31 results about "Central gray matter" patented technology

The present invention relates to devices and methods of use thereof for making semiderivative voltammetric and chronoamperometric measurements of chemicals, e.g. neurotransmitters, precursors, and metabolites, in vitro, in vivo, or in situ. The invention relates to methods of diagnosing and / or treating a subject as having or being at risk of developing a disease or condition that is associated with abnormal levels of one or more neurotransmitters including, inter alia, epilepsy, diseases of the basal ganglia, athetoid, dystonic diseases, neoplasms, Parkinson's disease, brain injuries, spinal cord injuries, and cancer. The invention provides methods of differentiating white matter from grey matter using microvoltammetry. In some embodiments, regions of the brain to be resected or targeted for pharmaceutical therapy are identified using Broderick probes. The invention further provides methods of measuring the neurotoxicity of a material by comparing Broderick probe microvoltammograms of a neural tissue in the presence and absence of the material.

The invention relates to a method for three-dimensional registration and brain tissue extraction of individual human brain multimodality medical images. The method comprises the following steps: reading DICOM medical image data, and converting DICOM format data into NIfTI format data; layering images adopting a nuclear magnetic resonance structure; establishing a mixture gaussian model through an east Asia brain structure template and an east Asia brain tissue probability graph of ICBM, and dividing nuclear magnetic data into a grey matter part, a white matter part and a cerebrospinal fluid part; enabling the registration methods of other modality data and nuclear magnetic structure images to be the same; according to a layering and registration result, removing a skull and other parts outside the skull of each modality, and reserving the structure of parts inside the skull; adding weighting of three tissue probability graphs of the grey matter, the white matter and the cerebrospinal fluid obtained through layering of structure images so as to obtain a brain tissue probability graph, and performing Gaussian kernel smoothness; setting a threshold, applying the threshold to each modality image data after registration and resampling, and removing the cranium and parts outside the cranium; outputting a save result in an NIfTI format. According to the method disclosed by the invention, the registration of various structure images and multiplanar reconstruction images of a tested person can be completed at the same time.

The invention provides a beagle spinal cord orientation channel stent which comprises a stent body. A hollow area for growing fasciculus gracilis, fasciculus cuneatus, tractus spinothalamicus, tractus corticospinalis lateralis, fasciculus cerebellospinalis, tractus spinothalamicus lateralis, tractus spinotectalis, tractus spinothalamicus anterior, direct pyramidal tract and grey matter is arranged inside the stent body. The stent body is 8-15mm in vertical diameter, 6-10mm in transverse diameter and 0.1-1.0mm in wall thickness. The beagle spinal cord orientation channel stent based on the three-dimensional printing technology has the advantages that the precision and structure problems in the prior art can be solved, and the beagle spinal cord orientation channel stent is applicable to treatment and researches of spinal cord injuries.

The invention discloses an Alzheimer's disease classification method and system based on an anatomical landmark and a residual network, and the method comprises the steps: carrying out the tissue segmentation modulation of a training image set, and obtaining a gray matter image; comparing voxels of the Alzheimer's disease image and the normal subject image in the training image set to identify ananatomical landmark; taking the obtained anatomical landmark as a center, and extracting grey matter blocks of the gray image; and connecting the grey matter blocks, inputting the obtained synthetic blocks into a residual error network model for feature extraction, taking features extracted by the residual error network model as input of a classifier, and classifying Alzheimer's disease patients and normal subjects in the test image set. The anatomical landmark is taken as the center, feature blocks of gray matters in three tissues of the brain are taken as the input of the residual network, the feature blocks are taken as the feature expression of each MR image, the residual network model is adopted to enhance the feature learning capability of the network, and the classification accuracyis enhanced.

The invention discloses a stability calculation method for a brain dynamic function mode. The method comprises the following steps of acquiring and preprocessing functional magnetic resonance imagingdata and structural magnetic resonance imaging data of a subject; extracting grey matter voxel with volume of brain grey matter being greater than 0.2 as a mask for subsequent calculation; calculatinga two-dimensional matrix of dynamic function connection between each gray voxel and other gray voxels of the whole brain under each time window by adopting a sliding time window method; calculating the obtained two-dimensional matrix of each gray voxel by taking a time window as a scorer to obtain a Kendall harmony coefficient of the two-dimensional matrix as a functional stability value of the gray voxel; calculating the tested whole-brain gray voxels one by one to obtain functional stability values of all the gray voxels; performing Z-score standardization of functional stability values ofall gray matter voxels and forming and outputting the brain dynamic function stability of the subject. . Calculation based on gray voxels is adopted. Brain activity signals are utilized to the maximumextent, and dynamic characteristics of brain functional activities can be accurately and comprehensively described.

Delivery devices, systems, and methods related thereto may be used in humans for spinal delivery of cells, drugs or vectors. Thus, the system enables subpial delivery, which leads to a near complete spinal parenchymal AAV9-mediated gene expression or ASO distribution in both white and grey matter.

The invention discloses a classification and identification method based on multi-modal multi-site data fusion. The method comprises the following steps: 1) acquiring multi-site sMRI and R-fMRI data; 2) preprocessing the sMRI data of each site to obtain an sMRI skull stripping brain image and a cerebral cortex surface model; (3) dividing the cerebral cortex into a plurality of different areas, and calculating dissection parameters such as the cortex average thickness, the cortex average surface area and the grey matter volume of each area; 4) inputting the sMRI skull dissection brain image into the ResNet3D deep network model to extract high-dimensional features; 5) fusing the extracted anatomical parameters and high-dimensional features to obtain one-dimensional vectors as sMRI data features; 6) preprocessing the R-fMRI data of each site to obtain a brain grey matter image; the multi-site data has the same or similar data distribution by adopting the method based on low-rank representation, so that the problem of multi-site data isomerism is solved or partially solved, the generalization of a diagnosis model is effectively improved, and actual requirements are better met.

The invention provides a high-simulation collagen spinal cord stent for people. The stent comprises a stent body, and the stent body is internally provided with hollow regions used for growing spinal grey matter, fasciculus gracilis, fasciculus cuneatus, tractuscorticospinalislateralis, tractusrubrospinalis, a reticulospinal tract, a lateral spinothalamic tract, a spinotectal tract, a reticulospinal tract, a direct pyramidal tract and a central canal. The vertical diameter of the stent body is 1.2-1.8 cm, the transverse diameter of the stent body is 1.4-1.8 cm, and the height of the stent body is 2.0 mm-8 cm. The stent body is made of collagen. According to the high-simulation collagen spinal cord stent for people, the main anatomical structure and the spatial relationship of thoracolumbar horizontal spinal segment white and grey matter of people are established, the stent is used for improving nerve directional and orderly connection after spinal cord injury, and rapid repair is promoted after spinal cord injury of people.

Delivery devices, systems, and methods related thereto may be used in humans for spinal delivery of cells, drugs or vectors. Thus, the system enables subpial delivery, which leads to a near complete spinal parenchymal AAV9-mediated gene expression or distribution in both white and grey matter.

The invention relates to a construction method of spinal cord tissues for repairing spinal cord injuries, in particular to a construction method for spinal cord tissues containing white matter structures on the periphery and grey matter structures in the center. When the construction method is used, the constructed spinal cord tissues containing white matter structure stem cell-derived oligodendroglia cells and grey matter structure stem cell-derived nerve cells are transplanted to transverse spinal cord injuries, and are integrated in a host neural network in a matching mode that a white matter structure corresponds to host white matters and a grey matter structure corresponds to host grey matters, so that the regeneration and functional repair of injured spinal cords are better promoted.

A device or system for delivering fluid to or removing fluid from a CSF-containing space of a brain and for recording electrical activity from white or grey matter in the brain includes a catheter including a proximal end, a distal end portion, a first lumen extending from the proximal end to the distal end portion, and one or more electrodes positioned relative to the catheter a distance from a distal end of the catheter, such that the one or more electrodes would be placed in contact with white or grey matter of the brain if the distal end of the catheter were positioned in the CSF-containing space. The catheter may include the electrodes or a lead adjacent the catheter may include the electrodes.

The invention discloses a cerebral cortex surface reconstruction method and a readable storage medium, and belongs to the field of medical image processing. The cerebral cortex surface reconstruction method comprises the following steps: performing data preprocessing on a medical image to obtain a standardized image; inputting the standardized image into a segmentation model to segment a brain anatomical structure adjacent to gray matter so as to obtain a distinguished brain anatomical structure segmentation image; inputting the distinguished brain anatomical structure segmentation image and the standardized image into a surface level set prediction model to obtain a level set representation image of an interface between the brain anatomical structure and the grey matter; the level set representation image of the interface between the brain anatomy and the grey matter is input into a topological repair module and a polygonal mesh representation reconstruction module to obtain a polygonal mesh representation of the interface between the brain anatomy and the grey matter.

The invention relates to a neural stem cell derived tissue engineering spinal cord tissue for simulating a spinal cord tissue structure and main cell constituents. The neural stem cell derived tissue engineering spinal cord tissue is obtained by in-vitro culture and construction through a biological tissue engineering technology and a neural stem cell induced differentiation technology, and has a white matter like structure in the peripheral part and a grey matter like structure in the central part. CNTF (ciliary neurotrophic factor) gene modified oligodendrocyte precursor cells (OPCs) are planted in the white matter like structure; and NT-3 gene and receptor thereof TrkC gene modified neural stem cells (NSCs) are planted in the grey matter like structure. When being transplanted to a spinal cord complete transection injury site, the tissue engineering spinal cord tissue has favorable cell activity and can secrete neurotrophic factors to accelerate regeneration of spinal cord injured central neuron axons; and the tissue engineering spinal cord tissue can realize corresponding functions of oligodendrocytes and neurons, and serves as a neural information transfer relay to form a synaptic connection with regenerated nerve fibers so as to repair spinal cord injured neural circuits. The tissue engineering spinal cord tissue can also be used as an in-vitro model for neural pharmacology and neural development researches.

The invention discloses a preparation method of bovine spinal cord peptide. The preparation method comprises the steps of unfreezing frozen bovine spinal cord at a room temperature, and carrying out high-temperature cooking, tissue homogenization, degreasing, enzymolysis, ultrafiltration, freeze drying or spray drying, so as to obtain bovine spinal cord peptide powder. Spinal cord is a part of a central nervous system of human or a vertebrate, the upper end of the spinal cord is connected with a medulla, paired nerves grow on two sides of the spinal cord, and an H-shaped grey matter region isformed in the spinal cord; and the spinal cord is a passage between the nerves and the brain and a low-level pivot for the many simple reflection activities, so that the spinal cord is a very specialtissue organ of the vertebrate and necessarily contains special functional factors according to a traditional Chinese medicine theory of reinforcing organs with organs. An analytical detection resultshows that fresh spinal cord contains nutrient substances, macroelements, microelements, fatty acids and various amino acids required by a human body, so that the spinal cord has a very high nutritional value. Prepared bovine spinal cord nutritional whole powder and prepared spinal cord polypeptide powder can be used for developing common foods and health products or can be applied to the medicinefield.

The invention discloses a method for carrying out deep brain nucleation positioning by using a quantitative magnetic susceptibility map, which uses a high-resolution QSM technology to obtain a quantitative magnetic susceptibility map of a head, can display a clear deep brain nuclei contour exceeding that of a traditional MRI method, and especially can distinguish thalamus bottom nuclei from blackmatter. A traditional CT image and a traditional T1 image can conduct spatial positioning on skull and grey matter, a quantitative magnetic susceptibility image, the CT image and the high-resolution T1 image are used in a combined mode, and deep brain nuclei can be accurately positioned. The method has the characteristics that the advantages of the QSM technology and the CT technology are integrated, and the accuracy of nucleus positioning is improved through clear brain deep nucleus imaging; the method does not depend on a specific machine type, is suitable for various commercial magnetic resonance devices, and is convenient to popularize.

The invention discloses a brain age assessment method for Rolandic epilepsy children based on machine learning, which comprises the following steps: S100, collecting MRI data of a control group object, and performing effective model feature extraction through dimension reduction; S200, constructing a prediction model based on the effective model features extracted in S100; and S300, collecting MRI data of epilepsy children, extracting features of the test model, inputting the features of the test model into the prediction model, and outputting a prediction result, wherein the MRI data in the S100 comprises the thickness of the brain grey matter, the surface area of the brain grey matter and the volume of the brain grey matter; and the effective model features comprise at least one of respective subdivision part indexes and mixing indexes in the thickness of the brain grey matter, the surface area of the brain grey matter and the volume of the brain grey matter. According to the invention, the SVR brain age prediction model based on the features of the brain grey matter morphological indexes is adopted; and the brain age and the brain maturity of the Rolandic epilepsy children can be preliminarily evaluated.

The invention relates to a brain model and preparation methods thereof. The brain model comprises a healthy brain model, a brain model provided with a hemorrhagic spot model and a brain model provided with an ischemic spot model. The invention also provides corresponding manufacturing methods. Meanwhile, different perfusates are designed according to the internal structure of the brain and comprise a cerebrospinal fluid perfusate, a grey matter perfusate, a white matter perfusate, a cerebellum perfusate, a brainstem perfusate, a hemorrhagic point perfusate and an ischemic point perfusate. The invention also provides corresponding preparation methods. According to the brain model obtained through configuration, the dielectric constant and the loss angle of the brain model are close to true values, the separation degree is good, and it can be ensured that layers cannot be fused or confused.

The invention provides a method for automatically calculating ischemic penumbra by using ASL of a brain template. Compared with the prior art, the invention provides multiple contrast options (left and right sides, large and small brains and grey matter) based on ASL perfusion, and improves the accuracy of calculating ischemic penumbra.

The invention is applicable to the field of image processing, and provides a data generation device and method, a terminal and a storage medium. The data generation method comprises the steps of obtaining a sample gray matter segmentation map of brain gray matter of a sample brain and a sample gray matter boundary map of a brain gray matter boundary of the sample brain; obtaining a to-be-detected T1W magnetic resonance image of a to-be-detected object; according to the to-be-detected T1W magnetic resonance image, determining a grey matter probability graph of the brain grey matter of the to-be-detected object and a first grey matter boundary graph of the brain grey matter boundary of the to-be-detected object; determining a grey matter extension graph of the to-be-detected object according to the grey matter probability graph and the sample grey matter segmentation graph; determining a second grey matter boundary map of the to-be-detected object according to the first grey matter boundary map and the sample grey matter boundary map; and generating comprehensive feature data according to the grey matter extension graph and the second grey matter boundary graph. According to the embodiment of the invention, the reliability of the FCD focus positioning data can be improved, and the focus can be accurately positioned.

The invention discloses a method of judging distribution of ventral horn motor neurons in cervical segments of the spinal cord of a rat. The method comprises the steps of: taking a plurality of frozen sections of cervical segments and cross sections of the spinal cord of an SD (Sprague Dawley) rat; dyeing the frozen sections by a cresyl violet and Pal-Weigert dyeing method; observing distribution of ventral horn motor neurons in the cervical segments of the spinal cord of SD rat by a light microscope technology; and researching the distribution characteristic of the ventral horn motor neurons in the cervical segments of the spinal cord of SD rat. According to the method of judging distribution of ventral horn motor neurons in cervical segments of the spinal cord of the rat provided by the invention, segments where cerebral nuclei appear and disappear in the spinal cord of the SD rat are refined to 1 / 4 section, thus showing that the appearance of different cerebral nuclei in same segments is probable, and a comparatively specific reference is provided for researching positioning of association of cerebral nuclei in different nerves and gray substances of the spinal cord.

The invention belongs to the technical field of neuroimaging, and particularly relates to a research method for a teenager muscular spasm epilepsy multi-brain-region co-activation mode. The method comprises the following steps: firstly, analyzing the grey matter volume of a JME patient by utilizing voxel-based morphological analysis (VBM); it is assumed that a brain region with gray matter volume atrophy is a key brain region causing disease generation, so that the brain region with gray matter volume atrophy is used as a region of interest, and brain dynamic activity changes of a JME patient and a healthy control group in the selected region of interest are analyzed through a co-activation mode. Therefore, the difference between the dynamic brain activities of the JME patient and the normal person in the resting state can be searched.

The invention relates to an application of a KIBRA rs17070145 detection reagent in preparation of an olfactory function evaluation kit. The olfactory function is an important reference basis for clinical disease diagnosis, and the correlation between a control brain region and a genotype of the olfactory function is researched. Researches show that the grey matter volume (GMV) of bilateral orbital frontal regions and left thalamus of a KIBRA TT carrier is obviously lower than that of a C allele carrier, and the KIBRA TT carrier can be judged as olfactory disorder. Or after correction of CC and CT genotypes and olfactory disorder is 0.687, and the olfactory function protection effect of the C allele is stronger and the olfactory disorder occurrence possibility is lower when the age is larger. Based on the achievements, the invention provides the application of the KIBRA rs17070145 detection reagent in preparation of the olfactory function evaluation kit.

The invention provides a grey white matter volume anomaly detection and correction method, which comprises the following steps of: based on a behavioral scale, obtaining a behavioral scoring result of a test object and a mapping template between behavioristics and a brain structure corresponding to the behavioral scale; weighting the behavioral scoring result and the mapping template to obtain a weighted template; performing VBM analysis on the magnetic resonance imaging data of the test object to obtain a probability template of a brain grey matter volume anomaly region; and comparing the weighted template with the probability template of the brain grey matter volume anomaly region, and extracting voxels which are simultaneously represented as significant anomaly as an analysis result. The invention further discloses a grey matter volume anomaly detection device, grey matter volume anomaly detection equipment and a computer readable storage medium. By adopting the embodiment of the invention, the false positive caused by multiple comparison errors in VBM analysis can be corrected by utilizing the relationship between the brain structure and behavioristics.

The invention discloses a QSM deep brain nucleus automatic segmentation method based on deep learning, and the method is realized based on a quick segmentation tool for a QSM deep brain grey matter nucleus structure, and is named as DeepQSMSeg. Based on an end-to-end model DeepQSMSeg of deep learning, five pairs of ROI (Region of Interest) structures (caudal nuclei CN, shell nuclei PUT, pale globe GP, nigra SN and red nuclei RN in left and right hemispheres) of a deep grey matter nucleus can be accurately, stably and quickly segmented by utilizing a QSM technology. The deep grey matter nuclei are accurately segmented, which is beneficial to the development of brain-iron related researches, especially nervous system degenerative diseases closely related to the deep brain nuclei, such as Parkinson's disease.

The invention discloses a brain function network intra-cortex work state and inter-cortex work state determination method comprising the following steps: a first acquisition step, collecting a plurality of first blood oxygen saturation level time point vectors of a plurality of first grey matter voxels of a first cortex of a brain function network template, wherein each the first blood oxygen saturation level time point vector respectively comprises blood oxygen saturation level signals of each the first grey matter voxel on various continuous time points in a specific time period; a first cluster step, clustering the first blood oxygen saturation level time point vectors as a plurality of first grey matter voxel cooperation time point classes by taking the blood oxygen saturation level signals as targets, wherein the first grey matter voxel cooperation time point classes refer to the cluster of each the first grey matter voxel at a plurality of discrete time points in a specific time period; a first determination step, determining the plurality of first grey matter voxel cooperation time point classes as the intra-cortex work state of the first cortex.

The invention relates to a brain image-based evaluation method and a neuropsychiatric disease evaluation system. The system comprising a storage module in which first to third determination models corresponding to grey matter, white matter and spinal fluid, respectively, are stored, a transmission module, an azimuth angle correction module, a brain tissue recognition module, and a brain tissue recognition module. The azimuth angle correction module is connected with the transmission module so as to receive a 3D brain image to be judged to correct the 3D azimuth angle of the center of the 3D brain image to be judged; the brain tissue recognition module is used for receiving the corrected 3D brain image to be judged and capturing a white matter image part to be judged, a grey matter image part to be judged and a spinal fluid image part to be judged; the processing module feeds white matter, grey matter and spinal fluid image parts of a 3D brain image to be judged into the first to third judgment models after dimension reduction, first to third judgment results are obtained after calculation, and an evaluation result is generated according to the first to third judgment results.

The invention discloses an MRI (Magnetic Resonance Imaging) brain tissue clustering segmentation method. The method comprises the following steps of: setting tissues except spinal fluid, grey matter and white matter in an MRI brain image as an image background and removing the image background; calculating the intensity and position similarity between pixels in the MRI brain image to form a bilateral similarity matrix Bsm; segmenting pixels of the MRI brain image into a set C = {Ck, k = 1, 2,... P} by using an FCM algorithm; calculating a weight coefficient set Vk = {gamma 1, gamma 2,... gamma N} of pixels in the Ck; sorting the weight coefficients in the Vk to obtain a first R weight coefficient set VkR = {gamma'1, gamma'2,... gamma'R}, wherein the set V = {VkR, k = 1, 2,... P} is a main multi-clustering center set of C; distributing labels in a K neighbor mode according to the bilateral similarity matrix Bsm and distribution of the set V, determining a point with the farthest distance in the K neighbor range of elements in the V as a secondary clustering center, forming a secondary clustering center set A1, enabling a plurality of times of secondary clustering to form a set Az, wherein the set A = {A1, A2,... Az} is a set of all the secondary clustering centers of the C. Compared with the prior art, the scheme of the invention avoids falling into local optimum, and the MRI brain image segmentation result distribution is more balanced.