223 results about "Brain network" patented technology

The invention discloses an epilepsy region positioning method and system based on brain power supply imaging and a dynamic brain network, and the method comprises the steps: obtaining T1 weighted magnetic resonance imaging data of a user, and constructing an individual three-dimensional head model through a boundary element method; acquiring scalp electroencephalogram data of a user, and preprocessing the scalp electroencephalogram data; based on an individual three-dimensional head model, performing inverse problem solving on the preprocessed scalp electroencephalogram data by using a standardized low-resolution brain power source imaging algorithm to obtain source current density signals of 68 brain regions; decomposing into six frequency bands, calculating the power spectrum density of each brain region and carrying out normalization processing, and screening effective frequency bands; based on the source current density signals of the 68 brain regions of the effective frequency band, information flow directions and intensities of different brain regions are calculated by adopting a directional transfer function method, a directional transfer function matrix of the effective frequency band is formed, and a directed brain network is constructed; and calculating a graph theory index and / or an epilepsy index of each brain region, carrying out maximum value normalization analysis, and determining an epilepsy region positioning result.

The invention relates to an infantile autism classification method based on a double-branch functional topological graph neural network. The infantile autism classification method can realize the classification of the infantile autism by using functional magnetic resonance imaging data. According to the provided autism classification network, long-distance connection and short-distance connection are divided based on the shortest path between brain intervals, then an exponential decay mask is introduced through a functional topological graph Transform branch to adjust attention weight and accurately extract long-distance dependency features, a graph isomorphic network in the other branch is subjected to multiple neighborhood aggregation operations, short-distance dependency features are captured, and the short-distance dependency features are extracted. According to the method, multi-scale dependence of the brain network is extracted in parallel through a double-branch structure, information redundancy is reduced by means of a topology perception attention mechanism, and the adaptive ability of the model to the heterogeneous brain network is improved by using the adaptive fusion module, so that multi-scale dependence of the heterogeneous brain network is balanced in a self-adaptive manner. The classification accuracy is remarkably superior to that of an existing mainstream method, objective and efficient technical support is provided for autism diagnosis, and high interpretability is achieved.

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.

The invention discloses a dementia identification method based on brain computer network space cross attention fusion. The dementia identification method comprises the steps that resting-state electroencephalogram signals are acquired, preprocessing and brain network construction are carried out, the frequency band power ratio is calculated, and a training set and a test set are divided; the brain network fuses the spatial information, and spatial features are obtained through an isotropic graph neural network and a local feature enhancement module; the frequency band power ratio is coded by a multi-layer perceptron to obtain frequency spectrum statistical characteristics; a bidirectional cross attention module is input, and complementary fusion features are extracted; and performing mixed pooling and splicing on the complementary fusion features, then sending the fused features to a Chebyshev Kolmogorov-Arnold network classifier, and outputting an Alzheimer's disease / frontotemporal dementia / health control (AD / FTD / HC) classification result and a cognitive scale evaluation score (MMSE). According to the method, the multi-feature complementary information is effectively integrated through joint modeling of the spatial diagram features and the global spectrum features, and the accuracy, stability and generalization ability of AD and FTD classification in a complex brain network are improved.

The invention discloses an fNIRS adaptive feedback emotion cognition cooperative training device and method, and relates to the technical field of emotion disorder cognition training. The device comprises a data acquisition module used for acquiring an original dual-wavelength light intensity signal from a brain; the data processing module is used for performing multi-stage preprocessing on the original dual-wavelength light intensity signal; the neural feedback module is used for calculating a multi-dimensional evaluation index according to the hemoglobin concentration data and adjusting a difficulty level and a feedback threshold value of a training task by using a dynamic threshold value control algorithm; the training module is used for training participants based on the emotion stimulation task and the cognitive training task; and the evaluation module is used for performing training evaluation according to the comparison result of the comprehensive performance score and the feedback threshold. Brain function equipment is closely combined with cognitive training and emotion stimulation tasks, dynamic evaluation and real-time feedback are added for traditional tasks, and through the brain network analysis technology, accurate evaluation of the user training effect is achieved.

The invention relates to the field of medical image analysis and neuropsychiatric disease diagnosis, and particularly discloses a brain network typing diagnosis system and method for attention deficit hyperactivity disorder. Comprising a data acquisition and preprocessing module, a topological feature extraction module, a supervised manifold learning module, a network reconstruction and diagnosis module, a parameter optimization module and a result verification module, continuous homology analysis is performed on a brain region function connection matrix based on an algebraic topology theory, and a brain region topological feature matrix is generated; utilizing supervised manifold learning to map the brain region topological feature matrix to a Riemannian manifold, and calculating a brain region importance weight map based on ADHD phenotypic features as supervised signals; according to the method, the brain network is subjected to subtype specific reconstruction through the curvature flow theory of Riemannian geometry, the topological difference between different subtypes is calculated, accurate typing diagnosis of ADHD is achieved, and the diagnosis accuracy is improved by 15%-20%.

The invention belongs to the technical field related to data processing, and provides a brain network analysis method and system based on multi-network collaborative topology analysis in order to solve the problem that an analysis result is unstable due to threshold selection subjectivity in existing Alzheimer disease brain network analysis. Extracting brain regions corresponding to the default mode network, the significance network and the execution control network, calculating correlation coefficients among different brain regions to construct a function connection matrix, and converting the function connection matrix into a distance matrix meeting complex construction requirements; constructing a Vietories-Rips complex based on the distance matrix, calculating a coherence group under each scale of the filtering sequence, and extracting topological features of a 0-dimensional Betti number and a 1-dimensional Betti number; according to the method, the Betti number is extracted, a curve that the Betti number changes along with the threshold value is drawn, the difference of the two groups in topological characteristics is compared, the specific topological biomarker related to the Alzheimer's disease is identified, and richer biomarker information is provided for early diagnosis of the Alzheimer's disease.

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 stroke motion cognition evaluation system and method based on spatial perception nerve enhancement. The stroke motion cognition evaluation system comprises a data acquisition module, a preprocessing module, a feature extraction module, a weight coefficient generation module, a motion cognition evaluation module and a visualization module. Extracting information of different frequency bands of the brain through a feature extraction module, and obtaining power spectral density, a phase locking value and cross-frequency coupling features; meanwhile, a weight coefficient generation module is used for obtaining weight coefficients for performing weight fusion on different features, and the features extracted by a feature extraction module are fused through a motion cognition evaluation module to completely describe complex brain network interaction, so that cognition and spatial perception state evaluation of the subject in the motion imagination process is completed. In addition, threshold adjustment and personalized feedback are performed based on the real-time change of the evaluation result, so that the quality and efficiency of motor imagery training can be remarkably improved.

The invention provides a method and system for evaluating the instant curative effect of acupuncture head acupoints on cerebral stroke cognitive impairment based on electroencephalogram signals, and the method comprises the steps: collecting the electroencephalogram signals before, during and after acupuncture in stages, and synchronously combining a MoCA / MMSE scale and a cognitive task test to obtain subjective curative effect data; an insulating acupuncture needle is adopted to reduce interference on electroencephalogram signals, and 4-8-conduction portable dry electrode electroencephalogram equipment is matched to collect signals in an area avoiding acupuncture points of the head, so that acupuncture operation and electroencephalogram collection are ensured not to influence each other. A condition vector is generated by using acupuncture point parameters and acupuncture manipulation, and a space-time Transform-GNN model is driven to dynamically focus alpha / theta / beta frequency band time sequence characteristics and a brain network connection mode of cognitive related brain regions such as a prefrontal lobe, a parietal lobe and a temporal lobe. According to the method, a multi-modal feature vector containing electroencephalogram frequency domain features, time domain components and clinical scale scores is constructed through an electroencephalogram-acupoint-curative effect triple data training model of at least 100 patients, and an obvious / effective / invalid level is output.

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 the technical field of psychiatric disease diagnosis, and discloses a defective schizophrenia prediction and analysis system based on multi-modal brain network characteristics, which comprises an image data acquisition module, an image data processing module, a deep learning model module and a prediction module which are connected in sequence, the image data acquisition module acquires a brain MRI image; the image data processing module carries out cortical and subcortical reconstruction on the brain MRI image to obtain a model input index; the deep learning model module performs training by using a training data set composed of a plurality of brain MRI images and defective schizophrenia diagnosis results thereof to obtain a target prediction model; and the prediction module inputs the real-time brain MRI image into the target prediction model to obtain a prediction result. According to the method, the multi-dimensional brain structure image features are integrated, diagnosis information contained in brain region changes is fully mined, prediction results of defective schizophrenia and non-defective schizophrenia are improved, and a more comprehensive biological basis is provided for clinical diagnosis.

The application discloses a kind of cognitive promotion method and system based on multi-modal data.The cognitive promotion method includes: obtaining the first multi-modal data before treatment of patient;Amyloid load grouping is carried out to patient based on the amyloid load in the brain of patient, to obtain the initial cognitive training scheme corresponding;Cognitive training is carried out based on initial cognitive training scheme during the treatment of patient, and the second multi-modal data during treatment is collected;Based on multi-modal data before and after treatment of patient, in combination with brain network group atlas, obtain the multi-modal change data of patient;The multi-modal change data of patient is input into preset model, to output the cognitive promotion scheme that cognitive index of patient is most improved, to push to patient and carry out cognitive training.The method will be integrated by A beta-PET data, functional nuclear magnetic resonance imaging data and event-related potential data, real-time evaluation of the neuroplasticity change of patient, to dynamically generate personalized cognitive promotion scheme.

The application provides a multi-modal brain network classification method based on feature decoupling and dynamic graph construction, and relates to the technical field of artificial intelligence, and the method comprises the following steps: acquiring functional magnetic resonance imaging (fMRI) data and structural magnetic resonance imaging (sMRI) data of a to-be-tested person, obtaining multi-modal brain network data according to the fMRI data and the sMRI data, processing the multi-modal brain network data based on a preset multi-modal brain network classification model, and obtaining the brain network state of the to-be-tested person. The application can fully utilize the advantages of the two modalities by combining the fMRI data and the sMRI data, thereby improving the accuracy and comprehensiveness of the brain network state classification, and the dynamic graph attention module based on a graph attention network (GAT) can capture the dynamic change characteristics of the brain network, construct a dynamic graph representation, and enhance the sensitivity of the model to the dynamic connection relationship between brain regions.

The invention discloses a dynamic brain network analysis method guided by neural heterogeneity, and is suitable for the technical field of brain image processing and recognition. The method comprises the steps that functional magnetic resonance imaging data are acquired and preprocessed, an overlapped sliding window is used for dividing the functional magnetic resonance imaging data to construct a dynamic functional brain network, and then the dynamic functional brain network is decoupled into a topological consistency network and a time trend network which conform to brain activities; capturing space and time heterogeneity weights of different brain regions in the brain based on a topological consistency network and a time trend network, and identifying key nodes for driving brain network recombination; further weighting the topology consistency network and the time trend network to obtain a heterogeneity dynamic function brain network; propagation of neural information in a time dimension is simulated based on time propagation graph convolution operation, and spatial-temporal features of brain images are extracted from a heterogeneous dynamic function brain network; and finally, inputting the obtained spatial-temporal characteristics of the brain image into a multi-layer perceptron to predict the data category of the brain image to be recognized, and analyzing the influence of the brain disease image characteristics on the spatial-temporal heterogeneity of the brain region to complete the dynamic brain network analysis guided by the neural heterogeneity.

The invention discloses an Alzheimer's disease classification prediction method and system. According to the method, firstly, a bimodal brain network diagram is constructed, brain region features serve as nodes, and edge features are constructed through single nucleotide polymorphism data and structural magnetic resonance imaging data; the method is characterized in that closed-loop information interaction between nodes and edges is realized through a mutual feedback backflow graph neural network: firstly, edge features are dynamically updated based on node similarity, and information transmission from the nodes to the edges is realized; carrying out aggregation propagation and key screening among edge features by utilizing a topological neighborhood relationship; and finally, returning the edge feature information subjected to cross-modal fusion to the nodes, and updating node features. According to the method, the limitation that an existing graph neural network only pays attention to edge-to-node one-way information flow is overcome, and by describing dynamic coupling of nodes and edges and complementation of multi-modal information on a connection layer, the capturing capacity of early and tiny pathological modes of the Alzheimer's disease is enhanced, so that the accuracy and interpretability of classification prediction are improved.

The application discloses a brain network analysis method based on a hypergraph and a gravity model, relates to the technical field of electroencephalogram signal analysis and complex network science, and comprises the following steps: S1, acquiring multi-channel stereoelectroencephalogram signals, and calculating the phase locking values between each pair of channels; and S2, based on the phase locking values, taking the stereoelectroencephalogram channels as nodes, and constructing a 3-consistent weighted hypergraph, wherein each hyperedge in the 3-consistent weighted hypergraph comprises three nodes, and the nodes in the 3-consistent weighted hypergraph are divided into multiple groups; the driving force between groups is obtained through layer-by-layer deduction, the high-order correlation characteristics between brain groups can be reflected in multiple dimensions, the interaction of different brain groups can be quantified from the aspect of the driving direction, the multiple quantitative indexes derived can enrich the analysis dimension of the driving relationship between brain groups, and the method is suitable for various brain signal research scenes, so as to meet the actual research and use requirements of the fine analysis of brain interaction mechanisms.

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 invention relates to a structural network-genetic map biological network model for predicting ischemic stroke and a construction method thereof, and the method comprises the steps: extracting and calculating seven multi-scale morphological features and pairwise Pearson correlation coefficients among the features from T1 weighted imaging data and diffusion tensor imaging data; constructing a 308 * 308 morphological similarity network matrix and a brain network module for identifying ischemic stroke neural dysfunction; 1782 sampling points are extracted from the Airy human brain map, and each sampling point comprises expression data of 10185 genes; the method comprises the following steps: mapping space coordinates of AHBA sampling points to a cortex package of a Desikan-Killiany map, carrying out normalization processing to output 308 * 10185 brain region gene-by-gene expression matrixes, and constructing a structural network-gene map biological network model for predicting ischemic stroke by adopting a partial least square regression method and a bootstrap method. Compared with the prior art, the model determines the specific molecular mechanism related to the phenotypic structure change of ischemic stroke injury, and the stroke occurrence probability is predicted according to the specific molecular mechanism.

The application discloses a kind of fNIRS adaptive feedback emotion cognitive collaborative training device and method, it is related to emotional disorder cognitive training technical field.The device includes: data acquisition module, for obtaining original double-wavelength light intensity signal from brain;Data processing module, for the original double-wavelength light intensity signal is carried out multistage pretreatment;Neural feedback module, for calculating multidimensional evaluation index according to hemoglobin concentration data, and using dynamic threshold control algorithm adjusts the difficulty level and feedback threshold of training task;Training module, for training participant based on emotional stimulation task and cognitive training task;Evaluation module, for training evaluation according to comprehensive performance score and feedback threshold comparison result.The present application closely combines brain function equipment with cognitive training and emotional stimulation task, adds dynamic evaluation and real-time feedback to traditional task, and realizes the precise evaluation of user training effect by brain network analysis technology.

The application discloses a kind of fNIRS motor imagination decoding methods based on double-flow cross attention and function connection fusion, obtain the fNIRS original signal of subject under motor imagination task, calculate HbO signal and HbR signal concentration variation sequence, respectively to HbO signal and HbR signal are first-order differential processing, generate the enhanced feature stream reflecting the change rate of HbO signal and HbR signal, and introduce residual connection;Double-flow feature encoder is constructed, and HbO signal depth feature map and HbR signal depth feature map are extracted using time convolution and depth space convolution respectively;Cross attention module is introduced, and the depth feature after double-flow dynamic complementary fusion is obtained;For each signal sample obtained, calculate the pearson correlation coefficient matrix between all acquisition channels, and flatten it into a global brain network connection vector matrix;The depth feature after double-flow dynamic complementary fusion is spliced with the global brain network connection vector matrix, and the category result of motor imagination is output through fully connected classifier.

This application provides a method, apparatus, device, and medium for denoising multimodal directed brain network signals. The method includes: acquiring a denoised signal and a multimodal brain network signal at time t, where the denoised signal at time t is either the denoised signal calculated in the previous loop or the initial noise signal; inputting the denoised signal and the multimodal brain network signal at time t into a denoising model, and extracting local and global features between brain regions in the denoised signal and the multimodal brain network signal at time t through the denoising model to obtain a denoised signal and a directed connectivity brain network at time t-1; repeating the above steps until a denoised signal at time 0 is obtained, and then using the corresponding directed connectivity brain network as the target directed connectivity brain network. Through some embodiments of this application, global and local features in multimodal brain network signals can be obtained, thereby improving the denoising effect.

This invention discloses a fast communication method for large-scale brain simulation, providing a communication method based on GPU-Direct technology. This reduces data interaction between memory and GPU memory through direct communication between GPU data. Furthermore, it proposes a multi-GPU-based data encoding and decoding scheme to address issues such as uneven pulse data and large pulse data volume. This scheme arranges neuron clusters by process during the establishment of large-scale brain-like networks, ensuring that neuron clusters within the same process are allocated in a contiguous address space. Encoding and decoding methods are designed accordingly to compress and transmit pulse data. This ensures that when pulse data is encoded, compressed, and decoded based on multi-computing node cluster information, the amount of data communicated each time is a fixed value that depends only on the number of neurons, reducing the amount of data communication between computing nodes. This solves the problems of communication time consumption, memory-GPU memory interaction time consumption, and unstable communication volume in traditional large-scale brain-like simulations, and is adaptable to various hardware devices.

The invention discloses a dynamic trajectory construction and visual mapping method for association of mental disorder brain network damage and systemic system diseases, and belongs to the field of artificial intelligence medical application. According to the method, high-resolution MRI images, biomarkers and clinical information of major mental disorder patients are collected, and the influence of factors such as age, gender, medication and diagnosis on the braingut axis and the cardio-cerebral axis is evaluated through multi-modal data fusion. By constructing a disease dynamic trajectory model, brain structures and function change modes corresponding to different mental disorders are identified. Large-scale samples are analyzed through machine learning, potential risks and protection factors are extracted, and a visual tool is developed to visually display changes of the brain under different disease systems. A closed-loop feedback mechanism is established through follow-up visit, the disease progress and the intervention effect are dynamically tracked, and key evaluation indexes are identified. According to the invention, theoretical basis and practical guidance are provided for early screening, precise intervention and personalized treatment of mental disorders, and the diagnosis and treatment accuracy and efficiency are remarkably improved.

The invention discloses an SEEG connection analysis method and system under brain region division, and relates to the technical field of computer vision, and the method comprises the steps: fusing MRI and CT images of a patient, constructing a three-dimensional brain region model based on a Destrieux atlas and an automatic segmentation algorithm, carrying out multi-modal registration to calibrate the spatial position of an SEEG electrode, and generating a primary electrode file with an anatomical tag; a virtual electrode point is generated by adopting bipolar combined guidance, and the affiliation of an electrode brain region is dynamically corrected in combination with spatial registration and prior implantation information, so that a three-level tag file is formed. According to the method, SEEG electrode pairs are grouped, sorted and identified according to brain regions, brain region-level summarized signals are output, connectivity analysis of cross-brain region electrode pair groups is supported, and spatial interpretability and analysis precision of SEEG data in brain network research are improved. According to the method, accurate grouping of SEEG data according to anatomical brain regions is realized through multi-modal image fusion, virtual electrode construction and a prior information guided brain region affiliation correction technology.

The invention discloses a magnetic resonance image analysis method for Alzheimer's disease, and relates to the technical field of image analysis. Comprising the following steps: acquiring a magnetic resonance image set of the Alzheimer's disease, and performing gender regression analysis through a multivariable regression analysis method to obtain a gender correlation grey matter structure brain network; performing paired sample T inspection to obtain absolute grey matter volumes of the grey matter masks; carrying out Pearson correlation analysis on the volume by adopting a graph theory analysis method to obtain a gender-specific grey matter structure brain network; and respectively training the machine learning model through the gender correlation grey matter structure brain network and the gender specificity grey matter structure brain network to obtain a gender mixed magnetic resonance image analysis model and a gender specificity magnetic resonance image analysis model. According to the method, the influence of individual difference on an analysis result is effectively reduced, and the reliability and accuracy of magnetic resonance image analysis are remarkably improved.

The application belongs to the technical field of electric digital data processing, and particularly relates to an adaptive ensemble learning model optimization method for electroencephalogram signal classification, which comprises the following steps: acquiring a multi-channel electroencephalogram sample segment, extracting features containing time-frequency energy, phase locking and spatial mode, the phase locking containing phase locking values among the multi-channels, and constructing three base learners; constructing a weighted brain network through the phase locking values and constructing a brain state feature vector; taking the fusion weight of each base learner as an optimization variable, combining the weight into a particle, grouping according to feature preferences, constructing fitness, iteratively updating the particle based on the fitness, and iteratively optimizing to obtain an optimal weight; weighting and fusing the prediction probabilities of each base learner by using the optimal weight, selecting the category corresponding to the maximum prediction probability as the classification result, and triggering re-optimization based on the change of the adjacent brain state feature vector. The method realizes online adaptive optimization of the weight, and improves the accuracy and long-time stability of motor imagery electroencephalogram signal classification.

The invention provides a brain dynamic mode classification method based on a graph auto-encoder and soft and hard clustering. The brain dynamic mode classification method comprises the steps that S1, data preprocessing and blood oxygen level dependence signal extraction are carried out; s2, constructing a dynamic brain network; s3, nonlinear dimensionality reduction of the graph auto-encoder is carried out; s4, soft and hard clustering conjoint analysis and dynamic mode feature extraction; s5, performing feature screening and validity verification; and S6, classifier training verification and classification result output. According to the method, rs-fMRI data is taken as core input, accurate classification of brain dynamic modes is realized through a whole-process design of data preprocessing, brain network construction, nonlinear dimension reduction, clustering analysis and classification verification, and the problem of low classification accuracy of a traditional magnetic resonance image data classification method is solved.

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 belongs to the technical field of biomedical signal processing and nerve regulation and control, and discloses a closed-loop time interference nerve regulation and control system and method based on synchronous electroencephalogram signals. The closed-loop nerve regulation and control system integrates multi-channel electroencephalogram collection, neural state real-time recognition, brain network function connection analysis, traceability positioning calculation and individualized time interference stimulation strategies, an upper computer module can obtain electroencephalogram signals and analyze brain function states in real time, abnormal network structures are automatically recognized, and the brain function states are analyzed in real time. A double-target stimulation path is formulated by combining a nerve source positioning result, so that the defects of inaccurate positioning, incapability of responding to brain state change in real time and unpredictable stimulation effect of a traditional nerve stimulation method are overcome; the closed-loop mechanism of'perception-decision-execution-evaluation 'of the system not only enhances the effectiveness of stimulation, but also remarkably improves the safety, controllability and individualization level of the regulation and control process.