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

Abstract

The invention relates to the technical field of transcranial magnetic stimulation target spot positioning, and provides a transcranial magnetic stimulation individual target spot positioning method and system and a program product, and the method comprises the steps: obtaining the magnetic resonance brain imaging data of a tested individual; preprocessing the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data, and performing superficial brain region division according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions; individual target stimulation targets of transcranial magnetic stimulation are determined according to the multiple functional sub-regions, fluctuation of functional connection strength between different voxels can be reduced, instability of resting state functional connection in a tested individual is effectively counteracted, and even if the connection strength of some voxels changes along with time, the tested individual cannot be affected. And the functional characteristics of the whole functional sub-region are still kept relatively consistent, so that the robustness of target spot positioning is improved.

Description

Technical Field

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

[0002] The resting-state functional connectivity between superficial brain areas and deep nuclei can be used as a marker for finding the optimal personalized stimulation target for transcranial magnetic stimulation (TMS). When finding the optimal personalized target, the existing algorithm for locating the optimal personalized stimulation target for transcranial magnetic stimulation determines the superficial brain areas that can be directly stimulated by TMS, and then searches for the location with the strongest resting-state functional connectivity with the target deep nuclei voxel by voxel in the superficial brain areas as the optimal personalized stimulation target. For example, the voxel location with the most negative resting-state functional connectivity with the subgenual anterior cingulate cortex in the left dorsolateral prefrontal cortex is found as the optimal personalized stimulation target for the treatment of depression.

[0003] However, voxel is the smallest unit of magnetic resonance imaging in space, not the smallest unit of functional area division of the cerebral cortex. Therefore, searching for the best stimulation target voxel by voxel will ignore the functional information of the brain. In addition, resting-state functional connectivity is not stable within an individual, and the connection strength between the same voxel and the deep nuclei may vary at different times. Therefore, the method of searching for individualized best targets voxel by voxel is easily affected by this instability, resulting in the calculated best stimulation target being not robust enough. Summary of the invention

[0004] In order to solve or at least partially solve the above-mentioned technical problems, the present application provides a method, system and program product for individual target positioning of transcranial magnetic stimulation, which can divide the superficial brain area into several functional sub-areas, and then use the functional sub-areas as small search units to search for individualized optimal targets one by one.

[0005] In a first aspect, the present application provides a method for locating individual targets for transcranial magnetic stimulation, comprising:

[0006] Acquiring magnetic resonance brain imaging data of the subject, wherein the magnetic resonance brain imaging data includes 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] The superficial brain regions are divided according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions.

[0009] Determine individual target stimulation points of transcranial magnetic stimulation according to the multiple functional sub-areas.

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

[0011] An acquisition module, used to acquire magnetic resonance data of a subject, wherein the magnetic resonance data includes a functional magnetic resonance image and a structural magnetic resonance image;

[0012] A preprocessing module, used for preprocessing the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data;

[0013] The division module is used to divide the superficial brain area according to the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-areas.

[0014] A positioning module is used to determine individual target stimulation targets of transcranial magnetic stimulation according to the multiple functional sub-areas.

[0015] In a third aspect, an embodiment of the present application also provides an individual target localization system for transcranial magnetic stimulation, comprising a processor and a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, the programs comprising steps for the individual target localization 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 localization method for transcranial magnetic stimulation.

[0017] The implementation mode of the present application is a method for positioning individual targets of transcranial magnetic stimulation, which obtains magnetic resonance brain imaging data of an individual subject, wherein 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, and the noise introduced in the signal acquisition process is eliminated by preprocessing the magnetic resonance brain imaging data, so as to retain the real functional activity of the brain of the individual subject, thereby providing a reliable basis for subsequent functional connection calculations; then, the superficial brain area is divided according to the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-areas; the individual target stimulation target of transcranial magnetic stimulation is determined according to the multiple functional sub-areas, and the superficial brain area is divided into several functional sub-areas, and then the functional sub-areas are used as the minimum search unit to search for the individual optimal target in each functional sub-area. This scheme comprehensively considers the large The superficial cortex has dual temporal and spatial characteristics. The spatial arrangement of neurons in the brain is continuous. Neurons with high functional similarity are usually closer in space. The temporal characteristic of the division of functional sub-regions of the superficial brain area 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 functional sub-regions. By dividing the superficial brain area into functional sub-regions, the smallest unit of target 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 and effectively offset the instability of resting-state functional connections within individual subjects. 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 positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the implementation of the present application, the following is a brief introduction to the relevant drawings. It is understood that the drawings described below are only used to illustrate some implementations of the present application, and those skilled in the art can also obtain many other technical features and connection relationships not mentioned in this document based on these drawings.

[0019] Figure 1 A schematic diagram of the process of an individual target positioning system for transcranial magnetic stimulation provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram showing a method of collecting magnetic resonance brain imaging data of a subject using transcranial magnetic stimulation technology provided in an embodiment of the present application;

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

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

[0023] Figure 5 A schematic diagram of the structure of another method for locating individual targets of transcranial magnetic stimulation provided in an embodiment of the present application;

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

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

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0028] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. Those skilled 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 the optimal individualized stimulation target for transcranial magnetic stimulation.

[0030] In multiple studies across species (mice, marmosets, macaques), comprehensive and high-resolution brain transcriptomes and spatial cell maps revealed a high correspondence between the transcriptome identity and spatial specificity of each cell type, that is, the spatial arrangement of neurons in the brain is continuous, and neurons with high functional similarity are closer in spatial position. This scheme is based on an individualized target algorithm based on spatiotemporal dual-feature segmentation. By using magnetic resonance brain imaging data to estimate the spatiotemporal dual features of neurons, namely functional connectivity strength and spatial position information, the functional sub-regions of the cerebral cortex are divided, and then the functional sub-regions that meet the principle of high correspondence of neuronal functional similarity and position specificity are used to calculate the optimal therapeutic target for the individual subject.

[0031] By dividing the superficial brain area into several functional sub-areas, and then taking the functional sub-areas as the minimum search unit, the optimal individualized target is searched for each functional sub-area. This scheme comprehensively considers the temporal and spatial dual characteristics of the superficial cortex of the brain. The spatial arrangement of neurons in the brain is continuous, and neurons with high functional similarity are usually closer in space. The temporal characteristics of the division of the functional sub-areas of the superficial brain area are based on the functional connection strength between voxels, which reflects the functional similarity between different voxels, and the spatial characteristics are the spatial distance between different voxels, which aims to constrain the division of functional sub-areas. By dividing the superficial brain area into functional sub-areas, the minimum unit of target search can better reflect the functional characteristics of the brain. The voxels in each functional sub-area correspond to similar cognitive functions. This division can reduce the fluctuation of the functional connection strength between different voxels and effectively offset the instability of the resting-state functional connection within the individual subject. Even if the connection strength of some voxels changes over time, the functional characteristics of the entire functional sub-area remain relatively consistent, thereby improving the robustness of target positioning.

[0032] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.

[0033] Embodiment 1

[0034] like Figure 1 As shown, Figure 1 A schematic diagram of a process flow of a method for locating individual targets for transcranial magnetic stimulation provided in an embodiment of the present application. An embodiment of the present application proposes a method for locating individual targets for transcranial magnetic stimulation, the method comprising:

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

[0036] Among them, a magnetic resonance imaging device can be used to collect magnetic resonance brain imaging data of the subject, and the magnetic resonance brain imaging data includes functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI).

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

[0038] The method of preprocessing the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data includes:

[0039] The magnetic resonance brain imaging data is preprocessed to perform at least one of the following preprocessing operations: head motion correction, spatial alignment, slice time correction, spatial smoothing, signal filtering, and denoising.

[0040] Specifically, head motion correction refers to the use of motion correction algorithms to correct image distortion caused by head movement of the subject; slice time correction refers to the synchronization of different slices at each time point to eliminate the signal time delay caused by different slice acquisition times; spatial smoothing refers to the application of Gaussian smoothing kernels to reduce noise, improve signal detectability, and improve the results of statistical analysis; signal filtering refers to the use of bandpass filters to retain frequency band signals related to neural activity; denoising refers to the removal of whole-brain physiological noise, specifically removing noise caused by physiological factors such as heartbeat, breathing, etc., which can affect neural activity signals in specific areas, such as Figure 3 , which is a schematic diagram of preprocessed resting-state cerebral functional magnetic resonance imaging (rs-fMRI) data.

[0041] Spatial alignment refers to segmentation, establishment of a coordinate change deformation field between the standard space and the structural MRI space, and establishment of a coordinate change matrix between the structural MRI space and the functional MRI space. By using the coordinate change deformation field between the standard space and the structural MRI space, and the coordinate change matrix between the structural MRI space and the functional MRI space, the group standard map, such as the superficial brain area range and the deep nucleus area range defined in the Broadmann area (BA) map, can be converted from the group standard space to the functional MRI space. For example, the superficial brain area can be the superficial left dorsal lateral prefrontal cortex (DLPFC), and the deep nucleus can be the subgenual anterior cingulate cortex (sgACC), DLPFC is BA46, and sgACC is BA25.

[0042] By preprocessing the functional magnetic resonance images, the noise introduced during the signal acquisition process is eliminated and the true functional activity of the subject's brain is retained, thus providing a reliable basis for subsequent functional connection calculations.

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

[0044] In the present application, by dividing the superficial brain area into multiple functional sub-areas, the positions with the strongest resting-state functional connections with the deep nuclei can be found one by one in the superficial brain area as the individualized optimal stimulation targets.

[0045] Among them, based on prior information and brain maps, a superficial brain area that can be directly affected by transcranial magnetic stimulation and is associated with the target deep nuclei can be selected, and the functional connection matrix of the superficial brain area can be calculated. Then, according to the functional connection matrix, the core voxels of several initial functional sub-regions can be determined, and regional growth can be performed with each core voxel to obtain multiple functional sub-regions.

[0046] In one embodiment, calculating functional connectivity (FC) is a common TMS treatment method for DLPFC precision positioning in patients with refractory major depressive disorder (MDD). The subgenual anterior cingulate cortex (sgACC), a core abnormal brain area of ​​depression located deep in the brain, is used as an anchor point. By setting the 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, Figure 2 The figure shows a demonstration diagram of using transcranial magnetic stimulation (TMS) technology to collect magnetic resonance brain imaging data of individual subjects. TMS technology uses the alternating current in the stimulation coil to generate an alternating magnetic field outside the brain to stimulate induced current in the brain, thereby changing the electrical activity of neurons, thereby inducing a series of neurophysiological effects and having a certain impact on the individual's cognition and behavior. Figure 2 In this study, the superficial brain area is DLPFC and the deep nucleus is sgACC. This protocol uses the location with the strongest resting-state functional connection with the deep nucleus sgACC in the superficial brain area as the individualized optimal stimulation target.

[0047] Optionally, in one embodiment, transcranial magnetic stimulation technology is used to treat obsessive-compulsive disorder. The superficial brain area can be the orbitofrontal cortex (OFC), and the deep nucleus is the amygdala. The location with the strongest resting-state functional connection with the target deep nucleus is sought within the superficial brain area as the individualized optimal stimulation target.

[0048] Optionally, in one embodiment, transcranial magnetic stimulation technology is used to enhance associative memory. The superficial cortex may be the posterior parietal cortex (PPC), and the deep nucleus may be the hippocampus. The location with the strongest resting-state functional connection with the target deep nucleus is searched voxel by voxel in the superficial brain region as the individualized optimal stimulation target.

[0049] In other words, this solution can be used in scenarios such as treating depression, treating obsessive-compulsive disorder, and improving associative memory.

[0050] Among them, by dividing the superficial brain area into several functional sub-areas, and then taking the functional sub-areas as the minimum search unit, the individualized optimal targets are found one by one in each functional sub-area. This scheme comprehensively considers the temporal and spatial dual characteristics of the superficial cortex of the brain. The spatial arrangement of neurons in the brain is continuous, and neurons with high functional similarity are usually closer in space. The temporal feature of the division of functional sub-areas of the superficial brain area is the functional connection strength between voxels, which reflects the functional similarity between different voxels. The spatial feature is the spatial distance between different voxels, which aims to constrain the division of functional sub-areas.

[0051] 104. Determine individual target stimulation targets for transcranial magnetic stimulation based on the multiple functional sub-areas.

[0052] Studies have shown that the resting-state functional connectivity between superficial brain areas and deep nuclei can be used as a marker for finding the optimal individualized stimulation target for transcranial magnetic stimulation.

[0053] Among them, the individual target stimulation target of transcranial magnetic stimulation is determined according to the multiple functional sub-regions, and specifically, the functional connection strength between the multiple functional sub-regions of the superficial brain area and the deep nuclei can be determined; then, the target functional sub-region is determined according to the functional connection strength between the multiple functional sub-regions and the deep nuclei, for example, the functional sub-region with the largest functional connection strength is determined as the target functional sub-region, and then the individual target stimulation target of transcranial magnetic stimulation is determined according to the target functional sub-region, for example, the center of gravity of the target functional sub-region is determined as the individual target stimulation target of transcranial magnetic stimulation, and the target functional sub-region is determined according to the functional connection strength. The greater the functional connection strength, the more likely it is that stimulating the functional sub-region will bring greater energy stimulation to the deep nuclei.

[0054] Alternatively, the individual target stimulation targets of transcranial magnetic stimulation are determined based on the multiple functional sub-regions, and specifically, the volumes of the multiple functional sub-regions can be determined; then, the target functional sub-region is determined based on the volumes of the multiple functional sub-regions; for example, the functional sub-region of the volume is the target functional sub-region, and then the individual target stimulation targets of transcranial magnetic stimulation are determined based on the target functional sub-region, for example, the center of gravity of the target functional sub-region is determined as the individual target stimulation targets of transcranial magnetic stimulation, and the target functional sub-region is determined based on the volume of the functional sub-region. The larger the volume, the more effectively the energy applied by the transcranial magnetic stimulation falls into the region.

[0055] Individual target stimulation targets for transcranial magnetic stimulation are determined based on multiple functional sub-regions. By dividing the superficial brain areas into functional sub-regions, the minimum unit of target 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 and effectively offset the instability of resting-state functional connections within individual subjects. 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 positioning.

[0056] The implementation mode of the present application is a method for positioning individual targets of transcranial magnetic stimulation, which obtains magnetic resonance brain imaging data of an individual subject, wherein 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, and the noise introduced in the signal acquisition process is eliminated by preprocessing the magnetic resonance brain imaging data, so as to retain the real functional activity of the brain of the individual subject, thereby providing a reliable basis for subsequent functional connection calculations; then, the superficial brain area is divided according to the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-areas; the individual target stimulation target of transcranial magnetic stimulation is determined according to the multiple functional sub-areas, and the superficial brain area is divided into several functional sub-areas, and then the functional sub-areas are used as the minimum search unit to search for the individual optimal target in each functional sub-area. This scheme comprehensively considers the large The superficial cortex has dual temporal and spatial characteristics. The spatial arrangement of neurons in the brain is continuous. Neurons with high functional similarity are usually closer in space. The temporal characteristic of the division of functional sub-regions of the superficial brain area 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 functional sub-regions. By dividing the superficial brain area into functional sub-regions, the smallest unit of target 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 and effectively offset the instability of resting-state functional connections within individual subjects. 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 positioning.

[0057] Optionally, the superficial brain area division is performed according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-areas, including:

[0058] Extracting the time series signal of each voxel in the superficial brain area from the preprocessed magnetic resonance brain imaging data, and calculating the signal correlation between these voxels to obtain the functional connection matrix of the superficial brain area;

[0059] Determine the average functional connection strength of each voxel with all other voxels in the superficial brain region according to the functional connection matrix, and sort the voxels in descending order according to the average functional connection strength;

[0060] Select multiple voxels as core voxels of the functional sub-region in descending order of average functional connection strength to obtain multiple core voxels;

[0061] Region growing is performed with each core voxel as the center to divide the superficial brain region into regions to obtain multiple functional sub-regions.

[0062] Among them, Figure 4 As shown, it is a schematic diagram of the functional sub-region division of a superficial brain area. The superficial brain area can be, for example, DLPFC. The functional connection matrix of the superficial brain area can be calculated first, and the time series signal of each voxel in the superficial brain area is extracted from the preprocessed magnetic resonance brain imaging data, and the signal correlation between these voxels is calculated to obtain the functional connection matrix of the superficial brain area.

[0063] Then, the average functional connection strength between each voxel and all other voxels in the superficial brain area is determined according to the functional connection matrix, and the voxels are sorted in descending order according to the average functional connection strength. The core voxels of the functional sub-area usually show higher functional connection characteristics with other voxels in the functional sub-area. Therefore, the average functional connection strength between each voxel and all other voxels in the superficial brain area can be calculated, and the results are sorted in descending order. Subsequently, several voxels with the highest average connection strength are selected as the core voxels of the initial functional sub-area, that is, multiple voxels are selected as the core voxels of the functional sub-area in descending order according to the average functional connection strength, and multiple core voxels are obtained.

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

[0065] By selecting multiple voxels as the core voxels of the functional sub-region in order from large to small in terms of the average functional connection strength, multiple core voxels are obtained, and then regional growth is performed with the core voxels as the growth center. In this way, functional sub-regions with larger average functional connection strength can be obtained. This not only enables the functional sub-regions to meet the continuity of the spatial arrangement of neurons, but also makes individualized treatment targets more suitable.

[0066] Optionally, the region growing is performed with each core voxel as the center to divide the superficial brain region into regions to obtain multiple functional sub-regions, including:

[0067] The core voxel is taken as the growth center, and the functional connection value between the growth center and the surrounding new voxels that are adjacent and not included in other functional sub-regions is calculated. 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 the new growth center, and the expansion is continued until no new voxel that meets the conditions can be found, and it is determined that the functional sub-region division has been completed.

[0068] Among them, the functional connectivity value is used to represent the functional connectivity strength. Specifically, the larger the functional connectivity value, the greater the functional connectivity strength. The first preset value may be 0.5, for example. When the functional connectivity 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 used as the new growth center, and the expansion continues. The inclusion condition is that the functional connectivity strength between the new voxel and the included voxel is greater than the first preset value. When no new voxel that meets the condition can be found, it is determined that the functional sub-region division is completed. Repeat the above steps 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 also includes: for a functional sub-region that has completed regional growth, when the functional connection strength between voxels in 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 that are not included in the functional sub-region are used as new core voxels, and regional growth is performed with the new core voxels to divide the functional sub-region.

[0070] Among them, 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 indicates that the core voxels 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 regional volume of the functional sub-region is too small, the core voxels of the functional sub-region may be missed. At this time, all voxels that are not included in the functional sub-region need to be used as new core voxels, and the functional sub-regions need to be searched again according to the process of regional growing algorithm to find all functional sub-regions.

[0071] Optionally, the method also includes: judging whether regional fusion is required for the functional sub-regions that have completed regional growth, and when two functional sub-regions meet the fusion conditions, regional fusion is performed on the two functional sub-regions that meet the fusion conditions to obtain multiple fused functional sub-regions; wherein the fusion condition is that the functional connection strength between the voxels in the functional sub-region is greater than the second preset value or the volume of the functional sub-region is greater than the fifth preset value.

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

[0073] Optionally, determining individual target stimulation points of transcranial magnetic stimulation according to the multiple functional sub-areas includes:

[0074] Determine the strength of functional connectivity between multiple functional sub-regions of superficial brain regions and deep nuclei;

[0075] determining the volumes of multiple functional sub-regions;

[0076] Determining a target functional sub-region according to the functional connection strengths between the multiple functional sub-regions and the deep nuclei and the volumes of the multiple functional sub-regions;

[0077] The individualized target stimulation point of transcranial magnetic stimulation is determined according to the target functional sub-area.

[0078] Among them, the target functional sub-region is determined according to the functional connection strength between multiple functional sub-regions and deep nuclei and the volume of multiple functional sub-regions; the individualized target stimulation target of transcranial magnetic stimulation is determined according to the target functional sub-region. The greater the functional connection strength, the more likely it is that stimulating the functional sub-region will bring greater energy stimulation to the deep nuclei, and the larger the volume, the more effectively the energy applied by transcranial magnetic stimulation falls into the region. The functional connection strength and the volume of the functional sub-region can be combined to obtain a more accurate target functional sub-region, and then locate the individualized target stimulation target.

[0079] Optionally, determining the target functional sub-region according to the functional connection strengths between the multiple functional sub-regions and the deep nuclei and the volumes of the multiple functional sub-regions includes:

[0080] Determine target score values ​​corresponding to the multiple 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 multiple functional sub-regions and the deep nuclei, and the volumes of the multiple functional sub-regions;

[0081] The functional sub-region with the largest target score value is determined as the target functional sub-region.

[0082] Among them, the first weight corresponding to the functional connection strength and the second weight corresponding to the volume of the functional sub-area can be set in advance, so that the functional connection strength and the volume of the functional sub-area can be combined to obtain a more accurate target functional sub-area, and then locate the individualized target stimulation target.

[0083] Optionally, determining the individual target stimulation target of transcranial magnetic stimulation according to the target functional sub-area includes:

[0084] Determine the center of gravity of the target functional sub-area, and use the center of gravity position of the target functional sub-area as the individual target stimulation target of transcranial magnetic stimulation. The center of gravity position usually represents the center of functional activity in the functional sub-area. Stimulating the center of gravity can more effectively affect the surrounding neuronal population and maximize the energy transfer of transcranial magnetic stimulation.

[0085] Embodiment 2

[0086] like Figure 5 As shown, one embodiment of the present application proposes another method for locating individual targets of transcranial magnetic stimulation. Figure 5 A schematic flow chart of another method for locating individual targets of transcranial magnetic stimulation provided in an embodiment of the present application, the method comprising:

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

[0088] 202. Preprocess the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data.

[0089] 203. Extract the time series signal of each voxel in the superficial brain area from the preprocessed magnetic resonance brain imaging data, and calculate the signal correlation between these voxels to obtain the functional connection matrix of the superficial brain area.

[0090] 204. Determine the average functional connection strength between each voxel and all other voxels in the superficial brain region according to the functional connection matrix, and sort them in descending order according to the average functional connection strength.

[0091] 205. Select multiple voxels as core voxels of the functional sub-region in descending order of average functional connection strength to obtain multiple core voxels.

[0092] 206. Taking the core voxel as the growth center, calculate the functional connectivity value between the growth center and the surrounding new voxels that are adjacent and not included in other functional sub-regions.

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

[0094] 208. If so, include the new voxel into the functional sub-region to which the growth center belongs; use the newly included voxel as the new growth center, and continue to expand until no new voxel that meets the conditions can be found, and determine that the functional sub-region division is completed, and obtain multiple functional sub-regions; if not, determine that the functional sub-region division is completed, and obtain multiple functional sub-regions.

[0095] 209. Determine the functional connectivity strength between multiple functional sub-regions of superficial brain regions and deep nuclei.

[0096] 210. Determine the volumes of multiple functional sub-regions.

[0097] 211. Determine target score values ​​corresponding to the multiple 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 between the multiple functional sub-regions and the deep nuclei, and the volumes of the multiple functional sub-regions.

[0098] 212. Determine a functional sub-region with a maximum target score as the target functional sub-region.

[0099] 213. Determine the center of gravity of the target functional sub-region, and use the center of gravity position of the target functional sub-region as the individual target stimulation target of transcranial magnetic stimulation.

[0100] In this embodiment, the time series signal of each voxel in the superficial brain area is extracted from the preprocessed magnetic resonance brain imaging data, and the signal correlation between these voxels is calculated to obtain the functional connection matrix of the superficial brain area; the average functional connection strength of each voxel with all other voxels in the superficial brain area is determined according to the functional connection matrix, and the voxels are sorted in descending order according to the average functional connection strength; multiple voxels are selected as core voxels of the functional sub-area in descending order according to the average functional connection strength to obtain multiple core voxels; the core voxels are used as the growth center, and the functional connection values ​​between the growth center and the surrounding new voxels that are adjacent and not included in other functional sub-areas are calculated, and when the growth center 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 used as the new growth center, and the expansion is continued until no new voxel that meets the conditions can be found, and it is determined that the functional sub-region division is completed; the functional connection strengths of multiple functional sub-regions of the superficial brain area and the deep nuclei are determined; the volumes of the multiple functional sub-regions are determined; the target score values ​​corresponding to the multiple functional sub-regions are determined 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 of the multiple functional sub-regions and the deep nuclei, and the volumes of the multiple functional sub-regions; the functional sub-region with the largest target score value is determined The energy sub-region is the target functional sub-region; the center of gravity of the target functional sub-region is determined, and the center of gravity position of the target functional sub-region is used as the individual target stimulation target of transcranial magnetic stimulation; multiple voxels are selected as the core voxels of the functional sub-region in the order of the average functional connection strength from large to small, and multiple core voxels are obtained, and then the core voxels are used as the growth center for regional growth, so that a functional sub-region with a large average functional connection strength can be obtained, which can not only make the functional sub-region meet the continuity of the spatial arrangement of neurons, but also make the individualized treatment target more suitable; the target functional sub-region is determined according to the functional connection strength of multiple functional sub-regions with deep nuclei and the volume of multiple functional sub-regions; according to The target functional sub-region determines the individualized target stimulation target of transcranial magnetic stimulation. The greater the functional connection strength, the more likely it is that stimulation of the functional sub-region will bring greater energy stimulation to the deep nuclei. The larger the volume, the more effectively the energy applied by transcranial magnetic stimulation falls into the region. The functional connection strength and the volume of the functional sub-region can be combined to obtain a more accurate target functional sub-region, and then locate the individualized target stimulation target; the center of gravity position of the target functional sub-region is used as the individual target stimulation target of transcranial magnetic stimulation. The center of gravity position usually represents the center of functional activity within the functional sub-region. The stimulation center of gravity can more effectively affect the surrounding neuronal population and maximize the energy transfer of transcranial magnetic stimulation.

[0101] Embodiment 3

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

[0103] An acquisition module 301 is used to acquire magnetic resonance data of a subject, wherein the magnetic resonance data includes a functional magnetic resonance image;

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

[0105] A division module 303 is used to divide the superficial brain area according to the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-areas;

[0106] The positioning module 304 is used to determine individual target stimulation points of transcranial magnetic stimulation according to the multiple functional sub-areas.

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

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

[0109] Optionally, in the aspect of dividing the superficial brain area according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-areas, the dividing module 303 is specifically used for:

[0110] Extracting the time series signal of each voxel in the superficial brain area from the preprocessed magnetic resonance brain imaging data, and calculating the signal correlation between these voxels to obtain the functional connection matrix of the superficial brain area;

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

[0112] Select multiple voxels as core voxels of the functional sub-region in descending order of average functional connection strength to obtain multiple core voxels;

[0113] Region growing is performed with each core voxel as the center to divide the superficial brain region into regions to obtain multiple functional sub-regions.

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

[0115] The core voxel is taken as the growth center, and the functional connection value between the growth center and the surrounding new voxels that are adjacent and not included in other functional sub-regions is calculated. 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 the new growth center, and the expansion is continued until no new voxel that meets the conditions can be found, and it is determined that the functional sub-region division has been completed.

[0116] Optionally, the division module 303 is also used for: for a functional sub-region that has completed regional growth, when the functional connection strength between the voxels in 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 that are not included in the functional sub-region are used as new core voxels, and regional growth is performed with the new core voxels to divide the functional sub-region.

[0117] Optionally, the division module 303 is also used to: determine whether regional fusion is required for the functional sub-regions that have completed regional growth, and when two functional sub-regions meet the fusion conditions, perform regional fusion on the two functional sub-regions that meet the fusion conditions to obtain multiple fused functional sub-regions; wherein the fusion condition is that the functional connection strength between the voxels in the functional sub-region is greater than the second preset value or the volume of the functional sub-region is greater than the fifth preset value.

[0118] Optionally, in determining the individual target stimulation points of transcranial magnetic stimulation according to the multiple functional sub-areas, the positioning module 304 is specifically used to:

[0119] Determine the strength of functional connectivity between multiple functional sub-regions of superficial brain regions and deep nuclei;

[0120] determining the volumes of multiple functional sub-regions;

[0121] Determining a target functional sub-region according to the functional connection strengths between the multiple functional sub-regions and the deep nuclei and the volumes of the multiple functional sub-regions;

[0122] The individualized target stimulation point of transcranial magnetic stimulation is determined according to the target functional sub-area.

[0123] Optionally, in terms of determining the target functional sub-region according to the functional connection strengths between the multiple functional sub-regions and the deep nuclei and the volumes of the multiple functional sub-regions, the positioning module 304 is specifically configured to:

[0124] Determine target score values ​​corresponding to the multiple 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 multiple functional sub-regions and the deep nuclei, and the volumes of the multiple functional sub-regions;

[0125] The functional sub-region with the largest target score value is determined as the target functional sub-region.

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

[0127] The center of gravity of the target functional sub-region is determined, and the center of gravity position of the target functional sub-region is used as the individual target stimulation target point of transcranial magnetic stimulation.

[0128] In this embodiment, a method for locating individual target points of transcranial magnetic stimulation is provided, by obtaining magnetic resonance brain imaging data of an individual subject, the magnetic resonance brain imaging data including functional magnetic resonance images and structural magnetic resonance images; preprocessing the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data, by preprocessing the magnetic resonance brain imaging data to eliminate noise introduced in the signal acquisition process, retaining the true functional activity of the brain of the individual subject, thereby providing a reliable basis for subsequent functional connection calculations; then, superficial brain regions are divided according to the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-regions; individual target stimulation targets of transcranial magnetic stimulation are determined according to the multiple functional sub-regions, by dividing the superficial brain regions into several functional sub-regions, and then using the functional sub-regions as the minimum search units to search for individualized optimal targets one by one. This scheme comprehensively considers the superficial brain regions, The temporal and spatial dual characteristics of the epidermis are that the spatial arrangement of neurons in the brain is continuous, and neurons with high functional similarity are usually closer in space. The temporal characteristic of the division of functional sub-regions of the superficial brain area 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 functional sub-regions. By dividing the superficial brain area into functional sub-regions, the smallest unit of target 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 and effectively offset the instability of resting-state functional connections within individual subjects. 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 positioning.

[0129] Embodiment 4

[0130] like Figure 7 , Figure 7The application embodiment provides a transcranial magnetic stimulation individual target positioning system, the transcranial magnetic stimulation individual target positioning system includes a processor 410 and a memory 420; and one or more programs, one or more programs are stored in the memory, the memory 420 can be a high-speed RAM memory, or a non-volatile memory (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 code stored in the memory 420 to perform the following operations:

[0131] Acquiring magnetic resonance brain imaging data of the subject, wherein the magnetic resonance brain imaging data includes functional magnetic resonance images;

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

[0133] Dividing superficial brain regions according to the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-regions;

[0134] Individual target stimulation points for transcranial magnetic stimulation are determined based on the multiple functional sub-areas.

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

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

[0137] In a possible example, in the aspect of dividing the superficial brain area according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-areas, the processor 410 is specifically used to:

[0138] Extracting the time series signal of each voxel in the superficial brain area from the preprocessed magnetic resonance brain imaging data, and calculating the signal correlation between these voxels to obtain the functional connection matrix of the superficial brain area;

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

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

[0141] Region growing is performed with each core voxel as the center to divide the superficial brain region into regions to obtain multiple functional sub-regions.

[0142] In one possible example, in the aspect of performing regional growth with each core voxel as the center to divide the superficial brain area into regions to obtain multiple functional sub-regions, the processor 410 is specifically used to: take the core voxel as the growth center, calculate the functional connection value between the growth center and the surrounding new voxels that are adjacent and not included in other functional sub-regions, 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; take 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 is completed.

[0143] In a possible example, the processor 410 is also used to: for a functional sub-region that has completed region growth, when the functional connection strength between voxels in 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, use all voxels that are not included in the functional sub-region as new core voxels, and perform region growth with the new core voxels to divide the functional sub-region.

[0144] In a possible example, the processor 410 is also used to: determine whether regional fusion is required for functional sub-regions that have completed regional growth, and when two functional sub-regions meet the fusion conditions, perform regional fusion on the two functional sub-regions that meet the fusion conditions to obtain multiple fused functional sub-regions; wherein the fusion condition is that the functional connection strength between the voxels in the functional sub-region is greater than the second preset value or the volume of the functional sub-region is greater than the fifth preset value.

[0145] In a possible example, in determining the individual target stimulation points of transcranial magnetic stimulation according to the multiple functional sub-areas, the processor 410 is specifically configured to:

[0146] Determine the strength of functional connectivity between multiple functional sub-regions of superficial brain regions and deep nuclei;

[0147] determining the volumes of multiple functional sub-regions;

[0148] Determining a target functional sub-region according to the functional connection strengths between the multiple functional sub-regions and the deep nuclei and the volumes of the multiple functional sub-regions;

[0149] The individualized target stimulation point of transcranial magnetic stimulation is determined according to the target functional sub-area.

[0150] In a possible example, in determining the target functional sub-region according to the functional connection strengths between the multiple functional sub-regions and the deep nuclei and the volumes of the multiple functional sub-regions, the processor 410 is specifically configured to:

[0151] Determine target score values ​​corresponding to the multiple 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 multiple functional sub-regions and the deep nuclei, and the volumes of the multiple functional sub-regions;

[0152] The functional sub-region with the largest target score value is determined as the target functional sub-region.

[0153] In one possible example, in terms of determining the individual target stimulation point of transcranial magnetic stimulation based on the target functional sub-region, the above-mentioned processor 410 is specifically used to: determine the center of gravity of the target functional sub-region, and use the center of gravity position of the target functional sub-region as the individual target stimulation point of transcranial magnetic stimulation.

[0154] The individual target positioning method of transcranial magnetic stimulation of the present embodiment obtains magnetic resonance brain imaging data of the individual subject, and 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, and the noise introduced in the signal acquisition process is eliminated by preprocessing the magnetic resonance brain imaging data, so as to retain the real functional activity of the brain of the individual subject, thereby providing a reliable basis for subsequent functional connection calculation; then, the superficial brain area is divided according to the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-areas; the individual target stimulation target of transcranial magnetic stimulation is determined according to the multiple functional sub-areas, and the superficial brain area is divided into several functional sub-areas, and then the functional sub-areas are used as the minimum search unit to search for the individual optimal target in each functional sub-area. This scheme comprehensively considers the superficial brain area and the functional sub-areas of the brain. The temporal and spatial dual characteristics of the epidermis are that the spatial arrangement of neurons in the brain is continuous, and neurons with high functional similarity are usually closer in space. The temporal characteristic of the division of functional sub-regions of the superficial brain area 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 functional sub-regions. By dividing the superficial brain area into functional sub-regions, the smallest unit of target 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 and effectively offset the instability of resting-state functional connections within individual subjects. 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 positioning.

[0155] The present application also provides a computer program product, wherein the computer program product includes a non-transitory computer-readable program product storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any of the individual target location methods for transcranial magnetic stimulation recorded in the embodiments of the present application. The computer program product may be a software installation package.

[0156] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0157] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices (equipment), or computer program products. Therefore, the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The computer program is stored / distributed in a suitable medium, provided together with other hardware or as a part of hardware, and other distribution forms may also be adopted, such as by Internet or other wired or wireless telecommunication systems.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (devices) and computer program products of the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable human-vehicle trajectory analysis device to produce a machine, so that the instructions executed by the processor of the computer or other programmable human-vehicle trajectory analysis device generate instructions for implementing the processes in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable vehicle trajectory analysis device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable vehicle trajectory analysis device, so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for locating individual targets for transcranial magnetic stimulation, characterized in that: include: Acquiring magnetic resonance brain imaging data of the subject, wherein the magnetic resonance brain imaging data includes functional magnetic resonance images and structural magnetic resonance images; Preprocessing the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data; Dividing superficial brain regions according to the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-regions; Individual target stimulation points for transcranial magnetic stimulation are determined based on the multiple functional sub-areas.

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

3. The method for individual target location of transcranial magnetic stimulation according to claim 1, characterized in that: The superficial brain area division is performed according to the pre-processed magnetic resonance brain imaging data to obtain multiple functional sub-areas, including: Extracting the time series signal of each voxel in the superficial brain area from the preprocessed magnetic resonance brain imaging data, and calculating the signal correlation between these voxels to obtain the functional connection matrix of the superficial brain area; Determine the average functional connection strength of each voxel with all other voxels in the superficial brain region according to the functional connection matrix, and sort the voxels in descending order according to the average functional connection strength; Select multiple voxels as core voxels of the functional sub-region in descending order of average functional connection strength to obtain multiple core voxels; Region growing is performed with each core voxel as the center to divide the superficial brain region into regions to obtain multiple functional sub-regions.

4. The method for individual target location of transcranial magnetic stimulation according to claim 3, characterized in that: The region growing is performed with each core voxel as the center to divide the superficial brain region into regions to obtain multiple functional sub-regions, including: The core voxel is taken as the growth center, and the functional connection value between the growth center and the surrounding new voxels that are adjacent and not included in other functional sub-regions is calculated. 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 the new growth center, and the expansion is continued until no new voxel that meets the conditions can be found, and it is determined that the functional sub-region division has been completed.

5. The method for individual target location of transcranial magnetic stimulation according to claim 4, characterized in that: The method further comprises: For a functional sub-region that has completed region growing, when the functional connection strength between voxels in 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 that are not included in the functional sub-region are used as new core voxels, and region growing is performed with the new core voxels to divide the functional sub-region.

6. The method for locating individual target points by transcranial magnetic stimulation according to claim 4, characterized in that: The method further comprises: It is judged whether the functional sub-regions that have completed regional growth need to be regionally fused. When two functional sub-regions meet the fusion conditions, the two functional sub-regions that meet the fusion conditions are regionally fused to obtain multiple fused functional sub-regions; wherein the fusion condition is that the functional connection strength between the voxels in the functional sub-region is greater than the second preset value or the volume of the functional sub-region is greater than the fifth preset value.

7. The method for locating individual target points by transcranial magnetic stimulation according to any one of claims 1 to 6, characterized in that: The step of determining individual target stimulation points of transcranial magnetic stimulation according to the multiple functional sub-areas comprises: Determine the strength of functional connectivity between multiple functional sub-regions of superficial brain regions and deep nuclei; determining the volumes of multiple functional sub-regions; Determining a target functional sub-region according to the functional connection strengths between the multiple functional sub-regions and the deep nuclei and the volumes of the multiple functional sub-regions; The individualized target stimulation point of transcranial magnetic stimulation is determined according to the target functional sub-area.

8. The method for individual target location of transcranial magnetic stimulation according to claim 7, characterized in that: The step of determining the target functional sub-region according to the functional connection strengths between the multiple functional sub-regions and the deep nuclei and the volumes of the multiple functional sub-regions comprises: Determine target score values ​​corresponding to the multiple 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 multiple functional sub-regions and the deep nuclei, and the volumes of the multiple functional sub-regions; The functional sub-region with the largest target score value is determined as the target functional sub-region.

9. The method for individual target location of transcranial magnetic stimulation according to claim 7, characterized in that: The step of determining the individual target stimulation point of transcranial magnetic stimulation according to the target functional sub-area comprises: The center of gravity of the target functional sub-region is determined, and the center of gravity position of the target functional sub-region is used as the individual target stimulation target point of transcranial magnetic stimulation.

10. An individual target positioning system for transcranial magnetic stimulation, characterized in that: include: An acquisition module, used to acquire magnetic resonance data of a subject, wherein the magnetic resonance data includes a functional magnetic resonance image and a structural magnetic resonance image; A preprocessing module, used for preprocessing the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data; A division module, used for dividing the superficial brain area according to the preprocessed magnetic resonance brain imaging data to obtain multiple functional sub-areas; A positioning module is used to determine individual target stimulation targets of transcranial magnetic stimulation according to the multiple functional sub-areas.

11. An individual target positioning system for transcranial magnetic stimulation, characterized in that: It comprises a processor and a memory; and one or more programs, which are stored in the memory and configured to be executed by the processor, and the programs include steps of the individual target positioning method for transcranial magnetic stimulation as described in any one of claims 1 to 9.

12. A computer program product, characterized in that The computer program of the computer program product, when executed by a processor, can implement the steps of the method for individual target localization of transcranial magnetic stimulation according to any one of claims 1 to 9.

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