90 results about "Hippocampus proper" patented technology

The invention discloses an intelligent orthopedic rehabilitation self-adaptive evaluation method and device based on multi-modal physiological feedback. The method comprises the steps that multi-modal physiological data including electromyographic signals, knee joint three-dimensional motion trails, plantar pressure distribution and cognitive input degree of a subject are synchronously collected; constructing a three-dimensional baseline vector according to the MMT muscle force test, knee joint ROM measurement and VAS pain score of the subject; inputting the multi-modal physiological data and the three-dimensional baseline vector into a personalized orthopedic rehabilitation model, and outputting a knee joint function index prediction value and personalized rehabilitation adjustment parameters; dynamically adjusting electrical stimulation parameters according to rehabilitation adjustment parameters output by the personalized orthopedic rehabilitation model; if the cognitive load index exceeds the early warning line, the electrical stimulation intensity is reduced; otherwise, activating the hippocampus body wave stimulation mode. Adjustment is carried out according to the real-time state of the subject, blindness and subjectivity in the rehabilitation process are avoided, and the rehabilitation efficiency and success rate are improved.

The invention discloses an Alzheimer disease risk prediction method based on feature fusion, a medium and equipment. The method comprises the following steps: firstly, acquiring an sMRI image, an FDG-PET image, a biomarker and intelligence score data of a target subject; then, sMRI and FDG-PET image data are respectively input into a three-dimensional convolutional neural network module, and a corresponding first image feature map and a corresponding second image feature map are extracted; secondly, inputting the two feature maps into a cross attention fusion module to obtain image fusion features; and inputting a U-shaped up-sampling network to segment a hippocampus region to obtain hippocampus features. And then, merging the image fusion features, the hippocampus features, the biomarker data and the intelligence score data, and inputting the merged data into an extreme learning machine for calculation, thereby obtaining an onset risk prediction result of the Alzheimer's disease. According to the method, by means of cross-modal data fusion, multi-modal data complementary information is extracted, and the accuracy of onset risk prediction can be effectively improved.

The invention provides a cheese protein hydrolysate as well as a preparation method and application thereof. The composition of the casein hydrolysate at least comprises at least one of a peptide fragment 1 with an amino acid sequence as shown in SEQ ID NO: 1, a peptide fragment 2 with an amino acid sequence as shown in SEQ ID NO: 2 and a peptide fragment 3 with an amino acid sequence as shown in SEQ ID NO: 3. The casein hydrolysate has good neuron protection and repair effects, and can improve cholinergic neuron and dopaminergic neuron damage; abeta protein deposition can be inhibited, so that neuronal synaptic disorder, neuronal apoptosis and brain injury caused by Abeta protein deposition are relieved; the cholinergic defect can be improved, so that the nerve development damage and the behavior and movement disorder caused by the cholinergic defect are relieved; and damaged neurons in hippocampus and hypothalamus can be repaired, so that the brain environment is kept stable.

The invention discloses an environment-emotion gradient association memristive neural network circuit based on a TAP cell regulation mechanism. The environment-emotion gradient association memristive neural network circuit is composed of a hippocampus module and an amygdala kernel module. The hippocampus module comprises an environment recognition neuron module and an emotion generation neuron module; the environment recognition neuron module and the emotion generation neuron module are each composed of a weight regulation and control module adj based on a TAP cell regulation mechanism, an adder, a memristor, a resistor, an operational amplifier, a function module ABM1 and a sampling holder. Wherein the weight regulation and control module adj is composed of a voltage-controlled switch, a summator, a function module ABM2 and a function module ABM3. The almond kernel module is composed of a weight regulation and control module adj, a memristor, a resistor, an operational amplifier and a sampling holder. The provided circuit simulates the hippocampus and amygdala nucleus of the brain in structure, the regulation principle of TAP cells on neural stem cells is used for reference in a weight regulation mechanism, and bidirectional gradient associated memory of the environment and the emotion can be achieved.

Verbal memory impairment is a common symptom of temporal lobe epilepsy (“TLE”). The ability to encode and recall verbal memories can be probed with the classic free recall task. This study had three overarching hypotheses: 1) increased seizure frequency will lead to decreased verbal memory, 2) stimulation will modulate memory performance, and 3) stimulation will modulate the underlying neural correlates of memory. The study investigated these hypotheses in patients receiving chronic DBS to bilateral ANT. Recordings were obtained from individuals with drug resistant mesial TLE implanted with an investigational MEDTRONIC SUMMIT RC+S™ sensing and stimulation device. As patient seizure diaries are notoriously inaccurate, the patient's continuous local field potential (“cLFP”) was scored for seizures using a validated seizure classifier with trained epileptologist review, resulting in a reliable seizure diary. The subjects completed free recall memory tasks in their home environment with cLFP and behavioral data streamed to a handheld device and cloud repository.Through implementation of generalized linear mixed models (GLMM), the study was able to determine that ANT stimulation modulates memory performance, but changes in the seizure rate were not predictive of changes in memory performance. Analyzing the continuous local field potential recordings from these tasks, the study found that spectral power in the theta frequency band in the left ANT and hippocampus correlated with acute memory performance and chronic therapeutic stimulation frequency received.Not all patients are candidates for devices with chronic sensing capabilities, and this study indicates that changes in verbal memory performance can provide a suitable metric of therapy effectiveness in lieu of a reliable, objective seizure diary. But for those who do receive these and similar devices, thalamic-hippocampal spectral activities can be used to track and predict memory performance acutely and chronically, as well as predict the effect of therapeutic deep brain stimulation. Our study demonstrates chronic improvement of verbal memory with a new biomarker-based technology for remote task administration and modulation of the associated neural activities.

The present invention provides a hippocampus segmentation method and system based on coronal T1-MRI, including: obtaining AD coronal T1MRI images and corresponding hippocampus labels, and obtaining a data set after preprocessing; constructing a feature extraction module to obtain a feature map; constructing an uncertainty attention module to calculate uncertainty factors; building a segmentation network NU-Mamba; constructing a loss function to balance dynamic balance through uncertainty factors; NU-Mamba is trained and tested on the data set and outputs the final model, and the T1MRI image is segmented to obtain a segmentation result. The present invention solves the problem that the existing model optimization can only achieve suboptimal results by constructing different loss functions to balance the importance of foreground and background. Compared with other hippocampus segmentation algorithms, the segmentation accuracy is improved by more than 10%; it has the characteristics of automation and intelligence, and provides help for doctors' clinical diagnosis and formulation of patient treatment plans through simpler operations and faster result presentation, and has better clinical practicality, high immediacy and popularity.

The invention belongs to the technical field of nutritious food, and particularly discloses a composition for promoting brain development and application thereof. The composition for promoting brain development provided by the invention comprises bifidobacterium longum subsp. Infantis i18 and sn-2 site docosahexaenoic acid. According to the composition provided by the invention, all the components have a synergistic effect, so that the accumulation of DHA in cerebral cortex and hippocampus can be remarkably improved, and the purpose of promoting brain development is achieved. The composition is suitable for health food, formula milk powder or fermented milk and other products for improving cognition and promoting brain development.

The invention relates to application of bifidobacterium animalis subsp. Lactis BLa80 in preparation of a preparation for relieving depression or anxiety. The bifidobacterium animalis subsp. Lactis BLa80 is a strain of the bifidobacterium animalis subsp. Lactis BLa80 with the preservation number of CGMCC (China General Microbiological Culture Collection Center) No.22547, and the strain of the bifidobacterium animalis subsp. Lactis BLa80 is a strain of the bifidobacterium animalis subsp. Lactis BLa80 with the preservation number of CGMCC No.22547. The invention develops a brand new application of the bifidobacterium animalis subsp. Lactis BLa80 strain, namely, the bifidobacterium animalis subsp. Lactis BLa80 strain is used for relieving depression or anxiety symptoms, specifically, the bifidobacterium animalis subsp. Lactis BLa80 strain can stimulate RIN14B cells to secrete 5-HT, can relieve depression and anxiety-like behaviors of mice suffering from depression and anxiety, can relieve hyperfunction of hypothalamic-pituitary-adrenal axis (HPA axis), and can relieve depression and anxiety-like behaviors of mice suffering from depression and anxiety. The inflammatory response of mice with depression and anxiety is relieved, the expression level of brain-derived neurotrophic factors in hippocampus of the mice with depression and anxiety is improved, the abundance of intestinal beneficial flora of the mice with depression and anxiety is improved, and the diversity of intestinal flora is increased.

The invention belongs to the technical field of organic synthesis, and particularly discloses a near-infrared organic amine BODIPY fluorescent probe capable of recognizing A beta fibers with high sensitivity and a preparation method and application thereof.The near-infrared organic amine BODIPY fluorescent probe is synthesized by enabling para-organic amine substituted benzaldehyde and BODIPY fluorescent dye to be subjected to a Knoevenagel condensation reaction; according to the present invention, the synthesized organic amine BODIPY compound can be combined with diphenylalanine dipeptide so as to achieve the selective recognition of the Abeta fiber in the hippocampus, the organic amine is combined with the BODIPY fluorescent dye, such that the optical property of the BODIPY can be effectively regulated due to the strong electron supply ability and the low steric hindrance so as to achieve the selective recognition of the Abeta fiber in the hippocampus; and the sensitivity and the performance in AD application are improved. The enhanced signal response is helpful for realizing the rapid identification of the A beta fiber, and provides a potential strategy for the prevention and treatment of AD (Alzheimer's disease).

The invention provides application of urolithin A in Alzheimer's disease, belongs to the technical field of medicinal chemistry and treatment of mental diseases, and aims to solve the problem of social anxiety caused by the Alzheimer's disease. According to the application disclosed by the invention, the mitochondrial autophagy effect of urolithin A is utilized, A beta is eliminated, a key brain region loop aBLA-vCA1 is repaired, and the anxiety symptom of a patient suffering from the Alzheimer's disease is relieved. According to the present invention, with the application of the urolithin A in the treatment of Alzheimer's disease, through the mitochondrial autophagy function of the urolithin A, the targeted removal of A beta and the repair of the damaged basal outer side amygdala nucleus and hippocampus loop are achieved so as to effectively relieve the anxiety symptom of the patient and provide the new strategy for the AD treatment.

The invention discloses a multi-modal feature fusion prediction method and system for early screening of Alzheimer's disease, and belongs to the technical field of brain disease prediction. The method comprises the following steps: acquiring multi-dimensional data containing cognitive test scores, brain image scanning results and biomarker concentration levels from a patient record database, and performing standardization processing to obtain a multi-dimensional data set in a unified format; key feature vectors are extracted through a dimensionality reduction analysis method to capture the covariant relation between cognitive test scores and brain image changes; when the reduction range of the cognitive test score exceeds a preset threshold value and the brain image displays an atrophy sign, a classification model is constructed through an integrated learning method to preliminarily classify abnormal signals; and fusing the biomarker concentration level to obtain an abnormal signal vector. According to the method, the accuracy and efficiency of early screening of brain diseases such as Alzheimer's disease are remarkably improved through accurate evaluation of key parts of the human brain, such as hippocampus.

The present disclosure relates to the treatment and prevention of impaired cognitive function, cognitive decline and / or dementia or cerebral nervous system degenerative conditions in a subject suffering from diabetes mellitus by administering a pharmaceutical composition comprising a compound capable of reducing copper levels or amounts elsewhere in the hippocampus or brain of the subject.

The invention provides a senile dementia early diagnosis method based on hippocampus magnetic resonance imaging, which comprises the following steps that: according to a segmented image, a contour extraction algorithm is adopted to obtain multi-layer two-dimensional contour data to obtain a contour sequence, and the contour extraction algorithm carries out gradient analysis and threshold filtering on geometric features of a hippocampus, and captures boundary details; the contour sequence is subjected to space alignment processing, an alignment contour set is obtained, displacement deviation between different layers is eliminated through a transformation correction method in the space alignment processing, and the alignment contour set is generated; calculating volume and morphological indexes according to the preliminary three-dimensional model, and determining the atrophy degree; and comparing the refined three-dimensional structure with a preset reference model to generate difference mapping, judging the diagnosis precision, and adopting a geometric feature deviation measurement method to evaluate the structural difference in comparison to generate the difference mapping. According to the method, the fidelity and diagnosis precision of three-dimensional reconstruction of the hippocampus are remarkably improved, and reliable technical support is provided for early diagnosis of neurodegenerative diseases.

The invention provides a nutritional composition capable of influencing protein expression of brain hippocampus and application of the nutritional composition. The nutritional composition of the present invention comprises sialic acid, sphingomyelin and phosphatidylcholine, in which the ratio of the sum of the contents of sphingomyelin and phosphatidylcholine to the content of sialic acid is 1: 1 by weight, and the ratio of sphingomyelin to phosphatidylcholine is 0.2: 1 to 1.4: 1 based on the total weight of sphingomyelin and phosphatidylcholine. The composition can increase the gray matter volume of the brain and can improve the expression of BDNFmRNA of the hippocampus and the prefrontal cortex.

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.

This invention discloses a method for identifying and processing abnormal hippocampal atrophy in functional MRI images of the brain, comprising: acquiring functional MRI image data of the brain to be processed; preprocessing the functional MRI image data of the brain to obtain preprocessed functional MRI image data of the brain; extracting the hippocampal region from the preprocessed functional MRI image data of the brain; constructing a three-dimensional image of the hippocampus based on the hippocampal region; performing feature extraction on the three-dimensional image of the hippocampal region based on a deep learning network to obtain feature extraction results; and classifying and identifying abnormal atrophy of the hippocampal region according to the feature extraction results to obtain classification and identification results.

The invention provides a hippocampus and prefrontal cortex function inspired mobile robot behavior decision-making method, and relates to the technical field of mobile robot control, the current state and the target position of a mobile robot are obtained from the environment, and a model-based reinforcement learning module is constructed through the spatial cognitive function of a hippocampus; reducing the dimension of the current state into a two-dimensional discrete state, inputting the two-dimensional discrete state into a model-based reinforcement learning module, and outputting an action to a decision controller; a model-free reinforcement learning module is obtained based on the basal ganglion and hippocampus memory function, the current state is input into the model-free reinforcement learning module, an action is output to a decision controller, and the decision controller is constructed by simulating the function that the prefrontal cortex is switched between the model-based reinforcement learning module and the model-free reinforcement learning module; and the decision controller outputs an execution action to complete the behavior decision of the mobile robot, so that the mobile robot is ensured to make a more reliable decision, and the performance of the mobile robot in the aspect of optimal decision is improved.

The invention belongs to the technical field of disease control in aquaculture, and particularly relates to a hippocampus enteritis pathogen separation and identification and medication screening method based on a high-throughput sequencing technology, which comprises the following steps: collecting intestinal tissues of a diseased hippocampus and a healthy hippocampus, respectively preparing tissue homogenate, and freezing for preservation; the method comprises the following steps: extracting DNA (Deoxyribose Nucleic Acid) of microorganisms of an intestinal tissue sample, performing 16SrDNA high-throughput sequencing, analyzing intestinal flora composition, and determining dominant bacteria; selecting and screening a culture medium according to dominant bacteria, and performing pathogenic bacterium separation and purification on intestinal tissues of the diseased hippocampus; dNA of the purified strain is extracted, 16S rDNA amplification and sequencing are carried out, and the species of pathogenic bacteria are identified through sequence comparison; according to the method, the diversity and abundance differences of microorganisms in the sick hippocampus and the healthy hippocampus are compared through a high-throughput sequencing technology, and targeted separation, identification and drug sensitivity screening are further carried out by taking statistically significant difference bacteria as the guidance of potential pathogenic bacteria.

Systems and methods for neural modeling are disclosed. A perfusable microfluidic platform with human hippocampal organoid integration and functional vasculature is disclosed. Vascularized human brain organoids can be developed on a microphysiological platform. The system can include a membrane configured to enable perfusion throughout the membrane. The membrane can couple with a carrier. The system can include the carrier including channels. The channel can be configured to deliver a first set of cells to the membrane through a lumen of the membrane; and deliver a second set of cells to the membrane through an exterior of the membrane. The system can include monitoring components to continuously and non-invasively measure the onset and development of neural oscillations.

To provide a gene construct for use in the treatment of a metabolic disorder in a mammal, particularly a human.SOLUTION: A gene construct provided comprising a nucleotide sequence encoding fibroblast growth factor 21 (FGF21) for use in the treatment and / or prevention of a metabolic disorder, wherein the therapy involves expression of the gene construct in the central nervous system (CNS), preferably in the brain, more preferably in the hypothalamus and / or cortex and / or hippocampus and / or cerebellum and / or olfactory bulb, and most preferably in the hypothalamus.SELECTED DRAWING: None

The invention relates to a bifidobacterium composition for relieving mood disorder caused by constipation and application of the bifidobacterium composition, and belongs to the technical field of probiotics. According to the bifidobacterium composition containing the animal bifidobacterium subsp. Lactis Bb-12 and the bifidobacterium longum subsp. Longum BB536, through the synergistic effect of the Bb-12 and the BB536, the effect of relieving constipation can be achieved by improving the water content of excrement, improving the intestinal tract movement capacity and increasing the content of short-chain fatty acid in the intestinal tract. On the basis that constipation is relieved, emotional disorders caused by constipation can be further relieved, and particularly, the content of anxiety and depression related cranial nerve factors BDNF in hippocampus is increased, and the relative expression quantity of inflammatory factors TNF-alpha is reduced. A new thought is provided for the development of nutritional supplement food for relieving anxiety and depression caused by constipation.

The invention discloses an emotion memory generation system based on a memristor, and belongs to the field of integrated circuit design. The system comprises a brain emotion learning model and a hippocampus model. The brain emotion learning model comprises a thalamus module, a sensory cortex module, an orbital frontal cortex module and an amygdala kernel module; the hippocampus model comprises a DG module, a CA3 module and a CA1 module; under the stimulation of the emotional signals, the thalamus module firstly preprocesses the emotional signals, then the sensory cortex module and the orbital frontal lobe module integrate and process the preprocessed emotional signals, and the processed signals are respectively transmitted to the CA3 module and the amygdala kernel module to generate output signals representing emotions; under the stimulation of a learning signal, the DG module firstly preprocesses the learning signal, then the CA3 region is responsible for receiving the learning signal and generating an output signal representing short-term memory, and the output signal is transmitted to the CA1 region; and after receiving the short-term memory signal, the CA1 area converts short-term memory into long-term memory and stores the long-term memory.

The present invention discloses a 3D intelligent analysis and diagnosis platform for MRI hippocampus based on deep learning, which relates to the field of auxiliary medical technology based on deep learning. The platform of this application includes a user login module, a front-end monitoring module, a user demand analysis module, a data loading and preprocessing module, an inference module, a first display module, a second display module, a diagnosis module, a storage module, a query module and an interaction module; this platform combines with a 3D data visualization library to realize a 3D dynamic display of the hippocampus, which can not only observe the structure of the hippocampus more comprehensively and in detail, but also can deeply understand the data from different angles and levels through interactive operations. This platform provides a user-friendly operation interface, even users who have never been exposed to deep learning can easily upload data and quickly start segmentation work. The simple and intuitive design reduces the user's learning cost and improves work efficiency.

The embodiment of the invention provides a hippocampus substructure morphology measurement method, device, equipment and medium, and relates to the technical field of image processing, and the method comprises the following steps: segmenting a magnetic resonance standard template image, constructing a boundary optimization surface, and determining skeleton representation; sampling the skeleton representation, determining sub-region thickness measurement points of each hippocampus sub-region, and constructing a thickness measurement template of the template hippocampus according to the sub-region thickness measurement points and skeleton points in the skeleton representation; determining a target boundary optimization surface corresponding to a target magnetic resonance image of a target hippocampus, and generating a target skeleton representation according to the target boundary optimization surface and the thickness measurement template; and determining the morphological characteristics of the target hippocampus according to the positions of the subregion skeleton points in the target skeleton representation. According to the embodiment of the invention, fine measurement can be carried out on the substructure of the hippocampus, and cross-individual local form stable correspondence is realized.

The invention relates to the technical field of image processing, in particular to a method for sketching a hippocampus area in a CT image of an Alzheimer's disease patient, and the method comprises the steps: obtaining the CT image of the Alzheimer's disease patient, and carrying out the edge recognition to obtain each edge area; every three adjacent marginal areas are grouped, and a first possibility that each group belongs to the hippocampus area is acquired based on the shape expression and the distribution condition of the three marginal areas in each group; the second possibility that the lateral ventricular temporal horn exists in the auxiliary judgment area located on the lower left portion of each group is obtained, the hippocampus area is screened from the groups in combination with the first possibility, the accurate hippocampus area can be obtained, and even if the CT image of the Alzheimer disease patient is blurred due to some reasons, the hippocampus area can be accurately obtained. And the accurate sketching of the hippocampus body area is not influenced.

The present application provides a kind of nutritional composition in the application of the product for improving learning, memory and cognitive ability.The nutritional composition includes animal bifidobacterium lactis subsp.CP-9 and 3'-sialyllactose.Animal bifidobacterium lactis subsp.CP-9 and 3'-sialyllactose can synergistically enhance the memory retention ability of the body, reduce the degree of learning and memory errors, and up-regulate the expression of oligodendrocyte transcription factor 2 coding gene, myelin basic protein coding gene and brain-derived neurotrophic factor coding gene, realize the double regulation of myelin formation and neurotrophic in hippocampus region of brain, synergistically improve the neural plasticity of central nervous system through gut-brain axis pathway, and then double improve the learning, memory and cognitive function of the body from the behavior and molecular level.

This invention discloses a novel application of Bifidobacterium breve CCFM1078 in relieving intestinal colic, belonging to the fields of microbial technology and pharmaceutical technology. The Bifidobacterium breve CCFM1078 provided by this invention can relieve intestinal colic in infants and young children, specifically in the following ways: (1) increasing the visceral pain threshold and reducing the frequency of defecation in rats to a certain extent; (2) reducing the number of chromaffin cells and 5-HT content in the colon of rats with intestinal colic; (3) reducing the serum TNF-α and IL-1β content in rats with intestinal colic; (4) reducing the hippocampal TNF-α and IL-6 content in rats with intestinal colic; and (5) increasing the propionic acid content in the cecal contents of rats with intestinal colic.

Provided are compositions containing microalgae extracellular vesicles (MEVs) formulated for intranasal delivery, whereby, upon intranasal administration the MEVs traffic through specific routes following intranasal administration to specific regions in the brain via the olfactory nerve and throughout the lateral olfactory tract (LOT) to interconnected brain regions. The MEVs traffic via neuronal axonal transport. The MEVs have the ability to cross-over synapses including: (i) the synapses between the olfactory sensory neurons (OSN) and the mitral / tufted neurons; (ii) the synapses between the mitral / tufted neurons and the local neurons in the various brain regions colonized by the lateral olfactory tract (LOT); and (iii) the synapses between the neurons in the brain regions colonized by the LOT and neurons from the frontal cortex, the hippocampus, the thalamus, and the hypothalamus. The compositions contain extracellular vesicles from microalgae (MEVs) that are loaded with bioactive cargo for treating, detecting, diagnosing, or monitoring a disease, disorder, or condition of the brain or involving the brain, particularly providing neuronal delivery of the cargo. The compositions and methods have a variety of applications as therapeutics and diagnostics for treating, diagnosing, and monitoring a disease, disorder, or condition of the brain or involving the brain. The compositions can be used in methods and uses for treating cancers involving the brain, and can be used, for example, to deliver therapeutics for psychiatric diseases, disorders, conditions, and to deliver therapeutics for neurodegenerative diseases, disorders, and conditions.

The invention discloses a near-infrared illumination treatment device for treating Alzheimer's disease, and relates to the technical field of medical equipment. The device comprises a treatment table, a support is movably connected to the top of the treatment table, a supporting shell is arranged at the other end of the support, and a first infrared lamp panel is installed in the supporting shell. By arranging the rotatable circular ring and the second infrared lamp panel installed in the circular ring and cooperating with a limiting assembly composed of a supporting frame, a moving rod, a rubber pad and a screw rod, accurate adjustment and reliable fixation of the irradiation angle of near-infrared light are achieved, and medical staff can accurately adjust the irradiation angle of the near-infrared light according to the structural difference of brain regions of patients. The irradiation direction of the second infrared lamp panel is dynamically adjusted, particularly, accurate directional irradiation can be conducted on key areas such as hippocampus, and compared with a fixed-angle irradiation mode in the prior art, the device remarkably improves treatment pertinence and adaptability and effectively solves the problem of irradiation blind areas caused by head shape difference.

The application discloses a kind of brain-like neural network multi-vehicle coordination decision-making method and system fusing hippocampus playback mechanism, method includes: vehicle-mounted terminal receives the environment data from vehicle-mounted sensor and traffic signal data transmitted by roadside end, utilize pulse coding conversion into space-time pulse sequence;Time-space pulse sequence is input into pulse neural network to carry out prediction, and based on the feedback data of predicted road condition and vehicle-mounted sensor, calculate prediction error, and the physical feedback of real-time monitoring vehicle;When prediction error or physical feedback exceeds prediction mismatch degree threshold value, activate hippocampus memory mechanism, intercept all perception pulse sequence and self state data in the preset time window before current time, and as high-risk scenario memory write vehicle-mounted short-term memory buffer area;When vehicle is idle or charging state, use counterfactual reasoning mechanism to read high-risk scenario memory, reconstruct high-risk scene in virtual environment, and generate candidate strategy through counterfactual reasoning mechanism to carry out virtual trial and error.