocessing, calculates the correlation between the BOLD signals of each brain region and all other brain regions respectively, and performs Fisher-z transformation to obtain a functional connectivity matrix; applies the Combat method to control the different site effects of the functional connectivity matrix and reduce the interference of site effects on the modeling process to obtain a functional connectivity matrix in the common imaging space; based on the functional connectivity matrix in the common imaging space, further calculates the functional connectivity strength of each brain region, that is, obtains the sum of the functional connections between each brain region and all other brain regions to get the functional connectivity strength values of each brain region; based on the functional connectivity strength values of the brain regions, extracts the functional connectivity strength values of the L_DLPFC subregion. Among them, L_DLPFC is defined as: the sum of spherical regions with a radius of 20 mm centered at the following four positions, specifically x = -36, y = 39, z = 43; x = -44, y = 40, z = 29; x = -41, y = 16, z = 54; and x = -39, y = 26, z = 49. Finally, the functional connectivity strength values of each subregion of L_DLPFC are obtained.

[0118] In the canonical model construction module, based on the functional connectivity strength values of the L_DLPFC subregion, uses the Gaussian process regression (GPR) method in the PCN toolbox to construct a canonical model of the functional connectivity strength of the L_DLPFC subregion related to age and gender for all healthy individuals. At the same time, uses the standardized mean squared error (SMSE) and mean squared logarithmic loss (MSLL) to evaluate the performance of the canonical model.

[0119] A functional connectivity strength deviation value calculation module for depression subjects compares the functional connectivity strength values of the L_DLPFC subregion of each depression subject with the functional connectivity strength values of the corresponding brain region in the healthy canonical model, and uses the Z value to quantify the deviation degree between the two;

[0120] An individualized target acquisition module, based on the functional connectivity strength deviation values of the L_DLPFC subregion of each depression subject, finds the maximum value among the functional connectivity strength deviation values of each depression subject in this subregion, and determines the corresponding brain region as the TMS target in the MNI standard space; transforms the TMS target in the MNI standard space to the individual space through the inverse transformation matrix, that is, the individualized TMS target, and maps the individualized TMS target in the T1-weighted MRI of the depression subject to locate the individualized TMS target.

[0121] As Figure 5 shown Figure 5 This is a schematic diagram of the composition structure of the positioning system in the present invention, clearly showing each component of the system and their interrelationships, so as to comprehensively understand and explain the positioning system of the present invention.

[0122] The present invention constructs a normative model of healthy people and measures the deviation value of the functional connectivity strength in the brain regions of depression subjects, aiming to identify the brain region with the largest deviation in the L_DLPFC of depression patients. Compared with traditional methods, this method fully considers the abnormal position differences of the L_DLPFC in different depression patients, aiming to target the sub-region of the L_DLPFC with the largest deviation.

[0123] Although the embodiments and drawings of the present invention are disclosed for illustrative purposes, those skilled in the art can understand that various substitutions, changes, and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the scope of the present invention is not limited to the content disclosed in the embodiments and drawings.

Claims

1. A method for personalized TMS localization of depression targeting the dorsolateral prefrontal cortex with the largest deviation, characterized by: The method steps are: Step 1: Based on the MRI data of a group of healthy individuals, the functional connectivity strength values ​​of the brain regions of healthy individuals are calculated; Step 2: constructing a normative model of the functional connectivity strength values ​​of brain regions of healthy individuals based on the functional connectivity strength values ​​of brain regions of healthy individuals; Step 3, based on the MRI data of a group of depression subjects, calculating the brain region functional connectivity strength values ​​of the depression subjects; Step 4, based on the brain region functional connection strength values ​​of the depression subjects, calculating the deviation value of the brain region functional connection strength values ​​of the depression subjects from the normative model of the brain region functional connection strength values ​​of the healthy individuals; Step 5: Based on the deviation values ​​of the functional connectivity strength of the brain regions of the depressed subjects, the brain region with the largest deviation in the left dorsolateral prefrontal cortex is found and used as an individualized TMS target.

2. The method for individualized TMS localization of depression targeting the dorsolateral prefrontal cortex with the largest deviation according to claim 1, characterized in that: In step 1, based on a group of MRI data of healthy individuals, the functional connection strength values ​​of brain regions of healthy individuals are calculated, and the specific method is: 1.

1. Collect a group of MRI data of healthy individuals, including resting fMRI and T1-weighted MRI data, and preprocess these data. T1-weighted MRI is mainly used for the image segmentation step in resting fMRI preprocessing and helps to more accurately align the space. The fMRI preprocessing steps include the following: removing the initial time point, performing time layer correction, correcting head motion, segmenting the image, regressing covariates, aligning to the MNI standard space, and performing frequency filtering to obtain the resting fMRI data of healthy individuals after preprocessing. 1.

2. Based on the pre-processed healthy individual resting-state fMRI data and the atlas with multiple partitions in L_DLPFC, extract the BOLD signal of each brain region in the atlas of the pre-processed healthy individual resting-state fMRI data; 1.

3. Based on the BOLD signals of each brain region, the correlation between the BOLD signals of each brain region and the BOLD signals of all other brain regions was calculated using DPABI software, and Fisher-z transformation was performed to obtain the functional connectivity matrix of the healthy group; 1.

4. Based on the functional connectivity matrix of the healthy group, the neuroCombat toolkit was used to apply the Combat method to control the different site effects of the functional connectivity matrix of the healthy group, reduce the interference of the site effect on the modeling process, and obtain the functional connectivity matrix of the healthy group in the common imaging space; 1.5 Based on the functional connectivity matrix of the healthy group in the common image space, the functional connectivity strength of each brain region was further calculated, that is, the sum of the functional connections between each brain region and all other brain regions was obtained to obtain the functional connectivity strength value of each brain region in healthy individuals; 1.

6. Based on the functional connectivity strength values ​​of each brain region of healthy individuals, the functional connectivity strength values ​​of the brain regions located in L_DLPFC were extracted. L_DLPFC was defined as the sum of the spherical areas with a radius of 20 mm centered at the following four positions: x=-36, y=39, z=43; x=-44, y=40, z=29; x=-41, y=16, z=54 and x=-39, y=26, z=49. Finally, the functional connectivity strength values ​​of each subregion of L_DLPFC of healthy individuals were obtained.

3. The method for individualized TMS localization of depression targeting the dorsolateral prefrontal cortex with the largest deviation according to claim 1, characterized in that: In step 2, based on the brain region functional connection strength values ​​of healthy individuals, a normative model of the brain region functional connection strength of healthy individuals is constructed, and the specific method is: 2.

1. Based on the functional connectivity strength values ​​of the L_DLPFC subregions, a normative model of the functional connectivity strength values ​​of the L_DLPFC subregions related to age and gender was constructed for all healthy individuals using Gaussian process regression (GPR) of the PCN toolkit. 2.

2. Use standardized mean square error SMSE and mean square logarithmic loss MSLL to evaluate the performance of the regularized model.

4. The method for individualized TMS localization of depression targeting the dorsolateral prefrontal cortex with the largest deviation according to claim 1, characterized in that: In step 3, based on the MRI data of a group of depression subjects, the brain region functional connection strength values ​​of the depression subjects are calculated, and the specific method is: 3.

1. Collect MRI data of a group of depression subjects, including resting-state fMRI and T1-weighted MRI, and preprocess these data. T1-weighted MRI is mainly used for the image segmentation step in resting-state fMRI preprocessing and helps more accurate spatial registration. The fMRI preprocessing steps include the following: The pre-processed resting-state fMRI data of depression subjects were obtained by removing the initial time point, performing time layer correction, correcting head motion, segmenting the image, regressing covariates, aligning to the MNI standard space, and performing frequency filtering; 3.

2. Based on the resting-state fMRI data of the pre-processed depression subjects and the atlas with multiple partitions in L_DLPFC, extract the BOLD signal of each brain region of the resting-state fMRI data of the pre-processed depression subjects in the atlas; 3.

3. Based on the BOLD signals of each brain region, the correlation between the BOLD signals of each brain region and the BOLD signals of all other brain regions was calculated using DPABI software, and Fisher-z transformation was performed to obtain the functional connectivity matrix of the subjects with depression; 3.

4. Based on the functional connectivity matrix of the depression subjects, the neuroCombat toolkit was used to adjust the functional connectivity matrix of the depression subjects to the image space of the healthy group using the Combat method to reduce the interference of the site effect on the modeling process and obtain the functional connectivity matrix in the unified image space, that is, the functional connectivity matrix of the depression subjects in the common image space; 3.

5. Based on the functional connectivity matrix of the depression subjects in the common image space, the functional connectivity strength of each brain region is further calculated, that is, the sum of the functional connections between each brain region and all other brain regions is obtained to obtain the functional connectivity strength value of each brain region of the depression subjects; 3.

6. Based on the functional connectivity strength values ​​of each brain region, the functional connectivity strength values ​​of the brain regions located in L_DLPFC were extracted. L_DLPFC was defined as the sum of the spherical areas with a radius of 20 mm centered on the following four positions: x=-36, y=39, z=43; x=-44, y=40, z=29; x=-41, y=16, z=54 and x=-39, y=26, z=49. Finally, the functional connectivity strength values ​​of each subregion of L_DLPFC of the subjects with depression were obtained.

5. The method for individualized TMS localization of depression targeting the dorsolateral prefrontal cortex with the largest deviation according to claim 1, characterized in that: In step 4, based on the brain region functional connection strength values ​​of the depression subject, the deviation value of the brain region functional connection strength values ​​of the depression subject from the normative model of the brain region functional connection strength values ​​of the healthy individual is calculated, and the specific method is: 4.

1. Based on the functional connectivity strength values ​​of each L_DLPFC subregion of the depressed subjects, the functional connectivity strength values ​​of the L_DLPFC subregion of each depressed subject were compared with the normative model of healthy subjects, and the degree of deviation was quantified using the Z value: ， is the observed functional connectivity strength value of the L_DLPFC subregion, is the predicted functional connectivity strength value of the L_DLPFC subregion, is the uncertainty of the prediction, is the variance obtained from the canonical distribution; The Z value provides a statistical estimate of the degree of deviation of the functional connectivity strength value of the L_DLPFC subregion of each depressed subject relative to the healthy population, and the deviation value of the functional connectivity strength value of the L_DLPFC subregion of each depressed individual is obtained.

6. The method for individualized TMS localization of depression targeting the dorsolateral prefrontal cortex with the largest deviation according to claim 1, characterized in that: In step 5, based on the obtained deviation value of the brain region functional connectivity strength value of the depression subject, the brain region with the largest deviation in the left dorsolateral prefrontal lobe is found and used as the individualized TMS target. The specific method is: 5.

1. Based on the functional connectivity strength deviation value of each depressed subject in the L_DLPFC subregion, find the maximum functional connectivity strength deviation value of each depressed subject in the L_DLPFC subregion; 5.

2. The brain area with the largest deviation value of functional connectivity intensity was determined as the TMS target in the MNI standard space; 5.3 The TMS target in the MNI standard space is transformed into the individual space through the inverse transformation matrix to obtain the individualized TMS target, and the individualized TMS target is mapped to the T1-weighted MRI of the depressed subject to locate the individualized TMS target.

7. A positioning system developed according to the individualized TMS positioning method for depression targeting the dorsolateral prefrontal cortex maximum deviation brain area according to any one of claims 1 to 6, specifically comprising: A computer and a data uploading module, a data preprocessing module, a functional connection strength calculation module, a standard model building module, a depression subject functional connection strength deviation value calculation module, and an individualized target acquisition module running in the computer; The data uploading module is used to upload MRI data of a group of healthy individuals and a group of depression subjects, including resting fMRI and T1-weighted MRI; The data preprocessing module is used to preprocess the collected MRI data of a group of healthy individuals and a group of depression subjects, including removing the initial time point, performing time layer correction, correcting head motion, segmenting images, regressing covariates, aligning to the MNI standard space, and performing frequency filtering; The functional connection strength calculation module calculates the correlation between the BOLD signals of each brain region and all other brain regions based on the resting-state fMRI of a group of healthy individuals and a group of depression subjects after preprocessing, and performs Fisher-z transformation to obtain the functional connection matrix; the Combat method is used to control the different site effects of the functional connection matrix, reduce the interference of the site effect on the modeling process, and obtain the functional connection matrix in the common image space; based on the functional connection matrix in the common image space, the functional connection strength of each brain region is further calculated, that is, the correlation between each brain region and all other brain regions is obtained. The functional connection strength values ​​of each brain region are obtained by summing up the functional connections of the brain regions; based on the functional connection strength values ​​of the brain regions, the functional connection strength values ​​of the L_DLPFC subregions are extracted, where L_DLPFC is defined as the sum of the spherical regions with a radius of 20 mm centered at the following four positions: x=-36, y=39, z=43; x=-44, y=40, z=29; x=-41, y=16, z=54 and x=-39, y=26, z=49. Finally, the functional connection strength values ​​of each L_DLPFC subregion are obtained; In the canonical model building module, based on the functional connectivity strength values ​​of the L_DLPFC subregion, the Gaussian process regression (GPR) method in the PCN toolkit was used to build a canonical model of the functional connectivity strength of the L_DLPFC subregion related to age and gender for all healthy individuals; at the same time, the standardized mean square error (SMSE) and mean square logarithmic loss (MSLL) were used to evaluate the performance of the canonical model; The module for calculating the deviation value of functional connectivity strength of depression subjects compares the functional connectivity strength value of the L_DLPFC subregion of each depression subject with the functional connectivity strength value of the corresponding brain region in the healthy normative model, and uses the Z value to quantify the degree of deviation between the two; The individualized target acquisition module finds the maximum value of the functional connectivity strength deviation value in the L_DLPFC subregion of each depressed subject based on the functional connectivity strength deviation value of the subregion, and determines the corresponding brain region as the TMS target in the MNI standard space; the TMS target in the MNI standard space is transformed to the individual space, i.e., the individualized TMS target, through the inverse transformation matrix, and the individualized TMS target is mapped in the T1-weighted MRI of the depressed subject to locate the individualized TMS target.

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