Individualized TMS Localization Method and System for Depression Targeting the Largest Deviation Brain Region in the Dorsolateral Prefrontal Cortex

Abstract

The present invention relates to an individualized TMS localization method and system for depression targeting the brain region with the largest deviation in the dorsolateral prefrontal lobe. Based on the MRI data of healthy individuals, the functional connectivity strength values of the brain regions of healthy individuals are calculated; 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 is constructed; based on the MRI data of depression subjects, the functional connectivity strength values of the brain regions of depression subjects are calculated; based on the functional connectivity strength values of the brain regions of depression subjects, the deviation values of the functional connectivity strength values of the brain regions of depression subjects in the normative model of the functional connectivity strength values of the brain regions of healthy individuals are measured; based on the obtained deviation values of the functional connectivity strength values of the brain regions of depression subjects, the brain region with the largest deviation in the left dorsolateral prefrontal lobe is found, and this brain region is used as the individualized TMS target. The present invention realizes an individualized TMS localization method targeting the most abnormal brain region by measuring the deviation of the functional connectivity strength of the brain regions of depression subjects using the normative model of healthy individuals.

Description

Technical Field

[0001] The present invention belongs to the technical field of TMS target localization, and particularly relates to an individualized TMS localization 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 treatment-resistant major depression. In 2022, the FDA further approved an innovative technology of TMS - Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT), which is also used 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. However, 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 graph used in pediatrics. This model defines the normal range of functional connectivity strength in each brain region of the healthy population. We mapped the functional connectivity strength data of depression patients into this normalization model, quantified the degree of deviation from the average level of the healthy population, and found 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 the healthy population, 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 positioning method and system for depression targeting the maximum deviation brain region of the dorsolateral prefrontal cortex.

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

[0008] An individualized TMS positioning method for depression targeting the maximum deviation brain region of 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 maximum 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, the T1-weighted MRI is mainly used for the image segmentation step in the preprocessing of the resting-state fMRI and helps for more accurate spatial registration; the fMRI preprocessing steps include the following contents: removing the initial time points, performing time 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, apply the Combat method to control the different site effects of the healthy group's functional connectivity matrix, 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 connections 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 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 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:

[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:

[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 slice-time 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 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 subjects with depression, the functional connectivity strength values of the L_DLPFC sub-regions of each subject with depression 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 subject with depression 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.

[0039] Moreover, based on the deviation values of the functional connectivity strength of the brain regions of the obtained subjects with depression 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 deviation values of the functional connectivity strength of each subject with depression in the L_DLPFC sub-region, find the maximum value of the deviation values of the functional connectivity strength of each subject with depression in the L_DLPFC sub-region;

[0041] 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;

[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 subject with depression, then 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 deviation value of the functional connectivity strength of subjects with depression, 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 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 sub-region from them, where L_DLPFC is defined as: the sum of spherical regions with a radius of 20 mm centered at the following four positions, specifically the 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 sub-region of L_DLPFC are obtained.

[0047] In the canonical model construction module, based on the functional connectivity strength values of the L_DLPFC sub-region, the Gaussian process regression (GPR) method in the PCN toolbox is used 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, the standardized mean squared error (SMSE) and mean squared logarithmic loss (MSLL) are used 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 regions 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 on the T1-weighted MRI of the depression subject, then the individualized TMS target can be located. Description of the Drawings

[0050] Figure 1 It is a computational flowchart of the individualized TMS localization method for depression targeting the maximum deviation brain region of the dorsolateral prefrontal cortex of the present invention;

[0051] Figure 2a It is a data point diagram of the deviation value of the functional connectivity strength 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 and the normative model of the functional connectivity strength of the healthy individual brain regions;

[0052] Figure 2b It is a data point diagram of the deviation value of the functional connectivity strength 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 and the normative model of the functional connectivity strength of the healthy individual brain regions;

[0053] Figure 3a It is a data point diagram of the correlation 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 data point diagram of 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 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 data point diagram of the correlation between the change in the deviation value of the brain region closest to the TMS target before and after treatment and the TMS antidepressant efficacy, and a correlation analysis is performed between the difference in the deviation value before and after treatment and 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 4bThe data point graph shows the correlation between the change in the deviation value of the brain region 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 This is the 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 for descriptive purposes, and do not constitute 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 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;

[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 connectivities 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 in healthy individuals.

[0067] We screened the resting-state fMRI data and T1-weighted MRI data of 897 healthy individuals 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 the 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 individuals from Tianjin Anding Hospital. The MRI data of these subjects were also preprocessed using 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 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.

[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 connectivities 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 was 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.

[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 subregions, the Gaussian process regression (GPR) of the PCN toolkit was used to construct a normative model of the functional connectivity strength values of the L_DLPFC subregions 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 subregions, the Gaussian process regression (GPR) of the PCN toolkit was used to construct a normative model of the functional connectivity strength values of 6 L_DLPFC subregions 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, there are two examples of using the functional connectivity strength data of brain regions of a group of healthy individuals to construct a normative model of the functional connectivity strength of brain regions in healthy individuals and the deviation values of the functional connectivity strength values 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 change in the normal male population. The blue solid line represents the normative trajectory of the functional connectivity strength of this sub-region with age change, 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 depressed subjects, and the distance from the red dots to the normative trajectory is the deviation value. Only a small part of the 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 change in the normal female population. The blue solid line represents the normative trajectory of the functional connectivity strength of this sub-region with age change, the blue dashed line is the 95% confidence interval, and the light blue dots are the functional connectivity strengths of healthy people.

[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 the 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 movement, segmenting images, regressing covariates, registering to the MNI standard space, and performing frequency filtering to obtain the preprocessed resting-state fMRI data of the depressed subjects;

[0082] 3.2. Based on the preprocessed resting-state fMRI data of the 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 the 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 the spherical regions with a radius of 20 mm centered at 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, 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 of depression from a Chinese public database, the REST-meta-MDD database, with ages 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 depressed patients and patients in remission. 2) Subjects with substandard imaging data quality (based on visual inspection, quality control score less than 4), 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 remission were excluded, that is, subjects with a Hamilton Depression Rating Scale (HAMD) score not higher than 7. 4) Subjects missing the signal of the complete region of interest (ROI) were excluded.

[0088] These data were preprocessed by the 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, reduce the interference of site effects on the modeling process, and finally obtain the functional connectivity matrix of the healthy group in the common imaging space.

[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 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, 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:

[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 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 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 depressive 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 degree of deviation is quantified using the Z-value.

[0102] As shown in 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 depressive subjects in the canonical model.

[0103] Figure 2a Shows the canonical model of the change with age of a sub-region of the left dorsolateral prefrontal cortex (central MNI coordinates: x = -16, y = 29, z = 54) in the normal male population. The blue solid line represents the canonical trajectory of the change in 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 depressive subjects, and the distance from the red dots to the canonical 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 depressive subjects in the left dorsolateral prefrontal cortex (L_DLPFC).

[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 blue solid 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 individuals, 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 cortex 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 value 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 value 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 cortex, 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 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 movement, 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, reduces the interference of site effects on the modeling process, and obtains the 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. 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, obtains the functional connectivity strength values of each sub-region of L_DLPFC.

[0118] 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 toolbox 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 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 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;

[0120] An 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 of each depression subject in this sub-region, 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 on the T1-weighted MRI of the depression subject, then the individualized TMS target can be located.

[0121] As Figure 5 shown Figure 5 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 of brain regions in 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 L_DLPFC sub-region 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. An individualized TMS localization method for depression targeting the dorsolateral prefrontal maximum deviation brain region, characterized in that: The method steps are as follows: Step 1: Calculate the functional connectivity strength values of brain regions of healthy individuals based on the MRI data of a group of healthy individuals; Step 2: Construct 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 the healthy individuals; Step 3: Calculate the functional connectivity strength values of brain regions of depression subjects based on the MRI data of a group of depression subjects; Step 4: Calculate the deviation values of the functional connectivity strength values of brain regions of depression subjects from the 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 depression subjects; Step 5: Based on the obtained deviation values of the functional connectivity strength of brain regions of depression subjects, find the brain region with the largest deviation in the left dorsolateral prefrontal cortex and use it as the individualized TMS target; In step 4, based on the functional connectivity strength values of brain regions of depression subjects, calculating the deviation values of the functional connectivity strength values of brain regions of depression subjects from the normative model of the functional connectivity strength values of brain regions of healthy individuals, the specific method is: Based on the functional connectivity strength values of each sub-region of the L_DLPFC in depression subjects, the functional connectivity strength values of the L_DLPFC sub-regions of each depression subject were compared with the normative model of healthy subjects, and the degree of deviation was quantified using Z values: ; is the functional connectivity strength value observed in the L_DLPFC sub-region, is the predicted functional connectivity strength value of the L_DLPFC sub-region, For the prediction uncertainty, 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 each depression subject from the L_DLPFC sub-region of the healthy population, and the deviation value of the functional connectivity strength value of the L_DLPFC sub-region of each depression individual is obtained; In step 5, based on the obtained deviation values of the functional connectivity strength values of brain regions of depression subjects, finding the brain region with the largest deviation in the left dorsolateral prefrontal cortex and using it as the individualized TMS target, the specific method is: 5.1: Based on the functional connectivity strength deviation value of each depression subject in the L_DLPFC sub-region, find the maximum value of the functional connectivity strength deviation value of each depression subject in the L_DLPFC sub-region; 5.2: Determine the brain region with the largest functional connectivity strength deviation value as the TMS target in the MNI standard space; 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 depression subject, then the individualized TMS target can be located.

2. The individualized TMS localization method for depression targeting the dorsolateral prefrontal maximum deviation brain region according to claim 1, wherein: In step 1, based on the MRI data of a group of healthy individuals, calculating the functional connectivity strength values of brain regions of healthy individuals, the specific method is: 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, 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 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 healthy individuals; 1.2: Based on the preprocessed resting-state fMRI data of 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 healthy individuals in each brain region of the atlas; 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; 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; 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; 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.

3. The individualized TMS localization method for depression targeting the dorsolateral prefrontal maximum deviation brain region according to claim 1, characterized in that: In step 2, based on the functional connectivity strength values of the brain regions of healthy individuals, construct a normative model of the functional connectivity strength of the brain regions of healthy individuals. The specific method is as follows: 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; 2.

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

4. The individualized TMS localization method for depression targeting the dorsolateral prefrontal maximum deviation brain region according to claim 1, wherein: 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: 3.

1. Collect the MRI data of a group of depression 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: Remove the initial time points, perform temporal slice correction, correct head motion, segment the images, regress covariates, register to the MNI standard space, and perform frequency filtering to obtain the preprocessed resting-state fMRI data of the depression subjects; 3.

2. Based on the preprocessed resting-state fMRI data of the depression 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 the depression subjects in each brain region of the atlas; 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; 3.

4. Based on the functional connectivity matrix of the depression subjects, use the neuroCombat toolkit 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; 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, calculate the sum of the functional connections of each brain region with all other brain regions, and obtain the functional connectivity strength values of each brain region of the depression subjects; 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 L_DLPFC is: the sum of the spherical regions with a radius of 20 mm centered at 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, obtain the functional connectivity strength values of each sub-region of the L_DLPFC of the depression subjects.

5. A positioning system developed according to the individualized TMS positioning method for depression targeting the maximum deviation brain region of the dorsolateral prefrontal cortex as described in any one of claims 1-4, specifically comprising: 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; 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; 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 time slice correction, correcting head movement, segmenting images, regressing covariates, registering to the MNI standard space, and performing frequency filtering; The functional connectivity strength calculation module calculates the correlation between the BOLD signals of each brain region and all other brain regions based on the preprocessed resting-state fMRI of a group of healthy individuals and a group of depression subjects, 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; further calculates the functional connectivity strength of each brain region based on the functional connectivity matrix in the common imaging space, that is, calculates the sum of the functional connections of each brain region with all other brain regions to obtain the functional connectivity strength values of each brain region; extracts the functional connectivity strength values of the L_DLPFC sub-region from the functional connectivity strength values of the brain regions, where 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 sub-region of L_DLPFC are obtained. In the canonical model construction module, based on the functional connectivity strength values of the L_DLPFC sub-region, the Gaussian process regression (GPR) method in the PCN toolbox is used 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, the standardized mean square error (SMSE) and mean square logarithmic loss (MSLL) are used to evaluate the performance of the canonical model. 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. The individualized target acquisition module, based on the functional connectivity strength deviation value of the L_DLPFC sub-region of each depression subject, finds the maximum value of the functional connectivity strength deviation value in this sub-region for each depression subject, 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 on the T1-weighted MRI of the depression subject to localize the individualized TMS target.

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