60 results about "Cerebral 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 cerebral cortex stimulation central nervous regulation system and method based on a pulse magnetic coil, and the system comprises a pulse magnetic stimulation module, a neural signal collection module, a central control module and a man-machine interaction module, and is additionally provided with a three-dimensional positioning module and a multi-level safety monitoring module. The pulse magnetic stimulation module generates a focused pulse magnetic field, the neural signal acquisition module acquires multi-brain-region electric signals, and the central control module dynamically regulates stimulation parameters after analyzing the signals to form closed-loop regulation; the three-dimensional positioning module ensures accurate positioning of the coil, the safety monitoring module monitors parameters in real time and triggers emergency protection, and the man-machine interaction module realizes convenient operation and data management. According to the method, adjustment is completed through initialization, signal acquisition and analysis, precise stimulation, positioning adjustment and safety monitoring. The scheme is non-invasive, accurate, safe and suitable for nerve research and disease treatment, the regulation effect and use safety are improved, and development of the nerve regulation technology is promoted.

The application discloses a new use of Leonurine or a pharmaceutically acceptable salt thereof in the preparation of a medicament for treating radiation-induced brain injury, specifically, providing a safe and effective therapeutic drug for iron death, neuroinflammation, neuron damage and lipid metabolism disorder accompanying the radiation-induced brain injury, solving the technical problem of lack of specific treatment means in the prior art, and having a wide clinical application prospect. Experiments adopt a C57BL / c mouse 30 Gy whole brain X-ray radiation model, and results show that Leonurine can significantly improve the body weight decrease and survival rate of the model mice, reduce the serum IL-6 and IFN-alpha levels, inhibit the activation of microglial cells in the cerebral cortex and hippocampus, protect and repair damaged neurons, and improve the ultrastructure of mitochondria and synapses; meanwhile, the expression of GPX4 and SLC7A11 proteins in the brain tissue is up-regulated, the GSH content is increased to inhibit iron death, and the levels of GPE, GPS and GPC related to lipid metabolism are restored.

The invention relates to a brain-computer interface system which comprises a flexible electrode array, a receiving device, a flexible connecting wire and external equipment, wherein the flexible electrode array is implanted into a meninx middle artery in a minimally invasive manner through a superficial temporal artery puncture and a vascular intervention procedure; the receiving device is embedded under the skin of a head and is provided with a signal transmitter; the external equipment is provided with a signal receiver. A flexible electrode array is implanted in an anterior branch or posterior branch of a main branch of a middle meninx artery through a superficial temporal artery intravascular minimally invasive technology, the number of electrodes and the array are arranged according to needs, and a receiving device buried in the subcutaneous part of the head is electrically connected with the flexible electrode array implanted in the middle meninx artery. Craniotomy is not needed, operative complications are avoided, minimally invasive implantation reaches the surface of the cerebral cortex, operation of a doctor is easier, the operation time is shortened, and the comfort level of a patient is improved.

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.

1. Name of the product in this design: Flash Stimulator. 2. Purpose of this design: To serve as an auxiliary diagnostic device in the field of neurology that induces abnormal electrical discharges in the cerebral cortex through light-sensing input from the visual system. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D rendering 1.

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 reversible adhesive flexible electrode material based on zinc ion activation, which is characterized in that polydimethylsiloxane is used as a main chain, and functionalization capable of responding to metal ions is realized through sulfydryl-alkene click reaction and subsequent imidazolation modification. The invention also provides a preparation method and application of the electrode material. The elastomer material disclosed by the invention has remarkably enhanced adhesive force, and can be stably attached to the surfaces of soft tissues such as cerebral cortex and spinal cord; under the action of Zn or after a competitive ligand is introduced, the adhesive force is reduced, and mild stripping is realized. The material is simple in preparation process and excellent in biocompatibility, can be used for constructing a large-area flexible electrode, is suitable for acute and chronic nerve electrophysiological signal acquisition, and is particularly suitable for epilepsy lesion positioning and brain function research.

The utility model relates to the technical field of fixing devices, in particular to an integrated fixing device for manufacturing rodent pathological sections, which comprises a fixing device shell and a fixing device cover plate which are rotatably connected. The fixing device comprises a fixing device shell, a plurality of brain cutting clamping grooves are formed in the periphery of the fixing device shell, brain clamping grooves are formed in the bottom of the fixing device shell, the brain clamping grooves comprise a cerebellum fixing groove, a brain fixing groove and an olfactory bulb fixing groove, and the cerebellum fixing groove, the brain fixing groove and the olfactory bulb fixing groove are integrated. The closed fixing groove can be adopted, formalin volatilization is reduced, the brain tissue fixing function and the material taking function are integrated, and the operation process is simplified.

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.

The invention relates to the technical field of neural electrodes, in particular to a magnetic drive gel type ECOG electrode based on a skull patch and a preparation and packaging method thereof, and the electrode comprises a skull patch unit, a flexible sensing circuit and a gel type sensing unit; a magnetic nano material is coupled in the gel type sensing unit, and the gel type sensing unit is driven by an external magnet to move to a target area on the surface of the cerebral cortex for signal acquisition; the flexible sensing circuit comprises an insulating layer and a sensing layer which are coaxially coupled; the two ends of the flexible sensing circuit are embedded into the skull patch unit and the gel type sensing unit respectively, and bonding sites and recording sites are exposed on the surfaces of the skull patch unit and the gel type sensing unit respectively. The manufacturing problems that a traditional preparation technology is incompatible with a gel material with high water content and the preparation and packaging process is complex are solved, and the problems that when an ECOG electrode is used for large-range multi-brain-region monitoring, the surgical wound surface is large, inflammatory response is likely to be induced, and the postoperative rehabilitation effect is poor are solved.

A brain-controlled unmanned aerial vehicle method and system based on SSVEP, the brain-controlled unmanned aerial vehicle method comprising: dividing a visual stimulator into a plurality of independent areas, each independent area corresponding to each action direction of an unmanned aerial vehicle, each independent area flickering and stimulating square blocks at different frequencies; sequentially observing the stimulation square blocks of each independent area to enable the brain occipital area to generate a plurality of different electroencephalogram signals; collecting a plurality of different electroencephalogram signals and converting the electroencephalogram signals into digital signals; the digital signals are extracted and classified, and control instructions of all action directions of the unmanned aerial vehicle are generated; and observing the stimulation blocks corresponding to the independent areas according to the control instruction, and controlling the unmanned aerial vehicle to move according to the control instruction. According to the design, a plurality of different electroencephalogram signals are generated by the cerebral cortex by observing the stimulation square blocks with different frequencies, the electroencephalogram signals are converted into the control instructions of the unmanned aerial vehicle, the unmanned aerial vehicle can be controlled to move in the specified direction only by observing the corresponding stimulation square blocks, and the control precision is high.

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 provides a semi-intrusive brain-computer interface device, which belongs to the technical field of brain-computer interfaces, and comprises an electroencephalogram signal acquisition unit, a signal processing unit, a decoding unit, a control interface unit and an external equipment unit which are electrically connected in sequence, the electroencephalogram signal acquisition unit adopts a flexible electrode structure, is configured to be implanted into a cranial cavity, does not penetrate through the cerebral cortex and is used for acquiring a cortex electroencephalogram signal; the signal processing unit is used for filtering, amplifying and digitally processing the electroencephalogram signals; the decoding unit is used for classifying and identifying the electroencephalogram signals; and the control interface unit supports a multi-mode interface. According to the invention, a semi-intrusive design is adopted, the invasiveness and safety are balanced, the full-intrusive rejection risk is also avoided, compared with a non-intrusive design, the signal analysis precision is improved, neural features of different patients are adapted in a personalized manner, fine regulation and control of an object track and force in a complex three-dimensional environment are realized, and the control limitation of a traditional system is solved.

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 invention relates to a Parkinson's disease auxiliary diagnosis device and a computer readable storage medium, belongs to the technical field of medical devices, and solves the problems that noise interference and information loss exist in existing sMRI and fMRI data, and jump connection in an encoder-decoder is relatively fixed. The device comprises a data acquisition module for acquiring a multi-modal nerve image and a gait video data set; the brain data processing module reconstructs a cerebral cortex grid surface by using an sMRI diffusion model and reconstructs blood oxygen level mapping by using an fMRI diffusion model, and brain data features are extracted after registration; the gait data processing module acquires a gait video data set, performs perception enhancement processing and extracts a three-dimensional skeleton sequence to extract gait data features; and the feature fusion and classification module performs deep interaction and complementary enhancement on the fused brain and gait data features through an axial, cross and channel composite attention mechanism, and then classifies the enhanced fusion features. And the capability of identifying the complex characteristics of the Parkinson's disease is effectively improved.

1. The name of the design product: insomnia treatment instrument of transcranial magnetic. 2. The use of the design product: for stimulating the cerebral cortex by magnetic field, adjusting the brain nerve activity. 3. The design points of the design product: in shape. 4. The picture or photo that can best show the design points: perspective drawing.

The present application belongs to the field of brain science and visualization technology, and specifically relates to a human brain digital twin 3D visualization method. The method comprises the following steps: S1: constructing a cerebral cortex outer surface model and an independent brain region model; S2: registering a voxelized brain atlas to a standard brain template space to obtain a correspondence relationship between the vertexes of the cerebral cortex outer surface model and the brain atlas region labels as a projection rule; S3: according to the projection rule, displaying BOLD signals and EEG backtracking signals to the cerebral cortex outer surface model; S4: constructing connection pathways between each brain region in the independent brain region model, and performing deterministic fiber bundle tracking, cleaning and integration on individual diffusion weighted imaging data to obtain biologically authentic and representative fiber bundles between any two brain regions as the connection pathways; and S5: integrating the connection pathways into the independent brain region model to visualize brain network and brain region function effects. The present application realizes dynamic and interactive visualization of multi-modal brain data in three-dimensional space.

The application relates to the technical field of brain-computer interface, and relates to an armless disabled person assisting device. In order to solve the technical problems that the existing wearable assisting device for providing life assistance for upper limb disabled persons is invasive and semi-invasive, needs to be operated, electrodes are implanted into the cerebral cortex, the operation risk is large, complications such as infection and bleeding may be caused, long-term maintenance is needed after implantation, the non-invasive brain interface has small risk, is convenient to use, simple to operate, low in cost, but has low control precision, and the technical scheme provided by the application is as follows: the armless disabled person assisting system comprises a brain signal acquisition module, a visual control module and a main control module. The brain signal acquisition module is used for acquiring the brain electrical signal of a user in a non-invasive manner. The visual control module is used for acquiring target object positioning information. The main control module is used for generating a grasping action group signal according to the signals acquired by the brain signal acquisition module and the visual control module. The application is suitable for application in the work of assisting grasping of armless disabled persons.

The invention relates to the technical field of biomedical signal processing and neural image analysis, in particular to an epileptic seizure prediction method combining electroencephalogram and image data, which comprises the following steps: acquiring diffusion tensor imaging data of a target object, calculating the diffusion tensor of whole brain voxels, and calculating the image data of the target object; an anisotropic transmission resistance tensor field representing the conduction performance of the white matter fiber bundle of the brain is constructed based on inverse transformation of the diffusion tensor; and based on the anisotropic transmission resistance tensor field, calculating an anisotropic geodesic distance between any two points in the cerebral cortex space, and constructing a Riemannian manifold geometric substrate containing anatomical conduction constraint. According to the method, by constructing the anisotropic transmission resistance field and the Riemannian manifold substrate, the minimum biophysical cost constrained by anatomy is calculated, so that the problems that correlation analysis lacking physical constraint is mostly adopted in the traditional technology, and due to the neglect of the white matter fiber conduction rule, the analysis accuracy is low are solved; therefore, the false alarm problem caused by false synchronization caused by the fact that the volume conductor effect cannot be eliminated is solved.

Provided herein are novel methods for delivering recombinant adeno-associated viral (rAAV) particles to the central nervous system of a mammal (e.g., a human). In aspects, the methods involve administering rAAV particles containing a heterologous nucleic acid to the striatum and causing expression of the heterologous nucleic acid in at least the cerebral cortex and the striatum of the mammal.

The invention discloses an intelligent evidence-based acupuncture diagnosis and treatment instrument for treating migraine, and belongs to the technical field of acupuncture medical equipment. The device comprises a migraine diagnosis and treatment head cover, an acupuncture point detection module, an acupuncture point positioning module, an acupuncture treatment module, a central control module and an evidence-based decision module, a plurality of acupuncture point probe assemblies which can be independently controlled are distributed on the head sleeve; the acupoint detection module measures biological impedance through a four-electrode method and automatically recognizes acupoint coordinates in combination with a three-dimensional positioning algorithm; the acupoint positioning module drives a miniature linear actuator to realize automatic and accurate positioning of the probe; the acupuncture treatment module outputs adjustable electrical stimulation based on a balance acupuncture principle, activates cerebral cortex neurons and adjusts the function of a central nervous system; the evidence-based decision-making module intelligently generates and dynamically optimizes stimulation intensity parameters of each acupuncture point probe to realize personalized evidence-based treatment; the problems that traditional acupuncture depends on experience, is inaccurate in positioning and cannot be standardized are solved, and the method has the advantages of being accurate in positioning, highly personalized, safe and reliable.

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.

The invention relates to an athletic skill recovery interface device (1) comprising: one or more cortical sensors (3; 31, 32,..., 3n), each cortical sensor being capable of detecting a respective cortical signal (SE1, SE2,..., SEn) from a respective portion of the cerebral cortex (C) of the patient (P); a decoding unit (2) associated with the one or more cortical sensors (3; 31, 32,..., 3n) arranged to be operatively connected to the one or more cortical sensors (3; -each of said cortical signals (SE1, SE2,..., SEn) transmitted by each of said cortical signals (SE1, SE2,..., SEn) transmitted by each of said cortical signals (SE1, SE2,..., SEn); one or more muscle electrical stimulators (4) each adapted to a muscle (M) to be stimulated; wherein the decoding unit (2) is configured to process, based on cortical signals received by the one or more cortical sensors (31, 32,..., 3n), the one or more muscle electrical stimulators (4; 41, 42,..., 4n), and one or more actuation signals (SM1, SM2,..., SMn) of the one or more signals (SM1, SM2,..., SMn) of the one or more signals (SM1, SM2,..., SMn). The invention also relates to a method for operating the interface device (1).

The invention provides a work load assessment method, device, equipment and medium, and the method comprises the steps: collecting the multi-modal physiological data of a work user in the task execution process of the work user, the multi-modal physiological data including electrocardiogram data, human body temperature and the oxyhemoglobin concentration of cerebral cortex; extracting physiological signal features in each mode from the multi-mode physiological data; and inputting the physiological signal features in each mode into a pre-trained work load classification model to obtain a classification result output by the work load classification model, the classification result being used for indicating the work load level of the work user. In this way, by collecting the multi-modal physiological data of the operation user, the comprehensiveness and accuracy of operation load evaluation are improved, the operation load level of the operation user is obtained through the operation load classification model, targeted analysis of the operation user is achieved, the accuracy of operation load evaluation is improved, and the user experience is improved. Therefore, operation distribution can be guided more scientifically and effectively.

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.