125 results about "Entire brain" patented technology

Systems and methods for modeling functional magnetic resonance image datasets using a multivariate auto-regressive model which captures temporal dynamics in the data, and creates a reduced representation of the dataset representative of functional connectivity of voxels with respect to brain activity. Raw spatio-temporal data is processed using a multivariate auto-regressive model, wherein coefficients in the model with high weights are retained as indices that best describe the full spatio-temporal data. When there are a relatively small number of temporal samples of the data, sparse regression techniques are used to build the model. The model coefficients are used to perform data processing functions such as indexing, prediction, and classification.

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.

Irradiated points and light receiving points are arranged such that sampling points are arranged over an entire spherical head with no vacant space. The sampling points are arranged to cover the entire spherical head by combining a plurality of rhombic shells and inverting the respective positions of irradiated points and light receiving points in the adjacent shells. As a result, a whole brain probe capable of covering the entire head is provided to allow an image of the whole brain to be viewed as a single image.

The invention relates to a method for calculating brain blood volume. The method is used for calculating the brain blood volume on the stable status CT (Computed Tomography) brain perfusion imaging principle. The method comprises the following steps of (1) carrying out common whole brain CT scanning to obtain a brain tissue structure image before contrast agent injection; injecting the contrast agent to the vessel exciting area to reach the stable status, and obtaining the brain tissue structure image of the same position; (3) deleting an overhigh brain blood volume value with a vessel pixel deleting method, and deleting the interference of the artery in the existing area to the brain blood content measuring value; (4) and calculating the brain blood flow value of the brain tissue by calculating the ratio of the brain tissue to the signal change in the vessel according to a formula. With the method, all brain scanning can be carried out and the radial dosage of the perfusion scanning can be reduced, thus the method for calculating brain blood volume can be widely applied into the clinical trial.

A quantitative analysis method for the power spectrum to process the functional magnetic resonance data of brain includes such steps as acquiring functional magnetic resonance data, preprocessing, Fourier transform of time sequence, and quantitative analyzing to power of each frequency range one brain cell by one brain cell.

This application discloses configurations for arranging transducer arrays on a person's head to impose tumor treating fields (TTFields) in the brain at field strengths that are as uniform as possible throughout the entire brain. In some embodiments, L-shaped sets of electrodes are positioned near the right and left ears, each with a horizontal arm above the ear and a vertical arm behind the ear. Optionally, these embodiments may be combined with a second pair of electrodes positioned on top of the head and behind the neck. In other embodiments, one pair of electrodes is positioned above the right ear and on the left / rear portion of the neck; and a second pair of electrodes is positioned above the left ear and on the right / rear portion of the neck. These configurations improve the uniformity of the electric fields imposed throughout the brain, and are particularly useful for preventing and / or treating metastases.

The invention discloses an automatic analysis method and system based on resting-state EEG frequency domain characteristics and a brain network, and the method comprises the steps: calling a Matlab data processing script, carrying out the preprocessing of the resting-state EEG of a patient in an eye-closed state for 3 minutes, calculating the frequency domain characteristics of the EEG, and further calculating and extracting the average brain network connection strength of different brain regions and the whole brain through the traceability positioning analysis. According to the method, the first 10% of strongest connections are visualized, and the two-dimensional electroencephalogram is quickly converted into high-readability digital and image information, so that automatic batch processing of electroencephalogram frequency domain characteristics and brain network analysis in scientific research work is facilitated, the working efficiency is improved, and the labor cost is saved. And on the other hand, the abnormal frequency domain and the brain region of the patient are accurately identified by comparing and identifying with a standardized reference value of a health contrast norm, and a powerful technical means is provided for realizing individualized and precise non-invasive nerve regulation and control.

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.

The invention relates to the field of brain development, in particular to a whole brain reading system for realizing three kinds of quick reading such as left brain linear scanning quick reading, right brain graph image-taking quick reading and subconscious data quick recording and corresponding brain training methods. The whole brain reading system comprises an online reading module and a vision training module, wherein the vision training module consists of a right brain training module, an enlarged vision amplitude training module, a view point movement training module, a smooth view point training module, a one-point staring module and an eye-brain direct mapping module. The whole brain reading system has the advantages of increasing the function of manually detecting the reading speed, increasing the function of right brain development and training and increasing the function of right brain block reading, does not need online registration, is convenient to use and carry, and can be used by multiple persons for learning.

The invention discloses a method for extracting a brain function connecting mode of migraine. The method comprises the following steps that firstly, brain resting-state functional magnetic resonance data of migraineurs and able-bodied persons are collected; then four steps of pretreatment of time horizon correction, head moving correction, standardization and smoothening are carried out on the collected data; then according to the pretreated data, each tested whole brain steady-state functional connection column vector set is acquired by using an automatic calculation mode; then clustering analysis is carried out on all tested whole brain steady-state functional connection column vector set by adopting an improved K mean value clustering algorithm; lastly, the brain function connecting mode of the migraine is extracted according to a threshold, thereby providing a basis for the subsequent further analysis. The method for extracting the brain function connecting mode of the migraine ishelpful for acquiring the brain function connecting mode of the migraine according to the dynamics.

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 relates to a whole-brain individualized brain function map construction method taking an independent component network as a reference. The method comprises the following steps: utilizingbrain resting state fMRI data of an individual subject; introducing an independent component analysis method to construct a group-level brain function sub-network; then, reversely reconstructing eachtested brain function sub-network and a characteristic time sequence corresponding to the function sub-network by utilizing space-time regression; taking a characteristic time sequence correspondingto the functional sub-network as a reference signal; and introducing an inverse distance weighting coefficient, a sub-network inverse variation coefficient weighting, a correlation factor and an iterative process to obtain a whole-brain individualized function map with an independent component network as a reference. The method has the advantages of pure data driving, complete correspondence of brain regions, whole-brain coverage, more flexible functional brain region subdivision and the like, and a more accurate objective imaging tool is provided for researching a normal human brain operationmechanism and brain function impairment related to diseases.

The invention discloses a brain network classification method combining node attributes and multi-level topology, and the method comprises the following steps: S1, obtaining functional magnetic resonance brain image data, and carrying out the preprocessing; S2, based on the preprocessed data, generating a whole-brain network function connection matrix by using an automatic anatomical marking template, and constructing an unbiased brain network by using a Kruskal algorithm and taking DMN as a region of interest; S3, extracting brain region node betweenness from the unbiased brain network to serve as local attribute features, and extracting brain region features with inter-group significant differences by using a double-sample t test method; S4, extracting multi-level topological features on the unpartial brain network by using sub-network kernels, generating a sub-network kernel matrix, and extracting optimal topological features by using a kernel principal component analysis method. According to the method, the classification performance is remarkably improved, an abnormal brain area can be found, multi-level topological characteristics of brain area nodes can be captured, and the method has important significance in clinical auxiliary diagnosis of schizophrenia.

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 discloses a laterality detection method for a brain structure network based on module connection, and the method comprises the following steps: carrying out the preprocessing of a diffusion tensor image, and carrying out the region segmentation of the preprocessed diffusion tensor image according to a selected standardized brain atlas; adopting a deterministic fiber bundle tracking algorithm; reconstructing fibers of the whole brain on the basis of tracking end conditions, and calculating the number of fiber bundles and partial anisotropy indexes of every two brain regions and the superficial area of each brain region to obtain a fiber bundle number matrix FN, a partial anisotropy index matrix FA and a brain region superficial area matrix Surface of the brain regions. Compared with a traditional laterality detection method, the laterality detection method of the brain structure network based on module connection has the advantages that the modularity and the laterality ofthe network are fused, the laterality of local network indexes of the brain can be mined more effectively and accurately, and certain help is provided for exploring a brain working mechanism.

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 rest-state brain network construction method, device and equipment, and a computer storage medium. The method comprises the steps of obtaining rest-state functional magneticresonance data corresponding to a whole brain image; determining brain network nodes in the whole brain image according to a standard brain template, and respectively determining an average time sequence of voxels corresponding to each brain network node according to the rest-state functional magnetic resonance data; calculating a distance correlation coefficient between any two brain network nodes according to the average time sequence; and determining a brain network connection edge in the whole-brain image according to the distance correlation coefficient so as to construct a brain networkaccording to the brain network node and the brain network connection edge. Therefore, the brain network is constructed by utilizing the distance correlation coefficient, the construction steps of thebrain network are simplified, and the accuracy of the brain network is improved.

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.

Novel DNAs containing the regions which encode proteins have been directly cloned from cDNA libraries derived from the human adult whole brain, the human adult hippocampus and the human embryonic whole brain, the nucleotide sequences thereof have been determined, and their functions have been identified. The present invention provides DNA which comprises the nucleotide sequence encoding the following polypeptide (a) or (b): (a) a polypeptide comprising an amino acid sequence which is identical or substantially identical to an amino acid sequence represented by any one of SEQ ID NOS: 1 to 70; (b) a polypeptide comprising an amino acid sequence derived from the amino acid sequence represented by any one of SEQ ID NOS: 1 to 70 by deletion, substitution or addition of a section of amino acids, and having biological activity which is substantially the same characteristic with the function of the polypeptide of (a); a recombinant polypeptide, which is encoded by the above DNA; and a protein containing the polypeptide.

The embodiment of the invention discloses a cortex target spot determination method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a first preset group number of initial resting state functional magnetic resonance data of a whole brain area of a target object; determining a target brain region, and determining brain function connection distribution of the target brain region for each group of initial resting state functional magnetic resonance data; combining the first preset group number of brain function connection distributions according to the second preset group number to obtain all brain function connection distribution combinations; for each brain function connection distribution combination, respectively calculating an average value of brain function connection values corresponding to each voxel in the target brain region to obtain average brain function connection distribution of the brain function connection distribution combinations; and determining a target voxel according to the average brain function connection distribution of the brain function connection distribution combination, and determining the space coordinate of the target voxel as the target coordinate of the cortex target of the target object. According to the technical scheme of the embodiment of the invention, the accuracy of target spot positioning is improved.

The invention discloses a genetic image multi-modal fusion analysis method based on white matter integrity and DNA methylation. The method comprises the following steps: analyzing the whole-brain white matter fiber integrity FA value of a subject, performing inter-group comparison on a patient suffering from a certain neuropsychiatric disease and a normal person, typing a difference region name in an Allen brain library to obtain names of genes highly expressed in the brain regions, extracting methylation levels of CpG sites corresponding to the genes from a genome methylation sample, and counting the correlation between the methylation level of the CpG sites and the FA value by adopting correlation analysis. According to the method, multi-modal fusion analysis is carried out on the brain structure development mode of the neuropsychiatric diseases and the epigenetic basis behind the neuropsychiatric diseases, the analysis method ingeniously combines FA and DNA methylation two-modal data for analysis, and great application and guidance values are brought to deep mining of existing image and epigenetic multi-modal data information.

The invention discloses a brain disease classification method based on 3D attention convolution and self-supervised learning, and belongs to the field of brain science research. The method specifically comprises the following steps: acquiring resting-state fMRI data and preprocessing; constructing functional connection data based on the fMRI whole brain voxels; dividing a data set; and performing brain disease classification based on attention convolution and self-supervised learning. According to the method, spatial features of whole-brain voxels are extracted from fMRI data by using 3DCNN of an attention mechanism, meanwhile, more meaningful characterization is mined by using self-supervised learning, and finally, combined training is performed on a classification task and a self-supervised auxiliary task to optimize parameters. The method provided by the invention can better explore the spatial information of the brain and mine the implicit features of the data, so that the classification effect is improved, and the method is reasonable and reliable.

The invention discloses an automatic segmentation method for highly-cohesive and multi-size brain neurons based on point marking, and the method comprises the following steps: establishing a database, randomly dividing the database into a training set and a test set, and marking the centroid positions of neurons; carrying out preprocessing to obtain a normalized training set image and a test set image, and taking the neuron centroid probability graph as a training set truth value graph and a test set truth value graph; constructing a parallel multi-receptive-field convolutional neural network, predicting the probability of the centroid of the neurons, detecting the centroid of the neurons, and finally segmenting the neurons. The problem that high-density and multi-size neurons cannot be segmented in the whole brain in the prior art is solved.

The invention discloses an optical computer interface system for whole-brain multi-mode neural activity detection, and relates to the technical field of multi-mode neural activity optical computer interfaces. The system comprises a wide-field optical imaging system, an electrophysiological record regulation system and an animal facial expression shooting system. The neural activity information, the electrophysiological activity information and facial expression change information can be synchronously acquired by the wide-field optical imaging system, the electrophysiological record regulation system and the animal facial expression shooting system, and constitute multi-modal neural activity information. The acquired multi-modal neural activity information can help technicians in the field to master more global information of the brain, can be used for researching a functional mechanism of interaction between the cortical layer and the subcortical nuclei related to the whole brain, and provides an idea for exploring a new biological mechanism.

The invention discloses an anesthesia depth monitoring system based on electroencephalogram micro-state analysis and a method thereof. The system comprises an electroencephalogram signal acquisition module A, a micro-state time sequence construction module B, a micro-state parameter calculation module C and a classification and identification module D. The method comprises the following steps: firstly, acquiring a tested whole electroencephalogram signal by using high-density electroencephalogram, and then constructing a corresponding micro-state time sequence according to a micro-state algorithm; calculating micro state parameters such as time proportion and energy of the time sequence; simplifying the micro-state parameters through principal component analysis to obtain characteristic values, and performing recognition and classification by using a support vector machine. On the basis of taking the brain as an organic whole, the time information and the space information of the electroencephalogram are combined to reflect the characteristics of the electroencephalogram topology in the time domain, and meanwhile, the anesthesia depth of a patient can be effectively and accuratelymonitored.

Disclosed is a computer-implemented method for determining a measure of the causal interdependence of a source dataset S and a target dataset Y in the simultaneous presence of a non-empty confounding dataset B. The method includes a dimensional modification step to reduce the complexity of the data and an augmentation step to add information to the dimensionally modified data without a significant increase in size or complexity of the data. The augmented dimensionally modified data is used to calculate a measure of the causality or relatedness between the source dataset S and the target dataset Y. The method enables linear or nonlinear causality analysis of high-dimension multivariate time-series or features datasets, such as time-series datasets generated by functional MRI full-brain scans.

The invention discloses an intelligent accompanying machine for whole-brain intelligence development of infants. The intelligent accompanying machine comprises a rear housing, a front housing, a head rear housing, a head cover, a bottom cover, an anti-slip protective pad, a mainboard, a color sensor, an identification control panel, a loudspeaker and a control key. The accompanying system for whole-brain intelligence development of infants comprises a mainboard and a display module, a color identification module, a voice module and a control module connected with the mainboard, wherein a default accompanying mode, a click bell-ringing mode, a click teaching mode and a click preschool education mode are set in the mainboard. The intelligent accompanying machine integrates multiple elements such as voice, colors and videos, achieves diversified formative education, adopts man-machine interactive structural design and improves the education effect through interaction between children and the machine.