24 results about "Basal ganglia" patented technology

The invention relates to the technical field of intelligent diagnosis systems for neurodegenerative diseases, and discloses a system and equipment for identifying an early biomarker of Parkinson's disease based on non-invasive electroencephalogram. The Parkinson's disease early-stage biomarker identification system comprises a multi-mode signal acquisition module which is provided with a 64-lead dry electrode electroencephalogram cap, an integrated preamplifier and an ADC converter and is used for synchronously acquiring resting-state electroencephalogram and event-related potential, dynamically completing switching of the resting-state electroencephalogram sampling rate and transmitting acquired original data to a preprocessing module. According to the method, revolutionary improvement is realized in three dimensions of acquisition, analysis and calculation through system-level collaborative innovation, a nanocrystalline shielding dry electrode is combined with a dynamic impedance adjustment technology, so that the signal-to-noise ratio of a home environment is increased to 28dB, and the pathological correlation of key markers such as frontal lobe-basal node loop gamma entropy is verified by Granger causality and has a good application prospect. The cloud distributed model server supports 2000 paths of concurrent analysis, and the real-time bottleneck of home screening is thoroughly solved.

A nervous system emulator engine includes working computational models of the vertebrate nervous system to generate lifelike animal behavior in a robot. These models include functions representing several anatomical features of the vertebrate nervous system, such as spinal cord, brainstem, basal ganglia, thalamus, and cortex. The emulator engine includes a hierarchy of controllers in which controllers at higher levels accomplish goals by continuously specifying desired goals for lower-level controllers. The lowest levels of the hierarchy reflect spinal cord circuits that control muscle tension and length. Moving up the hierarchy into the brainstem and midbrain / cortex, progressively more abstract perceptual variables are controlled. The nervous system emulator engine may be used to build a robot that generates the majority of animal behavior, including human behavior. The nervous system emulator engine may also be used to build working models of nervous system functions for clinical experimentation.

The invention belongs to the technical field of robot intelligent control, and relates to a robot behavior decision-making method and device for simulating a brain learning and memory mechanism. According to the method, a development network DN2, reinforcement learning and a self-organizing map SOM are combined, functions of a cerebellum learning system, a basal ganglion and a hippocampus learning system are simulated, and a self-organizing reinforcement learning model based on the development network DN2 is obtained; the equipment comprises one or more processors and a computer readable medium in which one or more computer readable instructions are stored, and is used for realizing the robot behavior decision-making method. According to the method, the functions of the cerebellum learning system, the basal ganglion and the hippocampus learning system are properly utilized, and the fusion algorithm combines reinforcement learning and supervised learning methods, so that the robustness of the self-organizing reinforcement learning model based on the development network DN2 is enhanced, the capability of the algorithm for obtaining an effective obstacle avoidance strategy is improved, and the method is suitable for being applied to the self-organizing reinforcement learning of the self-organizing reinforcement learning of the self-organizing reinforcement learning of the self-organizing reinforcement learning. And the intelligent agent can be helped to quickly learn an efficient solution.

The present invention discloses a gait control method for a quadruped robot using a dual-level central pattern generator (CPG) inspired by the basal ganglia, comprising: modeling the single legs of the quadruped robot using forward kinematics and inverse kinematics; using a dual-level central pattern generator for each leg, consisting of a rhythm generation part and a pattern formation part, wherein the rhythm generation part is mainly responsible for phase change, and the pattern formation part is mainly responsible for the coordination of each joint; determining the hierarchical structure and coupling topology of the CPG network; and introducing a behavior selection model of the basal ganglia, which can be used to select the optimal gait for different environments and improve environmental adaptability. Compared with traditional control methods, the control of the quadruped robot using the present invention has a high degree of biological rationality, stronger robustness to external interference, good adaptability to terrain, and can flexibly control various movement modes of the quadruped robot.

The inventors discovered that neural oscillatory activities in the sensorimotor cortex region of the brain and the basal ganglia system of the brain are indicative of physiological gait events. The invention utilizes this discovery, along with recent advances in neural interfaces and machine learning techniques, to provide new and useful methods for identifying gait events directly from the neural activity of an individual. In particular, methods and systems are provided that produce trained classification models capable of accurately identifying left and right leg events during walking. Trained classification models exhibiting above a threshold level of accuracy may then be used to generate control signals for real time adaptive deep brain stimulation in order to improve gait function in patients with Parkinson's disease.

This invention discloses a method and system for pilot safety risk analysis based on neural computation. The method includes: identifying safety risk factors; establishing a mapping relationship between safety risk factors and unsafe pilot behaviors; establishing a structural model of the cortico-basal ganglia-thalamus neural circuit; establishing AMPA and GABA neural synapse computational models based on the theories of double exponential synapses and α-β synapses, respectively; establishing a computational model of the cortico-basal ganglia-thalamus neural circuit; calculating the thalamic discharge rate under the influence of different safety risk factors, and simulating the regulatory mechanisms of dopamine and acetylcholine during the formation of safety risks. This invention conducts safety analysis from the perspective of pilot neurodynamics, providing key physiological basis and theoretical support for improving risk analysis models and optimizing system design by analyzing the neural regulatory mechanisms of acetylcholine and dopamine during the formation of safety risks.

This invention provides the application of fractalkine in the preparation of drugs for the prevention or treatment of white matter injury repair. In animal models of cerebral hemorrhage, it was found that the mass effect of hematoma in the basal ganglia and the neurotoxic products generated by its lysis lead to damage to surrounding white matter fiber bundles. Fractalkine plays an important role in regulating microglial activation after cerebral hemorrhage to promote white matter repair.

To provide compositions and methods for the treatment of leukodystrophy.SOLUTION: Provided is use of administering an effective amount of a compound that down-modulates the expression of both wild-type and mutant TUBB4-A in the manufacture of a pharmaceutical composition for the treatment or prevention of hypomyelination and atrophy of the basal ganglia (H-ABC) leukodystrophy, the pharmaceutical composition ameliorating symptoms of H-ABC, and the compound being selected from short hairpin RNA (shRNA), short interfering RNA (siRNA), antisense RNA, antisense DNA, chimeric antisense DNA / RNA, microRNA, and a ribozyme that is sufficiently complementary to either the gene or mRNA encoding TUBB4A.SELECTED DRAWING: Figure 1-1

A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.

Provided are compounds, methods, and pharmaceutical compositions for reducing the amount or activity of TUBB4A RNA in a cell or subject, and in certain instances reducing the amount of TUBB4A protein in a cell or subject. Such compounds, methods, and pharmaceutical compositions are useful to ameliorate at least one symptom of a disease or disorder associated with TUBB4A. Such symptoms and hallmarks include hypomyelination, demyelination, dysphonia, dystonia, ataxia, spasticity, atrophy of cerebellar and / or basal ganglia, poor vision, rigidity, microcephaly, focal cortical dysplasia, seizures, and early childhood death.

The application belongs to the technical field of robot intelligent control, and relates to a robot behavior decision method and equipment simulating brain learning and memory mechanism. The method combines a developmental network DN2, reinforcement learning and a self-organizing map SOM, simulates functions of a cerebellum learning system, a basal ganglia and a hippocampus learning system, and obtains a self-organizing reinforcement learning model based on the developmental network DN2. The equipment comprises one or more processors and a computer readable medium storing one or more computer readable instructions, and is used for implementing the above robot behavior decision method. The application properly utilizes functions of the cerebellum learning system, the basal ganglia and the hippocampus learning system. The fusion algorithm combines reinforcement learning and supervised learning methods, enhances robustness of the self-organizing reinforcement learning model based on the developmental network DN2, improves the ability of the algorithm to obtain an effective obstacle avoidance strategy, and can help an intelligent agent to quickly learn an efficient solution.

The present disclosure provides systems and methods for optimizing a treatment regimen in a subject in need thereof. The methods may include detecting a biomarker and, in response to detecting the biomarker, altering one or more aspects of a treatment regimen for the subject. In an aspect, the methods include: detecting a biomarker in the subject, the biomarker being associated with a food preoccupation state, the biomarker comprising an elevation in the 1-13 Hz spectrum power in the ventral basal ganglia region in the brain of the subject when provoked with food stimuli as compared to a control, and altering a treatment regimen of the subject in response to detecting the biomarker.

The application discloses an unmanned obstacle avoidance method based on brain-like pulse reinforcement evolution and related devices, and the application generates an obstacle avoidance action at the current time according to the state sequence of the unmanned vehicle at the current time and the cortex-basal ganglia-thalamus network, and optimizes the cortex-basal ganglia-thalamus network at the next time based on the obstacle avoidance action at the current time. In the optimization process of the cortex-basal ganglia-thalamus network, the alert value of the cortex-basal ganglia-thalamus network to the environment is accumulated according to the reward difference value at adjacent times. When the accumulated alert value does not exceed a second threshold, the cortex-basal ganglia-thalamus network is optimized based on a spike timing-dependent plasticity mechanism and a dopamine regulation mechanism in turn. When the accumulated alert value exceeds the second threshold, the cortex-basal ganglia-thalamus network is optimized based on an offline evolution method of a genetic algorithm, so that the obstacle avoidance method with fast adaptation to different complex environments and accurate decision-making is realized.

A transcranial ultrasound neuromodulation device for accelerating skill acquisition and the formation of muscle memory in athletes, consisting of: A head-mounted ultrasound transmitter array configured for ergonomic positioning on an athlete's head, the array consisting of the following: a multitude of ultrasonic transducers arranged in a hexagonal grid pattern, each transducer being a piezoelectric crystal capable of emitting focused ultrasonic waves with adjustable frequencies in the range of 0.5 MHz to 3.0 MHz with variable intensity and pulse duration; Sensors monitor their alignment and orientation in relation to specific brain regions such as the motor cortex, the cerebellum, and the basal ganglia, which are crucial for motor control and the learning of skills; a control unit that is operationally connected to the ultrasound transmitter array, the control unit comprising: a digital signal processor (DSP) structured to dynamically manage the frequency, amplitude, and focal point of the ultrasound waves emitted by each transducer in real time based on neuromodulation parameters; a wireless communication module configured to receive data from a biofeedback sensor module and to transmit control signals to the ultrasound emitter array, thereby enabling adaptive modulation of the ultrasound parameters; a biofeedback sensor module comprising several sensors, including: electrodes for electroencephalography (EEG); sensors for functional near-infrared spectroscopy (fNIRS); electrodes for electromyography (EMG), with each sensor structurally embedded in the headgear to monitor neuronal activity and physiological responses during training, and with the biofeedback sensor module configured to wirelessly transmit the collected data to the control unit for real-time processing and adjustment of the neuromodulation protocol.

The present invention relates to a method for producing a nonhuman primate animal model of cerebral infarction, comprising administering endothelin to basal ganglia and thalamic region of a nonhuman primate, and thereby inducing basal ganglia damage, thalamus damage, and internal capsule damage; and a pharmaceutical composition for the treatment of cerebral infarction at a subacute to chronic stage, penetrating branch infarction, or cerebral infarction having brain damage in a penetrating branch territory, comprising a NeuroD1 protein or a polynucleotide encoding the NeuroD1 protein.

The invention provides a transcranial magnetoacoustic stimulation system based on regulation and control of a cortical-basal node neural circuit. The transcranial magnetoacoustic stimulation system comprises a digital signal generator, a radio frequency power amplifier, an ultrasonic transducer, an oscilloscope, a microelectrode array and two permanent magnets, the digital signal generator is connected with the ultrasonic transducer through the radio frequency power amplifier, and the ultrasonic transducer is provided with a collimator and emits ultrasonic waves in a pulse form; the microelectrode array is arranged on the head of a stimulated object and is used for collecting electroencephalogram signals in real time when stimulation is executed; the permanent magnets are fixed to the two sides of the head of a stimulated object, static magnetic fields and focused ultrasonic stimulation are applied to a target brain area at the same time, and neural activities of the deep brain area are regulated and controlled through the magnetoacoustic coupling effect. According to the application, the magnetoacoustic coupling effect is utilized to perform non-invasive regulation and control on neural activity in the deep brain region, so that the neural oscillation synchronism between the prefrontal lobe and the striatum is enhanced, and the motor and cognitive dysfunction related to the Parkinson's disease is improved.

The invention provides a basal ganglion Beta rhythm regulation and control device and system based on reinforcement learning, and relates to the field of neural engineering.The basal ganglion Beta rhythm regulation and control device comprises an STN electrode and an intelligent regulator, and the STN electrode is connected with the intelligent regulator; the intelligent regulator is configured to obtain a local field potential signal collected by the STN electrode in real time, and extract a Beta rhythm amplitude at the current moment from the local field potential signal; constructing a current state based on the Beta rhythm amplitude at the current moment and the Beta rhythm amplitude at the previous moment, and inputting the current state into a trained stimulation current recommendation model to obtain the optimal stimulation current intensity; applying the stimulation current intensity to the STN electrode to perform basal ganglion Beta rhythm regulation and control; wherein the stimulation current recommendation model adopts a soft actor-commentator algorithm in reinforcement learning, takes a negative value of a deviation between a Beta rhythm amplitude at the current moment and a target value as a reward function, takes stimulation current intensity as an action, and regulates and controls a Beta rhythm in the brain to an expected level in a closed loop based on reinforcement learning.

A nervous-system emulator engine includes working computational models of the vertebrate nervous system to generate lifelike animal behavior in a robot. These models include functions representing several anatomical features of the vertebrate nervous system, such as spinal cord, brainstem, basal ganglia, thalamus, and cortex. The emulator engine includes a hierarchy of controllers in which controllers at higher levels accomplish goals by continuously specifying desired goals for lower-level controllers. The lowest levels of the hierarchy reflect spinal cord circuits that control muscle tension and length. Moving up the hierarchy into the brainstem and midbrain / cortex, progressively more abstract perceptual variables are controlled. The nervous-system emulator engine may be used to build a robot that generates the majority of animal behavior, including human behavior. The nervous-system emulator engine may also be used to build working models of nervous system functions for clinical experimentation.

The present invention generally relates to the field of computational brain modeling. It is challenging to analyse neuronal activities that governs neuronal dynamics during the decision-making process using conventional techniques. Thus, embodiments of present disclosure provide a method and system to identify neuronal ensembles in basal ganglia using hierarchical drift-diffusion modeling. Microelectrode recording data of neuronal activity of neurons in the nuclei within BG of a subject are obtained. Then, spikes and associated spike times are extracted from the obtained data using which Inter-Spike Intervals (ISIs) for each of the neurons are calculated. Response times of each neuron is determined based on the ISIs and they are classified as one of an active state and a resting state which inherently reflected the broader network states responsible for behavioral responses by the neurons. Finally, the neurons are grouped into neuronal ensembles based on HDDM latent variables like drift rate.

The present invention relates to a method for producing a nonhuman primate animal model of cerebral infarction, comprising administering endothelin to basal ganglia and thalamic region of a nonhuman primate, and thereby inducing basal ganglia damage, thalamus damage, and internal capsule damage; and a pharmaceutical composition for the treatment of cerebral infarction at a subacute to chronic stage, penetrating branch infarction, or cerebral infarction having brain damage in a penetrating branch territory, comprising a NeuroD1 protein or a polynucleotide encoding the NeuroD1 protein.

This invention discloses a method for establishing Alzheimer's disease in tree shrews. The specific steps involve mixing an ethanol solution of 0.5 μg / μl Microcystin-LR with a muscarine solution, and then injecting 2 μl into each of the bilateral Meynert basal ganglia of the tree shrew. After administration, the tree shrew is fed normally for two weeks to complete the Alzheimer's disease model. The muscarine solution is prepared under sterile conditions by adding 200 μl of sterile saline to a vial containing 1 mg of muscarine, repeatedly pipetting to ensure complete dissolution, resulting in a concentration of 5 μg / μl. The Microcystin-LR solution is a commercially available product. This invention involves injecting a mixture of 0.1 μg MC-LR and 3 μg IBO solution into the bilateral Meynert basal ganglia of tree shrews, establishing a stable tree shrew AD model after two weeks. Three days after administration, tree shrews exhibited AD behavioral manifestations, such as lethargy and reduced activity. Two weeks later, the tree shrew brains showed typical AD pathological manifestations, including Aβ deposition and elevated expression levels at multiple phosphorylation sites of Tau protein.

The invention provides a Parkinson disease course quantitative evaluation method and system based on multi-scale feature fusion, and the method comprises the steps: simulating the continuous disease course progress of a Parkinson state from health to severe based on a constructed cortex-basal node-thalamus neural circuit model, so as to obtain neuron pulse sequence signals of each brain region at different progress stages; the method comprises the following steps: performing multi-scale feature extraction on a neuron pulse sequence signal to obtain a microscopic discharge feature, a macroscopic oscillation feature and a network synchronization feature; performing contribution degree sorting on the microcosmic discharge features, the macroscopic oscillation features and the network synchronization features through a recursive feature elimination algorithm to screen out a core feature subset most sensitive to the progress of the disease course; and inputting the screened core feature subset into the constructed machine learning model to output an evaluation result of the Parkinson disease course. According to the method, the saturation effect of a single index in the later period of the disease course is overcome, the linear evaluation of the whole disease course is realized, and the severity of the disease can be more comprehensively and accurately quantified.

The invention relates to a multichannel fusion Parkinson's disease recognition system and system based on PETMR imaging, and the method comprises the steps: collecting a PETMR image of a patient, the PETMR image comprises a plurality of sequence images, and carrying out the registration of the sequence images based on a basal node region; carrying out sequence identification on the registered PETMR images, and carrying out image information segmentation and extraction of a basal section area on each identified image to obtain a sequence basal section image of each sequence image; performing sequence feature extraction based on the sequence base section image, calculating the weight of each sequence feature, and screening a target sequence and a corresponding key sequence feature based on the weights; carrying out Parkinson identification by using a feature fusion strategy based on the features of the target sequence and the key sequence; the feature fusion strategy is an input-level early-stage fusion strategy, a semantic-level medium-stage fusion strategy or a decision-level later-stage fusion strategy. Compared with the prior art, the invention provides a Parkinson's disease identification method with lesion area targeting and high reliability.

The invention provides a basal ganglion-based spiking neural network decision model, which comprises a ventral striatum module, a nigra module, a cortex module and a basal ganglion module, and is characterized in that the cortex module is connected with the basal ganglion module, and environment state information is transmitted to the basal ganglion module; the basal ganglion module performs action selection and action sequence learning; the ventral striatum module is connected with the nigra module, the ventral striatum module receives the current state and transmits a calculated state value to the nigra module, and the nigra module further receives an award fed back by the environment and calculates an award prediction error of an actual award and an expected award to obtain a TD error; the nigra module is respectively connected with the ventral striatum module, the cortex module and the basal ganglion module, and the TD error further updates plastic synapses between the ventral striatum module and between the cortex module and the basal ganglion module. The invention provides a new cortical-striatum synaptic plasticity updating rule, and the ability of the model to cope with complex scenes is improved.