390 results about "Brain network" patented technology

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The present disclosure relates to a multi-modal brain network computation method associated with structural function, apparatus, device, and medium. The method is applied to train a brain disease prediction model, and the brain disease prediction model includes an association perception dual-channel generation module, a disease feature regression module, a topological structure discriminator, and a time-space joint discriminator. In a model training process, by performing a multi-level interactive fusion learning on a high-order topological feature of brain functional magnetic resonance data and magnetic resonance diffusion tensor imaging data, a multi-modal time series activity signal of each brain region is obtained.

The invention relates to the technical field of electroencephalogram signal processing, and discloses an electroencephalogram emotion recognition method and system based on a deep neural network. The method comprises the following steps: collecting and preprocessing a multi-channel EEG signal; constructing a graph data structure, extracting multi-domain features by taking electroencephalogram channels as nodes, and constructing a self-adaptive dynamic adjacency matrix; constructing a graph convolution long and short-term memory network, learning spatial features by GNN, and extracting time dependence by LSTM; enhancing emotion capture by using a multi-scale time-frequency feature fusion method in combination with STF and CWT; constructing global topological information of an FCN brain extraction region in combination with brain network features; and outputting alertness and other emotion indexes by means of the classification model. According to the method, graph structure learning and time sequence modeling are combined, EEG signal emotion recognition is optimized, and personalized adaptation and emotion recognition accuracy is improved.

The present disclosure discloses a multi-modal brain network calculation method, apparatus, device, and storage medium. The method is configured to train a brain disease prediction model. After the brain region structural feature and the brain region functional feature are separately extracted from magnetic resonance diffusion tensor imaging data and brain functional magnetic resonance data, a graph representation diffusion learning network is used to separate the universal feature and the unique feature in the brain region structural feature and the brain region functional feature. And then, multi-modal universal and unique feature fusion is implemented based on an alignment algorithm and adaptive weighting technology. Thus, complementary information between the multi-modal data is fully mining. The model can learn an effective feature of a related disease in a training process, and a finally obtained brain region disease prediction model has higher precision and better prediction effect.

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 relates to the technical field of intelligent robots, and provides a cerebellum model and cross-modal attention fused control device, which comprises a three-layer cooperative control framework: a high-level strategy network for receiving global state data, outputting a macroscopic action element instruction and guiding the overall direction of task completion; the middle-layer cerebellar network is used for fusing multi-modal ontology sensing data in real time and outputting compensation torque; and the bottom layer actuator is used for controlling and executing joint torque output through a feed-forward PID. According to the invention, high-level action errors can be corrected online, and the dynamic anti-interference capability is remarkably improved; delay of torque compensation is reduced, and disturbances such as ground slipping and load sudden change are effectively handled; the strategy drift problem in traditional end-to-end training is solved.

The invention provides a brain age estimation method based on a dynamic fuzzy learnable brain network, and belongs to the technical field of medical image processing and artificial intelligence. According to the technical scheme, the method comprises the following steps that S1, brain nuclear magnetic resonance imaging of a subject is collected, and preprocessing and data division are carried out; s2, constructing graph structure data, and performing feature extraction and position information embedding on the data; s3, constructing a dynamic fuzzy learnable brain network model comprising a main branch and a local branch, and respectively extracting global and local connection features; s4, introducing a dynamic fuzzy multi-head self-attention module into the main branch to realize effective modeling of global features; s5, a local branch dynamically models a dependency relationship between channels through a convolution filter and a learnable graph attention module; s6, after the features of the main branches and the local branches are fused, brain age prediction is carried out through a multi-layer perceptron. According to the method, the modeling capability of the brain function connection mode is improved, and the brain age prediction task can be more effectively completed.

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 discloses a schizophrenia early warning and evaluation system based on human brain multi-region signals, and relates to the technical field of early warning and evaluation, and the system comprises a neural connection analysis unit which is used for recognizing neural connection characteristics of a brain network based on biomarker characteristics, comparing the neural connection characteristics with a database, and determining the neural connection characteristics of the brain network; clustering individuals with similar neural connection features according to a comparison result to generate a risk group; the emotion perception analysis unit is used for acquiring facial emotion states of the risk group in a preset time period and predicting evolution trends of neural connection features in different facial emotion states; and the early warning evaluation unit is used for inputting the evolution trend of the neural connection characteristics into a pre-constructed evaluation model, outputting a risk level evaluation result and formulating an early warning measure based on the risk level evaluation result. According to the method, neural connection features and emotion perception analysis are combined, so that early-stage neural connection abnormity and emotion turning intervals of schizophrenia can be accurately identified and positioned.

The invention discloses a cross-individual EEG emotion recognition method based on course learning and multi-source domain adaptation, and the method comprises the steps: constructing a multi-modal feature extraction network, and integrating feature extraction modules of time sequence, frequency domain, brain network connectivity and the like, so as to fully mine and fuse multi-dimensional emotion related information. Aiming at challenges of large individual difference, inconsistent data distribution and the like in cross-subject emotion recognition, a multi-source domain adaptation mechanism is introduced, and the discrimination ability and migration performance of a model on a target domain are improved by effectively aligning feature distribution of a source domain and a target domain. Besides, in order to relieve the problems of unstable convergence and local optimization caused by difficult samples in the initial training stage of the model, a course learning strategy is introduced, target domain data is guided to participate in training from shallow to deep according to the sample difficulty, and therefore the convergence, generalization ability and robustness of the model are remarkably enhanced, and the training efficiency is improved. And finally, the overall performance of cross-subject EEG emotion recognition is effectively improved.

The invention discloses a thinking cellular system construction method for simulating spatio-temporal evolution of industrial chain nodes, and relates to the technical field of brain network construction. Comprising the steps of obtaining a thinking cell vector space; obtaining a thinking cell state; obtaining an adjacent topological relation; establishing a logic evolution dynamic potential function; constructing a thinking cellular system according to the thinking cellular vector space, the thinking cellular state, the adjacency topological relation and the logic evolution dynamics potential function; and identifying the observed state and the hidden state of the industrial chain key node based on the thinking cellular system to obtain an identification result. According to the method, dynamic simulation is performed on spatio-temporal evolution of the industrial chain key node, the evolution path of the industrial chain key node state is explicitly expressed, the evolution rule is adjusted in real time to cope with environmental disturbance, adaptive identification and risk prediction of the industrial chain key node state are realized, and the external risk resistance of the industrial chain is improved.

The invention belongs to the field of brain-computer interfaces, and particularly relates to a decision-making brain-computer method and device based on virtual reality induction, which combines two psychological decision-making tasks of auditory stimulation and visual stimulation and utilizes virtual reality equipment to create a decision-making brain-computer interface normal form of panoramic interaction, so that a subject can fit a scene facing a decision in reality to the greatest extent, and the accuracy of decision making is improved. The sensory motor cortex is effectively activated, and cooperative activation of the cognitive-motor neural network is induced; a decision interaction feedback link is added to enhance a decision stimulation effect and a cranial nerve feedback mechanism by analyzing related characteristics P300, power spectral density and brain network function connection quantity of the acquired electroencephalogram signals during normal form execution decision reaction. Compared with the prior art, the method has the advantages that immersive audio-visual stimulation and task interaction feedback are brought into a decision-making brain-computer interface for the first time, and a new scheme with neural rehabilitation and human-computer interaction functions is provided for neural feedback training of cognitive functions.

The invention discloses an attention deficit hyperactivity disorder subtype recognition method based on brain network topology hub deviation, relates to the technical field of medical diagnosis, and has the technical key points that a norm model of a brain structure form similarity network is constructed based on multi-center big data; quantifying a brain network topology hub index as a target phenotype of the norm model; then, performing semi-supervised clustering analysis by utilizing the individual deviation phenotype of the topological hub index so as to divide different biological subtypes and reveal unique clinical and biological characteristics of the biological subtypes; finally, strict cross validation is carried out in an external independent queue, and it is ensured that the potential biological subtypes recognized by the typing model have good generalization and clinical effectiveness.

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.

According to the Alzheimer's disease auxiliary prediction system, method, medium and device based on the non-invasive multi-modal neural image.According to the Alzheimer's disease auxiliary prediction system, method, medium and device based on the non-invasive multi-modal neural image.According to the Alzheimer's disease auxiliary prediction system and method based on the non-invasive multi-modal neural image.Through a multi-modal brain network fusion graph neural network framework guided by graph reconstruction, a self-supervised normal form is used for fully utilizing MRI data, the limitation of rare PET data under a supervision strategy is broken through, and and reliable pathological characterization embedding is generated. Meanwhile, different from conventional brain network modeling which only constructs node features, the method constructs a bimodal brain network which integrates semantic and topological information according to gray matter function signals and white matter fiber structure connection, and designs a node-edge bidirectional encoder, so that the representation capability of the brain network 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.

The embodiment of the invention relates to the technical field of fatigue driving intervention, and particularly discloses a method and a system for identifying the influence of music intervention on a brain network under driving fatigue. According to the embodiment of the invention, through grouping experiment planning, dynamic simulation is carried out according to experiment planning data, and fatigue monitoring data is collected; eEG collection and fNIRS collection are carried out, and EEG collection data and fNIRS collection data are obtained; constructing a weighted brain network diagram, and performing time sequence synchronization on the weighted brain network diagram and the fatigue monitoring data to generate time sequence synchronization data; and performing grouping and intra-group comparative analysis on the time sequence synchronization data, identifying music intervention influence, and performing scene application recommendation. Grouping experiment planning and dynamic simulation can be carried out, a weighted brain network graph is constructed through EEG collection and fNIRS collection, time sequence synchronization is carried out on fatigue monitoring data, grouping and intra-group comparative analysis are carried out, the music intervention influence is recognized, and therefore the influence of music intervention on the brain network under driving fatigue is recognized.

The invention provides a voice recognition method and device based on a brain-like model, electronic equipment and a storage medium, and the method comprises the steps: obtaining a whole-brain network topological structure according to a brain function network generated by human brain image data, and carrying out the recognition of a whole-brain network through employing a multi-class neuron model as a node and a synaptic plasticity model as an edge, constructing a multi-brain-region pulse neural network as a brain-like model; constructing a speech recognition framework of the brain-like model; electromagnetic intervention is applied to different brain areas of the brain-like model, optimal electromagnetic intervention parameters are determined by analyzing the voice recognition accuracy of the brain-like model before and after electromagnetic intervention, and brain-like model voice recognition is carried out according to the optimal electromagnetic intervention parameters. According to the invention, the speech recognition performance of the brain-like model can be effectively improved, the biological interpretability and neural information processing capability of the brain-like model are further improved, and the development of brain-like intelligence in the application of a mode recognition task is promoted.

The invention discloses an anxiety state assessment method, system and device and a medium, and relates to the technical field of electroencephalogram signal assessment. An observed value of the electroencephalogram data sequence at a set moment is extracted, and a time-varying linear coupling relation between the electroencephalogram data sequence and the observed value of the electroencephalogram data sequence is established based on a multivariate adaptive autoregression model; a conversion relation function between states corresponding to the multivariate signals is obtained through a time-varying linear coupling relation between the electroencephalogram data sequence and an observed value of the electroencephalogram data sequence, the conversion relation function is solved to obtain an ATDF value, and a dynamic causal brain network sequence is estimated through the ATDF value; according to the dynamic causal brain network sequence, electroencephalogram network time sequence features are extracted, and then the clinical anxiety state score of the testee is estimated; according to the method, the response to the brain is analyzed from the two dimensions of time and space, and the accuracy of the evaluation result of the clinical anxiety state of the testee is improved.

The invention provides a network graph generation method based on brain image data, and relates to the technical field of brain connection.The method comprises the steps that brain image data (including fMRI data and sMRI data) of a target object are obtained, the brain image data are preprocessed and registered, and registered brain graph data are obtained; dividing the brain map data into a plurality of brain regions based on a set brain map, and determining time sequence data of each brain region; based on the time sequence data of each brain region, a function connection matrix is determined, and non-diagonal elements in the function connection matrix reveal the connection strength between the brain regions; and based on the brain region and the functional connection matrix of the brain map data, generating a brain network map (which can be visualized) of the target object. According to the scheme, the sMRI data (the precision is millimeter level) is introduced as a registration basis, so that the registration precision can be improved, and a brain network diagram with higher precision can be generated subsequently.

The invention provides a method for constructing a mood disorder assessment model based on hierarchical multilevel gating, which comprises the following steps: acquiring electroencephalogram signals of a plurality of testees, labeling labels, and constructing a first training sample set; training by using the first training sample set to obtain time sequence dynamic graph network feature extraction models corresponding to the multiple electroencephalogram parameter combinations and related time sequence dynamic graph network features; constructing a second training sample set by using the first training sample set and the time sequence dynamic graph network features, and training a hierarchical multilevel gating model; and obtaining a mood disorder assessment model based on the time sequence dynamic graph network feature extraction model and the hierarchical multi-level gating model corresponding to each electroencephalogram parameter combination. According to the method, the problem that the assessment accuracy is limited due to the fact that a mood disorder assessment model in the prior art does not consider the correlation between different parameter combinations among the brain region, the frequency band and the observation time length and the task and does not perform hierarchical screening on different parameter brain networks based on the task correlation is solved.

The invention discloses an AD diagnosis method fusing electroencephalogram frequency spectrum standardization deviation and a brain network. The AD diagnosis method comprises the steps that S1, band-pass filtering and fragment segmentation are conducted on multi-channel EEG signals of five brain regions; s2, for each segment, calculating brain region-frequency band characteristics of five different brain regions; s3, performing standardized modeling by adopting a GAMLSS model, identifying individual deviation information of a disease group about a specific brain region rPSD, training a LightGBM classifier by utilizing the individual deviation information, and extracting a structured feature vector by utilizing an internal structure of the classifier; s4, dividing each segment into a plurality of non-overlapping frequency boxes according to frequency resolution, calculating an FCN graph in a matrix form of each frequency box, constructing a three-dimensional image according to the FCN graph, and extracting a visual feature vector by using a Swin transformer network; and S5, constructing a connection vector, and inputting the connection vector into a classifier for classification. The method provides a new technical approach for accurate diagnosis and differential diagnosis of neurodegenerative diseases.

The invention discloses a method for detecting interpersonal nerve synchronization under audio-visual stimulation, which comprises the following steps of: designing a double-person super-scanning experiment normal form, collecting double-person electroencephalogram signals, and constructing a database of visual stimulation and auditory stimulation; preprocessing the data in the database to obtain electroencephalogram signals of four different frequency bands delta, theta, alpha and beta, extracting the electroencephalogram signals of the alpha frequency band, segmenting the electroencephalogram signals, and calculating a correlation ISC value between subjects of each segment; an intra-brain network and an inter-brain network are constructed by constructing a functional connection matrix, and the similarity of the intra-brain network and the global efficiency of the inter-brain network are calculated, so that the cooperation and synchronization degree between neural activities of subjects under visual stimulation and auditory stimulation is effectively evaluated. The invention further discloses a system for detecting interpersonal nerve synchronization under audiovisual stimulation. According to the method, the difference of subjects is reduced by standardizing experimental conditions, and the influence of single sensory stimulation on an intracerebral network activation mode is studied.

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 a three-dimensional brain network dynamic segmentation method based on deep learning. The method comprises the following steps: S1, constructing fused image volume data with consistent time and consistent space; s2, a multi-scale sparse Transform coding feature pyramid is generated; s3, obtaining a same-scale brain region map structure; s4, taking the cross-scale node alignment fusion graph structure as initial output of a cross-scale node alignment fusion mechanism; s5, obtaining a first round of fusion brain region graph node embedding feature; s6, an updated multi-scale sparse Transform coding feature pyramid is obtained, and the step S3 to the step S5 are repeated until interaction updating of the multi-scale sparse Transform-GNN is completed; and S7, obtaining a three-dimensional brain region dynamic segmentation result. According to the method, collaborative extraction of local fine granularity and global coarse granularity features is realized, redundant information can be effectively inhibited, and the modeling capability of spatial structure and functional connection features in a cross-modal fusion image can be enhanced.

The invention provides a working memory ability assessment method and device for dynamic brain network attention fusion, and belongs to the technical field of intelligent medical treatment, and the method comprises the steps: segmenting a BOLD signal time sequence in functional magnetic resonance imaging data, extracting time sequence data of a region of interest, and constructing a functional connection matrix; extracting and fusing spatial dependence and time fluctuation characteristics in the functional connection matrix based on an attention mechanism to obtain a full connection graph; clustering the full connection graph by using multi-head attention to obtain a functional magnetic resonance imaging embedded representation of each testee; training the prediction network by using the functional magnetic resonance imaging embedding representation of the testee; and using the trained prediction network to complete work memory evaluation of the target subject. According to the method, dynamic multi-graph fusion and neuroscience priori knowledge are combined, individual differences and specific noise are eliminated through comparative learning, accurate evaluation of the working memory ability is achieved, and an objective basis is provided for evaluation of the cognitive function.

The invention provides a surgical process management method and system, and relates to the technical field of surgical process management.The method comprises the steps that DTI, fMRI and 3D-T1WI image data are fused through a non-rigid registration algorithm, a three-dimensional brain network atlas is generated, then an LSTM deep learning model is constructed, a brain surface displacement field monitored in real time in an operation is used as input, and an LSTM deep learning model is constructed; a deep tissue displacement value is used as a label for training to obtain a deep tissue displacement predicted value, and a dynamic relation between brain neural element electrical activity and oxyhemoglobin saturation is analyzed by combining data monitored by high-frequency EEG and near infrared spectrum and utilizing a wavelet time-frequency coherence algorithm to obtain a dynamic influence coefficient; and finally, combining the deep tissue displacement predicted value with the dynamic influence coefficient, calculating a comprehensive evaluation index, and comparing the comprehensive evaluation index with a preset threshold value, so that a surgeon can quickly adjust an operation strategy and timely deal with possible risks.

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 relates to the technical field of brain network construction, in particular to a group prior embedded double-flow space-time brain network analysis method, which comprises the following steps of: S1, preprocessing a brain function image; s2, dividing the brain into a plurality of brain regions, and extracting an average time sequence; s3, calculating edges of a connection weight construction brain map, and outputting a symmetric correlation matrix # imgabs0 #; s4, defining a graph isomorphic network under spatial features, taking the correlation matrix R as the input of the graph isomorphic network, and outputting to obtain a tag Z1 related to the spatial features; S5, collecting BOLD signals of brain function images, and inputting the BOLD signals into a # imgabs1 # model to a # imgabs2 # model to obtain a tag Z2 related to the time features; s6, performing dimensionality reduction and aggregation on the spatial feature tag Z1 and the time feature tag Z2 to obtain a same-dimensional tag Z; and S7, establishing a group-based attraction graph # imgabs3 # by using the same-dimensional label Z to realize classification and identification. According to the method, the spatial-temporal feature tags are utilized to construct the group graph Gp, and node feature updating is matched to obtain new tags embedded into group priori, so that the classification and recognition accuracy of the brain function image can be effectively improved.