42 results about "Hippocampus proper" patented technology

Methods for measuring atrophy in a brain region occupied by the hippocampus and entorhinal cortex. In one example, MRI scans and a computational formula are used to measure the medial-temporal lobe region of the brain over a time interval. This region contains the hippocampus and the entorhinal cortex, structures allied with learning and memory. Each year this region of the brain shrank in people who developed memory problems up to six years after their first MRI scan. The method is also applicable for measuring the progression rate of atrophy in the region in an instance where the onset of Alzheimer's disease has already been established.

A brain-based device (BBD) having a physical mobile device NOMAD controlling and under control by a simulated nervous system. The simulated nervous system is based on an intricate anatomy and physiology of the hippocampus and its surrounding neuronal regions including the cortex. The BBD integrates spatial signals from numerous objects in time and provides flexible navigation solutions to aid in the exploration of unknown environments. As NOMAD navigates in its real world environment, the hippocampus of the simulated nervous system organizes multi-modal input information received from sensors on NOMAD over timescales and uses this organization for the development of spatial and episodic memories necessary for navigation.

The invention discloses a hippocampus extraction method of a human brain nuclear magnetic resonance image based on a 3D neural network. The method comprises the following steps: preprocessing an original image data set and a label, constructing a 3D hippocampus segmentation neural network model, defining a boundary enhancement loss function, optimizing the boundary enhancement loss function, and detecting a preprocessed to-be-detected image by using the trained 3D hippocampus segmentation neural network model to obtain a hippocampus extraction result. According to the method, the 3D hippocampus segmentation neural network model is utilized, efficient, automatic and accurate segmentation of the hippocampus structure in human brain MRI is achieved, and the time for a doctor to perform earlydiagnosis on Alzheimer's disease can be shortened.

Methods for measuring atrophy in a brain region occupied by the hippocampus and entorhinal cortex. In one example, MRI scans and a computational formula are used to measure the medial-temporal lobe region of the brain over a time interval. This region contains the hippocampus and the entorhinal cortex, structures allied with learning and memory. Each year this region of the brain shrank in people who developed memory problems up to six years after their first MRI scan. The method is also applicable for measuring the progression rate of atrophy in the region in an instance where the onset of Alzheimer's disease has already been established.

The invention relates to a distance-field-fusion-based hippocampus segmentation method of an MR image. The method comprises: (1), normalization processing is carried out on an initial to-be-segmented MR test image and a skull and a biased field are removed, thereby obtaining an MR test image after normalization processing; (2), registering of an MR training set image and a training set label image to the MR test image is carried out; (3), distance conversion is carried out on the registered label image to obtain a distance field DF; (4), for a point X in the MR test image, an image block X<MR> is taken and is converted into a column vector; (5) a searching window is defined and an MR dictionary and a DF dictionary are selected; (6), the MR dictionary is used for expressing an MR test sample locally and linearly and a dictionary weight coefficient vector is solved; (7), a DF prediction image block vector of the test sample is obtained and is converted into an image block X <DF>; (8), the steps from (4) to (7) are repeated to obtain a DF value of each point; and (9), a label corresponding to each point in the test image is obtained. According to the invention, a target can be segmented accurately in an MR image.

The invention relates to an STDP-based pulse neural network handwritten Chinese character recognition method, which comprises the following steps of S1, downloading an offline data set, i.e., an offline handwritten Chinese character data set; S2, preprocessing the offline data set: performing normalization processing on each picture in the data set; S3, determining the number of neurons for training; S4, constructing a network structure; S5, performing pulse coding on each pixel in the neural network; S6, determining a neuron model; S7, learning the neuron model by adopting an STDP learning rule; S8, putting the data sets into the network in sequence for training, and completing the training of the pulse neural network after iterating for three times. The recognition method can improve therecognition efficiency of the handwritten Chinese characters. An STDP learning mechanism adopted in the invention exists in conoid neurons of hippocampus at the earliest, and the relative timing sequence of pulse distribution before and after synapsis induces different synapsis change processes, so that the membrane potential of neurons is influenced.

The present invention describes a dissociated cell culture system comprising cells of the hippocampus, one of the brain areas affected by Alzheimer's Disease (AD) or amyloid beta-related diseases. This culture system comprises hippocampal neuronal and glial cells from animal models of AD, particularly, but not limited to, double transgenic mice expressing both the human APP mutation (K670N:M671L) (mAPP), and the human PS1 mutation (M146L) (mPS1), and serves as a powerful tool for the screening and testing of compounds and substances, e.g., drugs, for their ability to affect, treat, or prevent AD or β-amyloid-related diseases. The effects of a test substance on the cells in this culture system can be quantitatively assessed to determine if the test substance affects the cells biochemically and / or electrophysiologically, and / or optically, and / or immunocytochemically. The present in vitro culture system is advantageous for AD drug screening, because it is rapid and efficient. By contrast, even in the fastest animal model of AD, pathology does not start before the end of the second month. If such in vivo animal models are used, it is necessary to wait at least the two month time duration or longer to test for drug efficacy for AD treatment or prevention. At the same time the present invention provides a tool for production of amyloid-beta that can be used for electrophysiological, behavioral, and toxicological studies.

The invention discloses a method for inducing body color change of young hippocampuserectus. In the method, the 20-day-old young hippocampuserectus is induced under physical environmental factors, so that the body color of the young hippocampuserectus is changed from black to yellow or saffron yellow, the color can be kept for a long term, and the survival rate of the young hippocampuserectus canalso be remarkably improved. The body color of the young hippocampuserectus is subjected to induced change by using the method disclosed by the invention, thus the market value of the living hippocampuserectus can be increased, and the commercial culture efficiency of the hippocampuserectus is favorably improved.

The embodiment of the invention provides a hippocampal body segmentation method, a device and an electronic device applied to MRI. The method comprises the following steps: acquiring MRI three-dimensional images; constructing a target image having a preset size and containing a sensitive region of the MRI three-dimensional image; inputting the target image into at least two interactive neural networks trained in advance respectively, and obtaining the pre-segmentation results output by each interactive neural network; determining a segmentation result of the MRI three-dimensional image according to a predetermined weight of each of the interactive neural networks and a pre-segmentation result output by each of the interactive neural networks. The embodiment of the invention can improve theefficiency of segmenting the hippocampus.

The invention relates to a medical image processing device and method using a convolutional neural network. A data enhancement technology and a migration technology are utilized; supervised learning of a convolutional neural network for deep learning of artificial intelligence is achieved by using one to twenty two-dimensional slices passing through a coronal plane of a hippocampus of image data of nuclear magnetic resonance imaging of a three-dimensional skull of a subject; one or two two-dimensional images passing through the coronal plane of the hippocampus of the three-dimensional skull nuclear magnetic resonance imaging image data of the subject is selected as tested images so as to realize three-class distinguishing and identification of the normal senile group, the forgetting type mild cognitive impairment and the Alzheimer's disease.

The invention discloses a hippocampus three-dimensional semantic network segmentation method based on a multi-scale feature multi-path attention fusion mechanism, and belongs to the field of medical image processing. The method comprises the following steps: preprocessing public marked hippocampus image data, and cutting original data into image blocks containing a hippocampus; a brand new hippocampus three-dimensional semantic segmentation network structure is constructed based on multi-scale feature extraction, a multi-path attention fusion mechanism and a branch classifier ensemble learning strategy; dividing the data set; performing offline model training to obtain model weight parameters for the three-dimensional hippocampus structure; and segmenting the test set image by using the model file and evaluating a segmentation result. According to the method, the semantic segmentation network structure conforming to the characteristics of the three-dimensional hippocampus image is designed, so that the utilization rate of the network on multi-dimensional image information can be improved, the pixel intensive prediction capability is improved, and the hippocampus segmentation performance is improved.

The invention relates to the technical field of neural electrodes, in particular to a flexible neural electrode, a preparation method and equipment. The flexible neural electrode comprises a flexible implantation part and a rear end interface part, the flexible implantation part comprises at least one flexible probe, one end of the flexible probe is connected with the rear end interface part, and the flexible probe is used for being implanted into a deep brain area of a living body; the flexible probe comprises a metal electrode and a flexible supporting layer, and the metal electrode is arranged in the flexible supporting layer; electrode holes are formed in the flexible supporting layer, and the metal electrodes are exposed out of the flexible supporting layer through the electrode holes to form electrode points; and the flexible supporting layer is made of polyimide. According to the flexible neural electrode, the implantation depth and the transverse coverage area are increased on the device structure, polyimide is adopted as the flexible supporting layer, and the electrode structure is manufactured through the MEMS technology. The flexible neural electrode has good biocompatibility, and can be applied to clinical research aiming at use scenes of being implanted into deep brain areas such as hippocampus and the like.

The invention relates to a brain-like navigation method based on multi-scale grid cell path integration, and belongs to the field of navigation positioning and artificial intelligence. According to the method, a position neural mechanism is calculated by referring to mammal intracerebral olfactory cortex multi-scale grid cell network path integration and a hippocampus position cell cluster network. The method comprises the following steps: firstly, constructing a three-dimensional multi-scale grid cell network model based on an exponential gain factor and a three-dimensional attractor neural network, and encoding self-motion information (speed / heading) of the unmanned aerial vehicle into a multi-scale grid cell discharge rate wave packet; then, constructing a position cell cluster neural network model, and decoding the multi-scale grid cell discharge rate wave packet into unmanned aerial vehicle three-dimensional position information; the invention provides a robust, accurate and intelligent brain-like navigation method in a three-dimensional and large-scale space, which can be used for satellite denial and intelligent autonomous navigation and positioning of an unmanned aerial vehicle in an unknown complex environment.

The embodiment of the invention provides an MRI hippocampus segmentation method and device. The method comprises the steps that target MRI is acquired, wherein the target MRI comprises a hippocampus to be segmented; the target MRI is input into a preset multi-task learning model, and a hippocampus segmentation probability graph is output, wherein the multi-task learning model is obtained by training based on a human brain MRI hippocampus image sample and corresponding label information, and the multi-task learning model comprises a segmentation task module, an edge prediction task module and adistance regression task module; and binarization processing is performed on the hippocampus segmentation probability graph to obtain a hippocampus segmentation result. According to the MRI hippocampus segmentation method and device provided by the embodiment of the invention, the optimization process is constrained by adding an additional edge prediction task and distance regression task, so that the model is more sensitive to the hippocampus edge, the generalization ability of the model is improved, and the hippocampus segmentation accuracy is improved.

The invention discloses an improved hippocampus-forehead cortex network space cognition method, which comprises the following steps: firstly, a robot explores an environment to collect information, and hippocampus position cells receive the environment information and form a class topology cognition map; cortical column neurons in the forehead cortex receive and integrate hippocampus information projection and environmental award stimulation signals to evaluate the class topology cognitive map, in the evaluation process, if the robot detects that the environment is dynamically changed, the emergency change module is excited, and if multiple targets exist in the environment, the award re-estimation module is excited; finally, stimulating signals are diffused in the model in the form of neural activity waves, after evaluation is completed, reward stimulating signals are diffused in the forehead cortex network, synaptic connection between cortical column neurons in the forehead cortex is changed according to an STDP rule, and a global vector field is formed; and finally, target-oriented space navigation of the robot is realized.

A method of preventing or treating neurological disease includes administering to a subject in need thereof, a composition comprising an osmotin peptide selected from the group consisting of (a) an osmotin peptide having the amino acid sequence of SEQ ID NO: 1, and (b) an osmotin peptide having at least one amino acid residue substitution, deletion or insertion in the amino acid sequence of SEQ ID NO: 1. When a subject is treated with the osmotin peptide, nerve cells show no increase in growth rate, have little cytotoxicity, and suppress cell death, and when the osmotin peptide is administered to an animal model, the osmotin peptide infiltrated into the hippocampus of the brain and the hypothalamus of the brain, which is the deep part of the brain. Accordingly, the osmotin peptide is used as a composition for preventing, ameliorating, or treating neurological disease.

A hippocampal prosthesis for bypassing a damaged portion of a subject's hippocampus and restoring the subject's ability to form long-term memories. The hippocampal prosthesis includes a first set of hippocampal electrodes configured to receive an input signal from at least one of the subject's hippocampus or surrounding cortical region. The hippocampal prosthesis includes a processing device having a memory and one or more processors operatively coupled to the memory and to the first set of hippocampal electrodes. The processing device being configured to generate an output signal based on the input signal received from the first set of hippocampal electrodes. The hippocampal prosthesis includes a second set of hippocampal electrodes operatively coupled to the one or more processors and configured to receive and transmit the output signal to the subject's hippocampus.

An inhibiting or alleviating agent for amyloid beta (A beta)-induced damage in hippocampus including an extract from inflamed tissues inoculated with vaccinia virus. And the use of the extract from inflamed tissues inoculated with vaccinia virus in the preparation of an agent for inhibiting or alleviating A beta-induced damage in hippocampus.

The invention provides an MRI image hippocampus region segmentation method based on various loss and multi-scale features. The method comprises the following steps: step 1, acquiring a plurality of T1-modal brain MRI images and hippocampus tags; step 2, taking the left and right labels of the left and right hippocampus as a reference, cutting the brain MRI image of each T1 mode to obtain a plurality of cut left and right hippocampus cubes; and step 3, performing 3D dicing on each cut left and right hippocampus cubes to obtain 3D dices of all the cut left and right hippocampus cubes, screening out 3D blocks containing hippocampus voxels greater than a set threshold value from the 3D dices of all the cut left and right hippocampus cubes, and performing pretreatment on the screened 3D blocks. According to the invention, the labels of the hippocampus and the background can be accurately segmented, and the segmentation accuracy is improved through the combination of multi-scale information and various losses, so that the segmentation accuracy of the hippocampus in the brain image is remarkably improved.

The invention discloses a robot behavior learning system based on a striated body structure and a learning method thereof, and belongs to the technical field of bionics. The robot behavior learning system is composed of sensory cortex, motor cortex, hippocampus, thalamus, a black compact part, an abdominal side quilt area and a striated body, wherein the striated body comprises striated corpusclesand a matrix. The striped corpuscles receive positioning information generated by hippocampus position cells and dopamine information generated by a black compact part and an abdominal covered area,and update orientation information of the robot according to an operation conditioned reflex mechanism. The matrix receives orientation information of the striped corpuscles and performs action selection according to an improved epsilon-greedy algorithm. Behavior habits can be formed after the robot interacts with the environment for a period of time. According to the method, a possible explanation of animal habitual behavior generation is given, and robot autonomous behavior learning can be guided. The method can be applied to the fields of robot navigation, physiology, animal behavioristicsand the like.

The invention provides an MRI (Magnetic Resonance Imaging) hippocampus segmentation method and system based on a hypergraph numerical neural P system. The method comprises the following steps: acquiring an MR image to be segmented; inputting the MR image to be segmented into the hypergraph-based numerical neural P system to obtain a hippocampus segmentation result; wherein the supergraph-based numerical neural membrane system comprises superneurons, and an encoder and a decoder consisting of a plurality of basic neurons, the encoder and the decoder are used for learning up-sampling and down-sampling features of the hippocampus in the MR image, and the superneurons are used for jumping and series fusion between the encoder and the decoder. The high precision of the deep learning model in semantic segmentation is combined with the good parallelism and robustness of the membrane system, and the performance of hippocampus segmentation is improved.

The invention discloses application of a deacetylase inhibitor in preparation of a medicine for treating abnormal glucose metabolism, and relates to the field of abnormal glucose metabolism treatment. The invention provides application of a deacetylase inhibitor in preparation of a medicine for treating abnormal glucose metabolism. The invention provides a new application way of the deacetylase inhibitor, the deacetylase inhibitor can regulate the expression of differential proteins generated by abnormal glucose metabolism, and a new drug choice is provided for the treatment of abnormal glucose metabolism diseases, especially for abnormal glucose metabolism of hippocampus tissues caused by fluorine poisoning.