80 results about "Grey matter" patented technology

The invention discloses a multi-modal dynamic structure-function multilayer brain network calculation method and terminal, and the method comprises the steps: obtaining a multi-modal brain image, and carrying out the preprocessing of the multi-modal brain image, and obtaining a target multi-modal brain image; constructing a dynamic functional brain network, a structural brain network and a grey matter morphological similar brain network according to the target multi-modal brain image; and calculating according to the dynamic functional brain network, the structural brain network and the grey matter morphological similar brain network through a network topology similarity principle to obtain structural-functional coupling. According to the method, after MRI data is preprocessed, a morphological brain network is obtained through sMRI, a functional connection brain network is obtained through fMRI, a structural connection brain network is obtained through DTI, a multi-layer brain network coupling index is calculated, while structural and functional connection is considered, morphological information is also considered, then exploration of the model on the brain structure and function is improved, and the method has the advantages of being high in practicability and the like. And more comprehensive and accurate brain structure and function information can be provided.

The invention relates to the technical field of medical image processing, in particular to a neurosurgery image diagnosis method and system based on image processing, and the method comprises the following steps: obtaining gray matter edge nodes of a triaxial section, constructing a symmetric path unit, collecting an edge direction vector, and generating a direction trajectory diagram; and extracting continuous slices, constructing a rotation track sequence, identifying an abnormal region, filling gaps, combining path voxels, and dividing spatial levels to generate an image structure chart. According to the method, the grey matter edge nodes in the three-axis tangent plane are obtained, and the node paths with the symmetrical characteristics are screened out according to the space projection trend, so that the continuous region of the structure can be accurately recognized, the direction vectors in the continuous slices are extracted, the direction mutation region in the track is recognized, and the jump and fracture performance of the structure can be timely captured; and the abnormal region is accurately labeled, so that higher-dimensional expression and finer-grained recognition of the neural structure are realized, and spatial modeling and visual analysis of complex neuropathy are effectively supported.

The invention discloses a bionic nanofiber-hydrogel composite biological scaffold as well as a preparation method and application thereof. The bionic nanofiber-hydrogel composite biological scaffold is a composite scaffold for simulating a gray-white matter partition structure of a spinal cord tissue, and comprises bionic nanofibers and a bionic hydrogel material, wherein the bionic nanofibers are arranged in an oriented manner and simulate a white matter ordered structure around the spinal cord, and the bionic hydrogel material is combined with the bionic nanofibers and simulates gray matter in the center of the spinal cord. According to the bionic nanofiber-hydrogel composite biological scaffold, the biological activity of loaded stem cells or adult cells can be improved, the problem that a large number of transplanted exogenous cells die due to unbalanced oxidative stress generated after tissue damage can be solved, and it is guaranteed that the transplanted cells reside in a damaged area and play a role; therefore, the spinal cord microenvironment is improved, proliferation and differentiation of endogenous neural stem cells are promoted, functional recovery after spinal cord injury is finally promoted, and great potential is achieved in spinal cord injury treatment.

The invention discloses a multi-modal feature combined depression auxiliary diagnosis system. The system comprises a sampling unit which is used for constructing a multi-modal depression data set by acquiring a depression screening scale, an electroencephalogram, a magnetoencephalogram and functional magnetic resonance imaging based on acquisition equipment; the feature extraction unit is used for extracting multi-modal brain features based on the depression data set, and the multi-modal brain features comprise power spectral density obtained by electroencephalogram signals, event-related potential, micro-state, prefrontal lobe gamma frequency band power spectral density obtained by magnetoencephalogram and event-related magnetic field; gray matter volume and resting state functional connection density are obtained through functional magnetic resonance imaging; a data preprocessing unit; the diagnosis model unit is used for constructing a multi-modal depression diagnosis model and training the model on the basis of the multi-modal brain features in combination with a fusion strategy; and an analysis and prediction unit. The extracted features are comprehensive and reasonable, the defect of each mode is overcome by the feature fusion method, and the fused features are advanced.

According to the Alzheimer's disease auxiliary prediction system, method, medium and device based on the non-invasive multi-modal neural image.According to the Alzheimer's disease auxiliary prediction system, method, medium and device based on the non-invasive multi-modal neural image.According to the Alzheimer's disease auxiliary prediction system and method based on the non-invasive multi-modal neural image.Through a multi-modal brain network fusion graph neural network framework guided by graph reconstruction, a self-supervised normal form is used for fully utilizing MRI data, the limitation of rare PET data under a supervision strategy is broken through, and and reliable pathological characterization embedding is generated. Meanwhile, different from conventional brain network modeling which only constructs node features, the method constructs a bimodal brain network which integrates semantic and topological information according to gray matter function signals and white matter fiber structure connection, and designs a node-edge bidirectional encoder, so that the representation capability of the brain network is remarkably improved.

The invention discloses a method, device and equipment for generating a head model, a storage medium and a program product. The method is characterized by comprising the following steps of: receiving and verifying original three-dimensional T1 weighted image data and an original three-dimensional grid model; performing tissue segmentation based on the verified three-dimensional T1 weighted image data and the original three-dimensional T1 weighted image data to obtain a cerebral grey matter mask, a cerebral white matter mask, a scalp mask and a skull mask; performing topological structure repair on the brain grey matter mask and the brain white matter mask to obtain a brain mask; respectively converting the scalp mask, the skull mask and the brain mask into a scalp mesh model, a skull mesh model and a brain mesh model; calculating and applying a spatial transformation matrix from the verified three-dimensional grid model to the scalp grid model, and performing spatial registration and fusion on the scalp grid model, the skull grid model and the brain grid model to obtain a head model; the method has the advantages that the efficiency, precision and robustness of head model generation are effectively improved, and the method has good cross-platform deployment capability.

The invention provides a brain region grey matter layering method, device and equipment based on diffusion magnetic resonance imaging, and can be applied to the technical field of neural image calculation. The method comprises the following steps: acquiring diffusion magnetic resonance imaging data corresponding to a target brain area of a target object; processing the signal feature data by using an encoder to obtain encoding feature vectors corresponding to the N grey matter areas respectively; for the nth grey matter area of the N grey matter areas, clustering at least one voxel located on the nth grey matter area based on a preset clustering number k and the coding feature vector to obtain an nth clustering result; for the mth cluster of the k clusters, determining sorting information of the mth cluster according to the position information of voxels in the mth cluster, the position information of the first interface and the position information of the second interface; and sorting the k clustering clusters according to the sorting information of the k clustering clusters to obtain a brain region grey matter layering result for the target object.

The invention relates to a heterogeneous human head model construction method and system oriented to multi-physics field simulation, based on real human head T1 weighted magnetic resonance image data, key tissues such as white matter, gray matter and cerebrospinal fluid are accurately segmented, and a three-dimensional geometric model used for generating carrying tissue identification information is researched and developed; converting the model data into a file format conforming to the NASTRAN standard by utilizing an independently developed format conversion program, and completely retaining organization classification information; finally, different tissue areas are automatically recognized through physical field simulation software, corresponding frequency dependent material attributes are distributed, and a finite element model capable of being directly used for simulation is formed. According to the method, seamless integration of the high-precision heterogeneous human head model and the simulation platform is achieved, tedious manual repairing and assignment operation is not needed, the method has the advantages of being high in precision, high in efficiency and good in compatibility, and the authenticity and reliability of biomedical simulation such as transcranial magnetic stimulation and radio frequency coil design can be remarkably improved.

The invention discloses a brain connection coupling analysis method based on grey matter morphological similarity-white matter fiber bundles, relates to the technical field of neuropsychiatric imaging, and has the technical key points that the method can be used for analyzing grey matter-white matter brain connection group coupling modes of the whole brain level, the level in brain network communities and the level between the brain network communities; the integration characteristics of structural tissues of the brain on different scales can be understood; the reliability of statistical analysis is improved by adopting a spatial autocorrelation correction method; based on a specific machine learning fusion model, the clinical application value of the model is verified.

The invention discloses a multi-mode brain dysfunction auxiliary diagnosis method based on a dynamic function connection network. A two-stage collaborative learning framework from an individual brain graph to a group relation graph is constructed. Firstly, an individual multi-modal fusion brain map is constructed, node features of the individual multi-modal fusion brain map are obtained through node regularization regression analysis of an rs-fMRI time sequence, an adjacent matrix is obtained through calculation of the brain interval grey matter volume difference of a T1 image, and individual enhancement characterization is obtained through map convolutional network fusion. And then constructing a group relationship enhancement graph, taking individual representation as node features, constructing a dual-channel adjacency relationship for distinguishing homologous / heterologous connection according to age and gender, obtaining final discriminative representation through dual-channel graph attention network aggregation, and performing classification diagnosis according to the final discriminative representation. According to the method, deep fusion of multi-modal information and explicit modeling of key biological variables are realized, and an effective tool is provided for accurate and explainable auxiliary diagnosis of brain diseases.

The invention discloses a depression-anxiety complication-oriented disease mapping brain region positioning method and device, and relates to the technical field of medical signal processing, and the method comprises the steps: reading magnetic resonance data and identity information data of a target individual, and determining the gray matter volume corresponding to the brain of the target individual based on the magnetic resonance data and the identity information data; partitioning the brain to obtain a plurality of brain regions, and determining a brain region depression vector and a brain region anxiety vector corresponding to the brain according to the grey matter volume and the identity information data; classifying the plurality of brain regions based on the brain region depression vector and the brain region anxiety vector to determine a depression significant brain region and an anxiety significant brain region included in the plurality of brain regions; generating significant brain region tags corresponding to the multiple brain regions according to the depression significant brain region and the anxiety significant brain region, and generating network topology tags corresponding to the multiple brain regions according to the identity information data and the magnetic resonance data; and determining the disease type of the target individual based on the significant brain region tag and the network topology tag.

The invention discloses a method for identifying somatic depression by using brain structure and function abnormality, which comprises the following specific steps: selecting depression patients into groups, dividing the depression patients into a somatic depression patient group and a non-somatic depression patient group according to whether the score of a health questionnaire somatic symptom group scale of the patients is greater than or equal to 10, and recruiting a health control group at the same time. The three groups of subjects are subjected to resting state structure and functional magnetic resonance imaging scanning, and the brain grey matter volume and the brain region with significant difference are analyzed through a voxel-based morphological index and functional connection method. Pearson correlation analysis is adopted in SPSS26.0, and the relevance between the gray matter volume and the abnormal function connection of the patients in the SD group and the NSD group and the PHQ-15 scale score is compared. The method has the advantages that the illness state of the depression patient can be found in time, the patient can be treated at the best treatment opportunity, and early recognition of the body symptoms of the depression is beneficial for reducing waste of medical resources and relieving the pain of the patient.

This disclosure pertains to the field of tissue regeneration to repair damaged tissue and in particular brain tissue. The scaffold (1) of the present disclosure comprises a set of horizontal layers (11, 12, 13, 14), each layer extending in a horizontal plane orthogonal to the vertical direction (Z), each layer (11, 12, 13, 14) comprising a plurality of openings (15), each opening (15) of one layer (11, 12, 13, 14) being connected to one opening (15) of the directly adjacent layer (11, 12, 13, 14) by at least one first pillar (16) extending along the vertical direction (Z). Thus, the scaffold (1) of the present disclosure is designed to promote a cell growth radially along each layer (11, 12, 13, 14) to reconstruct the grey matter portion of the damaged area and the white matter along the vertical pillars (16) extending through the different layer (11, 12, 13, 14).

Exemplary system, method and computer accessible medium can be provided for creating or providing a (e.g., direct) visualization of an anatomical structure (e.g., subcortical anatomy). For example, it is possible to receive a magnetic resonance image, and apply a power regularization convolutional neural network to the MRI. In this manner, it is possible to generate the visualization of the anatomical structure. It is also possible to receive a fast gray matter acquisition T1 inversion recovery (FGATIR) MRI and apply a power regularization convolutional neural network to the FGATIR MRI in order to generate the visualization of the anatomical structure. Further, it is also possible to receive a FGATIR MRI and apply a convolutional neural network to the FGATIR MRI so as to generate the visualization of the anatomical structure.

The invention provides a premature infant language development evaluation method, system, equipment and medium, and belongs to the field of applying deep learning to neonatal brain function development disorder screening, and the method comprises the following steps: obtaining a brain image of a neonatal, extracting feature maps of the back curvature, the cortex grey matter thickness, the back depth and the myelin sheath, and obtaining a brain image of the neonatal; dividing the brain image into a plurality of triangular image blocks, and taking the feature maps of the back curvature, the cortex grey matter thickness, the back depth and the myelin sheath as feature channels of each vertex of each triangular image block to obtain feature embedding vectors of the left half brain and the right half brain; inputting the feature embedding vectors of the left half brain and the right half brain into a bidirectional scanning network for forward processing and reverse processing to obtain nonlinear features between the feature embedding vectors of the left half brain and the right half brain, and compressing the nonlinear features to a state space with a fixed size by using a state space model and outputting the nonlinear features. According to the invention, the language development evaluation efficiency of the premature infant can be improved.

The present invention discloses a method for automatically segmenting deep gray matter structures (caudate nucleus, putamen, globus pallidus, substantia nigra, red nucleus, dentate nucleus) of the brain based on T1W and QSM by combining deep learning with personalized atlas, including the steps of: collecting T1W and QSM magnetic resonance data, and manually annotating deep gray matter structures of the brain by doctors; extracting feature vectors of T1W brain images; performing unsupervised clustering on the feature vectors of all T1W brain images, selecting representative images and doctor's annotations to form a personalized atlas; registering the T1W images and annotations in the personalized atlas with other subjects' T1W images to obtain preliminary segmentation results, forming training set data; constructing a deep learning network model based on the attention mechanism; and training the model to segment deep gray matter structures of the brain. The present invention uses the contrast advantages of T1W and QSM images to accurately and automatically segment deep gray matter structures of the brain, and is expected to be used in brain imaging research and auxiliary clinical diagnosis of movement disorders such as Parkinson's disease in the future.

There is provided a method of reconstruction of a target brain structure(s), comprising: reconstructing at least a part of a brain comprising boundaries of brain structures that include the target brain structure(s), wherein the reconstruction is insufficient for parceling of the target brain structure(s) into sub-structures of a same type of gray or white matter, reconstructing and parceling the target brain structure(s) using a reference atlas, segmenting and parceling at least one originating brain structure using the reference atlas, filtering white matter fibers to isolate at least one target white matter tract connecting the originating brain structure(s) and the target brain structure(s), and creating a 3D reconstruction of the target brain structure(s) and the target white matter tract(s), wherein the target white matter tract(s) and the target brain structure(s) are transformed and / or mapped to an anatomical native space.

The invention provides a schizophrenia core epicenter region identification method based on a multi-mode nerve image, and belongs to the technical field of medical image analysis and neuropsychiatric disease diagnosis. According to the method, a macroscopic-mesoscopic-microscopic covariant network model is established by integrating multi-modal nerve image data (including structural magnetic resonance, functional magnetic resonance imaging and diffusion tensor imaging) and mesoscopic-microscopic scale data (gene expression atlas, neurotransmitter distribution and cell construction characteristics). The method comprises the following steps: firstly, constructing various connection networks, then identifying a core epicenter region with abnormal grey matter thickness based on a graph theory algorithm, carrying out cross-scale matching on the epicenter region and gene expression, neurotransmitter and cell structure characteristics by utilizing spatial correlation analysis, and analyzing a diffusion path of the epicenter region through connection omics. By fusing the multi-scale biomarkers, the limitation of single modal analysis is broken through, and systematic analysis of the schizophrenia pathological network is realized.

The invention discloses a grey matter brain region anti-magnetic component susceptibility map construction and analysis method and application thereof, which can be used for exploring a global distribution mode of cortical iron deposition of an AD patient on a voxel level and positioning a cortical region with susceptibility related to cognition. The method comprises the following steps: (1) carrying out MR data acquisition on a subject on a magnetic resonance scanner; (2) unwrapping the multi-echo phase image by using a phase unwrapping method based on Laplacian; performing brain stripping processing on the amplitude image of the first echo by using a BET algorithm built in FSL software and generating a binary brain mask image; removing a background field of the unwound phase diagram by using a V-SHARP algorithm in combination with the brain mask diagram; calculating a magnetic susceptibility map from the local field map by using an STAR-QSM algorithm; (3) calculating the cortex thickness by using the T1 weighted structure image, and performing post-processing on the 3D-FSPGR sequence structure image; and (4) carrying out voxel-based whole-brain QSM analysis and cortex thickness statistical analysis.

The invention provides a mental disease classification method and system based on an individual difference structure covariant network and machine learning, and belongs to the field of mental disease classification. The problem of classification performance bottleneck caused by heterogeneity of mental diseases in the prior art is solved. The method comprises the following steps: acquiring a structural magnetic resonance T1 weighted image, and preprocessing the image; performing brain region segmentation on the pre-processed T1 image based on an AAL brain map, and extracting the gray matter volume of each brain region; constructing an IDSCN network by calculating the Pearson's correlation coefficient of the brain grey matter volume of the paired brain regions; calculating the area under a node topological attribute curve of the IDSCN network; screening node attribute indexes with statistical differences between the patient group and the healthy control group through double-sample t test; and taking the screened node attribute indexes as feature vectors, and inputting the feature vectors into a support vector machine classification model for disease classification. The method is mainly used in the medical image processing field.

The invention discloses a system for analyzing double-task cost and brain micro-injury of a Parkinson type multi-system atrophy patient, and relates to the technical field of medical diagnosis, and the system comprises a gait collection module which is used for collecting single-task and double-task gait data of a participant, and processing the gait data to obtain gait feature data of the participant; the gait analysis module is used for analyzing the difference of the participants in the single-task gait aspect and the double-task gait aspect according to the gait feature data and calculating the double-task cost; the nuclear magnetic resonance imaging module is used for collecting nuclear magnetic resonance imaging data of participants; the morphological analysis module is used for performing voxel-based morphological analysis on the nuclear magnetic resonance imaging data and analyzing the correlation between the double-task cost of the participant and the grey matter under the brain curtain; and the diffusion tensor imaging analysis module is used for performing diffusion tensor imaging analysis on the nuclear magnetic resonance imaging data and analyzing the correlation between the double-task cost of the participant and the white matter under the brain screen.

The invention provides a nutritional composition capable of influencing protein expression of brain hippocampus and application of the nutritional composition. The nutritional composition of the present invention comprises sialic acid, sphingomyelin and phosphatidylcholine, in which the ratio of the sum of the contents of sphingomyelin and phosphatidylcholine to the content of sialic acid is 1: 1 by weight, and the ratio of sphingomyelin to phosphatidylcholine is 0.2: 1 to 1.4: 1 based on the total weight of sphingomyelin and phosphatidylcholine. The composition can increase the gray matter volume of the brain and can improve the expression of BDNFmRNA of the hippocampus and the prefrontal cortex.

The invention discloses a cerebral cortex penetrator small blood vessel magnetic resonance imaging method based on an inflow enhancement effect. The method comprises the following steps: modeling a penetrator small blood vessel to generate a blood vessel TOF (Time of Flight) image; calculating the blood vessel-grey matter contrast ratio of the blood vessel TOF image under different FAs and different TRs, and optimizing the FAs and the TRs according to the blood vessel-tissue contrast ratio; obtaining a structural image of the in-vivo brain tissue, performing cortex reconstruction, and calculating an angle of a surface normal of each cortex vertex relative to the B0 field to obtain a cortex angle graph; adjusting a single-layer blood vessel scanning plane angle according to the cortex angle diagram; and according to the optimized FA and TR and the single-layer blood vessel scanning plane angle determined in the step S4, carrying out magnetic resonance imaging on the cerebral cortex perforation small blood vessel. The invention further discloses a cerebral cortex puncturing small blood vessel magnetic resonance imaging device based on the inflow enhancement effect. According to the method and the device, magnetic resonance imaging of the cerebral cortex perforation small blood vessel can be realized under the non-invasive and contrast-agent-free conditions.

The invention provides a method, a system, a medium and equipment for regulating and controlling a brain function through vibration stimulation. The method comprises the following steps: step 1, designing and developing a vibration stimulation module suitable for a head by utilizing an electromagnetic induction principle; 2, a vibration stimulation module is placed at a preset position and started, and the vibration amplitude and the vibration frequency are adjusted; step 3, setting vibration duration and performing vibration to obtain functional magnetic resonance data; and 4, evaluating the vibration efficiency, scanning by using a functional magnetic resonance sequence before and after vibration, and processing and analyzing functional magnetic resonance data to obtain a low-frequency fluctuation amplitude image representing spontaneous brain activity and coupling of a whole-brain grey matter signal and a cerebrospinal fluid signal. According to the transcranial vibration stimulation system, the purpose of implementing accurate vibration stimulation on the transcranial is achieved, the transcranial vibration stimulation with high amplitude precision and high frequency precision is achieved in a mode of combining functional magnetic resonance with transcranial vibration stimulation based on the vibration stimulation module, and the influence of vibration on brain functions can be detected.

The application discloses a kind of whole brain segmentation method, system, equipment and medium, method includes obtaining CT image data, and standard image data is obtained by preprocessing;Standard image data is input into whole brain segmentation model, and whole brain segmentation result is obtained.Wherein, whole brain segmentation model is obtained based on sample annotation data training, and whole brain segmentation model includes CNN encoder, Transformer encoder, multiple cross-domain fusion modules and the feature enhancement module corresponding to cross-domain fusion module one by one, and decoder.The application has the advantages that the soft tissue contrast of CT image data is low, it is difficult to distinguish cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue problem, by CNN encoder extraction local feature and Transformer encoder extraction global semantic feature, and local feature and global semantic feature dynamic interaction and fusion, accurately segmented advantage is carried out to whole brain cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue.

A biomechanically realistic brain model for impact testing comprises white matter simulant materials and gray matter simulant materials positioned to correspond with anatomical brain structure. The white matter simulant comprises anisotropic hydrogels with embedded magnetically-responsive, electrically-responsive, thermally-responsive, and / or mechanically-responsive particles that exhibit directionally-dependent stress-strain responses. The gray matter simulant comprises isotropic hydrogels or silicones, particularly siloxanes, that exhibit uniform stress-strain responses to applied forces. The materials are cast, injected, printed, or formed in anatomically correct positions and share realistic interfaces. The brain model accurately imitates physical brain responses during impact tests, particularly angular impacts, providing realistic testing results for biomechanical analysis.

The application discloses application of TESC as an Alzheimer's disease prevention and treatment target and relates to the field of biological medicines. A TESC overexpression (TESC-OE) hippocampal neuron cell line is constructed through lentivirus transfection, and the result shows that TESC-OE has the ability to resist A beta-induced neuron apoptosis and synapse damage. A TESC overexpression model in the hippocampus of a mouse is constructed through stereotactic injection of an adeno-associated virus, and the result shows that the hippocampal gray matter volume in the brain of the TESC-OE mouse is significantly larger than that of a wild type mouse. A beta stereotactic modeling is performed on the basis of TESC overexpression in the hippocampus, and the result shows that the TESC-OE mouse can significantly resist A beta-induced hippocampal atrophy, learning and memory dysfunction and impaired synaptic plasticity. Immunoblotting analysis reveals the molecular mechanism that TESC can increase the expression of anti-apoptotic genes in the hippocampus and reduce the expression of pro-apoptotic genes to exert a neuroprotective effect.

The application relates to the technical field of image processing, in particular to an MRI image feature extraction method for dynamic evaluation of child brain development, which comprises the following steps: determining a cerebrospinal fluid coefficient of a child brain; dividing an MRI image into a cerebrospinal fluid region and a residual brain region based on the cerebrospinal fluid coefficient of the child brain of each pixel point; determining a white matter coefficient of each pixel point in the residual brain region according to the gray value of each pixel point in the decayed weighted image of each pixel point in different gradient directions, and the gray value of each pixel point in the first weighted image and the second weighted image in the residual brain region; segmenting the white matter region and the gray matter region of the residual brain region based on the white matter coefficient, and extracting image features in the white matter region and the gray matter region. In this way, the accuracy and reliability of the segmentation result of different tissue regions in the child brain MRI image are improved.

A method is to be implemented by a computing device that stores a risk assessment model, and includes steps of: obtaining an entry of target physiological data and target magnetic resonance imaging (MRI) images of a brain of a subject with mild cognition impairment (MCI); obtaining, based on the target MRI images, voxel values respectively of primitive voxels that are related to grey matter of the brain of the subject; selecting, from among the primitive voxels, any primitive voxel satisfying a filtering criterion as a selected voxel; calculating an average of the voxel value(s) respectively of the selected voxel(s) to obtain an average target voxel value; and obtaining a probability of the subject developing Alzheimer's disease within a predetermined time period by feeding the average target voxel value and the entry of target physiological data into the risk assessment model.

The invention relates to the technical field of virtual reality, in particular to a virtual reality-combined neurosurgery preoperative simulation training method, which comprises the following steps of: acquiring a grey matter boundary and analyzing a nerve fiber trend, delimiting a path to form a training path, screening out a path range in contact with a functional area, loading track comparison and extracting an overlapping path, and performing simulation training on the overlapping path. Identifying the corresponding relation between the path abnormity and the area, and recombining the action sequence and the execution range according to the problem distribution. According to the invention, by identifying the spatial continuity of nodes in an image, combining the contact condition of a path and a functional region, screening a response region which can be included, comparing the spatial correspondence between an operation track and the nodes, mining offset and fracture points in the path, and determining the spatial continuity of the nodes. The action sequence is rearranged according to the frequent occurrence positions of the actions in the path and the continuation relation of the nodes, so that the training path has continuity and fluency, the action connection between the regions is optimized, and the coordination and consistency of the path and the operation content are improved.