A parameter conversion method and system for a multi-modal brain network atlas

Abstract

This invention provides a parameter conversion method and system for multimodal brain network maps. The method includes: acquiring and comparing multiple source brain regions and multiple target brain regions of a single subject; constructing a set of overlapping brain regions between each target brain region and the source brain region; and statistically analyzing the number of white matter fiber tracts and brain functional connectivity coefficients between each set of overlapping brain regions. Based on this, the remapping coefficients of the white matter fiber brain network and the brain functional connectivity coefficient brain network are calculated respectively. The method also includes: acquiring and statistically analyzing the set of overlapping brain regions between the target brain regions and the source brain regions of multiple subjects, as well as the corresponding number of white matter fiber tracts and brain functional connectivity coefficients; calculating the variance of the brain connectivity strength of the first experimental group and the first control group under the source map, and the second experimental group and the second control group under the target map; and weighting and summing the source brain connectivity statistics between the overlapping brain region sets using influence weights to obtain the target brain connectivity statistics between the target brain regions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of brain network atlas conversion, and particularly relates to a parameter conversion method and system for multi-modal brain network atlas. BACKGROUND

[0002] In neuroimaging research, brain white matter fiber networks and brain functional connectivity coefficient networks constructed based on magnetic resonance imaging (MRI) are widely used to analyze the connection relationship between different regions of the brain. Network atlas can provide rich topological information and reveal the connection mode differences between healthy individuals and disease groups. Current research usually performs network analysis based on different brain atlases. Each atlas has a unique partition scheme and application scenario, enabling researchers to select the optimal atlas for analysis according to specific needs. However, due to the different region division methods of different atlases, the network features of each study are difficult to compare directly, affecting the feasibility of multi-center research and meta-analysis.

[0003] In the prior art, different modal brain network atlases are converted by region of interest (ROI) matching or using interpolation methods. However, these methods lack standardized cross-atlas conversion methods, lack brain connectivity statistic conversion methods suitable for brain white matter fiber networks, have large manual conversion errors, and the network brain connectivity statistics under different atlases are difficult to convert directly, resulting in a lack of comparability between different research results. The degree of overlap between different atlas regions is not considered, which may cause data loss or introduce additional errors. There is a lack of brain connectivity statistic conversion methods suitable for brain white matter fiber networks, which to some extent limits the feasibility of multi-center research and large-scale brain disease analysis based on magnetic resonance imaging data. Therefore, there is an urgent need for a standardized method that can accurately convert parameters between multi-modal brain atlases. SUMMARY

[0004] In view of this, the embodiments of the present application provide a parameter conversion method and system for multi-modal brain network atlas to eliminate or improve one or more defects in the prior art, solving the problem that parameters between different modal brain network atlases in the prior art cannot be converted and directly compared and analyzed.

[0005] One aspect of the present application provides a parameter conversion method for multi-modal brain network atlas, which comprises the following steps:

[0006] Obtaining a plurality of source brain regions in a source atlas and a plurality of target brain regions in a target atlas of a plurality of subjects, comparing the source brain regions and the target brain regions, screening the source brain regions overlapping with each target brain region, and constructing an overlapping brain region set for each target brain region;

[0007] When both the source and target maps are brain white matter fiber network maps, the individual map parameter conversion process includes: counting the number of source brain region white matter fiber bundles between each overlapping brain region set, and weighting and summing the number of source brain region white matter fiber bundles between each overlapping brain region set based on a preset first-level mapping coefficient to obtain the number of target brain region white matter fiber bundles between target brain regions; the group map parameter conversion process includes: determining the group to which the subjects belong according to set elements to obtain the first experimental group and the first control group for the source map, and the second experimental group and the second control group for the target map; calculating the variance of the brain connectivity strength of the first experimental group, the first control group, the second experimental group, and the second control group respectively to measure the variability difference and correcting each first-level mapping coefficient to obtain the influence weight on the target brain connectivity statistics; calculating the source brain connectivity statistics of each source brain region between overlapping brain region sets, and weighting and summing the source brain connectivity statistics based on the influence weight to obtain the target brain connectivity statistics between target brain regions;

[0008] When the source and target maps are brain functional connectivity coefficient network maps, the individual map parameter conversion process includes: statistically calculating the source brain region functional connectivity coefficients between overlapping brain region sets, and weighting and summing the source brain region functional connectivity coefficients between overlapping brain region sets based on the second mapping coefficient to obtain the target brain region functional connectivity coefficients between target brain regions; the group map parameter conversion process includes: determining the group to which the subjects belong according to the set elements, obtaining the first experimental group and the first control group for the source map, and the second experimental group and the second control group for the target map; calculating the variance of the brain connectivity strength of the first experimental group, the first control group, the second experimental group, and the second control group respectively to measure the variability difference and correcting each second mapping coefficient to obtain the influence weight on the target brain connectivity statistics; calculating the source brain connectivity statistics of each source brain region between overlapping brain region sets, and weighting and summing the source brain connectivity statistics based on the influence weight to obtain the target brain connectivity statistics between target brain regions;

[0009] The calculation process of the preset first mapping coefficient includes: acquiring multiple target brain regions of multiple sample subjects in the target brain map, and the set of overlapping brain regions corresponding to the target brain regions in the source brain map; counting the number of white matter fiber bundles of the source brain region between the overlapping brain regions corresponding to each target brain region; counting the number of white matter fiber bundles of the target brain region between each target brain region; mapping the weighted sum of the number of white matter fiber bundles of the source brain region to the number of white matter fiber bundles of the target brain region; solving for the independent mapping coefficients by weights; and normalizing the independent mapping coefficients of multiple sample subjects to obtain the first mapping coefficient.

[0010] In some embodiments, the method includes solving for the independent mapping coefficients using the least squares method based on the mapping relationship between the number of white matter fiber bundles in the source brain region samples among the sets of overlapping brain regions corresponding to each target brain region and the number of white matter fiber bundles in the target brain regions among each target brain region. The independent mapping coefficients k ij The mapping relation expression that is satisfied is:

[0011]

[0012] Among them, Y AB X represents the number of white matter fiber bundles in the target brain region between target brain region A and target brain region B. ij denoted by p, which represents the number of source brain region white matter fiber bundles between the sample overlapping brain region sets; p represents the number of source brain regions in the sample overlapping source brain region set of sample target brain region A; and q represents the number of source brain regions in the sample overlapping source brain region set of sample target brain region B.

[0013] The remapping coefficients are obtained by normalizing the independent mapping coefficients of multiple sample subjects, and the remapping coefficients k ij * Satisfy the following expression:

[0014]

[0015] Where, k m,ij X represents the independent mapping coefficient when the sample subject number is m. m,ij This represents the number of white matter fiber bundles in the source brain region samples among the overlapping brain regions when the sample subject number is m, and n represents the total number of samples.

[0016] In some embodiments, the number of white matter fiber bundles in the source brain region is obtained by weighted summation based on a preset first mapping coefficient, and the number of white matter fiber bundles in the target brain region is Y. EF Satisfy the following expression:

[0017]

[0018] Among them, Y EF k represents the number of white matter fiber bundles in the target brain region between target brain region E and target brain region F. xy * X represents the coefficient of the first mapping. xy The number of white matter fiber bundles in the source brain regions between the overlapping brain region sets is represented by s, which represents the number of source brain regions in the overlapping source brain region set of the target brain region E, and t represents the number of source brain regions in the overlapping source brain region set of the target brain region F.

[0019] In some embodiments, the target brain connectivity statistics T between target brain regions EF Satisfy the following expression:

[0020]

[0021] Where s represents the number of source brain regions in the overlapping source brain region set of target brain region E, t represents the number of source brain regions in the overlapping source brain region set of target brain region F, and L xy Indicates the influence weight, T xy k represents the source-brain connectivity statistics among the source brain regions of the overlapping brain region set. xy * Represents the remapping coefficients. This represents the variance of brain connectivity strength in the first experimental group; This represents the variance of brain connectivity strength in the first control group; This represents the variance of brain connectivity strength in the second experimental group; This represents the variance of brain connectivity strength in the second control group.

[0022] In some embodiments, the brain connectivity strength is represented by the number of white matter fiber bundles per unit area of ​​brain region; the more fiber bundles per unit area of ​​brain region, the greater the brain connectivity strength.

[0023] In some embodiments, the target brain region functional connectivity coefficients between target brain regions are obtained by weighted summation of the source brain region functional connectivity coefficients between each overlapping brain region set based on the second mapping coefficients. The target brain region functional connectivity coefficients satisfy the following expression:

[0024]

[0025] k cd * =w c w d ;

[0026] Among them, R GH This represents the target brain region functional connectivity coefficient between target brain region G and target brain region H. Denotes the second mapping coefficient, r cd The coefficient represents the source brain region functional connectivity between overlapping brain region sets, k represents the number of source brain regions in the overlapping source brain region set of the target brain region G, l represents the number of source brain regions in the overlapping source brain region set of the target brain region H, and w represents the number of source brain regions in the overlapping source brain region set of the target brain region H. c w represents the proportion of overlapping brain volumes in the set of overlapping brain regions of the target brain region G. d The coefficient representing the proportion of overlapping brain volumes in the set of overlapping brain regions of the target brain region H.

[0027] In some embodiments, the method further includes:

[0028] The `convertStatistics` function is called to input the source brain connectivity statistics, source atlas name, target atlas name, target atlas connectivity type, brain connectivity strength variance, file save type of the target atlas after parameter conversion, and target atlas display type into a preset integrated development environment. The target atlas containing the number of white matter fiber tracts in the target brain region and the target brain connectivity statistics is saved and displayed.

[0029] On the other hand, the present invention also provides a parameter conversion for multimodal brain network maps, including a processor, a memory, and a computer program / instructions stored in the memory, wherein the processor is used to execute the computer program / instructions, and when the computer program / instructions are executed, the system implements the steps of any of the methods described above.

[0030] On the other hand, the present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of any of the methods described above.

[0031] On the other hand, the present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0032] The beneficial effects of the present invention are at least as follows:

[0033] The parameter conversion method and system for multimodal brain network maps described in this invention compares the source brain region and the target brain region to obtain the degree of overlap between the source and target maps and eliminates brain region localization errors caused by modal differences between different brain network maps. The independent mapping coefficients calculated from multiple sample subjects are normalized to obtain remapping coefficients. The remapping coefficients are then corrected using the variance of brain connectivity strength between the experimental and control groups to obtain the influence weights on the target brain connectivity statistics. This adapts the parameter conversion process to the characteristics of different subject groups, improving the accuracy and stability of parameter conversion. Through the remapping coefficients and influence weights, the number of white matter fiber tracts and brain connectivity statistics in the source map are converted to the number of white matter fiber tracts and brain connectivity statistics in the target map, achieving parameter conversion between various types of brain network maps of different modalities and avoiding information loss during the parameter conversion process.

[0034] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the description and drawings.

[0035] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0036] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. In the drawings:

[0037] Figure 1 This is a flowchart illustrating a parameter conversion method for multimodal brain network maps according to an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of the parameter conversion method for multimodal brain network maps according to an embodiment of the present invention.

[0039] Figure 3 This is a schematic diagram illustrating the correlation between the true t-statistic and the target t-statistic before and after parameter transformation in an embodiment of the present invention.

[0040] Figure 4 This is a schematic diagram illustrating the correlation and retention ratio of the real Cohen's d and the target Cohen's d before and after parameter conversion according to an embodiment of the present invention.

[0041] Figure 5 A schematic diagram of the distribution of the target Cohen's d after conversion to DK-114 map according to an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0043] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0044] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0045] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0046] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0047] In existing technologies, some studies on cross-map conversion of white matter fiber brain networks use region-of-interest matching or interpolation methods for data mapping. However, these methods lack standardized cross-map conversion methods, parameter conversion methods, and manual conversion results in large errors. The difficulty in directly converting parameters under different maps leads to a lack of comparability between different research results. The lack of parameter conversion methods applicable to multimodal brain network maps limits the feasibility of multicenter studies and large-scale brain disease analysis based on magnetic resonance imaging data. This invention proposes a parameter conversion method and system for multimodal brain network maps. It acquires and compares multiple source brain regions and multiple target brain regions from multiple subjects, constructing overlapping brain region sets for each target brain region. When the source and target maps are white matter fiber brain network maps, individual map parameter conversion is performed, and the number of white matter fiber bundles in the source brain regions between each overlapping brain region set is counted. The number of white matter fiber bundles in the target brain regions between target brain regions is obtained based on the first mapping coefficient. When the source and target maps are brain functional connectivity coefficient network maps, individual map parameter conversion is performed, and the number of source brain regions between each overlapping brain region set is counted. Brain region functional connectivity coefficients are obtained based on the second remapping coefficients to determine the target brain regions' functional connectivity coefficients between target brain regions. When performing group map parameter conversion between the brain functional connectivity coefficient network map and the white matter fiber network map, the subjects are assigned to the first experimental group and the first control group regarding the source map, and the second experimental group and the second control group regarding the target map. The variance of the brain connectivity strength of each fiber bundle in each group is calculated, and the remapping coefficients are corrected to obtain the influence weight on the target brain connectivity statistics. The source-brain connectivity statistics of each source brain region between overlapping brain region sets are calculated. The target brain connectivity statistics between target brain regions are obtained by weighted summation based on influence weights. The calculation process of the first mapping coefficient includes: obtaining multiple sample target brain regions and corresponding sample overlapping brain region sets, counting the number of source brain region sample white matter fiber bundles between each sample overlapping brain region set and the number of target brain region sample white matter fiber bundles between each sample target brain region, solving multiple independent mapping coefficients that map the weighted summation of the number of source brain region sample white matter fiber bundles to the number of target brain region sample white matter fiber bundles, and normalizing them to obtain the first mapping coefficient.

[0048] Figure 1 This is a flowchart illustrating a parameter conversion method for multimodal brain network maps according to an embodiment of the present invention. Specifically, this application provides a parameter conversion method for multimodal brain network maps, which includes the following steps S101 to S103:

[0049] Step S101: Obtain multiple source brain regions in the source atlas and multiple target brain regions in the target atlas from multiple subjects, compare the source brain regions and target brain regions, screen the source brain regions that overlap with each target brain region, and construct a set of overlapping brain regions for each target brain region.

[0050] Step S102: When the source map and the target map are brain white matter fiber network maps, the individual map parameter conversion process includes: counting the number of source brain region white matter fiber bundles between each overlapping brain region set, and weighting and summing the number of source brain region white matter fiber bundles between each overlapping brain region set based on the preset first-fold mapping coefficient to obtain the number of target brain region white matter fiber bundles between target brain regions; the group map parameter conversion process includes: determining the group to which the subjects belong according to the set elements to obtain the first experimental group and the first control group for the source map, and the second experimental group and the second control group for the target map; calculating the variance of the brain connectivity strength of the first experimental group, the first control group, the second experimental group, and the second control group respectively to measure the variability difference and correcting each first-fold mapping coefficient to obtain the influence weight on the target brain connectivity statistics; calculating the source brain connectivity statistics of each source brain region between overlapping brain region sets, and weighting and summing the source brain connectivity statistics based on the influence weight to obtain the target brain connectivity statistics between target brain regions.

[0051] Step S103: When the source map and the target map are brain functional connectivity coefficient brain network maps, the individual map parameter conversion process includes: statistically calculating the source brain region brain functional connectivity coefficients between each overlapping brain region set, and weighting and summing the source brain region brain functional connectivity coefficients between each overlapping brain region set based on the second mapping coefficient to obtain the target brain region brain functional connectivity coefficients between target brain regions; the group map parameter conversion process includes: determining the group to which the subjects belong according to the set elements, obtaining the first experimental group and the first control group for the source map, and the second experimental group and the second control group for the target map; calculating the variance of the brain connectivity strength of the first experimental group, the first control group, the second experimental group, and the second control group respectively to measure the variability difference and correcting each second mapping coefficient to obtain the influence weight on the target brain connectivity statistics; calculating the source brain connectivity statistics of each source brain region between overlapping brain region sets, and weighting and summing the source brain connectivity statistics based on the influence weight to obtain the target brain connectivity statistics between target brain regions.

[0052] The calculation process of the preset first mapping coefficient includes: acquiring multiple target brain regions of multiple sample subjects in the sample target atlas, and the set of sample overlapping brain regions corresponding to the sample target brain regions in the sample source atlas; counting the number of source brain region sample white matter fiber bundles among the sample overlapping brain region sets corresponding to each sample target brain region; counting the number of target brain region sample white matter fiber bundles among each sample target brain region; mapping the weighted sum of the number of source brain region sample white matter fiber bundles to the number of target brain region sample white matter fiber bundles; solving for the weights to obtain the independent mapping coefficients; and normalizing the independent mapping coefficients of multiple sample subjects to obtain the remapping coefficients.

[0053] In step S101, the multimodal brain network atlas includes a white matter fiber brain network atlas and a brain functional connectivity coefficient network atlas. The source atlas and the brain network atlas are different types of atlases belonging to the same modality of brain network atlas during parameter conversion. The multimodal brain network atlas includes the Automatic Anatomical Label Atlas (AAL), the Decican-Killian 114 atlas (DK114), the Schaefer 200 area functional atlas (Schaefer200), the Human Connectome Project Multimodal Partition Atlas (HCP_MMP), the Decican-Killian atlas (DK), the Decican-Killian 219 area atlas (DK219), the Brain Network Atlas (BN), and the Arslan atlas. The Baldassano, Brodmann, Economo, Independent Component Analysis (ICA) atlas, NSPn500 preprint network atlas, Power functional network atlas, Shen functional network atlas, Schaefer 300 area functional atlas, and Schaefer 400 area functional atlas were used. The source and target atlases of each subject were compared, and the source brain regions contained in the corresponding positions of the target brain regions in the source atlas were obtained and constructed as overlapping brain region sets for each target brain region. Further, the process of comparing source and target brain regions, screening for source brain regions overlapping with each target brain region, and constructing overlapping brain region sets for each target brain region includes steps S1011–S1013:

[0054] Step S1011: Check whether the shapes of the source spectrum and the target spectrum are consistent. If they are inconsistent, issue an error message and stop the parameter conversion process.

[0055] Step S1012: Read the numbers of multiple source brain regions in the source map and the numbers of multiple target brain regions in the target map using a preset map processing tool.

[0056] Step S1013: Compare the source brain region and the target brain region, select the number of voxels of the source brain region that overlaps with each target brain region, and construct the set of overlapping brain regions for each target brain region by using multiple source brain region numbers, multiple target brain region numbers, and the number of voxels of the overlapping source brain regions.

[0057] Specifically, checking the shape consistency between the source and target brain maps ensures consistency between the subject's source and target brain regions when screening for source regions that overlap with each target brain region. When the pre-defined map processing tool reads the numbers of multiple source brain regions in the source map and multiple target brain regions in the target map, for example, the pre-defined map processing tool uses Python's nibabel and numpy tools. A voxel is the smallest unit in a brain network map, used to depict the complex morphology and internal structure of the brain; the higher the resolution of the brain network map, the greater the number of voxels. By counting voxels in each region of the brain network map, the connectivity and function of white matter fibers in the brain can be quantified and used in studies between individuals or groups.

[0058] In step S102, the overlapping brain regions of each target brain region in the white matter fiber brain network atlas are connected by fiber bundles, and the transmission and combination of neural information in the brain regions are carried out through the fiber bundles. The number of white matter fiber bundles reflects the strength of the connection between each brain region and affects the efficiency of the transmission of neural information in the brain regions. Based on the number of white matter fiber bundles, the topological structure between each brain region can be constructed to realize the study of the brain network of different subjects and the diagnosis of diseases. The weighted summation of the fiber counts is the sum of the number of white matter fiber bundles between all overlapping brain region sets. The first mapping coefficient maps the sum of the number of white matter fiber bundles between all overlapping brain region sets to the target atlas to obtain the number of white matter fiber bundles between the target brain regions.

[0059] Furthermore, in the calculation of the first mapping coefficient, the sample dataset includes, but is not limited to, the HCP subject dataset and the CHCP subject dataset. The HCP subject dataset contains diffusion-weighted imaging (DWI) data from multiple sample subjects. DWI data is image data obtained using magnetic resonance imaging (MRI) technology that reflects the diffusion motion characteristics of water molecules in the brain. DWI data is presented in the form of three-dimensional images, and each voxel contains diffusion information of water molecules. The brain white matter fiber network atlas is based on the DWI data and reflects the anatomical static physical connections of the brain. In some embodiments, the method includes using the least squares method to solve for the independent mapping coefficients based on the mapping relationship between the number of white matter fiber bundles in the source brain region samples and the number of white matter fiber bundles in the target brain region samples between the target brain regions of each sample. The independent mapping coefficient k ij The mapping relation expression that is satisfied is:

[0060]

[0061] Among them, Y AB X represents the number of white matter fiber bundles in the target brain region between target brain region A and target brain region B. ij denoted by p, which represents the number of white matter fiber bundles in the source brain regions between the sets of overlapping source brain regions of sample target brain region A, and q, which represents the number of source brain regions in the sets of overlapping source brain regions of sample target brain region B.

[0062] The first-order mapping coefficients are obtained by normalizing the independent mapping coefficients of multiple sample subjects. ij * Satisfy the following expression:

[0063]

[0064] Where, k m,ij X represents the independent mapping coefficient when the sample subject number is m. m,ij This represents the number of white matter fiber bundles in the source brain region samples among the overlapping brain regions when the sample subject number is m, and n represents the total number of samples.

[0065] In some embodiments, the number of white matter fiber tracts in the source brain region is obtained by weighted summation based on a preset first mapping coefficient, and the number of white matter fiber tracts in the target brain region is Y. EF Satisfy the following expression:

[0066]

[0067] Among them, Y EF k represents the number of white matter fiber bundles in the target brain region between target brain region E and target brain region F. xy * X represents the coefficient of the first mapping. xy The number of white matter fiber bundles in the source brain regions between the overlapping brain region sets is represented by s, which represents the number of source brain regions in the overlapping source brain region set of the target brain region E, and t represents the number of source brain regions in the overlapping source brain region set of the target brain region F.

[0068] Furthermore, the white matter fiber brain network atlas and the brain functional connectivity coefficient network atlas employ a consistent brain connectivity statistics transformation method when converting population atlas parameters. The set elements include, but are not limited to, population health status, age, gender, and occupation. The first experimental group and the first control group each contain the source brain regions of the two groups, while the second experimental group and the second control group each contain the target brain regions of the two groups. The variance of brain connectivity strength in the first experimental group and the first control group is the variance of brain connectivity strength between source brain regions in each overlapping brain region, and the variance of brain connectivity strength in the second experimental group and the second control group is the variance of brain connectivity strength between target brain regions. In the white matter fiber brain network atlas, in some embodiments, brain connectivity strength is represented by the number of white matter fiber bundles per unit area of ​​brain region; the more fiber bundles per unit area of ​​brain region, the greater the brain connectivity strength. In the brain functional connectivity coefficient network atlas, brain connectivity strength is related to the proportion of overlapping brain volume, where overlapping brain volume is the voxel overlap between the overlapping brain region set and the target brain region. For example, the target brain connectivity statistic uses the t-statistic to test whether the variability difference in brain connectivity strength between two groups is significant, and the variability difference in brain connectivity strength is obtained by calculating the variance; the source brain connectivity statistics of each source brain region between overlapping brain region sets are transformed into target brain connectivity statistics between target brain regions by using influence weights; in the brain white matter fiber network atlas, in some embodiments, the target brain connectivity statistic T between target brain regions is... EF Satisfy the following expression:

[0069]

[0070] Where s represents the number of source brain regions in the overlapping source brain region set of target brain region E, t represents the number of source brain regions in the overlapping source brain region set of target brain region F, and L xy Indicates the influence weight, T xy k represents the source-brain connectivity statistics among the source brain regions of the overlapping brain region set. xy * Represents the remapping coefficients. This represents the variance of brain connectivity strength in the first experimental group; This represents the variance of brain connectivity strength in the first control group; This represents the variance of brain connectivity strength in the second experimental group; This represents the variance of brain connectivity strength in the second control group.

[0071] In step S103, in some embodiments, the source brain region functional connectivity coefficients between overlapping brain region sets are weighted and summed based on the second mapping coefficients to obtain the target brain region functional connectivity coefficients between target brain regions. The target brain region functional connectivity coefficients satisfy the following expression:

[0072]

[0073] k cd * =w c w d ;

[0074] Among them, R GH This represents the target brain region functional connectivity coefficient between target brain region G and target brain region H. Denotes the second mapping coefficient, r cd The coefficient represents the source brain region functional connectivity between overlapping brain region sets, k represents the number of source brain regions in the overlapping source brain region set of the target brain region G, l represents the number of source brain regions in the overlapping source brain region set of the target brain region H, and w represents the number of source brain regions in the overlapping source brain region set of the target brain region H. c w represents the proportion of overlapping brain volumes in the set of overlapping brain regions of the target brain region G. d This represents the proportion coefficient of overlapping brain volume in the set of overlapping brain regions of the target brain region H. Specifically, the calculation process of the second mapping coefficient includes: acquiring multiple target brain regions of multiple sample subjects in the sample target atlas, and the set of overlapping brain regions corresponding to the target brain regions in the sample source atlas; calculating the source brain region sample brain volume between the set of overlapping brain regions corresponding to each target brain region, calculating the target brain region sample brain volume between each target brain region, obtaining the proportion coefficient of overlapping voxels between source brain regions in each target brain region and the set of overlapping brain regions through the source brain region sample brain volume and the target brain region sample brain volume, and calculating the second mapping coefficient.

[0075] In some embodiments, the parameter conversion method for multimodal brain network maps further includes: calling the `convertStatistics` function to input the source brain connectivity statistics, source map name, target map name, target map connectivity type, brain connectivity strength variance, file save type of the parameter-converted target map, and target map display type into a preset integrated development environment; and saving and displaying the target map containing the number of white matter fiber tracts in the target brain region and the target brain connectivity statistics. Specifically, the method of this invention runs in various programming language environments, including but not limited to Python or MATLAB, and the target map is saved as a CSV file and displayed as an SVG image.

[0076] On the other hand, the present invention also provides a parameter conversion for multimodal brain network maps, including a processor, a memory, and a computer program / instructions stored in the memory, wherein the processor is used to execute the computer program / instructions, and when the computer program / instructions are executed, the system implements the steps of the above method.

[0077] On the other hand, the present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0078] On the other hand, the present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0079] The present invention will now be described with reference to a specific embodiment:

[0080] Figure 2 This is a schematic diagram of the parameter conversion method for multimodal brain network maps according to an embodiment of the present invention. The present invention proposes a parameter conversion method (TACOS) for multimodal brain network maps, which realizes the conversion of parameters between different modal brain network maps through two-stage model calculation.

[0081] 1. Brain white matter fiber network atlas: Individual atlas parameter conversion based on the connection of overlapping fibers.

[0082] (1) Calculate the region overlap between the source and target maps. Extract the spatial locations of each brain region in the source and target maps, and calculate the corresponding region set {a1, a2, ..., a...} in the source map for each brain region A in the target map. p Given that target atlas brain region A corresponds to p brain regions in the source atlas, calculate the set of corresponding regions {b1, b2, ..., b} in the source atlas for each target atlas brain region B. q The target atlas brain region B corresponds to q brain regions in the source atlas. The steps to calculate the set of corresponding regions for each target atlas brain region in the source atlas are as follows: Use Python's nibabel and numpy tools to read NIFTI images; for each pair of target and source atlases, compare the overlapping regions of each brain region one by one, i.e., the overlapping parts of voxels with the same labels in the two atlases, and record the number of overlapping voxels; the source and target atlases are different forms of brain network atlases.

[0083] (2) Calculate the remapping coefficients of fiber bundles at the individual level. In HCP subject data, which includes diffusion-weighted imaging data from multiple sample subjects, calculate the source spectral region a. i and b j The number of white matter fiber bundles between X ij The number of white matter fiber bundles Y between target brain regions A and B AB Solve for the independent mapping coefficients k ij The expression is:

[0084]

[0085] Calculate the remapping coefficient at the population level The expression is:

[0086]

[0087] (3) Remapping is performed using the population-level remapping coefficients. The calculated population-level remapping coefficients k... ij * Mapping the connection number of the source graph to the target graph, the expression is:

[0088]

[0089] 2. Brain functional connectivity coefficients: Brain network atlases are converted based on the proportion of overlapping brain volumes to transform individual atlas parameters.

[0090] The time series data of target brain region A in the target atlas satisfies the following expression: Among them, V i Indicates a i Time series of brain regions. Region {a1, a2, ..., a p} represents the source map region that spatially overlaps with the target brain region A, and the variable w i It is the target brain region A and the source brain region a. i The ratio of overlapping voxels between them ranges from (0,1). The time series of target brain region B in the target atlas satisfies the following expression: The correlation coefficient between target brain region A and target brain region B satisfies the following expression: Among them, R AB The coefficient represents the correlation between brain region A and brain region B, Cov represents the covariance, and σ represents the standard deviation.

[0091] Due to spatial autocorrelation, voxel time series from the same brain region exhibit high correlation; therefore, time series U... A and time series V i They show a strong correlation, with their standard deviations being approximately equal. This similarity in standard deviations provides the basis for substitution in the equation, using V... i Replace U A , using V j Replace U B Thus, the following results were obtained: (3-9) Where, r ij Brain region a represents the spatial overlap between target brain region A and target brain region B. i and b j Specify the correlation coefficient between the links. This is the corresponding proportionality coefficient for the number of overlapping voxels. In this method, the average proportion of overlapping brain region volumes is used instead of the voxel overlap ratio.

[0092] 3. Brain white matter fiber network atlas and brain functional connectivity coefficients: Population atlas parameter transformation was performed based on variance-weighted t-statistics.

[0093] (1) Calculate the variance plot of the connection between the two groups of subjects on the source map. The two groups of subjects are the experimental group and the control group: calculate the variance of each connection in the experimental group (P). The variance in the control group (C)

[0094] (2) Calculate the influence weights and convert the t-statistic. The expression for the influence weights is:

[0095]

[0096] in, This represents the variance of brain connectivity strength between regions A and B in the target atlas in the experimental group (P). This represents the variance of brain connectivity strength between regions A and B in the target atlas and in the control group (C). This represents the variance of brain connectivity strength between regions A and B in the source map in the experimental group (P). This represents the variance of brain connectivity strength between regions A and B in the source map and the control group (C). Represents the remapping coefficient at the group level.

[0097] The t-statistic for the target map is calculated using the following expression:

[0098]

[0099] Among them, T AB L represents the combined t-statistic between regions A and B in the target map. ij T represents the weight of the influence of the ij-th pair of connections on the target region AB; ij This represents the t-statistic between region i and region j in the source map.

[0100] (3) After the above calculation, the t-statistic on the target spectrum is consistent with the source spectrum and can be used for comparison and merging analysis of different studies.

[0101] 4. In this invention, parameter conversion for multimodal brain network maps can be performed on brain network maps of different modalities. When the programming language is Python or MATLAB, the source brain connectivity statistics, source map name, target map name, target map connectivity type, brain connectivity strength variance, file saving type of the target map after parameter conversion, and target map display type are input into the preset integrated development environment by calling the convertStatistics function. The target map containing the number of white matter fiber tracts in the target brain region and the target brain connectivity statistics is saved and displayed.

[0102] 5. Figure 3 This diagram illustrates the correlation between the true t-statistic and the target t-statistic before and after parameter transformation in an embodiment of the present invention. The transformation from DK (n=68), DK-219 (n=219), HCP-MMP (n=360), BN (n=210), and Schaefer (n=200) maps to the DK-114 (n=114) map is shown. The target t-statistic matrix displays the transformed target t-statistic, with the depth of the points representing the magnitude of the target t-statistic. The correlation coefficient r represents the correlation between the target t-statistic and the true t-statistic; the vertical axis represents the target t-statistic, and the horizontal axis represents the true t-statistic. The red dashed line in the diagram represents the correlation between the target t-statistic transformed using the present invention and the true t-statistic. The permutation test results show that the target t-statistic generated by the present invention has a significantly higher correlation with the target t-statistic generated from other simulations (zero distribution), with all p < 0.001. Figure 4 This diagram illustrates the correlation and retention ratio of the true Cohen's d and the target Cohen's d before and after parameter transformation according to an embodiment of the present invention. The correlation is the Cohen's d correlation between the target Cohen's d mapping obtained from five different maps and a random mixture of multiple maps, and the true Cohen's d mapping in the DK-114 map. The Cohen's d mapping is an indicator used to measure the effect size, reflecting the magnitude of the difference in the mean values ​​and the degree of correlation between the two sets of parameters before and after the map transformation. The retention ratio diagram shows the proportion of connections that are still retained when the true effect size exceeds a set Cohen's d threshold. The vertical axis represents the retention ratio, and the horizontal axis represents the Cohen's d threshold. The true effect size includes positive and negative effect sizes. Figure 5 This invention provides a schematic diagram of the distribution of target Cohen's d after conversion to a DK-114 map, as described in one embodiment. The diagram represents the converted effect size distribution corresponding to connections where the true effect size |Cohen's d|>0.15; the vertical axis represents the converted target Cohen's d, and the horizontal axis represents the map type; the target effect size includes positive and negative effect sizes.

[0103] In summary, this invention provides a parameter conversion method and system for multimodal brain network maps. It acquires and compares multiple source brain regions in a source map and multiple target brain regions in a target map from multiple subjects, filters source brain regions overlapping with each target brain region, and constructs an overlapping brain region set for each target brain region. When performing individual map parameter conversion for white matter fiber brain network maps, it counts the number of white matter fiber bundles in the source brain regions between each overlapping brain region set, and performs a weighted summation of the number of white matter fiber bundles in the source brain regions based on a preset remapping coefficient to obtain the number of white matter fiber bundles in the target brain regions between target brain regions; the first remapping coefficient... The calculation process includes: acquiring multiple target brain regions of multiple sample subjects in the target brain map, and the set of overlapping brain regions corresponding to the target brain regions in the source brain map; counting the number of white matter fiber bundles of the source brain region among the overlapping brain regions corresponding to each target brain region; counting the number of white matter fiber bundles of the target brain region among each target brain region; mapping the weighted sum of the number of white matter fiber bundles of the source brain region to the number of white matter fiber bundles of the target brain region; solving for the independent mapping coefficients by weights; and normalizing the independent mapping coefficients of multiple sample subjects to obtain the remapping coefficients. When converting individual atlas parameters for brain network atlases using brain functional connectivity coefficients, the source brain region functional connectivity coefficients between overlapping brain region sets are statistically analyzed. Based on the second remapping coefficient, a weighted sum of these source brain region functional connectivity coefficients is performed to obtain the target brain region functional connectivity coefficients between target brain regions. The population atlas parameter conversion process for the two modalities of brain network images includes: dividing subjects into two groups according to set elements to obtain a first experimental group and a first control group for the source atlas, and a second experimental group and a second control group for the target atlas; calculating the variance of brain connectivity strength in the first experimental group, the first control group, the second group, and the second control group to measure variability differences, and correcting each remapping coefficient to obtain the influence weights on the target brain connectivity statistics; calculating the source brain connectivity statistics for each source brain region between overlapping brain region sets, and then weighted summing these source brain connectivity statistics based on the influence weights to obtain the target brain connectivity statistics between target brain regions.

[0104] This invention also provides a parameter conversion system for multimodal brain network maps, including a processor and a memory, wherein the processor and the memory can be connected via a bus or other means.

[0105] The processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0106] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the button blocking method of the vehicle display device in this embodiment of the invention. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory.

[0107] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] The one or more modules are stored in the memory, and when executed by the processor, they perform the method described in this embodiment.

[0109] This invention also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the aforementioned edge computing server deployment method. The computer-readable storage medium can be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.

[0110] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0111] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0112] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations of the embodiments of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A parameter conversion method for multimodal brain network maps, characterized in that, The method includes the following steps: Multiple source brain regions in the source atlas and multiple target brain regions in the target atlas were obtained from multiple subjects. The source brain regions and target brain regions were compared, and source brain regions that overlapped with each target brain region were screened. A set of overlapping brain regions for each target brain region was constructed. When both the source and target maps are brain white matter fiber network maps, the individual map parameter conversion process includes: counting the number of source brain region white matter fiber bundles between each overlapping brain region set, and weighting and summing the number of source brain region white matter fiber bundles between each overlapping brain region set based on a preset first-level mapping coefficient to obtain the number of target brain region white matter fiber bundles between target brain regions; the group map parameter conversion process includes: determining the group to which the subjects belong according to set elements to obtain the first experimental group and the first control group for the source map, and the second experimental group and the second control group for the target map; calculating the variance of the brain connectivity strength of the first experimental group, the first control group, the second experimental group, and the second control group respectively to measure the variability difference and correcting each first-level mapping coefficient to obtain the influence weight on the target brain connectivity statistics; calculating the source brain connectivity statistics of each source brain region between overlapping brain region sets, and weighting and summing the source brain connectivity statistics based on the influence weight to obtain the target brain connectivity statistics between target brain regions; When the source and target maps are brain functional connectivity coefficient network maps, the individual map parameter conversion process includes: statistically calculating the source brain region functional connectivity coefficients between overlapping brain region sets, and weighting and summing the source brain region functional connectivity coefficients between overlapping brain region sets based on the second mapping coefficient to obtain the target brain region functional connectivity coefficients between target brain regions; the group map parameter conversion process includes: determining the group to which the subjects belong according to the set elements, obtaining the first experimental group and the first control group for the source map, and the second experimental group and the second control group for the target map; calculating the variance of the brain connectivity strength of the first experimental group, the first control group, the second experimental group, and the second control group respectively to measure the variability difference and correcting each second mapping coefficient to obtain the influence weight on the target brain connectivity statistics; calculating the source brain connectivity statistics of each source brain region between overlapping brain region sets, and weighting and summing the source brain connectivity statistics based on the influence weight to obtain the target brain connectivity statistics between target brain regions; The calculation process of the preset first mapping coefficient includes: acquiring multiple target brain regions of multiple sample subjects in the target brain map, and the set of overlapping brain regions corresponding to the target brain regions in the source brain map; counting the number of white matter fiber bundles of the source brain region between the overlapping brain regions corresponding to each target brain region, counting the number of white matter fiber bundles of the target brain region between each target brain region, mapping the weighted sum of the number of white matter fiber bundles of the source brain region to the number of white matter fiber bundles of the target brain region, solving for the independent mapping coefficients by weights, and normalizing the independent mapping coefficients of multiple sample subjects to obtain the first mapping coefficient; Specifically, the target brain region functional connectivity coefficients between target brain regions are obtained by weighted summation of the source brain region functional connectivity coefficients among overlapping brain region sets based on the second mapping coefficients. The target brain region functional connectivity coefficients satisfy the following expression: ； in, This represents the target brain region functional connectivity coefficient between target brain region G and target brain region H. Indicates the second mapping coefficient. The coefficient represents the source brain region functional connectivity between overlapping sets of brain regions, and k represents the number of source brain regions in the overlapping source brain region set of the target brain region G. This indicates the number of source brain regions in the set of overlapping source brain regions of the target brain region H. This represents the proportion coefficient of overlapping brain volume in the set of overlapping brain regions of the target brain region G. The coefficient representing the proportion of overlapping brain volumes in the set of overlapping brain regions of the target brain region H.

2. The parameter conversion method for multimodal brain network maps according to claim 1, characterized in that, The method includes using the least squares method to solve for the independent mapping coefficients based on the mapping relationship between the number of white matter fiber bundles in the source brain region samples among the sets of overlapping brain regions corresponding to each target brain region and the number of white matter fiber bundles in the target brain regions among each target brain region. The mapping relation expression that is satisfied is: in, This indicates the number of white matter fiber bundles in the target brain region between target brain region A and target brain region B. This indicates the number of white matter fiber bundles in the source brain regions among the sets of overlapping brain regions. This represents the number of source brain regions in the set of overlapping source brain regions for target brain region A. This represents the number of source brain regions in the set of overlapping source brain regions of the target brain region B. The remapping coefficients are obtained by normalizing the independent mapping coefficients of multiple sample subjects. Satisfy the following expression: ； in, Indicates the sample subject number is Independent mapping coefficients at time, Indicates the sample subject number is The number of white matter fiber bundles in the source brain regions between the overlapping brain regions of the time sample. This indicates the total number of samples.

3. The parameter conversion method for multimodal brain network maps according to claim 2, characterized in that, The number of white matter fiber bundles in the source brain region is obtained by weighted summation based on a preset first mapping coefficient. Satisfy the following expression: ； in, This indicates the number of white matter fiber bundles in the target brain region between target brain region E and target brain region F. Indicates the first mapping coefficient. The number of white matter fiber bundles in the source brain regions between the overlapping brain region sets is represented by s, which represents the number of source brain regions in the overlapping source brain region set of the target brain region E, and t represents the number of source brain regions in the overlapping source brain region set of the target brain region F.

4. The parameter conversion method for multimodal brain network maps according to claim 3, characterized in that, Target brain connectivity statistics between target brain regions Satisfy the following expression: ； ； Where s represents the number of source brain regions in the set of overlapping source brain regions of target brain region E, and t represents the number of source brain regions in the set of overlapping source brain regions of target brain region F. Indicates the influence weight. This represents the source-brain connectivity statistics between different source brain regions in sets of overlapping brain regions. Represents the remapping coefficients. This represents the variance of brain connectivity strength in the first experimental group; This represents the variance of brain connectivity strength in the first control group; This represents the variance of brain connectivity strength in the second experimental group; This represents the variance of brain connectivity strength in the second control group.

5. The parameter conversion method for multimodal brain network maps according to claim 4, characterized in that, The brain connectivity strength is represented by the number of white matter fiber bundles per unit area of ​​brain region; the more fiber bundles per unit area of ​​brain region, the greater the brain connectivity strength.

6. The parameter conversion method for multimodal brain network maps according to claim 1, characterized in that, The method further includes: The `convertStatistics` function is called to input the source brain connectivity statistics, source atlas name, target atlas name, target atlas connectivity type, brain connectivity strength variance, file save type of the target atlas after parameter conversion, and target atlas display type into a preset integrated development environment. The target atlas containing the number of white matter fiber tracts in the target brain region and the target brain connectivity statistics is saved and displayed.

7. A parameter conversion system for multimodal brain network maps, comprising a processor, a memory, and a computer program / instructions stored in the memory, characterized in that, The processor is used to execute the computer program / instructions, and when the computer program / instructions are executed, the system implements the steps of the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method as described in any one of claims 1 to 6.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 6.

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