121 results about "Entire brain" patented technology

The invention relates to a children ASD diagnosis device based on a magnetoencephalogram and an electroencephalogram. The children ASD diagnosis device comprises a magnetic shielding system, a head-mounted electroencephalogram and magnetoencephalogram array type sensor system, an electroencephalogram and magnetoencephalogram data acquisition system, a sensory stimulation system, a signal processing system and a data analysis system. The magnetic shielding system can effectively reduce background magnetic field noise. The head-mounted electroencephalogram and magnetoencephalogram array type sensor system can carry out whole-brain collection on a tested child in a combined nesting mode. The electroencephalogram and magnetoencephalogram data acquisition system can record brain electrical activity information. The sensory stimulation system can present visual and auditory sensory stimulation for the tested child. The signal processing system removes biological magnetic noise and backgroundmagnetic noise. The data analysis system can extract pathological features in the signals for analysis. The children ASD diagnosis device provided by the invention has the advantages of high sensitivity and specificity, multiple information sources, high signal quality, easy acceptance by children and the like. The children ASD diagnosis device has the advantages of high sensitivity and specificity in the aspect of ASD diagnosis of children.

The invention discloses a method for preparing a whole brain specimen of a baby animal. The steps are as follows: a. a baby animal brain sample is fixed and imbued; b. the fixed and imbued brain sample is blackened by using blackening liquor that is alkali liquor with the pH value being more than 10, and the blackening period is 1-24 hours; c. the obtained brain sample is dehydrated, and the dehydration is carried out by 100% dehydrating agent in weight; d. the blackened brain sample is gummed by using an embedding agent, and two continuous gumming are carried out by 100% embedding agent in weight; e. after being gummed by the embedding agent, the brain sample is put into an embedding box and then submerged by adding the 100% embedding agent in weight; f. the embedding box for submerging the brain sample is heated, so that the embedding agent is polymerized, the polymerization temperature is 35 to 80 DEG C, and the polymerization period is 6-96 hours. The method for preparing the baby animal brain specimen of the invention can dye more neurons as well as neuron cell bodies, dendrite and axon; the contrast is high; the color is not faded easily; and whole brain neuron three dimensional morphology structures can be observed in the sub-micro scale.

The invention discloses a brain network specific structure extraction method for Alzheimer's disease and mild cognitive impairment. The method comprises the steps of 1, preprocessing functional magnetic resonance data; 2, constructing a brain network, partitioning whole brain functions and extracting a time sequence; 3, constructing a continuous homology model; 4, carrying out continuous homologyhigh-dimensional feature quantification, and performing statistical analysis on three groups of Landscapes of AD, MCI and HC through replacement inspection; 5, extracting a brain network specific structure; 6, outputting a result. According to the method, the problem of threshold selection in a graph theory method is avoided, the calculation burden can be effectively reduced, and the method is aninnovative method.

A method useful in diagnosing normal pressure hydrocephalus and monitoring treatment is presented. A database of mean brain diffusion constants and distribution width is created. Three-dimensional diffusion images of the subject are obtained and algorithms are applied to the images, which include reducing the three-dimensional images to one-dimensional maps of the average diffusion content for the entire brain. Distribution maps (diffusion histograms) are obtained for the brain, and the histograms are fit to a model. From the fit are obtained a mean brain diffusion constant, which is characteristic of the diffusion of the brain tissue in the three compartment model, and a distribution width for the tissue. These numbers are compared to the database for normals, and the significant deviation from the normals (higher in the case of mean brain diffusion constant and wider in the case of distribution width) is an indicator of the presence of normal pressure hydrocephalus.

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.

The invention relates to a medicine for preventing and treating ischemic stroke. In the mouse cerebral ischemia-reperfusion model, the compound of the present invention can significantly reduce the behavioral score, can significantly reduce the percentage of cerebral infarction / whole brain, and can significantly reduce the water content of ischemia-reperfusion damaged brain tissue, and relieve ischemia. The degree of lateral cerebral hemisphere edema can be used to prevent and treat ischemic stroke.

The invention discloses an epilepsy focus positioning method and system, and the method comprises the steps: firstly carrying out the segmentation of a T1 structural image of an image clearly displaying a brain region structure, obtaining a to-be-detected tissue set, manufacturing a corresponding mask, extracting all tissue regions of tested gradient weighted MRI data through the mask, estimatinga DKI parameter graph, and inputting the DKI parameter graph into a neural network, extracting features to obtain feature vectors, further inputting the feature vectors into a classifier for classification, and judging whether the epilepsy focus exists or not. According to the method, the epilepsy focus analysis accuracy is higher on the basis of the DKI parameter graph with higher nerve tissue representation sensitivity and specificity; the whole brain is replaced by the brain division organization structure for parameter graph estimation, so that the calculation amount is reduced; the neuralnetwork constructed by transfer learning is used for feature extraction, the problem that features extracted by directly training and extracting the feature network are not comprehensive due to smallmedical data volume is solved, the method and system are applied to conventional MRI negative epilepsy, and the illness state of a patient can be effectively controlled through timely and accurate focus positioning diagnosis.

The invention is suitable for the technical field of image processing, and particularly relates to a cerebral artery wave intensity and wave power measuring method, terminal equipment and a computer readable storage medium. The measuring method comprises the following steps: acquiring a nuclear magnetic resonance image containing brain information and neck information, and constructing a cerebral hemodynamic model according to the nuclear magnetic resonance image; and obtaining target ultrasonic flow velocity frequency spectrum waveforms of the left internal carotid artery, the right internal carotid artery, the left vertebral artery and the right vertebral artery respectively, and determining the wave intensity and the wave power of each cerebral artery according to the cerebral hemodynamic model and each target ultrasonic flow velocity frequency spectrum waveform. According to the embodiment of the invention, the measurement of the WI and WP of the whole cerebral artery is realized in a mode of combining non-invasive measurement and numerical simulation, so that the accuracy of the measurement of the WI and WP of the cerebral artery is greatly improved, and the method has relatively high usability and practicability.

The invention discloses a depression classification method based on graph embedding and a multi-modal brain network, and the method comprises the steps of modeling a brain into a brain network based on the thought of a graph theory, fusing a whole-brain function network and a structure network through a function-structure hierarchical mapping algorithm, and obtaining the brain network with the fusion of function and structure information, automatically learning the topological structure features and connection features of the brain network by using a graph embedding algorithm to obtain the vector representation of each node of the brain network, further combining into brain network representation, and finally performing depression classification based on each tested brain network representation by using a support vector machine model. According to the method, graph embedding and the multi-modal brain network are utilized, the defect of single-modal information is overcome, the feature vector suitable for a machine learning classification model is generated, the brain network level features related to depression are effectively mined, and the depression classification precision is improved.

The invention discloses a device for measuring human brain neurotransmitter information and human brain complex network information. The device comprises a brain wave input and amplification-lead device, a digital to analog conversion module, a central processing unit, a data storage device, an output device and an input control device; and operation procedures such as brain wave data acquisition, S spectrum generation, optimizing, frequency multiplication and fractal dimension processing of a coding area, correlation calculation, correlation judgment, full brain transmitter information, fly bird map generation and output, frequency multiplication and fractal dimension processing of a network area, fractal dimension optimization of the network area, HOLO map generation and output and the like, and a neurotransmitter module data procedure are involved. The measurement device acquires brain electrical signals, extracts ultra slow system information which is hidden in the brain electrical signals, and analyzes and processes the ultra slow system information to obtain the human brain neurotransmitter information and the human brain complex information data, and displays and outputs the fly bird map and the HOLO map according to the specified operation steps.

The invention discloses a CT brain parenchyma segmentation system based on a block region growing method, which comprises a preprocessing module, a coarse segmentation module, a brain parenchyma extraction module and a post-processing module. The preprocessing module is used for preprocessing original three-dimensional CT image data to obtain preprocessed data; the coarse segmentation module is used for segmenting the preprocessed data by adopting a threshold segmentation method to obtain a foreground and an initial skull part, and processing the initial skull part by adopting binary morphology to obtain a brain parenchyma mask; the brain parenchyma extraction module is used for acquiring seed points of a brain parenchyma region from the brain parenchyma mask, and performing block circulation segmentation through a three-dimensional region growing algorithm to obtain a whole brain parenchyma mask; and the post-processing module subtracts the initial skull part from the whole brain parenchyma mask to obtain a final brain parenchyma segmentation result. The idea of partitioning is applied to the region growing segmentation method, and rapid and stable segmentation of brain parenchyma is achieved.