69 results about "Brain cortex" patented technology

The invention discloses a closed-loop deep brain regulation and control system for epilepsy treatment, and the system comprises an electroencephalogram signal collection module which collects neural electrophysiological signals in real time by implanting a recording electrode in a cerebral cortex or hippocampal brain area; the electroencephalogram signal real-time analysis module is used for outputting the epileptic seizure period prediction probability xi through a seven-layer full-connection neural network based on the 33-dimensional feature vector so as to realize epileptic seizure early warning; when the zeta is greater than or equal to a threshold value, the electrical stimulation regulation and control module is used for triggering electrical stimulation, filtering out a frequency band required to be regulated and controlled according to filtering, calculating and predicting electrical stimulation intensity and then correcting and outputting pulse stimulation, so that personalized electrical stimulation regulation and control are carried out on a specific frequency band, and detection is continuously carried out until the frequency band becomes normal; and the medicine intervention module is used for triggering a micro-injection pump to carry out medicine infusion through a pulse signal. According to the system, epilepsy treatment is improved from empirical parameter adjustment to accurate regulation and control based on real-time nerve physiological signals, and a safer and more effective treatment choice is provided for a drug-refractory epilepsy patient.

The invention relates to the technical field of medical monitoring, and provides a full cortical nerve-immune monitoring and closed-loop regulation and control system based on nerve electrical stimulation, and the system comprises a regulation and control electrode which is used for detecting the cerebral cortex of a target brain region and the neuroelectric signals of a deep brain region, and is used for the electrical stimulation regulation and control of neurons of the deep brain region; the brain temperature detection assembly is used for detecting temperature information of a target brain region in real time; the cortex imaging device is used for collecting nerve and immune activity images of the whole cortex; the external control device is electrically connected with the regulation and control electrode, the brain temperature detection assembly and the cortex imaging device, and the external control device is used for receiving temperature information detected by the brain temperature detection assembly and image information detected by the cortex imaging device and controlling the regulation and control electrode to work according to the temperature information. According to the invention, high-precision synchronous monitoring and closed-loop regulation and control of deep brain area nerve electrical stimulation and whole cortical nerve-immune activity are realized, an innovative technical means is provided for neuroscience, immunology and clinical medicine research, and the method has important scientific research and clinical application values.

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 present invention belongs to the technical field of medical image segmentation methods and relates to an automatic segmentation method for the cerebral cortex surface based on a geometric neural network. The method comprises acquiring a brain sMRI image, reconstructing the cerebral cortex surface, and extracting multidimensional morphological features of the cerebral cortex surface; decomposing the multidimensional morphological features of the cerebral cortex surface using a graph Laplacian to obtain a spectral representation of the cerebral cortex surface manifold; extracting contextual information of the cerebral cortex surface of a target image using a segmentation module with a U-shaped hierarchical structure, extracting anatomical prior information of the cerebral cortex surface of an atlas image using an auxiliary module with the same structure, and fusing information from the segmentation module and the auxiliary module using a non-local attention feature fusion module to output a predicted probability map of the segmentation label of the cerebral cortex surface of the target image. The present invention has better real-time performance and makes full use of the intrinsic structural information of the cortical surface and the global information of brain regions.

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.

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 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 invention relates to a method for constructing a growth model of a brain in a life cycle, and the method comprises the steps: obtaining brain MRI images of individuals of different age groups, and dividing the cerebral cortex into n regions; constructing a morphological feature network for sMRI of the brain MRI image of the individual, and performing morphological feature similarity degree analysis to obtain a first phenotype; constructing a functional network for the fMRI of the brain MRI image of the individual; constructing a structure-function coupling matrix for the morphological feature network and the function network, and calculating to obtain a second phenotype; selecting the first phenotype and the second phenotype of each individual as dependent variables and the age of each individual as a smooth item to carry out GAMLSS modeling, and selecting JSU distribution to respectively fit the change of the first phenotype and the second phenotype along with the age to generate a reference growth curve. The method can be used for studying the growth mode of the brain network in the whole life cycle, studying life cycle association between morphological characteristics and functional characteristics of different areas and the like.

The invention provides a brain cortex stimulation device, and relates to the technical field of brain electrical stimulation devices.The stimulation device comprises a generation module and a stimulation module, the generation module generates stimulation signals, the stimulation module makes contact with the brain cortex and transmits the stimulation signals to the brain cortex, and electrical stimulation on the brain cortex is achieved. The stimulation module comprises at least one column arranged in the first direction, each column of the stimulation module comprises a wire and a plurality of stimulation contacts electrically connected to the wire, the wire extends in the second direction, and the stimulation contacts located on the same wire are arranged in the second direction. The wire is electrically connected with the generation module, the target area of the stimulation contact is in contact with the cortex of the brain, and the stimulation signal is transmitted to the cortex of the brain through the wire and the target area of the stimulation contact in sequence. Wherein in the plurality of stimulation contacts located on the same wire, a gap is formed between every two adjacent stimulation contacts.

The application discloses a kind of single-photon counting circuit and detection device based on time-domain near infrared spectrum, circuit includes: pulse light module, for generating near infrared light to cerebral cortex tissue is irradiated;Light signal receiving amplification module, for receiving after the light signal of diffusion absorption of the cerebral cortex tissue, after amplification conversion into electrical signal;Single-photon counting module, including counting window generation unit and D flip-flop one, for in the counting window of generation, through D flip-flop one output electrical signal's counting waveform;Control module, including counter, for receiving electrical signal's counting waveform, and according to counting waveform carries out counting, generates histogram.The single-photon counting circuit of time-domain near infrared brain function imaging disclosed in the application is low in cost, simple in structure, high in integration, and can effectively solve the shortcomings of existing timing instrument, such as complexity and high cost, and the limitation brought by conversion time.

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 present application discloses a pathological network connection method, device, equipment and storage medium for processing cortico-cortical evoked potentials. The method includes obtaining the original cortico-cortical evoked potential data, superimposing and averaging the electroencephalogram signals obtained from multiple repeated stimulations of the same evoked stimulus, performing false discovery rate verification on the thresholds obtained from all evoked stimuli, obtaining the connection relationship between the evoked electrode and the current electrode according to the verification, constructing a stimulus-response matrix diagram and constructing a connection relationship diagram between all brain electrode sites; the above solution provided by the present application obtains the position of the electrode site in the brain region through the CT and magnetic resonance imaging registration method, aggregates the stimulus responses of the electrode sites between the same brain regions together, obtains the connection relationship between the cerebral cortex brain regions, and thus can abstractly analyze the higher-level brain connection relationship, which helps to assist the surgical treatment that requires evoked stimulation and improves work efficiency.

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 application provides a cortical brain network construction method based on a causal convolutional graph neural network, and belongs to the field of cortical brain network construction. The method directly mines a brain cortex source interaction mode from collected head surface EEG signals, directly mines a cortical network from the head surface electroencephalogram signals in an end-to-end manner, and directly estimates a cortical directed network mode corresponding to the head surface recorded electroencephalogram signals based on a deep learning method to mine an end-to-end implicit learning relationship between the electroencephalogram signals and the cortical source space brain network, thereby avoiding problems such as ill-conditioning and instability of signal tracing in a traditional explicit solving framework, and problems such as model and hypothesis constraints in traditional causal network construction. The method can robustly estimate a physiological physiological significance cortical causal network mode from the head surface electroencephalogram signals, and has important significance for fine brain causal interaction exploration research in the time-space two-dimensional dimension.

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 application discloses a brain cortex super-vertex division method based on T1 magnetic resonance imaging, which comprises the following steps: S1, establishing a data set and performing pretreatment; S2, calculating vertex connection in a brain region to obtain a vertex connection matrix; S3, obtaining a group original brain region super-vertex space; S4, further obtaining a group whole brain super-vertex space according to step S3; S5, completing individualized super-vertex division according to the divided group super-vertex label; S6, constructing different group characteristic super-vertex connection networks and individualized characteristic super-vertex connection networks according to the vertex characteristics of the brain atlas space and the division results of the group level super-vertex and the individualized super-vertex; and S7, verifying the effectiveness of the super-vertex division according to a machine learning algorithm. Compared with the existing brain atlas, the method greatly improves the resolution of brain division, does not need to determine the number of sub-regions, and can automatically perform brain region division to construct super-vertices.

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 epilepsy operation prognosis prediction model obtaining method, the epilepsy operation prognosis prediction model using method and the related equipment are applied to the artificial intelligence technology. According to the method, individual brain network features are analyzed, key brain regions related to epilepsy operation prognosis are identified by using a grid statistics prediction tool, the change of cerebral cortex and the relationship between different brain regions are successfully constructed, the structural change of the brain of an epilepsy patient can be effectively reflected, and an epilepsy operation prognosis prediction model is combined to determine the epilepsy operation prognosis. The operation prognosis result of the patient is accurately predicted, so that a solid basis is provided for an operation decision-making process and personalized diagnosis and treatment.

The invention relates to the technical field of medical auxiliary instruments, and discloses a brain-controlled rehabilitation instrument, which comprises an electroencephalogram signal acquisition module, a signal processing and control module, a mechanical exoskeleton module and a sensory feedback module, the electroencephalogram signal acquisition module is used for acquiring an electroencephalogram signal of a brain motor cortex in real time; the signal processing and control module is used for carrying out preprocessing, feature extraction and motion intention decoding on the electroneurographic signals and outputting a control instruction to the mechanical exoskeleton module; the mechanical exoskeleton module drives joints to move according to a control instruction, and the sensory feedback module feeds motion parameters back to a patient, so that a closed-loop neural rehabilitation system of'intention recognition-action execution-sensory feedback 'is formed. The cerebral cortex is activated through motor imagery, the training efficiency is improved compared with that of traditional equipment, non-invasive electrode design is adopted, operation is not needed, the infection risk is avoided, the mechanical structure is light in weight, wearing is comfortable and free of pain, and safety is high.

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.