Brain function subregion positioning method and device, computer equipment and storage medium

Abstract

The invention relates to a brain function subregion positioning method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a first three-dimensional skull model of a target object based on the brain image data of the target object; determining feature information of a brain target area of the target object based on the brain image data; the feature information comprises a multi-modal fusion connection matrix and anatomical structure information corresponding to the brain target area; according to the feature information of the brain target area, dividing the brain target area to obtain a plurality of functional sub-areas; and determining first coordinate information of different functional sub-regions, and positioning the functional sub-regions on the first three-dimensional skull model based on the first coordinate information. Through the method and the device, the problem that the target function area cannot be accurately positioned is solved, and the target function area is accurately positioned.

Description

Technical Field

[0001] The present application relates to the field of medical image processing technology, and in particular to a method, apparatus, computer equipment and storage medium for locating functional subregions of the brain. Background Art

[0002] In neuroscience research, non-human primates (such as macaques and marmosets) are key models for studying the role of the prefrontal cortex in advanced cognitive functions. Due to the complex anatomical structure of the prefrontal cortex, existing methods for locating functional areas of the prefrontal cortex usually rely on regional divisions based on anatomical maps, but due to factors such as individual differences and rough partitioning, the above methods cannot accurately locate the target functional area.

[0003] With regard to the problem in related technologies that the target functional area cannot be accurately located, no effective solution has been proposed so far. Summary of the invention

[0004] In this embodiment, a method, apparatus, computer device and storage medium for locating functional subregions of the brain are provided to solve the problem in related technologies that the target functional area cannot be accurately located.

[0005] In a first aspect, a method for locating brain functional subregions is provided in this embodiment, and the method comprises: constructing a first three-dimensional skull model of the target object based on the brain image data of the target object; Based on the brain image data, determining feature information of a target brain region of the target object; the feature information includes a multimodal fusion connection matrix and anatomical structure information corresponding to the target brain region; According to the characteristic information of the brain target area, the brain target area is divided to obtain a plurality of functional sub-areas; The first coordinate information of the different functional sub-regions is determined, and the functional sub-regions on the first three-dimensional skull model are positioned based on the first coordinate information.

[0006] In some embodiments, constructing a first three-dimensional skull model of the target object based on the brain image data of the target object includes: Acquiring the brain image data of the target object; the brain image data includes a brain magnetic resonance image and a brain computer tomography image of the target object; constructing a second three-dimensional skull model of the target object based on the brain computer tomography image; fusing the registered brain magnetic resonance image and the brain computer tomography image; The second three-dimensional skull model is optimized based on the fusion result to obtain the first three-dimensional skull model of the target object.

[0007] In some embodiments, determining the multimodal fusion connection matrix corresponding to the target brain region based on the brain image data includes: Based on the brain magnetic resonance image of the target object in the brain image data, constructing a functional connection network and a structural connection network corresponding to the brain target area; Based on the functional connection network and the structural connection network, the multimodal fusion connection matrix corresponding to the brain target area is generated.

[0008] In some embodiments, the brain magnetic resonance image includes a functional magnetic resonance image and a structural magnetic resonance image; and constructing a functional connection network and a structural connection network corresponding to the target brain area based on the brain magnetic resonance image of the target object in the brain image data includes: Analyzing the functional magnetic resonance image and the structural magnetic resonance image of the target object in the brain image data to obtain the functional connection strength in the target brain area; Based on the functional connection strength in the target brain area, constructing the functional connection network corresponding to the target brain area; Analyzing the structural magnetic resonance image of the target object in the brain image data to obtain a structural connection pattern within the target brain region; Based on the structural connection pattern in the target brain region, the structural connection network corresponding to the target brain region is constructed.

[0009] In some embodiments, the target brain region is divided according to the characteristic information of the target brain region to obtain a plurality of functional sub-regions, including: Based on the characteristic information of the brain target area, the brain target area is identified by a community discovery algorithm to obtain each function-structure joint module in the brain target area; Based on the functional connection heterogeneity within the function-structure joint module and the similarity between adjacent function-structure joint modules, the function-structure joint module after boundary optimization is subdivided to obtain a plurality of functional sub-regions.

[0010] In some embodiments, determining the first coordinate information of different functional sub-regions includes: Obtaining second coordinate information of each functional sub-area in the current image space; The second coordinate information of each functional sub-region is converted to the target space through a space conversion algorithm to obtain the first coordinate information of each functional sub-region; the target space is the space where the first three-dimensional skull model is located.

[0011] In some embodiments, positioning each of the functional sub-regions on the first three-dimensional skull model based on the first coordinate information includes: The first coordinate information of the functional sub-region is input into a target device to control the target device to locate the functional sub-region on the first three-dimensional skull model based on the first coordinate information.

[0012] In a second aspect, a device for locating brain functional subregions is provided in this embodiment, and the device comprises: A construction module, configured to construct a first three-dimensional skull model of the target object based on the brain image data of the target object; An analysis module, configured to determine feature information of a target brain region of the target object based on the brain image data; the feature information includes a multimodal fusion connection matrix and anatomical structure information corresponding to the target brain region; A division module, used for dividing the target brain area according to the characteristic information of the target brain area to obtain a plurality of functional sub-areas; A positioning module is used to determine the first coordinate information of different functional sub-regions, and to position the functional sub-regions on the first three-dimensional skull model based on the first coordinate information.

[0013] In a third aspect, a computer device is provided in this embodiment, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for locating functional subregions of the brain as described in the first aspect when executing the computer program.

[0014] In a fourth aspect, in this embodiment, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method for locating brain functional subregions described in the first aspect above is implemented.

[0015] Compared with the related art, the method, device, computer equipment and storage medium for locating functional sub-regions of the brain provided in this embodiment construct a first three-dimensional skull model of the target object based on the brain image data of the target object; determine the characteristic information of the target brain region of the target object based on the brain image data; the characteristic information includes the multimodal fusion connection matrix and anatomical structure information corresponding to the target brain region; divide the target brain region according to the characteristic information of the target brain region to obtain multiple functional sub-regions; determine the first coordinate information of different functional sub-regions, and locate the functional sub-regions on the first three-dimensional skull model based on the first coordinate information, thereby solving the problem of being unable to accurately locate the target functional region and achieving accurate positioning of the target functional region.

[0016] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 It is a hardware structure block diagram of a terminal device of a method for locating brain functional subregions provided in an embodiment of the present application; Figure 2 is a flow chart of a method for locating brain functional subregions provided in one embodiment of the present application; Figure 3 is a flow chart of a method for constructing a three-dimensional skull model provided in one embodiment of the present application; Figure 4 is a flow chart of a method for dividing functional sub-areas provided in an embodiment of the present application; Figure 5 is a flow chart of a method for locating brain functional subregions provided in a preferred embodiment of the present application; Figure 6 It is a structural block diagram of a brain functional sub-region positioning device provided in one embodiment of the present application.

[0018] In the figure: 102, processor; 104, memory; 106, transmission device; 108, input and output device; 10, construction module; 20, analysis module; 30, division module; 40, positioning module. DETAILED DESCRIPTION

[0019] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0020] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by people with general skills in the technical field to which this application belongs. The words "one", "a", "the", "these" and the like in this application do not indicate a quantitative limitation, and they may be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone. Generally, the character " / " indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", "third", etc. in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0021] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 FIG. 1 is a hardware structure diagram of a terminal of the method for locating brain functional subregions in this embodiment. Figure 1 As shown, the terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 and a memory 104 for storing data, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0022] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for localizing functional subregions of the brain in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0023] The transmission device 106 is used to receive or send data via a network. The above network includes a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0024] In this embodiment, a method for locating brain functional subregions is provided. Figure 2 is a flow chart of the method for locating brain functional subregions in this embodiment, such as Figure 2 As shown, the process includes the following steps: Step S210, constructing a first three-dimensional skull model of the target object based on the brain image data of the target object; Step S220, determining feature information of a target brain region of the target object based on the brain image data; the feature information includes a multimodal fusion connection matrix and anatomical structure information corresponding to the target brain region; Step S230, dividing the target brain region according to the characteristic information of the target brain region to obtain a plurality of functional sub-regions; Step S240: determine first coordinate information of different functional sub-regions, and locate the functional sub-regions on the first three-dimensional skull model based on the first coordinate information.

[0025] Specifically, the brain imaging data of the target object is collected, and the brain imaging data includes the brain magnetic resonance imaging (MRI) image, brain computed tomography (CT) image, etc. of the target object. The brain MRI image includes high-resolution T1-weighted structural image data, T2-weighted structural image data, and resting-state functional magnetic resonance (fMRI) data, and the brain imaging data is preprocessed, and the preprocessing operations include but are not limited to denoising, normalization, and alignment processing. For example, the brain MRI image data is subjected to head motion correction, field strength uniformity correction, brain tissue segmentation, and skull removal processing; the brain CT image data is subjected to noise suppression, bone tissue segmentation, and enhancement processing; and the brain MRI image data and the brain CT image data are spatially normalized using standardized methods to ensure the comparability of data from different modalities.

[0026] Further, based on the preprocessed brain image data, a first three-dimensional skull model of the target object is constructed. For example, the brain CT image of the target object is analyzed, the skull image data of the target object is accurately segmented from the image data, and the skull image data is converted into the corresponding first three-dimensional skull model through a surface reconstruction algorithm, so as to construct an individualized three-dimensional skull model using the CT image data to provide accurate spatial reference. The functional magnetic resonance image and structural magnetic resonance image of the target object in the brain image data are extracted, and the functional magnetic resonance image and structural magnetic resonance image of the target object are analyzed and calculated to obtain the functional connection strength in the target brain area. Based on the functional connection strength in the target brain area, a functional connection network corresponding to the target brain area is constructed, and the interaction relationship between different functional areas in the target brain area is identified. In addition, the structural connection pattern in the target brain area is obtained by combining the structural magnetic resonance image of the target object. Based on the structural connection pattern in the target brain area, a structural connection network corresponding to the target brain area is constructed, and then based on the functional connection network and the structural connection network, a multimodal fusion connection matrix corresponding to the target brain area is generated. It should be noted that the target brain area is the target area to be divided and located, and the target brain area can be set according to actual application requirements, such as the prefrontal cortex, hippocampus or cerebellum, etc., which is not limited here.

[0027] Based on the characteristic information of the target brain area, the target brain area is identified through the community discovery algorithm to obtain the functional-structural joint modules within the target brain area. When the functional connection heterogeneity within the functional-structural joint module is greater than the preset coefficient of variation, the functional-structural joint module after boundary optimization is subdivided, and the similarity between adjacent functional-structural joint modules is judged. The sub-regions with similarity greater than the preset threshold are merged to obtain multiple functional sub-regions that are finally divided.

[0028] In addition, the brain network group atlas method can be used to finely divide the functional sub-regions of the target brain area based on individualized functional connection patterns and anatomical structure information. Specifically, multimodal image processing technology is used to perform standardized preprocessing of diffusion magnetic resonance images. The preprocessing methods include denoising, eddy current distortion correction, calculation of voxel-level fiber orientation distribution probability, and cortical surface reconstruction; white matter fiber bundles are analyzed based on the preprocessed magnetic resonance images, and whole-brain probability tracking is implemented with the gray-white matter interface surface as the seed area, a vertex-fiber bundle connection probability model is established, and a vertex-level structural connection matrix is ​​generated; based on the reconstructed cortical surface mesh model, the minimum path distance between vertices, that is, the geodesic distance, is calculated, and based on the geodesic distance and the preset attenuation coefficient, a spatial neighborhood matrix is ​​constructed through an exponential decay model; when fusing multimodal features, a similarity network fusion algorithm is used to achieve robust fusion of dual-modal matrices, and a vertex similarity network is constructed for each modality with the structural connection matrix and the spatial neighborhood matrix as input, and dual-modal information fusion is achieved through a cross-diffusion mechanism; based on the weighted adjacency matrix, a weighted adjacency matrix is ​​constructed. Construct a non-normalized Laplace matrix and a symmetric normalized Laplace matrix to define the spectral analysis framework of the weighted adjacency matrix, perform eigendecomposition on the Laplace matrix, select the first k non-trivial eigenvectors from the obtained eigenvector matrix, construct a feature space, and achieve low-dimensional embedding; calculate the similarity matrix between the individual feature matrix and the group feature matrix, and construct the corresponding extended matrix based on the similarity matrix, the individual feature matrix and the group feature matrix. Perform graph Laplace decomposition on the extended matrix to obtain a low-dimensional spectral space representation, and then establish a spherical neighborhood containing multiple group-level reference vertices with the individual vertex as the center. Perform probabilistic modeling through prior probability and likelihood function to quantify the possibility that individual vertices belong to different labels, and select the maximum a posteriori probability label according to the Bayesian formula to assign the optimal label to the individual vertex, so as to achieve accurate redefinition of individual brain areas by constructing a group-individual joint feature space. After the partitioning is completed, quantitative verification is performed from three dimensions: partitioning quality assessment, topological consistency, and method superiority. Partitioning quality assessment includes quantifying the degree of difference in connection patterns inside and outside the brain region through the structural consistency index and evaluating the spatial gradient changes of partition boundaries through boundary sharpness. Topological consistency verification can use multi-scale Dice coefficient and topological preservation rate, while method superiority verification can use improved gain and statistical significance test.

[0029] After that, the second coordinate information of each functional sub-region in the current image space is obtained, and the second coordinate information of each functional sub-region is converted to the target space through the space conversion algorithm to obtain the first coordinate information of each functional sub-region, and a functional sub-region map corresponding to the target brain area is generated. The functional sub-region map usually contains multiple sub-regions. For example, a functional sub-region map of the prefrontal cortex is generated, which contains sub-regions such as the rostral part of the superior frontal gyrus, the rostral middle part of the superior frontal gyrus, and the dorsal caudal part of the inferior frontal gyrus.

[0030] Among them, the current image space is determined by the image data used to divide the sub-regions. For example, when MRI images are used for division, the current image space is the MRI space, and the target space is the space where the first three-dimensional skull model is located. For example, when the first three-dimensional skull model is constructed based on brain CT images, the target space is the CT space.

[0031] When performing the positioning operation, the first coordinate information of the target functional sub-area is input into the target device to control the target device to position the target functional sub-area on the first three-dimensional skull model based on the first coordinate information. Exemplarily, the first coordinate information of the target functional sub-area is input into the surgical robot control system, and the high-precision mechanical arm of the robot is used to realize automatic positioning of the target functional sub-area. At the same time, the infrared tracking technology or the optical positioning system is combined to correct the small displacement caused by the head fixation method or environmental factors in real time to ensure the navigation accuracy; the first coordinate information of the target functional sub-area is input into the stereo positioning device such as the stereo positioning frame system, and the microelectrode, optical fiber probe or drug injection device is precisely targeted and positioned based on the first coordinate information; in the study of the prefrontal cortex, the optimal placement position and angle of the cranial magnetic stimulation coil on the skull surface are calculated according to the first coordinate information of the target functional sub-area, so as to ensure that the magnetic field can target the specific fine sub-area of ​​the prefrontal cortex to the greatest extent, and the electric field modeling method is used to simulate the cranial magnetic stimulation effect to optimize the coil parameters and improve the targeting and repeatability of the stimulation.

[0032] It should be noted that the above-mentioned functional sub-region positioning method can be applied to a variety of studies, including advanced cognitive function analysis, neural regulation and brain disease research, etc. For example, in the research on advanced cognitive function of macaques such as transcranial magnetic stimulation intervention experiments, electrophysiological recording experiments and pharmacological research, surgical robots or stereotactic equipment can be used to achieve precise navigation of the target brain area, providing a high-precision positioning method for the research on the prefrontal cortex function of macaques. Among them, in transcranial magnetic stimulation intervention experiments, the above-mentioned positioning method is used to accurately locate the functional sub-regions of the prefrontal cortex, so that transcranial magnetic stimulation can be accurately applied during the macaque's cognitive tasks (such as delayed matching tasks and attention switching tasks), and the regulatory effects of different prefrontal cortex fine sub-regions on cognitive functions can be studied; in electrophysiological recording experiments, combined with microelectrode recording technology, the effects of transcranial magnetic stimulation or other intervention methods on the neural activity of the target prefrontal cortex sub-region are analyzed to provide data support for understanding the network function of the prefrontal cortex; in pharmacological research, stereotactic equipment is used to accurately inject neuromodulators into specific prefrontal cortex fine sub-regions to study the effects of neuromodulators on macaque behavior and neural activity.

[0033] In neuroscience research, non-human primates (such as macaques and marmosets) are key models for studying the role of the prefrontal cortex in advanced cognitive functions. Due to the complex anatomical structure of the prefrontal cortex, existing methods for locating functional areas of the prefrontal cortex usually rely on regional divisions based on anatomical maps, but due to factors such as individual differences and rough partitioning, the above methods cannot accurately locate the target functional area.

[0034] Compared with the prior art, the present application constructs a first three-dimensional skull model of the target object based on the brain image data of the target object; determines the characteristic information of the target brain area of ​​the target object based on the brain image data; the characteristic information includes the multimodal fusion connection matrix and anatomical structure information corresponding to the target brain area; divides the target brain area according to the characteristic information of the target brain area to obtain multiple functional sub-areas; determines the first coordinate information of different functional sub-areas, and locates the functional sub-areas on the first three-dimensional skull model based on the first coordinate information. Based on this, by combining the multimodal brain image data of the target object, analyzing the multimodal connection pattern and anatomical structure information of the target brain area, individualized fine functional partitioning of the prefrontal cortex can be achieved, so that accurate positioning can be performed based on the partitioning results, solving the problem of being unable to accurately locate the target functional area, achieving accurate positioning of the target functional area, avoiding low positioning accuracy, ignoring individual differences, etc., and helping to improve the accuracy and repeatability of fine functional research.

[0035] In some of the embodiments, Figure 3 As shown, constructing a first three-dimensional skull model of the target object based on the brain image data of the target object in step S210 includes the following steps: Step S211, acquiring brain image data of the target object; the brain image data includes a brain magnetic resonance image and a brain computer tomography image of the target object; Step S212, constructing a second three-dimensional skull model of the target object based on the brain computer tomography image; Step S213, fusing the registered brain magnetic resonance image and the brain computer tomography image; Step S214: Optimize the second three-dimensional skull model based on the fusion result to obtain a first three-dimensional skull model of the target object.

[0036] Specifically, the brain CT image of the target object is analyzed, the skull image data of the target object is accurately segmented from the image data, and the skull image data is converted into a corresponding second three-dimensional skull model through a surface reconstruction algorithm.

[0037] Furthermore, the brain MRI images and brain CT images are registered using a rigid registration algorithm based on feature points and a non-rigid registration algorithm based on morphological features. For example, the positioning marks of the head bracket in the brain MRI images and brain CT images are extracted, the brain MRI images and brain CT images are preliminarily aligned through rigid body transformation, and the nonlinear registration of the brain MRI images and brain CT images is performed through an improved symmetric normalization algorithm.

[0038] Among them, when performing nonlinear registration, mutual information is used as a similarity measure, and constraints are imposed based on the prior knowledge of the target object's skull anatomy. For example, the deformation field is constrained according to the features of the temporal bone and frontal sinus contours, so that the registration process is more in line with the actual anatomical structure of the target object's skull. At the same time, a multi-resolution registration strategy is used to optimize the parameters to achieve gradual refinement from 1 / 4 resolution to full resolution. By setting reasonable regularization weights, local excessive deformation is suppressed to ensure the stability and reliability of the registration results. Therefore, the registration effect is continuously optimized through multiple iterations, ultimately achieving high-precision nonlinear refined registration.

[0039] It should be noted that the verification methods of the registration results include quantitative evaluation, visual verification, etc. For example, during the registration verification, the skull surface in the brain CT image and the meningeal boundary in the brain MRI image are extracted respectively, and the Dice coefficient between the skull surface and the meningeal boundary is calculated. If the calculated Dice coefficient is greater than the preset threshold, it indicates that the skull surface in the brain CT image and the meningeal boundary in the brain MRI image have a high degree of overlap in spatial position, and the current registration effect is good. Alternatively, the brain CT image and the brain MRI image are superimposed and displayed to determine whether the key landmarks in the image (such as the optic canal and cribriform plate) are aligned. If the key landmarks are accurately aligned, it indicates that the registration effect is good.

[0040] Afterwards, the functional information of the registered brain MRI images and the structural information of the brain CT images are fused through image fusion technology. The second three-dimensional skull model is optimized based on the fusion results to obtain the first three-dimensional skull model of the target object, and a visual display is provided to optimize navigation accuracy.

[0041] Exemplarily, a dual-branch U-Net network is constructed, and the functional information of the registered brain MRI image and the brain CT image are input respectively. Among them, the encoder of the dual-branch U-Net network adopts a backbone network to extract the multi-scale features of the input image; the channel attention mechanism is used in the fusion layer to perform weighted fusion of the bony features extracted from the CT data and the soft tissue features extracted from the MRI data; after network processing, the fused three-dimensional volume data is output, one channel contains CT bone density information, and the other channel contains MRI gray matter signal information. When the fusion is completed, the morphological closing operation is used to eliminate the fusion gap between the inner plate of the skull and the meninges, and the fused three-dimensional volume data is reconstructed in multiple planes to generate coronal and sagittal fusion views, so as to highlight the spatial relationship between the target area of ​​the brain and the skull from different angles.

[0042] Through this embodiment, brain image data of the target object is obtained, the brain image data including the brain magnetic resonance image and the brain computed tomography image of the target object, a second three-dimensional skull model of the target object is constructed based on the brain computed tomography image, the registered brain magnetic resonance image and the brain computed tomography image are fused, the second three-dimensional skull model is optimized based on the fusion result, and the first three-dimensional skull model of the target object is obtained, so as to integrate functional information and structural information and improve the accuracy and reliability of the model.

[0043] In some embodiments, determining a multimodal fusion connection matrix corresponding to a target brain region based on brain image data includes the following steps: Based on the brain magnetic resonance images of the target object in the brain imaging data, construct a functional connection network and a structural connection network corresponding to the target brain area; Based on the functional connection network and the structural connection network, a multimodal fusion connection matrix corresponding to the target brain area is generated.

[0044] Specifically, based on the brain magnetic resonance imaging of the target subject, a multimodal connection pattern analysis is performed on the target brain area of ​​the target subject, the functional connection strength within the target brain area is obtained through functional connection pattern analysis, and the structural fiber density data within the target brain area is obtained through structural connection pattern analysis.

[0045] Furthermore, according to the functional connection strength in the target brain area, a functional connection network corresponding to the target brain area is constructed, and according to the structural fiber density data, a corresponding structural connection network is constructed, and the functional connection network and the structural connection network are feature fused to obtain a multimodal fusion connection matrix corresponding to the target brain area. The obtained functional connection strength and structural fiber density data are Z-score standardized, and based on a preset weight ratio, the standardized functional connection strength and structural fiber density data are linearly superimposed to generate a multimodal fusion connection matrix.

[0046] Through this embodiment, based on the brain magnetic resonance images of the target object in the brain imaging data, a functional connection network and a structural connection network corresponding to the target brain area are constructed, and based on the functional connection network and the structural connection network, a multimodal fusion connection matrix corresponding to the target brain area is generated, so as to realize the multimodal connection pattern analysis of the target brain area, which is helpful for the subsequent fine sub-region division of the target brain area to identify and define different functional sub-regions.

[0047] In some embodiments, the brain magnetic resonance image includes a functional magnetic resonance image and a structural magnetic resonance image; based on the brain magnetic resonance image of the target object in the brain image data, constructing a functional connection network and a structural connection network corresponding to the target brain area includes the following steps: Analyze the functional magnetic resonance images and structural magnetic resonance images of the target object in the brain imaging data to obtain the functional connection strength in the target brain area; Based on the functional connection strength within the target brain area, a functional connection network corresponding to the target brain area is constructed; Analyze the structural magnetic resonance images of the target object in the brain imaging data to obtain the structural connection pattern in the target brain area; Based on the structural connectivity pattern within the target brain region, a structural connectivity network corresponding to the target brain region is constructed.

[0048] Specifically, based on the geometric network division method of structural magnetic resonance imaging, the target brain area is divided into multiple initial seed points, which are used as nodes of the functional connection network. The time series corresponding to various sub-points are extracted from the resting-state functional magnetic resonance imaging data, and the Pearson correlation coefficients of the extracted time series of various sub-points are calculated pairwise to obtain the corresponding correlation coefficient matrix. Each element in the correlation coefficient matrix represents the functional connection strength between two seed points, which is used to describe the functional connection relationship between various sub-points, thereby completing the establishment of the functional connection network. In the process of extracting the time series, the data is band-pass filtered to limit the frequency range, such as 0.01 to 0.1 Hz.

[0049] Furthermore, the brain diffusion tensor imaging data of the target object is obtained, and the brain diffusion tensor imaging data is processed by a fiber tracking algorithm to reconstruct the trajectory of the white matter fiber bundles in the target brain area to obtain the fiber bundle distribution of each sub-area of ​​the target brain area and other areas of the whole brain. Based on the fiber tracking results, the fiber density between each sub-area of ​​the target brain area and other areas of the whole brain is counted, and the number of fiber bundles between each connection pair is calculated to complete the fiber bundle quantification, so as to realize the construction of the structural connection network.

[0050] Through this embodiment, the functional magnetic resonance images and structural magnetic resonance images of the target object in the brain imaging data are analyzed to obtain the functional connection strength within the target brain area. Based on the functional connection strength within the target brain area, a functional connection network corresponding to the target brain area is constructed. The structural magnetic resonance images of the target object in the brain imaging data are analyzed to obtain the structural connection pattern within the target brain area. Based on the structural connection pattern within the target brain area, a structural connection network corresponding to the target brain area is constructed, thereby realizing multimodal connection pattern analysis of the target brain area.

[0051] In some of the embodiments, Figure 4 As shown, step S230 divides the target brain region according to the characteristic information of the target brain region to obtain multiple functional sub-regions, including the following steps: Step S231, based on the characteristic information of the brain target area, the brain target area is identified by a community discovery algorithm to obtain each function-structure joint module in the brain target area; Step S232, based on the functional connection heterogeneity within the function-structure joint module and the similarity between adjacent function-structure joint modules, the function-structure joint module after boundary optimization is subdivided to obtain multiple functional sub-regions.

[0052] Specifically, based on the multimodal fusion connection matrix and anatomical structure information corresponding to the target brain region, the target brain region is identified by a community discovery algorithm to obtain the functional-structural joint modules in the target brain region. The community discovery algorithm includes but is not limited to the Louvain community discovery algorithm, the spectral clustering algorithm, and the random block algorithm. Among them, a reasonable resolution parameter γ is pre-set, such as taking γ as 1.0, to balance the module size and number, and the number of iterations of the community discovery algorithm is set. After reaching the preset number of iterations, the consensus clustering method is used to extract the partition results with higher consistency from the different partition results obtained by multiple iterations.

[0053] Furthermore, the corresponding gray matter gradient is calculated according to the structural magnetic resonance imaging data, and the boundary of the function-structure joint module is smoothed based on the gray matter gradient, so that the sub-region boundary and the actual anatomical structure are more spatially consistent through spatial constraints. If it is detected that the functional connection heterogeneity within the function-structure joint module is greater than the preset coefficient of variation, the function-structure joint module after boundary optimization is subdivided to obtain multiple dynamically adjusted functional sub-regions, and the similarity between adjacent function-structure joint modules is calculated at the same time, and the sub-regions with similarity greater than the preset threshold are merged to obtain the functional sub-regions that are finally divided. Among them, the similarity calculation method includes cosine similarity, edit distance algorithm, Jaccard similarity coefficient algorithm, etc., which are not limited here.

[0054] Through this embodiment, based on the characteristic information of the brain target area, the brain target area is identified through the community discovery algorithm to obtain each function-structure joint module in the brain target area, and based on the functional connection heterogeneity within the function-structure joint module and the similarity between adjacent function-structure joint modules, the function-structure joint module after boundary optimization is subdivided to obtain multiple functional sub-regions, thereby achieving accurate division of functional sub-regions of the brain target area, which is helpful for precise positioning of subsequent target functional regions.

[0055] In some embodiments, determining the first coordinate information of different functional sub-regions in step S240 includes the following steps: Obtain the second coordinate information of each functional sub-area in the current image space; Through the space conversion algorithm, the second coordinate information of each functional sub-region is converted to the target space to obtain the first coordinate information of each functional sub-region; the target space is the space where the first three-dimensional skull model is located.

[0056] Specifically, the second coordinate information of each functional sub-region in the current image space is determined, and the second coordinate information of each functional sub-region is converted to the target space by using the space conversion algorithm and the registration transformation matrix to obtain the first coordinate information of each functional sub-region, and the functional sub-region after the coordinate conversion is superimposed and displayed on the first three-dimensional skull model by three-dimensional reconstruction technology. Among them, the current image space is determined by the image data used to divide the sub-regions, and the target space is the space where the constructed three-dimensional skull model is located.

[0057] For example, when a three-dimensional skull model is constructed based on brain CT images and functional subregions are divided using MRI images, the current image space is the MRI space, and the target space is the CT space.

[0058] Through this embodiment, the second coordinate information of each functional sub-area in the current image space is obtained, and the second coordinate information of each functional sub-area is converted to the target space through a space conversion algorithm to obtain the first coordinate information of each functional sub-area. The target space is the space where the first three-dimensional skull model is located, thereby ensuring the coordinate consistency between different modal image data, which helps to improve the subsequent navigation accuracy.

[0059] In some embodiments, the step S240 of positioning each functional sub-region on the first three-dimensional skull model based on the first coordinate information includes the following steps: The first coordinate information of the functional sub-region is input into the target device to control the target device to locate the functional sub-region on the first three-dimensional skull model based on the first coordinate information.

[0060] Specifically, when performing the positioning operation, the first coordinate information of the target functional sub-area is input into the target device to control the target device to position the target functional sub-area on the first three-dimensional skull model based on the first coordinate information. Exemplarily, the first coordinate information of the target functional sub-area is input into the surgical robot control system, and the high-precision mechanical arm of the robot is used to realize automatic positioning of the target functional sub-area. At the same time, the infrared tracking technology or the optical positioning system is combined to correct the small displacement caused by the head fixation method or environmental factors in real time to ensure the navigation accuracy; the first coordinate information of the target functional sub-area is input into the stereo positioning frame system and other stereo positioning devices, and the microelectrode, optical fiber probe or drug injection device is precisely targeted and positioned based on the first coordinate information; in the study of the prefrontal cortex, the optimal placement position and angle of the cranial magnetic stimulation coil on the skull surface are calculated according to the first coordinate information of the target functional sub-area, so as to ensure that the magnetic field can target the specific prefrontal cortex fine sub-area to the greatest extent, and the electric field modeling method is used to simulate the cranial magnetic stimulation effect to optimize the coil parameters and improve the targeting and repeatability of the stimulation.

[0061] The above functional sub-region localization method can be applied to a variety of studies, including analysis of advanced cognitive functions, neural regulation and brain disease research, etc. For example, in the research on advanced cognitive functions of macaques such as transcranial magnetic stimulation intervention experiments, electrophysiological recording experiments and pharmacological studies, surgical robots or stereotactic devices can be used to achieve precise navigation of the target brain area, providing a high-precision positioning method for the research on the prefrontal cortex function of macaques. Among them, in transcranial magnetic stimulation intervention experiments, the above positioning method is used to accurately locate the functional sub-regions of the prefrontal cortex, so that transcranial magnetic stimulation can be accurately applied during the macaque's cognitive tasks (such as delayed matching tasks and attention switching tasks), and the regulatory effects of different fine sub-regions of the prefrontal cortex on cognitive functions can be studied; in electrophysiological recording experiments, combined with microelectrode recording technology, the effects of transcranial magnetic stimulation or other intervention methods on the neural activity of the target prefrontal cortex sub-region are analyzed to provide data support for understanding the network function of the prefrontal cortex; in pharmacological studies, stereotactic devices are used to accurately inject neuromodulators into specific fine sub-regions of the prefrontal cortex to study the effects of neuromodulators on macaque behavior and neural activity.

[0062] Through this embodiment, the first coordinate information of the functional sub-region is input into the target device to control the target device to locate the functional sub-region on the first three-dimensional skull model based on the first coordinate information. In this way, on the basis of constructing individualized functional sub-regions, with the help of a surgical robot or a stereo positioning device, precise navigation of the target brain area is achieved, providing a high-precision positioning method for functional research of the target brain area, thereby improving the reliability and accuracy of the research results.

[0063] The present embodiment is described and illustrated by means of specific examples below.

[0064] Taking the fine functional sub-region localization of the prefrontal cortex of macaques as an example, brain MRI images and brain CT images of macaques were collected. The brain MRI images include structural MRI data and functional MRI data. The brain MRI image data were preprocessed, including bias field correction, skull stripping and cortical surface reconstruction for structural MRI data to extract the prefrontal cortex mask, head motion correction and denoising for resting-state functional MRI data, and eddy current correction and fiber tracking for diffusion tensor imaging to generate a whole-brain white matter connection matrix. At the same time, an iterative reconstruction algorithm was used to remove metal artifacts from the brain CT image data, correct the beam hardening artifacts caused by the head fixation screws, and a standardized method was used to spatially normalize the brain MRI image data and the brain CT image data to ensure the comparability of data from different modalities.

[0065] Based on the brain CT images, the second three-dimensional skull model of the macaque is generated by the surface reconstruction algorithm. The positioning marks of the head bracket in the brain MRI images and brain CT images are extracted, and the brain MRI images and brain CT images are preliminarily aligned through rigid body transformation, and the nonlinear registration of the brain MRI images and brain CT images is performed through the improved symmetric normalization algorithm. After that, the functional information of the registered brain MRI images and the structural information of the brain CT images are fused through image fusion technology, and the second three-dimensional skull model is optimized based on the fusion results to obtain the first three-dimensional skull model of the target object, and a visual display is provided to optimize the navigation accuracy.

[0066] Furthermore, based on the geometric network division method of structural magnetic resonance imaging, the prefrontal cortex is divided into multiple initial seed points, which are used as nodes of the functional connection network. The time series corresponding to various sub-points are extracted from the resting-state functional magnetic resonance imaging data, and the Pearson correlation coefficients of the extracted time series of various sub-points are calculated pairwise to obtain the corresponding correlation coefficient matrix. Each element in the correlation coefficient matrix represents the functional connection strength between two seed points, which is used to describe the functional connection relationship between various sub-points, thereby completing the establishment of the functional connection network. At the same time, the brain diffusion tensor imaging data of the target object is obtained, and the brain diffusion tensor imaging data is processed by the fiber tracking algorithm to reconstruct the trajectory of the white matter fiber bundles of the prefrontal cortex to obtain the fiber bundle distribution of each sub-area of ​​the prefrontal cortex and other areas of the whole brain. Based on the fiber tracking results, the fiber density between each sub-area of ​​the prefrontal cortex and other areas of the whole brain is counted, and the number of fiber bundles between each connection pair is calculated to complete the fiber bundle quantification to achieve the construction of the structural connection network.

[0067] Afterwards, the obtained functional connection strength and structural fiber density data were Z-score standardized, and based on the preset weight ratio, the standardized functional connection strength and structural fiber density data were linearly superimposed to generate a multimodal fusion connection matrix. Based on the multimodal fusion connection matrix and anatomical structure information corresponding to the prefrontal cortex, the prefrontal cortex was identified by the Louvain community discovery algorithm to obtain each functional-structural joint module in the prefrontal cortex, and the corresponding gray matter gradient was calculated according to the structural magnetic resonance imaging data. The boundary of the functional-structural joint module was smoothed based on the gray matter gradient. If the functional connection heterogeneity detected in the functional-structural joint module was greater than the preset coefficient of variation, the functional-structural joint module after boundary optimization was subdivided to obtain multiple dynamically adjusted functional sub-regions, and the similarity between adjacent functional-structural joint modules was calculated at the same time, and the sub-regions with similarity greater than the preset threshold were merged to obtain multiple functional sub-regions that were finally divided.

[0068] The second coordinate information of each functional sub-region in the magnetic resonance imaging space is obtained, and the second coordinate information of each functional sub-region is converted to the CT space through the space conversion algorithm to obtain the first coordinate information of each functional sub-region, and generate a prefrontal sub-region atlas including the dorsolateral prefrontal cortex, orbitofrontal cortex, anterior cingulate cortex and other sub-regions. The first coordinate information of the functional sub-region is input into the surgical robot control system, the stereotactic device and other target devices to control the target device to locate the functional sub-region on the first three-dimensional skull model based on the first coordinate information.

[0069] The present embodiment is described and illustrated below through preferred embodiments.

[0070] Figure 5 is a flow chart of the method for locating brain functional subregions in this preferred embodiment, such as Figure 5 As shown, the method for localizing brain functional subregions includes the following steps: Step S510, constructing a second three-dimensional skull model of the target object based on the brain computer tomography image of the target object; Step S520, fusing the registered brain magnetic resonance image and the brain computer tomography image; Step S530, optimizing the second three-dimensional skull model based on the fusion result to obtain a first three-dimensional skull model of the target object; Step S540, constructing a functional connection network and a structural connection network corresponding to the target brain region based on the brain magnetic resonance image of the target object in the brain image data; Step S550, generating a multimodal fusion connection matrix corresponding to the target brain region based on the functional connection network and the structural connection network; Step S560, based on the multimodal fusion connection matrix and anatomical structure information of the brain target area, the brain target area is identified by a community discovery algorithm to obtain each function-structure joint module in the brain target area; Step S570, based on the functional connectivity heterogeneity within the function-structure joint module and the similarity between adjacent function-structure joint modules, the function-structure joint module after boundary optimization is subdivided to obtain a plurality of functional sub-regions; Step S580: determine first coordinate information of different functional sub-regions, and locate the functional sub-regions on the first three-dimensional skull model based on the first coordinate information.

[0071] Through this embodiment, a second three-dimensional skull model of the target object is constructed based on the brain CT image of the target object, the aligned brain magnetic resonance image and the brain CT image are fused, and the second three-dimensional skull model is optimized based on the fusion result to obtain the first three-dimensional skull model of the target object.

[0072] Furthermore, based on the brain magnetic resonance images of the target object in the brain image data, a functional connection network and a structural connection network corresponding to the target brain area are constructed, and based on the functional connection network and the structural connection network, a multimodal fusion connection matrix corresponding to the target brain area is generated. Based on the multimodal fusion connection matrix and anatomical structure information of the target brain area, the target brain area is identified by a community discovery algorithm to obtain each function-structure joint module in the target brain area, and based on the functional connection heterogeneity within the function-structure joint module and the similarity between adjacent function-structure joint modules, the function-structure joint module after boundary optimization is subdivided to obtain multiple functional sub-areas, and the first coordinate information of different functional sub-areas is determined. The functional sub-areas on the first three-dimensional skull model are located based on the first coordinate information, which solves the problem of being unable to accurately locate the target functional area and achieves accurate positioning of the target functional area.

[0073] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0074] In this embodiment, a brain functional sub-region positioning device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be repeated hereafter. The terms "module", "unit", "sub-unit", etc. used below can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0075] Figure 6 is a structural block diagram of the brain functional sub-region positioning device of this embodiment, such as Figure 6 As shown, the device comprises: A construction module 10 is used to construct a first three-dimensional skull model of the target object based on the brain image data of the target object; The analysis module 20 is used to determine the characteristic information of the target brain region of the target object based on the brain image data; the characteristic information includes the multimodal fusion connection matrix and anatomical structure information corresponding to the target brain region; A division module 30 is used to divide the target brain region according to the characteristic information of the target brain region to obtain a plurality of functional sub-regions; The positioning module 40 is used to determine the first coordinate information of different functional sub-regions, and to position the functional sub-regions on the first three-dimensional skull model based on the first coordinate information.

[0076] Through the device provided in this embodiment, a first three-dimensional skull model of the target object is constructed based on the brain image data of the target object; based on the brain image data, characteristic information of the target brain area of ​​the target object is determined; the characteristic information includes a multimodal fusion connection matrix and anatomical structure information corresponding to the target brain area; according to the characteristic information of the target brain area, the target brain area is divided to obtain multiple functional sub-areas; first coordinate information of different functional sub-areas is determined, and the functional sub-areas on the first three-dimensional skull model are positioned based on the first coordinate information, which solves the problem of being unable to accurately position the target functional area and achieves accurate positioning of the target functional area.

[0077] In some of the embodiments, the construction module 10 is used to obtain brain image data of the target object; the brain image data includes a brain magnetic resonance image and a brain computed tomography image of the target object; based on the brain computed tomography image, a second three-dimensional skull model of the target object is constructed; the registered brain magnetic resonance image and the brain computed tomography image are fused; and the second three-dimensional skull model is optimized based on the fusion result to obtain a first three-dimensional skull model of the target object.

[0078] In some of the embodiments, the analysis module 20 is used to construct a functional connection network and a structural connection network corresponding to the target brain area based on the brain magnetic resonance image of the target object in the brain imaging data; and generate a multimodal fusion connection matrix corresponding to the target brain area based on the functional connection network and the structural connection network.

[0079] In some of the embodiments, the analysis module 20 is used to analyze the functional magnetic resonance images and structural magnetic resonance images of the target object in the brain imaging data to obtain the functional connection strength within the target brain area; based on the functional connection strength within the target brain area, construct a functional connection network corresponding to the target brain area; analyze the structural magnetic resonance images of the target object in the brain imaging data to obtain the structural connection pattern within the target brain area; based on the structural connection pattern within the target brain area, construct a structural connection network corresponding to the target brain area.

[0080] In some of the embodiments, the division module 30 is used to identify the target brain region through a community discovery algorithm based on the characteristic information of the target brain region, and obtain various function-structure joint modules within the target brain region; based on the functional connection heterogeneity within the function-structure joint module and the similarity between adjacent function-structure joint modules, the function-structure joint module after boundary optimization is subdivided to obtain multiple functional sub-regions.

[0081] In some of the embodiments, the positioning module 40 is used to obtain the second coordinate information of each functional sub-region in the current image space; through the space conversion algorithm, the second coordinate information of each functional sub-region is converted to the target space to obtain the first coordinate information of each functional sub-region; the target space is the space where the first three-dimensional skull model is located.

[0082] In some of the embodiments, the positioning module 40 is used to input the first coordinate information of the functional sub-region into the target device to control the target device to position the functional sub-region on the first three-dimensional skull model based on the first coordinate information.

[0083] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0084] In this embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0085] Optionally, the computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0086] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program: S1, constructing a first three-dimensional skull model of the target object based on the brain image data of the target object; S2, determining feature information of a target brain region of a target subject based on brain imaging data; the feature information includes a multimodal fusion connection matrix and anatomical structure information corresponding to the target brain region; S3, dividing the target brain area according to the characteristic information of the target brain area to obtain multiple functional sub-areas; S4, determining first coordinate information of different functional sub-regions, and locating the functional sub-regions on the first three-dimensional skull model based on the first coordinate information.

[0087] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0088] In addition, in combination with the brain functional subregion positioning method provided in the above embodiments, a storage medium can also be provided in this embodiment to implement the method. The storage medium stores a computer program; when the computer program is executed by a processor, any one of the brain functional subregion positioning methods in the above embodiments is implemented.

[0089] It should be understood that the specific embodiments described herein are only used to explain the application, rather than to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of this application.

[0090] Obviously, the drawings are only some examples or embodiments of the present application. For ordinary technicians in the field, the present application can also be applied to other similar situations based on these drawings without creative work. In addition, it is understandable that although the work done in this development process may be complicated and lengthy, for ordinary technicians in the field, certain changes in design, manufacturing or production based on the technical content disclosed in this application are only conventional technical means and should not be regarded as insufficient content disclosed in this application.

[0091] The term "embodiment" in this application refers to a specific feature, structure or characteristic described in conjunction with the embodiment that can be included in at least one embodiment of the present application. The appearance of this phrase in various locations in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is clearly or implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

[0092] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of patent protection. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the attached claims.

Claims

1. A method for locating brain functional subregions, characterized in that: The method comprises: constructing a first three-dimensional skull model of the target object based on the brain image data of the target object; Based on the brain image data, determining feature information of a target brain region of the target object; the feature information includes a multimodal fusion connection matrix and anatomical structure information corresponding to the target brain region; According to the characteristic information of the brain target area, the brain target area is divided to obtain a plurality of functional sub-areas; The first coordinate information of the different functional sub-regions is determined, and the functional sub-regions on the first three-dimensional skull model are positioned based on the first coordinate information.

2. The method for locating brain functional subregions according to claim 1, characterized in that: The step of constructing a first three-dimensional skull model of the target object based on the brain image data of the target object comprises: Acquiring the brain image data of the target object; the brain image data includes a brain magnetic resonance image and a brain computer tomography image of the target object; constructing a second three-dimensional skull model of the target object based on the brain computer tomography image; fusing the registered brain magnetic resonance image and the brain computer tomography image; The second three-dimensional skull model is optimized based on the fusion result to obtain the first three-dimensional skull model of the target object.

3. The method for locating brain functional subregions according to claim 1, characterized in that: Determining a multimodal fusion connection matrix corresponding to the target brain region based on the brain image data includes: Based on the brain magnetic resonance image of the target object in the brain image data, constructing a functional connection network and a structural connection network corresponding to the brain target area; Based on the functional connection network and the structural connection network, the multimodal fusion connection matrix corresponding to the brain target area is generated.

4. The method for locating brain functional subregions according to claim 3, characterized in that: The brain magnetic resonance images include functional magnetic resonance images and structural magnetic resonance images; and constructing a functional connection network and a structural connection network corresponding to the target brain area based on the brain magnetic resonance images of the target object in the brain image data includes: Analyzing the functional magnetic resonance image and the structural magnetic resonance image of the target object in the brain image data to obtain the functional connection strength in the target brain area; Based on the functional connection strength in the target brain area, constructing the functional connection network corresponding to the target brain area; Analyzing the structural magnetic resonance image of the target object in the brain image data to obtain a structural connection pattern within the target brain region; Based on the structural connection pattern in the target brain region, the structural connection network corresponding to the target brain region is constructed.

5. The method for locating brain functional subregions according to claim 1, characterized in that: The brain target area is divided according to the characteristic information of the brain target area to obtain multiple functional sub-areas, including: Based on the characteristic information of the brain target area, the brain target area is identified by a community discovery algorithm to obtain each function-structure joint module in the brain target area; Based on the functional connection heterogeneity within the function-structure joint module and the similarity between adjacent function-structure joint modules, the function-structure joint module after boundary optimization is subdivided to obtain a plurality of functional sub-regions.

6. The method for locating brain functional subregions according to claim 1, characterized in that: The determining of the first coordinate information of different functional sub-regions includes: Obtaining second coordinate information of each functional sub-area in the current image space; The second coordinate information of each functional sub-region is converted to the target space through a space conversion algorithm to obtain the first coordinate information of each functional sub-region; the target space is the space where the first three-dimensional skull model is located.

7. The method for locating brain functional subregions according to claim 1, characterized in that: The positioning of each functional sub-area on the first three-dimensional skull model based on the first coordinate information includes: The first coordinate information of the functional sub-region is input into a target device to control the target device to locate the functional sub-region on the first three-dimensional skull model based on the first coordinate information.

8. A device for locating brain functional subregions, characterized in that: The device comprises: A construction module, configured to construct a first three-dimensional skull model of the target object based on the brain image data of the target object; An analysis module, configured to determine feature information of a target brain region of the target subject based on the brain image data; the feature information includes a multimodal fusion connection matrix and anatomical structure information corresponding to the target brain region; A division module, used for dividing the target brain area according to the characteristic information of the target brain area to obtain a plurality of functional sub-areas; A positioning module is used to determine the first coordinate information of different functional sub-regions, and to position the functional sub-regions on the first three-dimensional skull model based on the first coordinate information.

9. A computer device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps of the method for locating brain functional subregions according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for locating brain functional subregions described in any one of claims 1 to 7 are implemented.

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