Individual target site localization method, system, and program product for transcranial magnetic stimulation

Abstract

The application relates to the technical field of transcranial magnetic stimulation target positioning, and provides a transcranial magnetic stimulation individual target positioning method, system and program product, which obtains magnetic resonance brain imaging data of a subject individual; carries out preprocessing on the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data, divides superficial brain regions according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions; and determines an individual target stimulation target point of transcranial magnetic stimulation according to the plurality of functional sub-regions, so that the fluctuation of functional connection strength between different voxels can be reduced, the instability of the resting state functional connection in the subject individual can be effectively offset, the functional characteristics of the entire functional sub-region remain relatively consistent even if the connection strength of certain voxels changes over time, and the robustness of target positioning is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transcranial magnetic stimulation target positioning, in particular to a transcranial magnetic stimulation individual target positioning method, system and program product. BACKGROUND

[0002] The resting state functional connectivity of superficial brain regions and deep nuclei can be used as a marker for finding the individualized optimal stimulation target of transcranial magnetic stimulation (TMS). The existing transcranial magnetic stimulation individualized optimal stimulation target positioning algorithm finds the individualized optimal target by determining the superficial brain region that can be directly stimulated by transcranial magnetic stimulation, and then finding the position with the strongest resting state functional connectivity with the target deep nucleus in the superficial brain region, as the individualized optimal stimulation target. For example, finding the voxel position with the most negative resting state functional connectivity between the left dorsolateral prefrontal cortex and the subgenual anterior cingulate cortex as the individualized optimal stimulation target for depression treatment.

[0003] However, the voxel is the smallest unit in space of magnetic resonance imaging, not the smallest unit of functional region division of the cerebral cortex, so voxel-by-voxel search for the optimal stimulation target will ignore the functional information of the brain. In addition, the resting state functional connectivity is not stable within an individual, and the connection strength between the same voxel and the deep nucleus may vary at different times, so the voxel-by-voxel search for the individualized optimal target is easily affected by this instability, resulting in an optimal stimulation target that is not robust enough. SUMMARY

[0004] To solve or at least partially solve the above technical problems, the present application provides a transcranial magnetic stimulation individual target positioning method, system and program product, which can divide the superficial brain region into several functional sub-regions, and then search for the individualized optimal target in each functional sub-region.

[0005] In a first aspect, the present application provides a transcranial magnetic stimulation individual target positioning method, comprising:

[0006] Obtaining magnetic resonance brain imaging data of a subject individual, the magnetic resonance brain imaging data comprising functional magnetic resonance images and structural magnetic resonance images;

[0007] Preprocessing the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data;

[0008] Dividing the superficial brain region according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions.

[0009] Determining the individual target stimulation target of transcranial magnetic stimulation according to the plurality of functional sub-regions.

[0010] In a second aspect, the present application provides an individual target point positioning system for transcranial magnetic stimulation, comprising:

[0011] an acquisition module configured to acquire magnetic resonance data of a subject, the magnetic resonance data comprising functional magnetic resonance images and structural magnetic resonance images;

[0012] a preprocessing module configured to preprocess the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data;

[0013] a division module configured to divide superficial brain regions according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions.

[0014] a positioning module configured to determine an individual target stimulation target point for transcranial magnetic stimulation according to the plurality of functional sub-regions.

[0015] In a third aspect, the embodiments of the present application further provide an individual target point positioning system for transcranial magnetic stimulation, comprising a processor and a memory; and one or more programs stored in the memory and configured to be executed by the processor, the program comprising the steps of the individual target point positioning method for transcranial magnetic stimulation as described in the first aspect.

[0016] In a fourth aspect, the embodiments of the present application further provide a computer program product, which, when executed by a computer, enables the computer to perform the aforementioned individual target point positioning method for transcranial magnetic stimulation.

[0017] The individual target point positioning method of transcranial magnetic stimulation provided by the embodiments of the present application comprises the following steps: acquiring magnetic resonance brain imaging data of a subject, wherein the magnetic resonance brain imaging data comprises functional magnetic resonance images and structural magnetic resonance images; pre-processing the magnetic resonance brain imaging data to obtain pre-processed magnetic resonance brain imaging data; performing pre-processing on the magnetic resonance brain imaging data to eliminate noise introduced in the signal acquisition process and retain the real functional activities of the brain of the subject, thereby providing a reliable basis for subsequent functional connection calculation; then, dividing superficial brain regions according to the pre-processed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions; and determining an individual target stimulation target point of transcranial magnetic stimulation according to the plurality of functional sub-regions, wherein the superficial brain regions are divided into a plurality of functional sub-regions, and then the functional sub-regions are taken as the minimum search unit to find the individualized optimal target point in each functional sub-region. The present scheme comprehensively considers the time and space double characteristics of the cerebral superficial cortex, the spatial arrangement of neurons in the brain has continuity, and neurons with high functional similarity are usually more close in space. The time characteristic on which the functional sub-regional division of the superficial brain region is based is the functional connection strength between voxels, which reflects the functional similarity between different voxels, and the space characteristic is the spatial distance between different voxels, which aims to constrain the division of the functional sub-regions. By dividing the superficial brain region into functional sub-regions, the minimum unit of target point search can better reflect the functional characteristics of the brain, the voxels in each functional sub-region correspond to similar cognitive functions, this division can reduce the fluctuation of the functional connection strength between different voxels, effectively offset the instability of the resting-state functional connection in the subject, even if the connection strength of some voxels changes over time, the functional characteristics of the entire functional sub-region still remain relatively consistent, thereby improving the robustness of target point positioning. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application, the related drawings will be briefly introduced below. It can be understood that the drawings described below are only used to illustrate some embodiments of the present application, and those skilled in the art can also obtain many other technical features and connection relationships not mentioned in the present text from these drawings.

[0019] Figure 1 A flowchart of an individual target point positioning system of transcranial magnetic stimulation provided by the embodiments of the present application;

[0020] Figure 2 A demonstration schematic diagram of acquiring magnetic resonance brain imaging data of a subject by using transcranial magnetic stimulation technology provided by the embodiments of the present application;

[0021] Figure 3 A schematic diagram of pre-processed resting-state functional magnetic resonance imaging data provided by the embodiments of the present application;

[0022] Figure 4 A functional sub-region division diagram of a superficial brain area provided by an embodiment of the present application;

[0023] Figure 5 Another structure diagram of an individual target positioning method of transcranial magnetic stimulation provided by an embodiment of the present application;

[0024] Figure 6 A structure diagram of an individual target positioning system of transcranial magnetic stimulation provided by an embodiment of the present application;

[0025] Figure 7 A structure diagram of an individual target positioning system of transcranial magnetic stimulation provided by an embodiment of the present application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative work, fall within the scope of protection of the present application.

[0027] The terms “first”, “second”, “third”, and “fourth” and the like in the specification of the present application and claims, and the drawings are used to distinguish different objects, but not to describe a particular order. In addition, the terms “include” and “have” and any variations thereof are intended to cover the inclusions without exclusivity. For example, a process, method, system, product, or device including a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or device.

[0028] In this document, the term “embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase does not necessarily mean that all embodiments described in the specification are the same or mutually exclusive or alternative embodiments. The skilled person in the art will explicitly and implicitly

[0029] Studies have shown that the resting state functional connectivity between superficial brain areas and deep nuclei can be used as a marker for finding individualized optimal stimulation targets of transcranial magnetic stimulation.

[0030] In multiple cross-species studies (mice, marmosets, and macaques), comprehensive and high-resolution brain transcriptomes and spatial cellular atlases have revealed a high degree of correspondence between transcriptome identity and spatial specificity for each cell type. This indicates a continuity in the spatial arrangement of neurons in the brain, with neurons exhibiting high functional similarity being spatially closer together. This approach, based on a spatiotemporal dual-feature segmentation algorithm, uses magnetic resonance imaging (MRI) data to estimate the spatiotemporal dual features of neurons—functional connectivity strength and spatial location information—to segment functional subregions of the cerebral cortex. Then, it utilizes these functional subregions, adhering to the principle of high correspondence between neuronal functional similarity and location specificity, to calculate the optimal therapeutic target for each individual subject.

[0031] By dividing the superficial brain region into several functional sub-regions and then using these sub-regions as the smallest search unit, this approach seeks individualized optimal targets for each sub-region. This method comprehensively considers the spatiotemporal characteristics of the superficial brain cortex. The spatial arrangement of neurons in the brain exhibits continuity, and neurons with high functional similarity are typically spatially closer. The temporal characteristic of dividing the superficial brain region into functional sub-regions is the strength of functional connections between voxels, reflecting the functional similarity between different voxels. The spatial characteristic is the spatial distance between different voxels, aiming to constrain the division of functional sub-regions. By dividing the superficial brain region into functional sub-regions, the smallest unit for target search can better reflect the functional characteristics of the brain. Voxels within each functional sub-region correspond to similar cognitive functions. This division can reduce fluctuations in the strength of functional connections between different voxels, effectively offsetting the instability of resting-state functional connections within the individual subject. Even if the connection strength of some voxels changes over time, the functional characteristics of the entire functional sub-region remain relatively consistent, thereby improving the robustness of target localization.

[0032] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0033] Example 1

[0034] like Figure 1 As shown, Figure 1 This is a flowchart illustrating an individual target localization method for transcranial magnetic stimulation (TMS) provided in an embodiment of this application. One embodiment of this application proposes an individual target localization method for TMS, which includes:

[0035] 101. Obtain magnetic resonance brain imaging data of the subject individual, wherein the magnetic resonance brain imaging data includes functional magnetic resonance images and structural magnetic resonance images.

[0036] The magnetic resonance brain imaging data includes functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI).

[0037] 102. Preprocessing the functional magnetic resonance imaging and the structural magnetic resonance imaging to obtain preprocessed magnetic resonance brain imaging data.

[0038] The preprocessing of the magnetic resonance brain imaging data includes:

[0039] The preprocessing of the magnetic resonance brain imaging data includes at least one of the following preprocessing operations: head motion correction, spatial alignment, slice time correction, spatial smoothing, signal filtering, and noise removal.

[0040] Specifically, the head motion correction refers to using a motion correction algorithm to correct image distortion caused by head motion of the subject; the slice time correction refers to synchronizing different slices at each time point to eliminate signal time delay caused by different slice acquisition times; the spatial smoothing refers to applying a Gaussian smoothing kernel to reduce noise, improve signal detectability, and improve statistical analysis results; the signal filtering refers to retaining frequency band signals related to neural activity through a band-pass filter; and the noise removal refers to removing physiological noise in the whole brain, specifically removing noise caused by physiological factors such as heartbeat and respiration, which can affect neural activity signals in specific regions, such as Figure 3 As shown in FIG. 1, a schematic diagram of preprocessed resting-state functional magnetic resonance imaging (rs-fMRI) data is shown.

[0041] Spatial alignment refers to establishing a coordinate transformation field between the segmentation and the standard space and the structural magnetic resonance image space, and establishing a coordinate transformation matrix between the structural magnetic resonance image space and the functional magnetic resonance image space. Using the coordinate transformation field between the standard space and the structural magnetic resonance image space, and the coordinate transformation matrix between the structural magnetic resonance image space and the functional magnetic resonance image space, the superficial brain region range and the deep nuclear group range defined in the population standard atlas, such as the superficial brain region range and the deep nuclear group range defined in the Broadmann area (BA) atlas, can be converted from the population standard space to the functional magnetic resonance image space. For example, the superficial brain region can be the dorsal lateral prefrontal cortex (DLPFC) on the left side of the superficial brain region, and the deep nuclear group can be the subgenual anterior cingulate cortex (sgACC). The DLPFC is BA46, and the sgACC is BA25.

[0042] The functional magnetic resonance image is preprocessed to eliminate noise introduced in the signal acquisition process and retain the true functional activity of the subject's brain, thereby providing a reliable basis for subsequent functional connection calculation.

[0043] 103. Dividing the superficial brain region according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions.

[0044] In this application, by dividing the superficial brain region into a plurality of functional sub-regions, the position with the strongest resting-state functional connection with the deep nuclear group can be found in each functional sub-region in the superficial brain region as the individualized optimal stimulation target.

[0045] Among them, according to prior information and brain atlas, a superficial brain region which can be directly affected by transcranial magnetic stimulation and is associated with the target deep nuclear group can be selected, the functional connection matrix of the superficial brain region is calculated, and then the core voxels of a plurality of initial functional sub-regions are determined according to the functional connection matrix. Region growing is performed based on each core voxel to obtain a plurality of functional sub-regions.

[0046] In an embodiment, the calculation of functional connectivity (FC) is a common method for precise positioning of DLPFC in TMS treatment of patients with major depressive disorder (MDD) who are refractory to common TMS treatment. The core abnormal brain area of depression, subgenual anterior cingulate cortex (sgACC), is located in the deep brain. By setting sgACC as the region of interest (ROI), the whole brain level functional connectivity is calculated to find the best treatment target in the superficial left dorsal lateral prefrontal cortex (DLPFC). Specifically, as shown in Figure 2 Fig. 1 is a schematic diagram of a demonstration of collecting magnetic resonance brain imaging data of a subject using transcranial magnetic stimulation (TMS) technology. The TMS technology uses alternating current in the stimulating coil to generate an alternating magnetic field outside the brain to excite induced current in the brain to change the electrical activity of neurons, thereby triggering a series of neuroelectrophysiological effects and having a certain impact on the cognition and behavior of the individual. Figure 2 In this embodiment, the superficial brain region is DLPFC, and the deep nucleus is sgACC. The present scheme finds the position with the strongest resting state functional connectivity with the deep nucleus sgACC in the superficial brain region as the individualized best stimulation target.

[0047] Optionally, in an embodiment, TMS technology is used for the treatment of obsessive-compulsive disorder. The superficial brain region can be the orbitofrontal cortex (OFC), and the deep nucleus is the amygdala. The position with the strongest resting state functional connectivity with the target deep nucleus is found in the superficial brain region as the individualized best stimulation target.

[0048] Optionally, in an embodiment, TMS technology is used to improve associative memory. The superficial cortex can be the posterior parietal cortex (PPC), and the deep nucleus is the hippocampus. The position with the strongest resting state functional connectivity with the target deep nucleus is found in the superficial brain region as the individualized best stimulation target.

[0049] That is, in the scenarios of depression treatment, obsessive-compulsive disorder treatment, and improvement of associative memory, the present scheme can be used.

[0050] The method comprises the following steps: dividing the superficial brain region into a plurality of functional sub-regions, and searching for individualized optimal target points in the functional sub-regions one by one by taking the functional sub-regions as the minimum search unit. The method comprehensively considers the time-space double characteristics of the superficial cerebral cortex. The spatial arrangement of neurons in the brain has continuity. Neurons with high functional similarity are usually more close in space. The time characteristic for dividing the functional sub-regions of the superficial brain region is the functional connection strength between voxels, which reflects the functional similarity between different voxels. The spatial characteristic is the spatial distance between different voxels, which aims to constrain the division of the functional sub-regions.

[0051] 104. Determining an individual target stimulation point of transcranial magnetic stimulation according to the plurality of functional sub-regions.

[0052] Research shows that the resting state functional connection between the superficial brain region and the deep nucleus can be used as a marker for searching for the individualized optimal stimulation target point of transcranial magnetic stimulation.

[0053] The method comprises the following steps: determining the functional connection strength between the plurality of functional sub-regions of the superficial brain region and the deep nucleus; determining a target functional sub-region according to the functional connection strength between the plurality of functional sub-regions and the deep nucleus; and determining an individual target stimulation point of transcranial magnetic stimulation according to the target functional sub-region. For example, the functional sub-region with the largest functional connection strength is determined as the target functional sub-region. The center of gravity of the target functional sub-region is determined as the individual target stimulation point of transcranial magnetic stimulation. The greater the functional connection strength is, the more likely it is to bring greater energy stimulation to the deep nucleus by stimulating the functional sub-region.

[0054] Alternatively, the method comprises the following steps: determining the volume of the plurality of functional sub-regions; determining a target functional sub-region according to the volume of the plurality of functional sub-regions; and determining an individual target stimulation point of transcranial magnetic stimulation according to the target functional sub-region. For example, the functional sub-region with the largest volume is determined as the target functional sub-region. The center of gravity of the target functional sub-region is determined as the individual target stimulation point of transcranial magnetic stimulation. The greater the volume is, the more effective the energy of transcranial magnetic stimulation is applied to the region.

[0055] According to the plurality of functional sub-regions, the individual target stimulation target point of the transcranial magnetic stimulation is determined, by dividing the superficial brain region into functional sub-regions, the minimum unit of the target point search can better reflect the functional characteristics of the brain, the voxels in each functional sub-region correspond to similar cognitive functions, and the division can reduce the fluctuation of the functional connection strength between different voxels, effectively offsetting the instability of the resting state functional connection in the individual subject, even if the connection strength of some voxels changes over time, the functional characteristics of the entire functional sub-region still remain relatively consistent, thereby improving the robustness of the target point positioning.

[0056] The embodiment of the present application, the individual target point positioning method of the transcranial magnetic stimulation, by acquiring the magnetic resonance brain imaging data of the subject, the magnetic resonance brain imaging data includes functional magnetic resonance images and structural magnetic resonance images; the magnetic resonance brain imaging data is preprocessed to obtain preprocessed magnetic resonance brain imaging data, by preprocessing the magnetic resonance brain imaging data, the noise introduced in the signal acquisition process is removed, and the real functional activity of the brain of the subject is retained, thereby providing a reliable basis for subsequent functional connection calculation; then, according to the preprocessed magnetic resonance brain imaging data, the superficial brain region is divided to obtain a plurality of functional sub-regions; according to the plurality of functional sub-regions, the individual target stimulation target point of the transcranial magnetic stimulation is determined, by dividing the superficial brain region into a plurality of functional sub-regions, and then taking the functional sub-region as the minimum search unit, the individualized best target point is searched for each functional sub-region, the present scheme comprehensively considers the time and space double characteristics of the cerebral superficial cortex, the spatial arrangement of neurons in the brain has continuity, and neurons with high functional similarity are usually more close in space, the time characteristic for dividing the functional sub-region of the superficial brain region is the functional connection strength between voxels, which reflects the functional similarity between different voxels, and the spatial characteristic is the spatial distance between different voxels, which aims to constrain the division of the functional sub-region; by dividing the superficial brain region into functional sub-regions, the minimum unit of the target point search can better reflect the functional characteristics of the brain, the voxels in each functional sub-region correspond to similar cognitive functions, and the division can reduce the fluctuation of the functional connection strength between different voxels, effectively offsetting the instability of the resting state functional connection in the individual subject, even if the connection strength of some voxels changes over time, the functional characteristics of the entire functional sub-region still remain relatively consistent, thereby improving the robustness of the target point positioning.

[0057] Optionally, the superficial brain region is divided according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions, including:

[0058] In the preprocessed magnetic resonance brain imaging data, the time sequence signal of each voxel in the superficial brain region is extracted, and the signal correlation between the voxels is calculated to obtain the functional connection matrix of the superficial brain region;

[0059] The average functional connectivity strength between each voxel and all other voxels in the superficial brain region is determined based on the functional connectivity matrix, and the voxels are sorted in descending order of average functional connectivity strength.

[0060] Multiple voxels are selected as core voxels of functional subregions in descending order of average functional connectivity strength, resulting in multiple core voxels.

[0061] Region growth is performed centered on each core voxel, and the superficial brain regions are divided to obtain multiple functional sub-regions.

[0062] Among them, such as Figure 4 The diagram shows a functional subregion division of a superficial brain region. For example, the superficial brain region can be the DLPFC. The functional connectivity matrix of the superficial brain region can be calculated first. The time series signal of each voxel in the superficial brain region can be extracted from the preprocessed magnetic resonance brain imaging data, and the signal correlation between these voxels can be calculated to obtain the functional connectivity matrix of the superficial brain region.

[0063] Then, based on the functional connectivity matrix, the average functional connectivity strength between each voxel and all other voxels in the superficial brain region is determined, and they are sorted in descending order of average functional connectivity strength. Core voxels of functional sub-regions usually exhibit high functional connectivity characteristics with other voxels in that functional sub-region. Therefore, the average functional connectivity strength between each voxel and all other voxels in the superficial brain region can be calculated, and the results are sorted in descending order. Subsequently, several voxels with the highest average connectivity strength are selected as the core voxels of the initial functional sub-region, that is, multiple voxels are selected as the core voxels of the functional sub-region in descending order of average functional connectivity strength, resulting in multiple core voxels.

[0064] Finally, region growth is performed centered on each core voxel to divide the superficial brain region into multiple functional sub-regions. Specifically, a region growth algorithm can be used for region growth, which is an image segmentation method based on pixel similarity, with the core voxel as the growth center for region growth.

[0065] By selecting multiple voxels as core voxels of functional subregions in descending order of average functional connectivity strength, multiple core voxels are obtained. Then, by using the core voxels as growth centers for regional growth, functional subregions with higher average functional connectivity strength can be obtained. This not only ensures the continuity of neuronal spatial arrangement in the functional subregions, but also makes individualized treatment targets more suitable.

[0066] Optionally, the region growth centered on each core voxel is used to divide the superficial brain regions into multiple functional sub-regions, including:

[0067] Taking the core voxel as a growth center, a functional connection value between the growth center and a new voxel adjacent to the growth center and not included in other functional sub-regions is calculated, and when the functional connection value between the growth center and the new voxel is greater than a first preset value, the new voxel is included in the functional sub-region to which the growth center belongs; the newly included voxel is taken as a new growth center, and the expansion is continued until no new voxel meeting the condition can be found, and it is determined that the functional sub-region division is completed.

[0068] The functional connection value is used to represent the functional connection strength, and specifically, the greater the functional connection value, the greater the functional connection strength; the first preset value may be 0.5, for example, and when the functional connection value between the growth center and the new voxel is greater than 0.5, the new voxel is included in the functional sub-region to which the growth center belongs; the newly included voxel is taken as a new growth center, and the expansion is continued, and the inclusion condition is that the functional connection strength between the new voxel and the included voxel is greater than the first preset value; when no new voxel meeting the condition can be found, it is determined that the functional sub-region division is completed; the above steps are repeated to determine all functional sub-regions one by one until the functional sub-regions of all core voxels are identified and determined.

[0069] Optionally, the method further includes: performing region growing on the functional sub-regions on which the region growing has been completed, and taking all voxels not included in the functional sub-regions as new core voxels, and performing region growing on the new core voxels to perform functional sub-region division.

[0070] When the functional connection strength between the voxels in the functional sub-region is less than the third preset value or the volume of the functional sub-region is less than the fourth preset value, it is indicated that the core voxel of the functional sub-region may be missed; specifically, when the functional connection strength between the voxels in a functional sub-region is too low or the volume of the functional sub-region is too small, the core voxel of the functional sub-region may be missed, and at this time, all voxels not included in the functional sub-regions are taken as new core voxels, and the region growing process is performed again to find the functional sub-regions according to the region growing algorithm again to find all functional sub-regions.

[0071] Optionally, the method further includes: judging whether region fusion is needed for the functional sub-regions on which the region growing has been completed, and when two functional sub-regions meet a fusion condition, performing region fusion on the two functional sub-regions meeting the fusion condition to obtain a plurality of fused functional sub-regions; wherein the fusion condition is that the functional connection strength between the voxels of the functional sub-region is greater than a second preset value or the volume of the functional sub-region is greater than a fifth preset value.

[0072] The function sub-regions which have completed region growing can be fused and adjusted, and the division of the function sub-regions highly depends on the core voxels of the determined initial function sub-regions. When the connection strength between voxels in a function sub-region is too high or the volume of the sub-region is too large, that is, the functional connection strength between voxels of the function sub-region is greater than a second preset value or the volume of the function sub-region is greater than a fifth preset value, multiple voxels originally belonging to the same function sub-region can be proposed as the core voxels of separate function sub-regions. At this time, it is necessary to judge whether the function sub-regions which have completed growing need to be fused. When two function sub-regions meet the fusion condition (for example, the functional connection strength between voxels in the two function sub-regions is greater than 0.5), the two function sub-regions are fused to ensure that the division of the function sub-regions is more accurate.

[0073] Optionally, the method further comprises:

[0074] determining the functional connection strength between the multiple function sub-regions and the deep nuclei;

[0075] determining the volume of the multiple function sub-regions;

[0076] determining a target function sub-region according to the functional connection strength between the multiple function sub-regions and the deep nuclei and the volume of the multiple function sub-regions;

[0077] determining the individualized target stimulation target of the transcranial magnetic stimulation according to the target function sub-region.

[0078] Optionally, the method further comprises:

[0079] determining a target function sub-region according to the functional connection strength between the multiple function sub-regions and the deep nuclei and the volume of the multiple function sub-regions, comprising:

[0080] determining a target score value corresponding to the multiple function sub-regions according to a first weight corresponding to the functional connection strength, a second weight corresponding to the volume of the function sub-regions, the functional connection strength between the multiple function sub-regions and the deep nuclei, and the volume of the multiple function sub-regions;

[0081] determining the target functional sub-region with the maximum target score value as the target functional sub-region.

[0082] wherein the first weight corresponding to the functional connection strength and the second weight corresponding to the volume of the functional sub-region can be pre-set, so that the functional connection strength and the volume of the functional sub-region can be comprehensively considered to obtain a more accurate target functional sub-region, and then the individual target stimulation target point is located.

[0083] Optionally, the method further comprises:

[0084] determining the center of gravity of the target functional sub-region, and taking the center of gravity position of the target functional sub-region as the individual target stimulation target point of the transcranial magnetic stimulation, wherein the center of gravity position generally represents the center of functional activity in the functional sub-region, and the stimulation center of gravity can more effectively affect the surrounding neuron population, thereby maximizing the energy transmission of the transcranial magnetic stimulation.

[0085] Embodiment Two

[0086] As shown in the following, Figure 5 an embodiment of the present application provides another individual target point positioning method of transcranial magnetic stimulation, Figure 5 a flowchart of another individual target point positioning method of transcranial magnetic stimulation provided by the embodiment of the present application, which comprises:

[0087] 201, acquiring magnetic resonance brain imaging data of a subject individual, wherein the magnetic resonance brain imaging data comprises functional magnetic resonance images and structural magnetic resonance images.

[0088] 202, pre-processing the magnetic resonance brain imaging data to obtain pre-processed magnetic resonance brain imaging data.

[0089] 203, extracting the time series signal of each voxel in the superficial brain region in the pre-processed magnetic resonance brain imaging data, and calculating the signal correlation between the voxels to obtain the functional connection matrix of the superficial brain region.

[0090] 204, determining the average functional connection strength of each voxel with all other voxels in the superficial brain region according to the functional connection matrix, and sorting in descending order of the average functional connection strength.

[0091] 205, selecting a plurality of voxels as core voxels of the functional sub-region in descending order of the average functional connection strength to obtain a plurality of core voxels.

[0092] 206, taking the core voxels as the growth center, and calculating the functional connection value between the growth center and the new voxels adjacent to the growth center and not included in other functional sub-regions.

[0093] 207. determining whether the functional connection value between the growth center and the new voxel is a first preset value.

[0094] 208. if yes, including the new voxel into the functional sub-region to which the growth center belongs; taking the newly included voxel as a new growth center, continuing to expand until no new voxel meeting the condition can be found, determining that the functional sub-region division is completed, and obtaining a plurality of functional sub-regions; if no, determining that the functional sub-region division is completed, and obtaining a plurality of functional sub-regions.

[0095] 209. determining the functional connection strength between the plurality of functional sub-regions of the superficial brain region and the deep nucleus.

[0096] 210. determining the volume of the plurality of functional sub-regions.

[0097] 211. determining a target score value corresponding to the plurality of functional sub-regions according to a first weight corresponding to the functional connection strength, a second weight corresponding to the volume of the functional sub-region, the functional connection strength between the plurality of functional sub-regions and the deep nucleus, and the volume of the plurality of functional sub-regions.

[0098] 212. determining that the functional sub-region with the maximum target score value is the target functional sub-region.

[0099] 213. determining the center of gravity of the target functional sub-region, and taking the position of the center of gravity of the target functional sub-region as an individual target stimulation point of transcranial magnetic stimulation.

[0100] The present embodiment extracts the time series signal of each voxel in the superficial brain region in the pre-processed magnetic resonance brain imaging data, calculates the signal correlation between the voxels, and obtains the functional connectivity matrix of the superficial brain region; determines the average functional connection strength of each voxel with all other voxels in the superficial brain region according to the functional connectivity matrix, and sorts them in descending order of the average functional connection strength; selects a plurality of voxels as the core voxels of the functional sub-region in descending order of the average functional connection strength, and obtains a plurality of core voxels; takes the core voxels as the growth center, calculates the functional connection value between the growth center and the new voxels adjacent to the growth center and not included in other functional sub-regions, and when the functional connection value between the growth center and the new voxels is greater than a first preset value, the new voxels are included in the functional sub-region to which the growth center belongs; take the newly included voxels as the new growth center, continue to expand, until no new voxels meeting the conditions can be found, and determine that the functional sub-region division is completed; determine the functional connection strength of the plurality of functional sub-regions of the superficial brain region and the deep nuclear group; determine the volume of the plurality of functional sub-regions; determine the target score value corresponding to the plurality of functional sub-regions according to the first weight corresponding to the functional connection strength, the second weight corresponding to the volume of the functional sub-region, the functional connection strength of the plurality of functional sub-regions and the deep nuclear group, and the volume of the plurality of functional sub-regions; determine the functional sub-region with the maximum target score value as the target functional sub-region; determine the center of gravity of the target functional sub-region, and take the position of the center of gravity of the target functional sub-region as the individual target stimulation target point of transcranial magnetic stimulation; by selecting a plurality of voxels as the core voxels of the functional sub-region in descending order of the average functional connection strength, a plurality of core voxels are obtained, and then the region growth is performed with the core voxels as the growth center, the functional sub-region with larger average functional connection strength can be obtained, which not only makes the functional sub-region meet the continuity of the spatial arrangement of neurons, but also makes the individualized treatment target more appropriate; determine the target functional sub-region according to the functional connection strength of the plurality of functional sub-regions and the deep nuclear group and the volume of the plurality of functional sub-regions; determine the individualized target stimulation target point of transcranial magnetic stimulation according to the target functional sub-region, the greater the functional connection strength represents the greater the energy stimulation to the deep nuclear group when stimulating the functional sub-region, the greater the volume represents that the energy applied by transcranial magnetic stimulation falls more effectively into the region, which can comprehensively consider the functional connection strength and the volume of the functional sub-region to obtain a more accurate target functional sub-region, and then locate the individualized target stimulation target point; take the position of the center of gravity of the target functional sub-region as the individual target stimulation target point of transcranial magnetic stimulation, the center of gravity usually represents the center of functional activity in the functional sub-region, and stimulating the center of gravity can more effectively affect the surrounding neuron population, maximizing the energy transmission of transcranial magnetic stimulation.

[0101] Example Three

[0102] As Figure 6 ,Figure 6 The application provides a transcranial magnetic stimulation individual target positioning system 300, comprising:

[0103] An acquisition module 301 is configured to acquire magnetic resonance data of a subject, wherein the magnetic resonance data comprises functional magnetic resonance images;

[0104] A preprocessing module 302 is configured to preprocess the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data;

[0105] A division module 303 is configured to divide a superficial brain region according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions;

[0106] A positioning module 304 is configured to determine an individual target stimulation target of transcranial magnetic stimulation according to the plurality of functional sub-regions.

[0107] Optionally, in the preprocessing of the magnetic resonance brain imaging data to obtain the preprocessed magnetic resonance brain imaging data, the preprocessing module 302 is specifically configured to:

[0108] perform at least one of the following preprocessing operations on the preprocessing of the magnetic resonance brain imaging data: head motion correction, slice time correction, spatial smoothing, signal filtering, and denoising processing.

[0109] Optionally, in the division of the superficial brain region according to the preprocessed magnetic resonance brain imaging data to obtain the plurality of functional sub-regions, the division module 303 is specifically configured to:

[0110] extract a time series signal of each voxel in the superficial brain region from the preprocessed magnetic resonance brain imaging data, and calculate signal correlation between the voxels to obtain a functional connection matrix of the superficial brain region;

[0111] determine an average functional connection strength of each voxel to all other voxels in the superficial brain region according to the functional connection matrix, and sort the voxels in a descending order of the average functional connection strength;

[0112] select a plurality of voxels as core voxels of the functional sub-regions in the descending order of the average functional connection strength to obtain a plurality of core voxels;

[0113] perform region growing with each core voxel as a center to perform region division on the division of the superficial brain region to obtain the plurality of functional sub-regions.

[0114] Optionally, in the region growing with each core voxel as the center to perform the region division on the division of the superficial brain region to obtain the plurality of functional sub-regions, the division module 303 is specifically configured to:

[0115] Taking the core voxel as a growth center, a functional connection value between the growth center and a new voxel adjacent to the growth center and not included in other functional sub-regions is calculated, and when the functional connection value between the growth center and the new voxel is greater than a first preset value, the new voxel is included in the functional sub-region to which the growth center belongs; the new voxel is taken as a new growth center, and the expansion is continued until no new voxel meeting the condition can be found, and it is determined that the functional sub-region division is completed.

[0116] Optionally, the division module 303 is further configured to: perform region growing on the functional sub-regions on which the region growing has been completed, and when a functional connection strength between voxels in the functional sub-region is less than a third preset value or a volume of the functional sub-region is less than a fourth preset value, take all voxels not included in the functional sub-region as a new core voxel, and perform region growing on the new core voxel to perform functional sub-region division.

[0117] Optionally, the division module 303 is further configured to: perform region growing on the functional sub-regions on which the region growing has been completed, and when a functional connection strength between voxels in the functional sub-region is less than a third preset value or a volume of the functional sub-region is less than a fourth preset value, take all voxels not included in the functional sub-region as a new core voxel, and perform region growing on the new core voxel to perform functional sub-region division.

[0118] Optionally, in the aspect of determining an individual target stimulation target point of transcranial magnetic stimulation according to the plurality of functional sub-regions, the positioning module 304 is specifically configured to:

[0119] determine functional connection strengths between the plurality of functional sub-regions of the superficial brain area and the deep nuclear mass;

[0120] determine volumes of the plurality of functional sub-regions;

[0121] determine a target functional sub-region according to the functional connection strengths between the plurality of functional sub-regions and the deep nuclear mass and the volumes of the plurality of functional sub-regions;

[0122] determine an individualized target stimulation target point of transcranial magnetic stimulation according to the target functional sub-region.

[0123] Optionally, in the aspect of determining a target functional sub-region according to the functional connection strengths between the plurality of functional sub-regions and the deep nuclear mass and the volumes of the plurality of functional sub-regions, the positioning module 304 is specifically configured to:

[0124] determine target score values corresponding to the plurality of functional sub-regions according to the first weight corresponding to the functional connection strength, the second weight corresponding to the volume of the functional sub-region, the functional connection strengths between the plurality of functional sub-regions and the deep nuclear mass, and the volumes of the plurality of functional sub-regions;

[0125] determining the target functional sub-region with the maximum target score value as the target functional sub-region.

[0126] Optionally, in the aspect of determining the individual target stimulation point of the transcranial magnetic stimulation according to the target functional sub-region, the positioning module 304 is specifically configured to:

[0127] determining the barycenter of the target functional sub-region, and taking the barycenter position of the target functional sub-region as the individual target stimulation point of the transcranial magnetic stimulation.

[0128] In this embodiment, the individual target point positioning method of the transcranial magnetic stimulation comprises the following steps: acquiring magnetic resonance brain imaging data of a subject, wherein the magnetic resonance brain imaging data comprises functional magnetic resonance images and structural magnetic resonance images; pre-processing the magnetic resonance brain imaging data to obtain pre-processed magnetic resonance brain imaging data; pre-processing the magnetic resonance brain imaging data to eliminate noise introduced in the signal acquisition process and retain the real functional activity of the brain of the subject, thereby providing a reliable basis for subsequent functional connection calculation; then, dividing the superficial brain region according to the pre-processed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions; determining the individual target stimulation point of the transcranial magnetic stimulation according to the plurality of functional sub-regions, wherein the superficial brain region is divided into a plurality of functional sub-regions, and then the functional sub-regions are taken as the minimum search unit to find the individualized optimal target point one by one, and the scheme comprehensively considers the time-space double characteristics of the cerebral superficial cortex, the spatial arrangement of neurons in the brain has continuity, and neurons with high functional similarity are usually more close in space, the time characteristic for dividing the functional sub-regions of the superficial brain region is the functional connection strength between voxels, which reflects the functional similarity between different voxels, and the spatial characteristic is the spatial distance between different voxels, which aims to constrain the division of the functional sub-regions; by dividing the superficial brain region into functional sub-regions, the minimum unit of target point search can better reflect the functional characteristics of the brain, the voxels in each functional sub-region correspond to similar cognitive functions, this division can reduce the fluctuation of the functional connection strength between different voxels, effectively offsetting the instability of the resting-state functional connection of the subject, even if the connection strength of some voxels changes over time, the functional characteristics of the entire functional sub-region still remain relatively consistent, thereby improving the robustness of target point positioning.

[0129] Embodiment Four

[0130] As Figure 7 , Figure 7An individual target positioning system for transcranial magnetic stimulation according to an embodiment includes a processor 410 and a memory 420; and one or more programs stored in the memory, the memory 420 can be a high-speed RAM memory, or a non-volatile memory such as a disk memory. The memory 4000 is used to store a set of program codes, and the processor 410 is used to call the program codes stored in the memory 420 to perform the following operations:

[0131] Obtain magnetic resonance brain imaging data of a subject, the magnetic resonance brain imaging data including functional magnetic resonance images;

[0132] Preprocess the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data;

[0133] Divide the superficial brain region according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions;

[0134] Determine an individual target stimulation target of transcranial magnetic stimulation according to the plurality of functional sub-regions.

[0135] In one possible example, in the preprocessing of the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data, the processor 410 is specifically configured to:

[0136] The preprocessing of the magnetic resonance brain imaging data is at least one of the following preprocessing operations: head motion correction, slice time correction, spatial smoothing, signal filtering, and denoising processing.

[0137] In one possible example, in the division of the superficial brain region according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions, the processor 410 is specifically configured to:

[0138] Extract the time series signal of each voxel in the superficial brain region in the preprocessed magnetic resonance brain imaging data, and calculate the signal correlation between the voxels to obtain a functional connectivity matrix of the superficial brain region;

[0139] Determine the average functional connection strength of each voxel to all other voxels in the superficial brain region according to the functional connectivity matrix, and sort them in descending order of average functional connection strength;

[0140] Select a plurality of voxels as core voxels of the functional sub-regions in descending order of average functional connection strength to obtain a plurality of core voxels;

[0141] The region growing is performed with each core voxel as a center to regionally divide the superficial brain region division, and a plurality of functional sub-regions are obtained.

[0142] In one possible example, in the region growing with each core voxel as a center to regionally divide the superficial brain region division, and obtaining a plurality of functional sub-regions, the processor 410 is specifically configured to: take the core voxel as a growth center, calculate a functional connection value between the growth center and a new voxel adjacent to the growth center and not included in other functional sub-regions, and when the functional connection value between the growth center and the new voxel is greater than a first preset value, include the new voxel in the functional sub-region to which the growth center belongs; continue to expand with the newly included voxel as a new growth center until no new voxel satisfying the condition can be found, and determine that the functional sub-region division is completed.

[0143] In one possible example, the processor 410 is further configured to: for the functional sub-region after the region growing is completed, when a functional connection strength between voxels in the functional sub-region is less than a third preset value or a volume of the functional sub-region is less than a fourth preset value, take all voxels not included in the functional sub-region as a new core voxel, and perform region growing with the new core voxel to perform functional sub-region division.

[0144] In one possible example, the processor 410 is further configured to: for the functional sub-region after the region growing is completed, determine whether region fusion is needed, when two functional sub-regions satisfy a fusion condition, perform region fusion on the two functional sub-regions satisfying the fusion condition, and obtain a plurality of functional sub-regions after fusion; wherein the fusion condition is that a functional connection strength between voxels in the functional sub-region is greater than a second preset value or a volume of the functional sub-region is greater than a fifth preset value.

[0145] In one possible example, in the determining the individualized target stimulation target point of the transcranial magnetic stimulation according to the plurality of functional sub-regions, the processor 410 is specifically configured to:

[0146] determine functional connection strengths between the plurality of functional sub-regions of the superficial brain region and the deep nuclear mass;

[0147] determine volumes of the plurality of functional sub-regions;

[0148] determine a target functional sub-region according to the functional connection strengths between the plurality of functional sub-regions and the deep nuclear mass and the volumes of the plurality of functional sub-regions;

[0149] determine the individualized target stimulation target point of the transcranial magnetic stimulation according to the target functional sub-region.

[0150] In one possible example, in the aspect of determining the target functional sub-region according to the functional connection strength of the plurality of functional sub-regions and the deep nuclear group and the volume of the plurality of functional sub-regions, the processor 410 is specifically configured to:

[0151] determine a target score value corresponding to the plurality of functional sub-regions according to the first weight corresponding to the functional connection strength, the second weight corresponding to the volume of the functional sub-region, the functional connection strength of the plurality of functional sub-regions and the deep nuclear group, and the volume of the plurality of functional sub-regions.

[0152] determine the functional sub-region with the maximum target score value as the target functional sub-region.

[0153] In one possible example, in the aspect of determining the individual target stimulation target point of the transcranial magnetic stimulation according to the target functional sub-region, the processor 410 is specifically configured to: determine the center of gravity of the target functional sub-region, and take the position of the center of gravity of the target functional sub-region as the individual target stimulation target point of the transcranial magnetic stimulation.

[0154] The individual target point positioning method of the transcranial magnetic stimulation in the embodiment, by acquiring the magnetic resonance brain imaging data of the subject individual, the magnetic resonance brain imaging data includes functional magnetic resonance images and structural magnetic resonance images; the magnetic resonance brain imaging data is preprocessed to obtain preprocessed magnetic resonance brain imaging data, by preprocessing the magnetic resonance brain imaging data, to eliminate the noise introduced in the signal acquisition process, and to retain the real functional activity of the brain of the subject individual, thereby providing a reliable basis for subsequent functional connection calculation; then, according to the preprocessed magnetic resonance brain imaging data, the superficial brain region is divided to obtain a plurality of functional sub-regions; the individual target stimulation target point of the transcranial magnetic stimulation is determined according to the plurality of functional sub-regions, by dividing the superficial brain region into a plurality of functional sub-regions, and then taking the functional sub-region as the minimum search unit, the individualized best target point is searched for each functional sub-region, the present scheme comprehensively considers the time and space double characteristics of the cerebral superficial cortex, the spatial arrangement of neurons in the brain has continuity, and the neurons with high functional similarity are usually more close in space, the time characteristic for the division of the functional sub-region of the superficial brain region is the functional connection strength between voxels, which reflects the functional similarity between different voxels, and the spatial characteristic is the spatial distance between different voxels, which aims to constrain the division of the functional sub-region; by dividing the superficial brain region into functional sub-regions, the minimum unit of the target point search can better reflect the functional characteristics of the brain, the voxels in each functional sub-region correspond to similar cognitive functions, this division can reduce the fluctuation of the functional connection strength between different voxels, effectively offsetting the instability of the resting state functional connection in the subject individual, even if the connection strength of certain voxels changes over time, the functional characteristics of the entire functional sub-region still remain relatively consistent, thereby improving the robustness of the target point positioning.

[0155] The embodiment of the present application further provides a computer program product, wherein the computer program product comprises a non-transitory computer readable program product storing a computer program, and the computer program is operable to cause a computer to execute all or part of the steps described in any one of the transcranial magnetic stimulation individual target point positioning methods disclosed in the embodiments of the present application. The computer program product can be a software installation package.

[0156] Although the present application is described herein in conjunction with various embodiments, numerous modifications and alterations to the described embodiments are possible, which will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that all such modifications and alterations be considered part of this disclosure in conjunction with the scope of the claimed application. The word "comprising" does not exclude the presence of elements or steps other than those listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. A single processor or other unit can fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to an advantage.

[0157] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, apparatuses (devices), or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) having computer usable program code embodied in the medium. The computer program product can be distributed over a network, for example, the Internet, or over other telecommunication or computer networks.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses (devices) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing equipment to produce a machine, so that the instructions executed by the computer or other programmable data processing equipment produce the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0159] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions Figure 1 stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flow Figure 1 diagram or diagrams and / or blocks in the flowchart or diagrams and / or blocks in the flowchart or diagrams and / or blocks in the flowchart or diagrams.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions Figure 1 which implement the function specified in the flow Figure 1 diagram or diagrams and / or blocks in the flowchart or diagrams and / or blocks in the flowchart or diagrams and / or blocks in the flowchart or diagrams.

[0161] Although the present application has been described in connection with certain specific features, embodiments, and implementations, it is evident that many alternatives, modifications, combinations, and permutations of these concepts are possible. Accordingly, it is intended to embrace all such alterations, modifications, combinations, and permutations as fall within the scope of the appended claims. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover all modifications and variations of this application that come within the scope of the appended claims and their equivalents.

Claims

1. A method for individual target localization in transcranial magnetic stimulation, characterized in that, include: Acquire magnetic resonance brain imaging data of individual subjects, including functional magnetic resonance images and structural magnetic resonance images; The magnetic resonance brain imaging data is preprocessed to obtain preprocessed magnetic resonance brain imaging data. Based on the preprocessed magnetic resonance brain imaging data, superficial brain regions are divided to obtain multiple functional sub-regions; The individual target stimulation points for transcranial magnetic stimulation are determined based on the aforementioned multiple functional sub-regions; The process involves dividing the superficial brain regions based on the preprocessed magnetic resonance imaging (MRI) data to obtain multiple functional sub-regions, including: The time-series signal of each voxel in the superficial brain region was extracted from the preprocessed magnetic resonance brain imaging data, and the signal correlation between these voxels was calculated to obtain the functional connectivity matrix of the superficial brain region. The average functional connectivity strength between each voxel and all other voxels in the superficial brain region is determined based on the functional connectivity matrix, and the voxels are sorted in descending order of average functional connectivity strength. Multiple voxels are selected as core voxels of functional subregions in descending order of average functional connectivity strength, resulting in multiple core voxels. Region growth is performed with each core voxel as the center, and the superficial brain regions are divided into regions to obtain multiple functional sub-regions; The determination of individual target stimulation points for transcranial magnetic stimulation based on the multiple functional sub-regions includes: Determine the functional connectivity strength between multiple functional subregions in the superficial brain and deep nuclei; Determine the volume of multiple functional sub-regions; The target functional sub-region is determined based on the functional connection strength between the plurality of functional sub-regions and the deep nucleus and the volume of the plurality of functional sub-regions; Based on the target functional subregions, individualized target stimulation points for transcranial magnetic stimulation are determined.

2. The method for individual target localization of transcranial magnetic stimulation according to claim 1, characterized in that, The step of preprocessing the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data includes: The magnetic resonance brain imaging data is preprocessed by at least one of the following preprocessing operations: head motion correction, slice time correction, spatial smoothing, signal filtering, and noise reduction.

3. The method for individual target localization of transcranial magnetic stimulation according to claim 1, characterized in that, The process of region growth centered on each core voxel is used to divide the superficial brain regions into multiple functional sub-regions, including: Using the core voxel as the growth center, calculate the functional connectivity value between the growth center and the surrounding new voxels that are not included in other functional sub-regions. When the functional connectivity value between the growth center and the new voxel is greater than a first preset value, the new voxel is included in the functional sub-region to which the growth center belongs. Using the newly included voxel as the new growth center, continue to expand until no new voxel that meets the conditions can be found, and determine that the functional sub-region division has been completed.

4. The method for individual target localization of transcranial magnetic stimulation according to claim 3, characterized in that, The method further includes: For a functional sub-region that has completed regional growth, if the functional connection strength between voxels within the functional sub-region is less than a third preset value or the volume of the functional sub-region is less than a fourth preset value, all voxels not included in the functional sub-region are used as new core voxels, and regional growth is performed using these new core voxels to divide the functional sub-region.

5. The method for individual target localization of transcranial magnetic stimulation according to claim 3, characterized in that, The method further includes: For functional sub-regions that have completed regional growth, it is determined whether region fusion is required. When two functional sub-regions meet the fusion conditions, the two functional sub-regions that meet the fusion conditions are merged to obtain multiple fused functional sub-regions. The fusion conditions are that the functional connection strength between voxels of the functional sub-regions is greater than a second preset value or the volume of the functional sub-region is greater than a fifth preset value.

6. The method for individual target localization of transcranial magnetic stimulation according to claim 1, characterized in that, The step of determining the target functional sub-region based on the functional connectivity strength between the plurality of functional sub-regions and the deep nucleus and the volume of the plurality of functional sub-regions includes: The target score value corresponding to the multiple functional sub-regions is determined based on the first weight corresponding to the functional connectivity strength, the second weight corresponding to the volume of the functional sub-region, the functional connectivity strength between the multiple functional sub-regions and the deep nucleus, and the volume of the multiple functional sub-regions. The functional sub-region with the largest target score is determined as the target functional sub-region.

7. The method for individual target localization of transcranial magnetic stimulation according to claim 6, characterized in that, The step of determining the individual target stimulation point for transcranial magnetic stimulation based on the target functional sub-region includes: Determine the centroid of the target functional sub-region and use the centroid location of the target functional sub-region as the individual target stimulation point for transcranial magnetic stimulation.

8. A transcranial magnetic stimulation (TMS) individual target localization system, characterized in that, include: The acquisition module is used to acquire magnetic resonance brain imaging data of the subject individual, the magnetic resonance brain imaging data including functional magnetic resonance images and structural magnetic resonance images; The preprocessing module is used to preprocess the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data. The segmentation module is used to segment the superficial brain regions based on the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-regions. The positioning module is used to determine the individual target stimulation point for transcranial magnetic stimulation based on the multiple functional sub-regions; The partitioning module is also used for: The time-series signal of each voxel in the superficial brain region was extracted from the preprocessed magnetic resonance brain imaging data, and the signal correlation between these voxels was calculated to obtain the functional connectivity matrix of the superficial brain region. The average functional connectivity strength between each voxel and all other voxels in the superficial brain region is determined based on the functional connectivity matrix, and the voxels are sorted in descending order of average functional connectivity strength. Multiple voxels are selected as core voxels of functional subregions in descending order of average functional connectivity strength, resulting in multiple core voxels. Region growth is performed with each core voxel as the center, and the superficial brain regions are divided into regions to obtain multiple functional sub-regions; The positioning module is also used for: Determine the functional connectivity strength between multiple functional subregions in the superficial brain and deep nuclei; Determine the volume of multiple functional sub-regions; The target functional sub-region is determined based on the functional connection strength between the plurality of functional sub-regions and the deep nucleus and the volume of the plurality of functional sub-regions; Based on the target functional subregions, individualized target stimulation points for transcranial magnetic stimulation are determined.

9. A transcranial magnetic stimulation (TMS) individual target localization system, characterized in that, It includes a processor and a memory; and one or more programs stored in the memory and configured to be executed by the processor, the programs including steps for an individual target localization method for transcranial magnetic stimulation as claimed in any one of claims 1 to 7.

10. A computer program product, characterized in that, When the computer program of the computer program product is executed by a processor, it can implement the steps of the individual target localization method for transcranial magnetic stimulation as described in any one of claims 1 to 7.

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