92 results about "Functional network" patented technology

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention discloses an epilepsy abnormal brain network identification method based on a multi-scale static-dynamic fusion network, and belongs to the field of brain image analysis. The method comprises the following steps: firstly, constructing a static function connection weighted graph and a dynamic function connection graph; and fusing the static and dynamic representations by adopting a cross attention module. In order to describe a multi-scale spatial relationship, performing lexical meta-processing on brain connection according to anatomical partition and a functional network; and the local-global fusion module is used for integrating the fine granularity and the macroscopic relationship, so that the brain region with diagnostic significance is highlighted. In the training stage, cross entropy, reverse contrast loss and sparse regularization based on contrast graph adjacency matrix entropy are jointly used. The method is verified on multi-center functional magnetic resonance data, compared with other mainstream depth models, the classification accuracy, generalization and interpretability are remarkably improved, an abnormal brain region consistent with an epilepsy network can be positioned, and brain image markers with biological significance can be connected and recognized.

System and methods that improve the use of network functions on edge data networks are disclosed. A mechanism that supports edge-assisted UE context and trigger collection is described herein, where a UE can actively send its context information and / or any trigger (e.g. a request for a network function / service to be deployed in edge data networks, etc.) to an edge enabler server, which processes the received context and triggers from its UEs. The edge enabler server may also forward UE context and triggers to 5G core network (5GC) upon receiving a solicitation request from the 5GC, or if the 5GC has already made a subscription for getting notification on UE context and triggers. A network repository function is provided as a new network function collect, store, and manage edge data network information.

The invention relates to a depression risk detection method based on self-adaptive multi-scale neighborhood perception fusion space-time diagram convolution, and aims to solve the problems that traditional depression diagnosis is subjective and existing electroencephalogram detection space-time feature modeling is insufficient. The method comprises the following steps: acquiring electroencephalogram signals of a testee, and extracting frequency spectrum features through a frequency domain feature extraction module; in combination with an adjacent matrix (independent of an electrode physical distance) which is adaptively learned and normalized in training, embedded features are generated through a graph convolutional neural network; and then capturing long-time-history dependence through a dynamic feature extraction module, realizing feature multi-level fusion by means of a multi-cascade multi-scale convolution module, finally integrating local and global features through an adaptive feature fusion module, and inputting a prediction layer to output a detection result. According to the method, electroencephalogram signal time-space domain joint modeling is achieved, brain region function connection dynamic and multi-scale neural features are effectively captured, information redundancy and key feature loss are avoided, classification accuracy and generalization ability are remarkably improved, depression-related key brain region and function network features can be revealed, and the method has high interpretability and potential clinical application value.

The invention provides an automatic test system and method for a third-party high-power repeater, and relates to the technical field of automatic test equipment, and the system comprises an integrated reconfigurable passive link box, a control host, a vector signal generator and a vector signal analyzer. The integrated reconfigurable passive link box comprises a box body, a radio frequency switch matrix network, an embedded function network, a control port, an input port, an output port, an uplink interface and a downlink interface. The radio frequency switch matrix network and the embedded function network are arranged in the box body. The input port, the output port, the uplink interface and the downlink interface are arranged on the surface of the box body. The vector signal generator is connected with the input port, the vector signal analyzer is connected with the output port, and a third-party high-power repeater to be tested is arranged between the uplink interface and the downlink interface. According to the invention, the radio frequency switch matrix network and the embedded function network are packaged in the integrated reconfigurable passive link box, and the control host is utilized to carry out automatic testing on the third-party high-power repeater according to the preset process, so that frequent operation processes are avoided, and damage to instruments is reduced.

The invention relates to the technical field of neurotrauma prognosis image analysis, and discloses a craniocerebral trauma prognosis prediction analysis system based on a three-dimensional model. According to the system, multi-scale segmentation and topology construction are carried out on a craniocerebral three-dimensional image of a patient, the morphological evolution rate of a trauma area is tracked, and key signal events in the trauma evolution process are accurately recognized in combination with an edema signal change curve. The system further quantitatively analyzes dynamic deviations associated with the integrity of normal brain tissue fiber bundles when a signal event occurs, thereby generating a lesion propagation path and mapping it to functional network nodes of a standard brain map, ultimately identifying a prognostic key brain network. According to the technical scheme, key event capture and path foresight prediction in the dynamic propagation process of the secondary injury after the craniocerebral trauma are realized, and the accuracy of prognosis evaluation is improved.

The invention discloses a personalized MCI electrical stimulation intervention method based on a dynamic brain network, and belongs to the technical field of electrical stimulation target determination. Individual heterogeneity of a brain function network of a patient is considered, specific frequency band selection is carried out on electroencephalogram data sets of a normal group and a cognitive impairment group, an effective data set is constructed, and the effective data set is determined. The method comprises the following steps: respectively constructing average dynamic brain network connection of total test times of a normal group and a cognitive impairment group based on an adaptive method of dynamic time-varying weight optimization of reaction time, and then determining a main abnormal frequency band through the difference of the average dynamic brain network connection of the two groups; and then target points are determined for the core nodes connected with the average dynamic brain network of the main abnormal frequency band, and compared with traditional single-frequency-band analysis, the application proposes that the effective data set is constructed by the specific frequency band to determine the main abnormal frequency band; in addition, stimulation targets are positioned according to the core nodes connected with the average dynamic brain network of the abnormal frequency band, and the reliability of target selection is improved.

Disclosed in the embodiments of the present application are a quantization-based data processing method and apparatus, and a device and a medium. The method comprises: using a plurality of bit quantization modes to respectively perform quantization processing on an original model parameter corresponding to a model function network, so as to obtain a quantized model parameter corresponding to each bit quantization mode; then, acquiring a first output result of the model function network under the original model parameter, and acquiring a second output result of the model function network under each quantized model parameter; and next, on the basis of the first output result and a plurality of second output results, selecting a target bit quantization mode from among the plurality of bit quantization modes, wherein the target bit quantization mode is used for performing quantization processing on the original model parameter during an inference phase of the model function network. The technical solution of the present application improves the accuracy of quantization processing, and achieves good model performance.

The invention relates to the technical field of brain function network analysis, in particular to a post-stroke aphasia patient fMRI focal recognition method based on deep learning. The method comprises the following steps of: acquiring a region consistency value for geometric structures of matched local regions of two adjacent fMRI images in an fMRI image sequence, similarity of gray distribution and an image matching degree, and dividing a same time window by using the region consistency value; and obtaining a functional connection coefficient according to the difference of the region consistent values of the fMRI images in two adjacent time windows and the difference of the geometric structure features and the gray level distribution features of the local regions, determining the convolution kernel size of deep learning of the fMRI images in the time windows by using the functional connection coefficient, and performing focal recognition on the fMRI images in the time windows. According to the method, the function connection coefficient presenting the cooperation degree of the brain region activity modes in the adjacent time windows is analyzed, the convolution kernel size of the time windows is determined in a self-adaptive mode, and the recognition effect on the local focus of the patient is improved.

Various systems and methods for providing decentralized reputation management in a named-function network are described herein. A compute node is configured to access an information centric network (ICN) interest packet from a user device, the ICN interest packet including a function name and a data name; construct a named-function network (NFN) interest packet using the function name; transmit the NFN interest packet to a function provider; receive an NFN data packet with a version of a function corresponding to the function name; construct a named-data network (NDN) interest packet using the data name; receive an NDN data packet with a data value corresponding to the data name; determine that the version of the function is not on a denylist; and initiate execution of the version of the function with the data value in response to determining that the version of the function is not on the denylist.

The invention discloses a coal gasification device key parameter diagnosis method and system based on digital twinning, and relates to the field of coal gasification device parameter diagnosis, and the method comprises the following operation steps: S1, multi-source data acquisition and intelligent preprocessing, S2, dynamic digital twinning hybrid modeling, S3, dynamic digital twinning hybrid modeling, S4, dynamic digital twinning hybrid modeling, and S5, dynamic digital twinning hybrid modeling. Constructing a dynamic digital twinborn body in which the mechanism model and the data driving model are deeply coupled; s3, performing real-time diagnosis on the health degree of the key parameters; s4, fault root cause tracing and model dynamic correction; and S5, optimizing the coal types. According to the coal gasification device key parameter diagnosis method and system based on the digital twinning, based on strong correlation parameter linkage verification of a function network topology library, the misjudgment rate of a single parameter can be greatly reduced; the graded response mechanism realizes stepped processing of digital twinborn optimization in a normal state, automatic fine adjustment of an early warning state and rapid alarm in an abnormal state, so that potential faults can be found in advance, and the problem of non-planned shutdown is avoided.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The invention relates to a brain anomaly network positioning method and system based on functional connection network mapping in the technical field of neural image data processing. The brain abnormal network positioning method comprises the following steps: calculating a whole brain function network diagram connected with each abnormal site by using resting state function connection data of large-scale health subjects based on a plurality of dispersed abnormal sites reported in previous literatures; then superposing the function network diagrams to obtain a network probability graph; and finally, filtering the probability graph through a threshold value to obtain a final core anomaly network graph. According to the method, the inherent functional connection architecture of the brain is used as a reference system, abnormal sites which seem to be uncorrelated in different researches are successfully traced and unified to a common and stable functional network, and compared with a single brain region marker, the generated network-level biomarker integrates more source evidences, so that the network-level biomarker has the advantages that the network-level biomarker can be widely applied to the field of biomarkers of the brain region. Therefore, the problems of result heterogeneity and inconsistency in brain abnormality discovery are solved.

Technologies for programming flexible accelerated network pipelines include a comping device with a network controller. The computing device loads a program binary file that includes a packet processing program and a requested hint section. The binary file may be an executable and linkable format (ELF) file with an extended Berkeley packet filter (eBPF) program. The computing device determines a hardware configuration for the network controller based on the requested offload hints and programs the network controller. The network controller processes network packets with the requested offloads, such as packet classification, hashing, checksums, traffic shaping, or other offloads. The network controller returns results of the offloads as hints in metadata. The packet processing program performs actions based on the metadata, such as forwarding, dropping, packet modification, or other actions. The computing device may compile an eBPF source file to generate the binary file. Other embodiments are described and claimed.

The present application relates to medical image analysis and brain network research technical field, specifically to glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises obtaining patient fMRI and structural MRI data and preprocessing, excluding tumor area by lesion mask registration strategy, reducing quality effect interference; dividing tumor core area, peritumoral abnormal area and normal brain area; registering Yeo-17 network template to individual brain area to realize mapping; defining tumor core area as independent network unit, and 17 normal networks to form a new set; calculating whole brain voxel and network functional connection strength, and determining functional connection voxel according to threshold; quantifying intratumoral function proportion RIFR and peritumoral connection proportion RPTR, and generating visualization atlas. The present application accurately maps individual brain function network, overcomes tumor heterogeneity interference, provides repeatable quantitative index, and provides reliable imaging analysis tool for brain glioma function protection and clinical research.

The application discloses a critical patient sedation state evaluation method based on a multi-modal network model, relates to the technical field of medical monitoring and signal processing, and comprises the following steps: analyzing a plurality of channel physiological signals of a patient, constructing a dynamic time-varying function network, and performing multi-scale community detection to identify stable function modules in the network and dynamic connection modes thereof. The network topology mode is fused with instantaneous phase information of the signals to form a fusion feature space-time atlas. After the atlas is subjected to deep feature extraction and structured coding, a sequence modeling network is used to capture time sequence dependence, and finally a quantitative sedation evaluation signal is output by a decoder. Through dynamic modular structure analysis of the brain function network and cross-level feature fusion, the method realizes more continuous and accurate objective evaluation of the sedation state.

A method for identifying brain-state dependent functional areas of unitary pooled activity (FAUPAs) using a statistical model that does not require a priori knowledge of the activity-induced ideal response signal time course is provided. A system for identifying a functional network in a brain of a living object includes a FAUPA identifier configured to identify FAUPAs by analyzing a plurality of images of the brain over a predetermined period. The plurality of images include a plurality of voxels, and the FAUPA identifier analyzes each voxel of the plurality of voxels in relation to one or more surrounding voxels of each voxel until each voxel of the plurality of voxels is evaluated. A brain network identification module configured to construct the functional network based on the identified FAUPAs that are functionally connected. A display module configured to display images of the brain depicting the FAUPAs included in the functional network.

The invention relates to the technical field of medical image analysis and brain network research, in particular to a glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises the steps of obtaining and preprocessing fMRI and structural MRI data of a patient, eliminating a tumor area through a focus shielding registration strategy, and reducing mass effect interference; dividing a tumor core region, a peritumoral abnormal region and a normal brain region; a Yeo-17 network template is registered to an individual brain region to realize mapping; defining a tumor core area as an independent network unit, and forming a new set with 17 normal networks; calculating the connection strength of whole brain voxels and network functions, and judging functional connection voxels according to a threshold value; and quantifying the intratumoral function ratio RIFR and the peritumoral ligation ratio RPTR, and generating a visual map. According to the method, an individual brain function network is accurately mapped, tumor heterogeneity interference is overcome, repeatable quantitative indexes are provided, and a reliable imaging analysis tool is provided for brain glioma function protection and clinical research.

The invention discloses a brain cognitive function analysis method based on an EEG-fNIRS cross-modal multilayer network. The method comprises the following steps: firstly, collecting multichannel EEG and fNIRS signals under a working memory training normal form, and preprocessing the EEG and fNIRS signals; source space reconstruction is carried out on the signals, corresponding position information is distributed for EEG and fNIRS source time sequences, and regions of interest are divided based on a Desikan-Killiany atlas; the method comprises the following steps: quantizing decomposable directed mutual information between systems by using IID-PSIT, and constructing an EEG-fNIRS multiplexing network; quantizing cross frequency coupling in the brain function network by using GCSIT, and constructing an EEG-fNIRS full-connection multi-layer network; topological characteristics of a multiplexing network and a multi-layer network are analyzed and extracted by using graph theory parameters, brain function changes under different cognitive loads are revealed, and the accuracy of cognitive load classification tasks is improved.

The application relates to a method and device for predicting the risk of depression of a post-stroke patient, a medium and an electronic device. The method comprises: acquiring a brain image of the post-stroke patient and a standard graph of a depression functional network, the standard graph of the depression functional network being used to represent a brain functional network related to post-stroke depression; calculating a network damage score of the post-stroke patient according to the brain image and the standard graph of the depression functional network; acquiring a standard graph of depression structural disconnection, the standard graph of the depression structural disconnection being used to represent a brain disconnection distribution related to post-stroke depression; calculating a structural disconnection score of the post-stroke patient according to the brain image and the standard graph of the depression structural disconnection; inputting the network damage score, the structural disconnection score and clinical information data of the post-stroke patient into a prediction model to determine the risk of depression of the post-stroke patient after several months of onset through the prediction model. The application can predict the risk of depression according to the direct impact of a stroke lesion on a brain network responsible for emotion regulation in the brain.

[0009] An embodiment of the present application provides a communication method and a communication device. The method includes a step in which a second network function network element receives a service request message from a first network function network element and determines whether to provide a service to the first network function network element based on a first token. The service request message is used to request the second network function network element to provide a service to the first network function network element, and the service request message includes a first token and second service domain information, where the second service domain information indicates a service area of ​​the service requested by the first network function network element, and the first token includes the first service domain information, where the first service domain information indicates a service area range in which the first network function network element can obtain the service from the second network function network element. In the solution of the present application, the first service domain information is added to the first token to reduce or prevent the first network function network element from accessing the service without authorization, thereby ensuring network security.

The application discloses an online self-adaptive seizure prediction method and system, relates to the technical field of medical signal processing and artificial intelligence, and comprises the following steps: acquiring multi-channel electroencephalogram signals of a target user; constructing an online self-adaptive seizure prediction model according to spatially constrained independent component analysis, brain function network and a transfer learning mechanism; inputting the multi-channel electroencephalogram signals into the online self-adaptive seizure prediction model, identifying and predicting a pre-seizure state, and obtaining a prediction result; and performing online early warning judgment according to the prediction result, and triggering an alarm if early warning conditions are met. The application solves the problem of unstable early warning effect caused by the variability of electroencephalogram data of epilepsy, realizes online seizure prediction with clinical accuracy and rapidness, and provides a basis for the treatment of intractable epilepsy and the research on the seizure mechanism of epilepsy.

The application belongs to the technical field of communication, and particularly relates to a network performance testing method, a network performance testing device, a computer readable medium, an electronic device and a computer program product. The method comprises the following steps: establishing a network performance testing channel for communication connection with a specified network element, wherein the specified network element is a functional network element specified in a core network of a communication system; and performing data communication with the specified network element based on the network performance testing channel, so as to obtain network performance parameters of communication traffic. The application can improve the accuracy of network performance optimization.

The application provides a lightweight satellite network security management system and method. The system realizes unified management of the satellite network security control plane through a centralized network controller. The system has functions such as security authentication of satellite nodes, dynamic security policy generation and distribution, real-time network monitoring, and rapid security event response. The network controller includes multiple modules such as authentication, policy generation, monitoring, and event response, and can customize security policies according to satellite characteristics and adjust them in real time to respond to security threats. The application also includes lightweight design, especially suitable for resource-constrained satellite environments, and a method for continuously optimizing security policies based on monitoring data and event records. In addition, the provided user interface allows satellite network administrators to easily configure policies and handle events. Through centralized management and dynamic adjustment, the system significantly improves the security and efficiency of satellite networks, has advantages such as lightweight, flexibility, and real-time, and meets the security management needs of modern satellite networks.

The invention provides a multi-level team cooperation performance evaluation method and system based on EEG superscanning, and belongs to the field of neural engineering and human-computer interaction. The method comprises the steps that electroencephalogram signals and behavior performance signals are synchronously collected by designing a multi-person team cooperation task; calculating an average time difference based on the behavior signal to quantify a collaboration level and grade; the electroencephalogram signals are preprocessed; extracting multi-task-stage, multi-frequency-band and multi-index interbrain connectivity features to construct a high-dimensional feature set; single-stage and cross-stage fusion models are trained by adopting a machine learning method to perform collaborative level classification, and key neural features are identified by utilizing an interpretability method; and finally, constructing and analyzing a team-level brain function network based on a graph theory, and revealing neural network characteristic differences of teams with different cooperation levels. According to the method, multi-dimensional and dynamic analysis of a team collaboration neural mechanism is realized, and a new objective and quantitative approach is provided for team performance evaluation and collaboration capability improvement.

The invention discloses a multi-modal network fusion analysis method based on a multi-layer network. The method comprises the following steps: 1, constructing a single-modal brain network based on canonical correlation analysis; 2, brain network structure-function coupling is extracted; and step 3, constructing and analyzing a high-order multi-mode brain network. The method has the advantages that the constructed high-order multi-mode brain network integrates brain structure and function information and structure-function coupling information, information loss caused by independent analysis of a structure network and a function network is overcome, multi-scale understanding of a brain mechanism and an abnormal mode is achieved, and a neural mechanism can be revealed more comprehensively; and 2, a core-peripheral tissue analysis method is introduced, the degeneration phenomenon of the core brain region of the brain is found from a multi-mode perspective, a new perspective is provided for analyzing the brain information processing process, and the understanding of the key brain region of the brain mechanism is further deepened.

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 belongs to a network construction evaluation method, and aims to solve the technical problems that an existing brain function network construction method is lack of personalized partitions, insufficient in nonlinear neural association description, weak in cross-scene generalization ability and difficult to support precision and large-scale clinical application of brain-computer interface neural regulation. According to the brain-computer interface regulation-oriented personalized brain function network construction and evaluation method provided by the invention, a personalized brain function network is constructed based on neural activity mode characterization, and personalized brain function network construction of heterogeneous fMRI data is realized by fusing neurodynamics knowledge constraint and data-driven modeling; the method can be used as a universal modeling tool for heterogeneous fMRI data, and provides reliable technical support for brain-computer interface nerve regulation and control.

This invention discloses a hypergraph representation method for brain functional networks. The steps include: preprocessing resting-state functional magnetic resonance imaging (fMRI) to obtain time series data for all brain regions; dividing the entire time series into multiple overlapping sub-sequence segments using a sliding window; constructing a dynamic brain functional network and transforming it into an optimization model; constructing a hypergraph of the dynamic brain functional network using the nearest neighbor algorithm; dynamically modifying the hypergraph structure through convolution operations and extracting features to obtain a new dynamic hypergraph; extracting the Laplacian matrix of the dynamic hypergraph; constructing the manifold regularization term of the Laplacian matrix and simultaneously introducing the manifold regularization term and the L1 norm regularization term into the optimization model to obtain the hypergraph representation of the brain functional network. This invention is used to represent functional interactions and higher-order relationships between multiple brain regions, determine discriminative brain functional network classification features, and effectively improve the classification performance of brain disease features.