44 results about "Brain cortex" patented technology

A Bifidobacterium animalis subsp. lactis for reducing Abeta42 deposition and an application, a name of the Bifidobacterium animalis subsp. lactis is Bifidobacterium animalis subsp. lactis IOB-LO7, and classified as Bifidobacterium animalis subsp. lactis, the Bifidobacterium animalis subsp. lactis IOB-LO7 is preserved in the General Microbiology Center of the China General Microbiological Culture Collection Center (CGMCC) on Dec. 23, 2021, with the collection number of CGMCC No. 24185. By reducing the levels of Aβ42 in the cerebral cortex and hippocampus, clearing Aβ amyloid plaques, improving communication between neurons, reducing brain neuroinflammation, and protecting nerve cells, it helps to restore cognitive function and improve Alzheimer's disease. Additionally, it can also improve gut microbiota imbalance caused by Alzheimer's disease.

The invention discloses a brain age prediction method, system and device and a medium, and relates to the technical field of biomedical image analys.The method comprises the steps that firstly, precise local detail features representing the cerebral cortex and brain tissue are precisely captured, then the dependency relationship between local areas of different images is obtained based on a windowed multi-head self-attention mechanism, and the brain age prediction result is obtained; a channel attention mechanism is introduced, importance weights of different feature channels are learned, and the importance weights are applied to feature fusion, so that long-distance dependency relationships and fine structure features are accurately captured; and then based on a cross attention mechanism, guiding global detail features to focus on a region with rich local features and guiding the local detail features to focus on a most relevant local region so as to mine deeper detail features, and finally dynamically adjusting the contribution proportion of the local detail features and global context detail features in a final decision. And the features are fused into final features so as to perceive local detail features and global detail features in a deeper level.

The invention discloses a brain tumor real-time identification system and method based on cortical electroencephalogram signals, and relates to the technical field of medical artificial intelligence, and the method comprises the following steps: obtaining multi-channel ECoG signals in real time; the multi-channel ECoG signals are updated in real time, the multi-channel ECoG signals updated in real time are preprocessed, and the preprocessed multi-channel ECoG signals are obtained; performing channel feature extraction on the ECoG signal of each channel to obtain various ECoG signal features under each channel as channel features; calculating segmentation thresholds of different channel characteristics under each channel based on an automatic threshold segmentation algorithm; carrying out confidence evaluation on the segmentation threshold values of different channel characteristics under each channel; and marking the channel of which the confidence coefficient is smaller than a set tumor marking threshold value as a tumor area. Accurate positioning of a tumor area can be achieved, functional state changes of the cerebral cortex can be reflected in real time, and doctors can conveniently master real-time changes of cortex functions in time.

A cerebral cortex model-based simplicial network for diagnosing mental disorder is disclosed. A cerebral cortex model-based method for diagnosing mental disorder performed by a mental disorder diagnosis system, according to one embodiment, may comprise the steps of: modeling a cerebral cortex mesh as a cerebral cortex simplicial complex; and performing message passing for diagnosing a mental disorder using relationship information of the modeled cerebral cortex simplicial complex through a message passing neural network.

The application relates to the technical field of brain image analysis, and discloses a fine-grained brain age prediction method, a system, a terminal and a storage medium.The method comprises the following steps: a plurality of brain tissue structures are segmented from a brain structure nuclear magnetic resonance image of a target object after preprocessing; a plurality of feature maps are generated according to the brain tissue structures and are mapped to a plurality of positive faces; a four-stage network model is used to process the mapped samples; and dynamic adaptive lateral attention is added after each stage to predict the brain age of each brain region.The application predicts the brain age by using the fine-grained brain cortex region level, and introduces a dynamic adaptive lateral attention mechanism to simulate the real brain structure lateral relationship, so that the topological structure and local features of the cortex can be effectively expressed, and the error caused by the brain morphology difference is reduced.

The invention relates to a cerebral cortex metabolism visualization method and system. The method comprises the steps of obtaining a first functional image and a structure image of the brain of a target patient; based on the target neural network model, performing denoising processing on the first functional image to obtain a second functional image; wherein the target neural network model is obtained by training an initial neural network model based on a sample set, the sample set comprises multiple groups of samples, and each group of samples comprises a first functional image sample and a structure image sample of the brain of a sample patient; generating a three-dimensional grid model of the cerebral cortex according to the structure image; and mapping the metabolic information in the second functional image into the three-dimensional grid model, and visualizing the metabolic information in the three-dimensional grid model. By adopting the method, accurate correspondence and visual fusion expression of the brain anatomical structure information and metabolism information of the target patient can be realized, and the observation efficiency of a doctor is improved.

The invention provides a cerebral cortex thickness measurement method and system based on a level set image segmentation algorithm, and belongs to the technical field of neural image analysis, and the method comprises the steps: obtaining a brain magnetic resonance imaging image, and carrying out the skull stripping processing; performing three-dimensional visualization on the stripped image through volume rendering, and acquiring three-dimensional coordinates of points of interest and a region of interest on the surface of the cortex by using a preset interaction means; using a level set method to carry out brain grey matter and brain white matter segmentation on the region of interest to obtain a brain grey matter segmentation layer and a brain white matter segmentation layer, and storing three-dimensional coordinate data of points in the segmentation layers; calculating the minimum Euclidean distance according to the three-dimensional coordinate data of the points in the segmentation layer and the three-dimensional coordinates of the points of interest to obtain the cortex thickness of the points of interest; the method solves the problems that the existing cerebral cortex thickness measurement lacks a visual means, is not supported by a mathematical model, cannot be accurate to the thickness of an interest point, and cannot display and retain comprehensive information.

The embodiment of the invention provides an autism auxiliary diagnosis method. The autism auxiliary diagnosis method comprises the following steps: constructing an individual brain function connection diagram; based on a community detection algorithm of a graph theory, performing functional region division on the individual brain function link graph to generate an individual brain map; taking the surface area percentage of the functional network of the healthy control group in the cerebral cortex as a response variable, taking the individual age as a smooth nonlinear independent variable, introducing an average head movement parameter as a linear covariable, and constructing a normal population development reference model; and comparing the surface area percentage of the functional network of the individual brain map with the normal population development reference model of the same age, and quantifying the deviation degree to realize auxiliary diagnosis of the autism. According to the autism auxiliary diagnosis method provided by the embodiment of the invention, individualized autism auxiliary diagnosis considering individual differences can be realized. The embodiment of the invention further provides an autism auxiliary diagnosis device and electronic equipment.

The invention discloses a training method and a recognition method of a brain morphological network analysis model, and the training method comprises the steps: carrying out the brain network construction of a normal cerebral cortex image set and an abnormal cerebral cortex image set, and obtaining a first brain morphological network group and a second brain morphological network group; performing inter-group network redundancy analysis on the first brain morphological network group and the second brain morphological network group to obtain a first node feature set; performing inter-group long and short distance information analysis on the first brain morphological network group and the second brain morphological network group to obtain a second node feature set; and according to the first node feature set and / or the second node feature set, performing parameter updating on the initialized brain morphological network analysis model to obtain a trained brain morphological network analysis model. According to the training method, a brain morphological network analysis model can be provided, and the accuracy and interpretability of brain morphological network analysis can be improved. The invention relates to the technical field of image processing.

Magnetic field generating means (2) is disposed on a scalp (1) of a subject (P) and generates a magnetic field that is radiated from a north pole, passes through a cerebral cortex (3) of the subject (P) and returns to a south pole in a loop path. Magnetic field detecting means (5) is disposed on the scalp (1) and detects a change in the magnetic field (4) as a signal reflecting an activity status of the cerebral cortex (3).

The application relates to a magnetoencephalography device and a method for registering the magnetoencephalography device and MRI, and belongs to the field of magnetoencephalography research. The method comprises the following steps: acquiring a head image of a subject wearing a magnetoencephalography device; point cloudizing the head image of the subject to obtain a scene point cloud; performing corresponding point clustering processing on the scene point cloud and a marker module model point cloud, identifying the marker module, and determining the position coordinates of the marker module; determining the sensor probe position according to the position coordinates and the relative position relationship between the marker module and the sensor array; acquiring a three-dimensional MRI image of the head of the subject, and separating the three-dimensional MRI image into a scalp three-dimensional image and a cerebral cortex anatomical structure; extracting an MRI facial image according to the scalp three-dimensional image; and registering the MRI facial image and the head image of the subject based on the sensor probe position, so that the sensor array and the cerebral cortex anatomical structure are in a unified coordinate system. The method improves the accuracy of registration.

The invention relates to a target spot optimization method, system and device and a storage medium. The method comprises the steps of obtaining a target device model; acquiring a head model of the detected object, and determining a target intervention target area on the head model; calculating cerebral cortex induced electric field intensity on the target intervention target spot area when the target equipment is in different poses; and taking the pose of the target equipment when the cerebral cortex induced electric field is optimal as the target pose of the target equipment. A head model of a detected object is acquired, a target intervention target area of the detected object on the head model is determined, so that target equipment can accurately determine a brain area, needing to be intervened, of the detected object, and cerebral cortex induced electric field intensities under different poses on the target intervention target area are calculated through simulation; and the treatment effect of target intervention is improved according to the obtained optimal placement pose of the target equipment.

The invention belongs to the technical field of coffee products, and particularly relates to a coffee composition for relieving depressive symptoms and a preparation method thereof. 25-30 parts of coffee beans, 6-10 parts of fructus rosae laevigatae, 6-10 parts of semen boitae, 8-12 parts of cortex albiziae and 1-3 parts of seaweed are limited, and different raw materials are extracted, decocted or subjected to enzymolysis, so that separation and retention of active ingredients of the raw materials are facilitated, and the active ingredients in fructus rosae laevigatae, semen boitae, cortex albiziae and seaweed have a protection effect on brain cells; the traditional Chinese medicine composition is beneficial to recovering the balance of a nervous system, relieving the inflammatory response in the brain, reducing the generation of oxygen free radicals, reducing the activity of xanthine oxidase in the cerebral cortex and improving the symptom of depression; and chlorogenic acid, nicotinic acid and the like in coffee can generate positive influence on depression symptoms. Therefore, the coffee composition provided by the invention can be used for relieving depression symptoms and provides a basis for adjuvant therapy of depression.

The application provides a brain cortex thickness morphological analysis method and system of MR images, and relates to the technical field of medical image data processing.The method comprises the following steps: obtaining a head three-dimensional high-resolution magnetic resonance T1 weighted image sequence of a patient with intractable epilepsy to be evaluated; performing bias field correction and brain tissue stripping on the image sequence to construct an initial brain parenchyma three-dimensional voxel model containing individual lateral ventricle expansion morphological characteristics; based on the initial brain parenchyma three-dimensional voxel model, extracting an initial isosurface of the interface between gray matter and white matter; calculating a local curvature tensor for a deep brain sulcus region in the initial isosurface; performing topological defect repair and mesh smoothing on the initial isosurface according to the local curvature tensor to obtain an individualized cortex surface mesh model eliminating ventricle expansion deformation artifacts.The application realizes accurate registration of image space and surgical physical space.

The invention discloses a method for constructing a mouse model with a Cep295 gene knocked out in a cerebral cortex area, a targeting vector and a kit, the structure of the targeting vector sequentially comprises a 5'homologous arm from a 5'end to a 3 'end, the 5' homologous arm is derived from a genome sequence at the upstream of a No.11 exon of the mouse Cep295 gene, and the 5'homologous arm is derived from a genome sequence at the upstream of a No.11 exon of the mouse Cep295 gene; a flox region, wherein the flox region comprises exons from the 11th exon to the 15th exon of the mouse Cep295 gene; an Frt-pGK-Neo-polyA-Frt element, wherein the pGK-Neo-polyA is a selection marker gene expression cassette, and the Frt-pGK-Neo-polyA is an Frt-pGK-Neo-polyA-Frt element. The 3'homologous arm is derived from a genome sequence at the downstream of a No.15 exon of the mouse Cep295 gene; and an MC1-TK-polyA element, wherein the MC1-TK-polyA element is a negative selection marker gene expression cassette.

The invention belongs to the technical field of microorganisms, and particularly relates to ackermania muciniphila AKK016 and application thereof in preparation of a product for improving and / or preventing and treating cognitive impairment, and the preservation number of the ackermania muciniphila AKK016 is CGMCC No.41190. The Ackermania muciniphila AKK016 provided by the invention can promote improvement of cognitive impairment, and the result of the embodiment shows that the result of a dark avoidance experiment in an animal experiment is positive. The ackermania muciniphila AKK016 disclosed by the invention can also improve cognition by regulating neurotransmitters related to cognition in the cerebral cortex and the hippocampus, and can also regulate the content of inflammatory factors and oxidative stress markers in the cerebral cortex. Therefore, the Ackermania muciniphila AKK016 has a better application prospect in preparation of a product for improving and / or preventing and treating cognitive impairment.

The present invention addresses the problem of providing a therapeutic agent for frontotemporal lobe degenerative disease (FTLD), a composition for treating FTLD, and a method for treating FTLD, which have not been developed so far. The inventor of the present invention finds that: a prefrontal lobe cerebral cortex neuron is differentiated and induced by an iPS cell derived from an FTLD patient; a therapeutic agent for frontotemporal lobe degenerative disease (FTLD), comprising a compound represented by formula (1-1), a compound represented by formula (2-1), or a compound represented by formula (3-1), a pharmaceutically acceptable salt thereof, or a solvate thereof, is administered to a prefrontal cerebral cortex neuron exhibiting a pathological state of FTLD. Therefore, the pathological state of FTLD of the forehead lobe type cerebral cortical neurons is improved.

The application relates to an electroencephalogram signal decoding method, a terminal device and a storage medium, the method comprising the following steps: converting electroencephalogram signal data into a three-dimensional signal matrix; constructing an electroencephalogram signal classification model; after the three-dimensional signal matrix is input into a convolution module, the output is input into a space and channel attention module respectively; the outputs of the two attention modules are combined, subjected to sigmoid operation, and multiplied by the output of the convolution module; after the multiplication result is taken off the output of the convolution module, the result is sequentially input through other convolution modules, and the output result of the last convolution module is input into a classification module to obtain a classification result. The low-dimensional electroencephalogram signal data is mapped to a high-dimensional brain cortex space, the local correlation between channels is effectively retained, a local convolution strategy and an attention mechanism are adopted to strengthen features, the accuracy and universality of electroencephalogram motor imagination recognition are improved, and real-time efficiency of brain-computer interaction is realized.

This invention discloses a method, apparatus, electronic device, and storage medium for modeling forward EEG problems. The method includes: segmenting brain tissue structures based on brain structural images; dividing the head domain into a global mesh based on the segmented brain tissue structures; creating a skin-like electrode template based on the electrode line distribution of skin-like electrodes; locally refining the mesh based on the center position, shape, and electrode line cross-sectional diameter of the skin-like electrodes to obtain a head model; obtaining a skin-like electrode model on the head model with electrode locations based on the skin-like electrode template; obtaining a corresponding source model based on mesh division of the cerebral cortex surface; and constructing a personalized EEG forward EEG model using the finite element method based on the head model, source model, and skin-like electrode model. This invention addresses the problem of complex electric field distribution in the scalp region covered by the electrodes due to the complex fractal serpentine mesh structure of skin-like electrodes, thus improving the modeling accuracy of forward EEG problems.

A method for achieving super-resolution in localizing electrical fields measured at the head surface with electroencephalography through a generative model of the cerebral cortex that has a very high resolution of cortical surface dipoles constructed from the known properties of human cerebral cortex and adapted to optimize the Bayesian explanation the individual's cortical surface electrical fields. The iterative optimization of the prior (generative) with the posterior (observed) fields with extensive data from extended recordings provides a probabilistic estimation of the individual's functional brain activity that can be used to train artificial neural network approximations of the individual's mental activity.

The application discloses a kind of based on multi-modal brain image fusion mood disorder identification method, belong to medical image processing and artificial intelligence auxiliary diagnosis technical field;Including the following steps: obtaining multi-modal brain image, registration to spherical space and alignment;After extracting and fusing features, spherical space-time self-attention coding and gate modulation are carried out;Modulated features are recursively down-sampled from initial level to penultimate level, and the aforementioned operation is repeated at each layer;In this level, features are processed in parallel, fused and compressed to the lowest level, while extracting clustering embedding features;Global features are aggregated at the lowest level, and after fusing with clustering embedding features, the recognition result is output by the clustering perception classifier;The application realizes multi-modal fusion and dynamic feature extraction under the maintenance of cerebral cortex topological structure, improves recognition accuracy and generalization ability.

The application discloses a brain cortex surface morphology high-order feature extraction method, which comprises the following steps: firstly, acquiring brain magnetic resonance T1 structural image data; for individual brain magnetic resonance T1 weighted structural image data, using an open source magnetic resonance data processing toolkit to reconstruct a brain cortex surface; secondly, acquiring brain cortex surface vertex original morphology features, constructing brain cortex surface morphology vertex node strength, and obtaining high-order features; then, constructing a brain region level complex network based on the cortex vertex morphology high-order features, and obtaining complex network features; finally, based on the high-order features and the complex network features, using feature selection combined with machine learning to realize a cognitive ability evaluation model, and completing effectiveness evaluation. The application helps to expand traditional brain cortex surface morphology research to the field of brain connection and brain network research, improves the validity of brain network research, reveals the brain cortex morphology connection mechanism of brain cognition and brain development, and promotes the progress of brain science research.

The application discloses application of a rabies virus mutant in traditional two-photon microscope deep brain cortex neuron imaging, and the rabies virus mutant is CVS-N2c-ΔG-GCaM6s. The application also discloses a calcium imaging virus containing the rabies virus mutant. The rabies virus mutant in the application is used as a retrograde labeling virus, has the advantages of high brightness of fluorescent expression of the labeled neuron, and has fast expression speed, thereby greatly saving time cost. In addition, the rabies virus mutant has the ability of labeling the complete structure of a cell, can completely record the functional signal from a dendritic spine to a cell body, provides a suitable method for studying input-output integration of single neuron information, especially deep neuron. The rabies virus mutant can realize high-quality functional imaging of deep neuron under the conventional two-photon microscope, and has the technical characteristics of high safety and low use cost.

The present invention relates to uses of regulatory T cells manipulated to overexpress CX3CR1. The regulatory T cells induced to overexpress CX3CR1 according to the present invention reduce microglial activation in an LPS-induced neuroinflammation animal model and suppress immune inflammatory responses of activated microglia by inhibiting the expression of pro-inflammatory markers and cytokines in the cortical and hippocampal regions of the brain, and thus can be used for reducing microglial activation or for treating neuroinflammatory diseases or neurodegenerative diseases caused by hyperactivation of microglia in the brain.

The invention relates to the technical field of brain-computer interfaces, and discloses a sensing system and method based on an optical genetic technology, and the method comprises the steps: transmitting a control signal to a main control module through an upper computer, converting the received control signal into a driving signal through the main control module, enabling an LED driver to recognize the driving signal, transmitting the driving signal to the LED driver through the main control module, and enabling the LED driver to drive the brain-computer interface. The LED driver drives the LED array to emit LED light to irradiate photosensitive protein in the cerebral cortex of the organism based on the driving signal, so that light stimulation is generated and visual neurons are accurately stimulated, and damage to tissues around the visual neurons is avoided.

The invention relates to a processor for cognitive ability evaluation and an analysis method of the processor. The processor is connected with a near-infrared brain function imaging monitoring assembly and a testing assembly. The processor is configured to receive a near-infrared brain function spectral image of a cerebral cortex, related to first time, of a test object in the test process, wherein the near-infrared brain function spectral image is collected by the near-infrared brain function imaging monitoring assembly; receiving action feedback information and feedback time of the test object sent by the test component; a cerebral cortex activity area and light absorption parameters are determined based on a near-infrared brain function spectrum image fed back by stimulation information, and real response time is determined based on the near-infrared brain function spectrum image in stimulation time. And determining the cognitive level of the first cognitive ability based on the proportion of the real response time to the complete feedback time of the test object to the stimulation information. According to the processor provided by the invention, the accurate cognitive competence condition of the test object can be obtained while the test frequency is reduced.

The invention relates to the technical field of biological signal processing, in particular to a cerebral cortex activation law analysis method and related products, and the method comprises the steps: firstly, respectively obtaining EEG signals corresponding to unilateral limb motor imagery and bilateral limb motor imagery, and preprocessing the EEG signals; and then, grouping the preprocessed EEG signals according to motion types, and carrying out cerebral cortex activation rule analysis in parallel. The analysis process comprises event correlation spectrum disturbance and difference analysis, power spectrum density analysis and brain function network analysis so as to reveal the activation rule of the cerebral cortex under different motion types. The method provides a scientific basis for motor imagery related brain mechanism research and neural rehabilitation application through multi-dimensional analysis, has the technical characteristics of high efficiency and accuracy, and is suitable for the fields of brain-computer interface, neural feedback training and the like.

The invention discloses a cross-species mapping method based on cerebral cortex geometric features. The method comprises the following steps: firstly, acquiring cross-species brain image data and preprocessing the data; extracting three-dimensional coordinates of each vertex of the cerebral cortex surface to obtain surface geometric information; splicing the coordinate matrixes of different species, calculating a cosine distance between each pair of vertexes and obtaining a distance matrix; performing data dimension reduction on the distance matrix to obtain a main gradient reflecting a brain physical structure; mapping of cross-species brain images is completed by taking geometric gradients of different species as features; the method is verified based on the consistency of cerebral cortex myelin distribution map mapping among different species. According to the cross-species brain image mapping method, cross-species brain image mapping is achieved by extracting geometric features from the aspect of species evolution, and a new thought and a new method are provided for cross-species brain image comparison.

This invention relates to the field of data analysis technology, specifically to a method and system for analyzing neural functional characteristics based on multimodal data. The method includes the following steps: acquiring multimodal neural data, analyzing rhythmic and reactive and motor characteristics, calculating coupling and blockage parameters, and normalizing them into feature vectors to determine abnormalities. In this invention, a unified time axis coordinate system is constructed to precisely align cortical neural potentials with limb movement trajectories. Hilbert transform is used to analyze signal phase, and neural pathway blockage mapping parameters are calculated using response dispersion to quantify conduction stability. For the phenomenon of phase inversion in precursor potential segments during motor fluctuations, precursor neural coupling parameters are established to characterize the motor readiness state. Based on the comparison of multidimensional feature vectors with a standard baseline, automated and accurate classification of neural functional abnormalities is achieved, effectively solving the pain points of being unable to quantitatively analyze neural behavioral coupling mechanisms and the difficulty in distinguishing between conduction blockage and readiness defects.

The application discloses a kind of brain-computer interface auditory space attention detection system driven by auditory system, including: signal acquisition module, signal processing module and integration module;The signal acquisition module is used to collect electroencephalogram and the electroencephalogram is preprocessed, obtains electroencephalogram discrimination window;The signal processing module is used to decode the electroencephalogram discrimination window, obtains time characteristic and spatial characteristic;The integration module is used to integrate the time characteristic and the spatial characteristic, obtains attention detection result.The application can simulate the information coding mechanism of human auditory pathway to sound signal and the information processing mechanism of left and right brain cortex to coded information, solve the weak explainability and poor real-time of current auditory attention detection model, so as to realize low delay, short decision window under high recognition accuracy, provide effective method for developing cognitive hearing aid based on brain-computer interface.