41 results about "Brain subcortex" patented technology

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 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 discloses a cerebral cortex penetrator small blood vessel magnetic resonance imaging method based on an inflow enhancement effect. The method comprises the following steps: modeling a penetrator small blood vessel to generate a blood vessel TOF (Time of Flight) image; calculating the blood vessel-grey matter contrast ratio of the blood vessel TOF image under different FAs and different TRs, and optimizing the FAs and the TRs according to the blood vessel-tissue contrast ratio; obtaining a structural image of the in-vivo brain tissue, performing cortex reconstruction, and calculating an angle of a surface normal of each cortex vertex relative to the B0 field to obtain a cortex angle graph; adjusting a single-layer blood vessel scanning plane angle according to the cortex angle diagram; and according to the optimized FA and TR and the single-layer blood vessel scanning plane angle determined in the step S4, carrying out magnetic resonance imaging on the cerebral cortex perforation small blood vessel. The invention further discloses a cerebral cortex puncturing small blood vessel magnetic resonance imaging device based on the inflow enhancement effect. According to the method and the device, magnetic resonance imaging of the cerebral cortex perforation small blood vessel can be realized under the non-invasive and contrast-agent-free conditions.

The present invention relates to a neuromodulation / neurostimulation system (100) for restoring motor function of an upper limb and / or a lower limb of a patient (P) suffering from spinal cord injury (SCI) and / or other neurological conditions and promoting neurological restoration, the system (100) comprising: a processing unit (10) configured and adapted to provide stimulation commands; an implantable or external stimulation unit (12) configured and adapted to deliver neuromodulation therapy, in particular electrical epidural stimulation (EES), to the dorsal root of the spinal cord (SC) of the patient (P); at least one implantable or external pulse generator (14), the at least one implantable or external pulse generator being operatively connected to the stimulation unit (12); an implantable neural sensor (16), the implantable neural sensor comprising an electronic component configured and adapted to detect a cerebral electrocorticogram (ECoG) signal representing a motor intent of the patient (P) from a sensory motor cortex (C) of the patient (P), wherein the processing unit (10), the stimulation unit (12) and the implantable neural sensor (16) are operatively connected to define a cerebrospinal cord interface (BSI), and wherein the processing unit (10) is configured and adapted to: preferably collect the ECoG signals from the sensory motor cortex (C) in real time and decode the ECoG signals into corresponding motor intent of the patient (P); the decoded motion intent is converted, preferably in real time, into a corresponding stimulation command, and the stimulation command is forwarded, preferably in real time, to the at least one pulse generator (14).

The application discloses a kind of implanted nerve electrode devices of magnetic control telescopic contact and separation cerebral cortex, belong to brain signal research technical field.It includes: flexible substrate, magnetostrictive deformation substrate, magnetostrictive film, elastic component and electrode;The flexible substrate side is provided with several rectangular grooves arranged in order;Two pieces of the magnetostrictive film are pasted to the two sides of the magnetostrictive deformation substrate, and the magnetostrictive film on one side is used to fix the magnetostrictive deformation substrate in the rectangular groove of the flexible substrate;The electrode is fixed on the magnetostrictive deformation substrate, for collecting electroencephalogram;The elastic component is used to pull the deformed magnetostrictive deformation substrate to restore flat state.The device is fixed under dura mater, and can be bent under the drive of magnetic field, realizes the effective contact of nerve electrode and cerebral cortex, compared with traditional semi-invasive nerve electrode, improves the detection quality and stability of electroencephalogram.

The application belongs to the technical field of neural network simulation and analog, and particularly relates to a large-scale cortex pulse neural network asynchronous parallel simulation algorithm based on GPU. The application utilizes the advantages of multi-thread and texture memory of a computing graphics card, combines the general form of a biological brain receiving external stimulation and the general connection mode between neurons in the cortex, designs an asynchronous parallel algorithm framework of GPU and CPU, GPU is responsible for parallel evolution of neuron dynamics equations and block parallel calculation of isotropic connection in a local network, CPU is responsible for processing anisotropic long-range connection, and different neuron dynamics equations and plasticity learning rules can be compatible. Compared with the prior art, the application can effectively improve simulation speed, provides a tool for simulating a biological brain cortex in a single computing node, and is suitable for a single node multi-graphics card and a multi-node multi-graphics card distributed operation model.

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.

The present application relates to the technical field of pattern recognition, in particular to a stroke rehabilitation evaluation method based on brain muscle function network features, comprising the following steps: S1, collecting electroencephalogram signals and electromyogram signals, collecting electroencephalogram signals and electromyogram signals of a stroke patient under a hand dynamometer test; S2, respectively pre-processing the electroencephalogram signals and electromyogram signals; S3, constructing a variational mode decomposition transfer entropy; S4, constructing a CMFN model based on EEG and EMG; S5, brain muscle function network analysis. The method considers that the research on motor recovery of stroke stays in analyzing the consistency or coupling relationship changes between the cerebral cortex and the muscle, realizes the quantification of stroke evaluation, and greatly improves the evaluation accuracy.

The invention belongs to the technical field of postoperative rehabilitation, and particularly relates to a brain postoperative rehabilitation training nursing device which comprises a frame, universal wheels are arranged at the bottom of the frame, a rear fixing plate is arranged on one side of the frame, an upper limb training device is arranged on the upper side of the rear fixing plate, a lower limb training device is arranged on one side of the rear fixing plate, and a main controller is arranged on the rear fixing plate. The upper limb training device is electrically connected with the lower limb training device, and the upper limb training device and the lower limb training device are both electrically connected with the main controller; according to the invention, training of upper and lower limbs is integrated, a patient is forced to do coordinated movement through an electric control system, a natural movement mode of a human body is met, daily life actions are simulated, and cerebral cortex functional recombination and neural network reconstruction can be more effectively stimulated.

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.

A method and a system for fully automatically segmenting a cerebral cortex surface based on a graph network, the method including: registering a cerebral magnetic resonance image to a standard template space; performing cerebral cortex surface reconstruction on the registered cerebral magnetic resonance image based on a deep neural network; calculating adjacency matrices among the vertexes of the reconstructed cerebral cortex; acquiring corresponding distinguishing features of each grid vertex of the cerebral cortex surface as corresponding feature vectors of the point so as to obtain a cerebral cortex surface segmentation result; and mapping the cerebral cortex surface segmentation result back to the original coordinate space from the standard template space. Graph structure modeling is performed on the reconstructed cerebral cortex surface, and the global topological structure features are learned based on the graph network, thus realizing the accurate segmentation of the cerebral cortex surface.

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 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 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.

A soft tissue cuff apparatus for subcortical surgery of the brain includes a cuff body. The cuff body forms a ring-like shape in an expanded position and is formed of a flexible material. The cuff body extends between a first lip and a second lip, thereby forming a concave outer periphery where, during use, the cuff body is configured to contact and cradle at least a portion of the cortex. The first and second lips are configured to form a hold on to the cortex such that the soft tissue cuff apparatus creates a stable fit within the cortex to form a surgical corridor and allow access to a surgical site under the cortex without subjecting the surrounding soft tissue to further manipulation.

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.

This invention discloses a method for segmenting cerebral blood vessels in non-angiographic images, comprising the following steps: S1, inputting the original image A, i.e., the original non-angiographic image; S2, filtering out air, outputting image B; S3, dissecting the skull, outputting image C; S4, performing preliminary ablation of the cerebral cortex, outputting image D; S5, iteratively ablating the cerebral cortex, outputting image E, i.e., the final cerebral blood vessel image. This invention can dissect the skull and iteratively ablate the cerebral cortex without affecting the vascular components, thereby obtaining the purest and most complete cerebral blood vessel components possible.

The invention relates to a transcranial ultrasonic nerve regulation and control device and a control method. The transcranial ultrasonic nerve regulation and control device comprises an ultrasonic generation unit, ultrasonic waves are controllably provided to an intracranial target area, and the ultrasonic generation unit is configured to provide the ultrasonic waves with the frequency ranging from 3.0 MHz to 8.0 MHz to the intracranial target area. According to the transcranial ultrasonic nerve regulation device and the control method, the intracranial nerves are stimulated through the high-frequency ultrasonic waves with the working frequency of 3.0-8.0 MHz, the transcranial ultrasonic nerve regulation device and the control method have remarkable advantages in cortex regulation, energy is more easily absorbed by superficial tissue, the stimulated area covers the cerebral cortex and superficial white matter, and interference of a non-targeted deep structure is avoided. Meanwhile, the spatial resolution of the ultrasound of the frequency band can reach the submillimeter level, the ultrasound can be accurately focused on a tiny functional subregion, and the ultrasound is particularly suitable for nerve regulation scenes needing accurate positioning intervention and is suitable for nerve regulation of focal epilepsy, chronic pain syndromes, visual and auditory cortex dysfunction and mental and psychological disorders.

Methods of assessing or obtaining an indication of the presence of a cognitive disorder by analysing microstructural changes in regions of the brain are provided. The invention particularly relates to methods of assessing or obtaining an indication of the presence of types of dementia, for example Alzheimer's disease, by analysing changes in minicolumns in regions or layers of the cortex of the brain or of the whole brain.

The invention relates to the field of light industry, especially to the knitwear production of hosiery. In particular, the invention relates to electrically conductive tights (1) that affect the general functional state of a person, including the cognitive functions of the brain. It is applicable for everyday long-term usage both at home and in professional activities that require enhanced attention concentration and increased reaction speed, for example, when driving vehicles, or when it is used by dispatchers or athletes. The electrically conductive tights (1) comprise a torso portion (2), leg portions (3) and footlet portions (4) and they are made from dielectric threads and modified threads including a first-class conductor. The modified threads are placed and made in such a way that the resistance of the electrical circuit of the tights from one footlet portion (4) to the other (4) is no more than 8 kOhm. The invention allows to increase the speed of visual-motor reactions by stimulating the neural activity of the cerebral cortex.

The invention belongs to the technical field of brain science and visualization, and particularly relates to a human brain digital twin 3D visualization method. Comprising the following steps: S1, constructing a cerebral cortex outer surface model and an independent brain region model; s2, registering the voxelized brain atlas to a standard brain template space, and obtaining a corresponding relation between the vertex of the cerebral cortex outer surface model and a brain atlas brain region label as a projection rule; s3, displaying the BOLD signal and the EEG traceability signal on the cerebral cortex outer surface model according to a projection rule; s4, constructing a connection path of each brain region in the independent brain region model, performing deterministic fiber bundle tracking on the individual diffusion weighted imaging data, and cleaning and integrating fiber bundles with biological authenticity and representativeness in any two brain regions as the connection path; and S5, integrating the connection path into the independent brain region model for visualizing the brain network and the brain region function effect. According to the method, the dynamic and interactive visualization of the multi-modal brain data in the three-dimensional space is realized.

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 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 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.

A near-infrared brain massager according to the present invention relates to a near-infrared phototherapy device designed to facilitate blood supply to the brain and prevent and treat brain diseases such as dementia in a non-invasive manner by irradiating major acupuncture points such as the Baihui acupoint with near-infrared rays, using the characteristics of near-infrared rays of increasing body temperature and facilitating blood circulation in the human body. In particular, the near-infrared brain massager is worn on the head as a helmet and has the objective of maximizing the effects of increasing body temperature, promoting blood circulation, and preventing and treating brain diseases by irradiating acupuncture points with near-infrared rays so that the near-infrared rays are effectively transmitted to the acupuncture points and reach the cerebral cortex. Accordingly, the near-infrared brain massager according to the present invention comprises: headgear; a light source module unit; a control unit; and a head fastening unit.

A method of rapid constructing human cerebral cortical organoids by 3D bioprinting and an application including preparing microfluidic chips, preparation of hydrogel of human cerebral cortical organoids, and printing of human cerebral cortical organoids. The microfluidic chip comprises a mixed-flow channel layer, liquid pool layer, microporous array layer, human cerebral cortical organoid culture layer, and culture medium recovery layer; the human cerebral cortical organoid hydrogel has gelatin, alginate, and hyaluronic acid; printing directly human cerebral cortical organoids in microfluidic chips by FRESH printing method, obtaining human cerebral cortical organoid chips after packaging. The application directly constructs large-scale human cerebral cortex-like with three layers of mutually connected structures in situ in organ chip through 3D bioprinting, simulates cerebrospinal fluid circulation through perfusion culture.

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 application discloses a gait recognition method based on a brain cortex algorithm, and comprises the following steps: acquiring a gait image; performing gait segmentation on the gait image to obtain a binary contour image; performing gait image coding on the binary contour image to obtain an encoding result of the gait segmentation binary image; performing gait image sparse discrete representation on the encoding result of the gait segmentation binary image to obtain a gait segmentation image sparse representation; and comparing the gait segmentation image sparse representation with a pre-stored gait sparse discrete representation to obtain a gait recognition result. In the gait segmentation process, time information is aggregated, the stability of foreground extraction is enhanced, the extraction speed is improved, and the gait image can be effectively extracted in a complex environment. The binary contour image is subjected to first and second spatial pooling, so that the reliability of recognition is improved, the calculation is reduced, and the calculation efficiency is improved. The application can be widely applied to the technical field of image recognition.

The invention provides a brain functional activity nonlinear modeling method and system and a storage medium, and relates to the technical field of functional magnetic resonance imaging data analysis, and the method comprises the steps: dividing a BOLD signal of a cerebral cortex in a task period into a plurality of task segments, and respectively mapping the BOLD signal to a group level unit spherical manifold and an individual level unit spherical manifold; obtaining a distance matrix between the BOLD signals according to the group horizontal unit spherical manifold; performing dimension reduction processing on the group horizontal unit spherical manifold according to the distance matrix, and distributing a functional neighborhood for each cerebral cortex vertex according to a low-dimensional embedded manifold obtained by the dimension reduction processing; and according to the average signal corresponding to the functional neighborhood, determining local synchronism measurement of the vertex of the cerebral cortex, and according to the local synchronism measurement, obtaining a related functional activation map of the cerebral cortex. According to the method, distance calculation and manifold learning are carried out on the group horizontal unit spherical manifold, so that the capacity of capturing nonlinear features of the brain function activity is enhanced, and the modeling precision of the brain function activity is improved.