332 results about "Functional connectivity" patented technology

The invention discloses a functional connection and dynamic model-based anatomical connection prediction method, which comprises the following steps of: firstly, constructing a neurodynamic model, taking a random matrix as initial input, combining functional connection data and the neurodynamic model, and generating a coupling matrix which is more in line with neurodynamic characteristics by iteratively optimizing the random matrix; and the coupling matrix is used for predicting anatomical connection and replacing anatomical connection data to construct a high-precision brain model. According to the method, the initial random matrix is iteratively optimized, so that the generated coupling matrix gradually approaches real anatomical connection, non-intrusive anatomical connection prediction is realized, dependence on intrusive structure connection data is remarkably reduced, and compared with an existing neurodynamics modeling method depending on dMRI data, the method has the advantages that the modeling efficiency is improved, and the modeling cost is reduced. The used DMF model achieves higher fitting degree in structure and function, has higher flexibility and adaptability, is beneficial to revealing deep connection between functional connection and anatomical connection, and provides more efficient technical support for brain network research.

The invention provides a multichannel transcranial direct current stimulation cognitive function assessment method and system, and belongs to the field of neuroscience and brain cognitive function assessment. The method comprises the steps that electroencephalogram signal data of a subject under multi-channel transcranial direct current stimulation are collected and preprocessed; respectively performing micro-state analysis, brain function connectivity analysis and oscillation power analysis on the preprocessed data; constructing a linear mixed effect model, and inputting the results of the micro-state analysis, the brain function connectivity analysis and the oscillation power analysis into the model for quantitative evaluation of the cognitive function state; and a time sequence database is constructed, and the time sequence database is used for storing cross-time-point analysis results and analyzing the long-term change trend of the cognitive function based on a statistical model. Dynamic, accurate and high-temporal-spatial-resolution evaluation of the cognitive state of the subject is achieved, the long-term change trend of the cognitive state is dynamically monitored, and technical support is provided for early warning and rehabilitation effect tracking of neurodegenerative diseases.

The invention belongs to the field of electroencephalogram signal processing, and provides a time-frequency space electroencephalogram emotion recognition method based on three-dimensional space position embedding, which comprises the following steps of: firstly, constructing a three-dimensional electrode space position matrix based on an international 10-20 system standard, determining a space adjacency relation between electrodes, and calculating a phase locking value to obtain a functional connection matrix; then, deep feature fusion of an electrode spatial position matrix and a functional connection matrix is realized by adopting a hierarchical cross Transform architecture, the spatial position matrix represents spatial distribution features of a cerebral cortex region, and the functional connection matrix quantifies phase synchronization features of cross-brain region neural oscillation and simulates a brain spatial topological structure; and finally, extracting time, frequency and spatial features of the electroencephalogram signals through combination of a graph attention network and bidirectional long-short-term memory with an attention mechanism for emotion recognition. The method can effectively extract space structure information highly related to the emotional state, and significantly improves the accuracy of emotion recognition.

The invention discloses a Parkinson's dyskinesia individualized SCAN network positioning method based on a multi-modal image and deep learning. The method comprises the steps of obtaining multi-modal medical image data, preprocessing the multi-modal medical image data, obtaining a multi-modal structure image and functional connection data, and calculating a spontaneous neural activity index of a whole-brain voxel level; taking a priori brain region related to the spontaneous neural activity index and dyskinesia as a seed point, constructing a seed point voxel function connection graph representing individual brain function connection, and performing nonlinear feature fusion and extraction through the deep learning network model; the bilinear attention network is adopted to capture the interaction information of the feature data and the individual dyskinesia symptom which is significantly related, an individualized SCAN network positioning result is obtained, the structure-function coupling characteristics of the individual brain are comprehensively described, the cross-modal pathological features related to the dyskinesia can be more sensitively recognized, and the accuracy and accuracy of the diagnosis and treatment of the dyskinesia can be improved. And the accuracy and robustness of abnormal brain region detection are obviously improved.

The invention relates to the technical field of medical care informatics, and discloses an intelligent post-stroke cognitive impairment assessment method based on brain network dynamic reconstruction, which comprises the following steps: acquiring multi-modal brain image data of a patient, dynamically compensating the difference between haemodynamic delay and white matter conduction velocity through a nerve-blood vessel coupling model, and calculating the post-stroke cognitive impairment; precise space-time alignment of function and structure signals is realized; a pulse neural network dynamic entropy change model is constructed based on the alignment data, functional connection strength change is coded into a pulse distribution sequence, and Gamma wave band synchronization enhancement and other compensatory features are extracted; constraining rationality of brain network reconstruction by adopting an energy optimal transmission model, and generating a compensatory thermodynamic diagram; and real-time calculation of the edge end is realized through pulse time sequence compression coding. The limitation of traditional static analysis is avoided, the algorithm achieves microsecond response on embedded equipment through the pulse coding technology, and a reliable stroke rehabilitation evaluation tool is provided for primary hospitals.

The invention discloses a multi-modal feature combined depression auxiliary diagnosis system. The system comprises a sampling unit which is used for constructing a multi-modal depression data set by acquiring a depression screening scale, an electroencephalogram, a magnetoencephalogram and functional magnetic resonance imaging based on acquisition equipment; the feature extraction unit is used for extracting multi-modal brain features based on the depression data set, and the multi-modal brain features comprise power spectral density obtained by electroencephalogram signals, event-related potential, micro-state, prefrontal lobe gamma frequency band power spectral density obtained by magnetoencephalogram and event-related magnetic field; gray matter volume and resting state functional connection density are obtained through functional magnetic resonance imaging; a data preprocessing unit; the diagnosis model unit is used for constructing a multi-modal depression diagnosis model and training the model on the basis of the multi-modal brain features in combination with a fusion strategy; and an analysis and prediction unit. The extracted features are comprehensive and reasonable, the defect of each mode is overcome by the feature fusion method, and the fused features are advanced.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention discloses a GNN-based multi-mode brain imaging infantile autism spectrum disorder detection method, which is characterized in that a function connection network which is different from traditional characteristics and takes an edge as a center is introduced, higher-order information interaction in a brain interval is mined, and a brain function abnormity mode of an infantile autism patient is accurately captured. Meanwhile, integrating brain imaging data and clinical text information into a unified multi-modal graph by utilizing the graph structure learning ability of GNN, capturing a complex nonlinear relationship, and realizing deep combination of multi-modal features; according to the method, the accuracy and robustness of autism detection are effectively improved, technical support is provided for personalized diagnosis and treatment, key technical problems in multi-modal data fusion and brain network analysis are solved, and the accuracy and reliability of ASD early diagnosis are improved.

The invention discloses a brain age prediction method based on structure and functional MRI image multi-modal fusion, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, extracting spatial structure characteristics of a structural magnetic resonance image by using DenseNet121; meanwhile, a function connection matrix is constructed according to the time sequence of the functions, a graph structure is constructed on the basis of the matrix, the characteristic of each node is the connection strength between the node and other nodes, and the edge is converted into sparse graph representation from the absolute value of the connection strength; then extracting functional features by using a graph attention network, and fusing the structure and the functional features by using a cross attention mechanism; and applying a gating mechanism fusion result to a brain age prediction regression task. According to the brain age prediction method, complementary information of multi-modal data is fully utilized, and biological markers of brain aging can be accurately captured.

The invention discloses a white matter fiber bundle structure-function connection coupling method based on a distance function. The method comprises the steps that brain imaging data are collected and preprocessed; extracting white matter fiber bundle structure attributes and function signals of the preprocessed brain imaging data; constructing a structure connection matrix and an individualized function connection matrix according to the white matter fiber bundle structure attributes and the function signals; measuring the connection weight similarity between corresponding columns of the structure connection matrix and the function connection matrix through a distance function, and carrying out white matter fiber bundle structure-function connection coupling according to the connection weight similarity; through the method, the relationship of the white matter fiber bundle structure and function of the brain can be quantified, and the method is suitable for brain structure and function analysis based on the white matter fiber bundle and research on brain development and mental diseases.

The invention discloses a time interference electrical stimulation system for suppressing, controlling and adjusting, which comprises an image acquisition module for acquiring functional magnetic resonance images to acquire functional connection data; the stimulation module is used for respectively generating time interference electrical stimulation waveforms in the plurality of stimulation areas so as to stimulate the corresponding stimulation areas and adjusting stimulation parameters; the evaluation module is electrically connected with the image acquisition module and the stimulation module, acquires cognitive emotion evaluation data, and obtains an emotion and cognitive neural circuit regulation result by combining the emotion evaluation data and the function connection data as an inhibition control capability evaluation index; and the control module is electrically connected with the stimulation module and the evaluation module, and controls the stimulation module to adjust the stimulation parameters based on the emotion and cognitive neural circuit adjustment result. According to the system, effective time interference electrical stimulation is carried out on a plurality of inhibition control intervention brain regions, stimulation parameters are improved by implementing stimulation effect evaluation, and a favorable adjustment means is provided for inhibition control of multi-region and complex network modulation.

The invention discloses a transcranial magnetic stimulation positioning system based on a deep effect brain region, and the system comprises the steps: obtaining the data of a tested magnetic resonance image, which comprises a resting state functional image and a high-resolution T1 structure image; performing data preprocessing on the function image and the structure image, and establishing an individual space; the deep effect brain region template is converted to an individual space through nonlinear registration, and a target region of interest is defined; calculating functional connections between voxels of the deep effect brain region and the cortical region by adopting a Granger causality analysis method based on wavelet transformation; and screening Top block masses according to the functional connection strength, calculating the gravity centers of the Top block masses, and determining individualized stimulation targets in combination with the skull surface distance. According to the method, spontaneous brain activity function variation of the brain of an individual and the brain signal propagation direction are fully considered, accurate positioning of different individual levels is achieved, relatively stable cortex stimulation targets are obtained, and therefore the clinical curative effect of TMS is optimized.

The invention discloses a disturbance of consciousness stimulation regulation and control system and method fused with electroencephalogram connection recognition. The system comprises a simulated electroencephalogram signal data acquisition stage, a connection recognition analysis stage, a stimulation parameter optimization stage and an executable stimulation instruction conversion stage. The method has the following advantages and effects that a whole-electroencephalogram activity distribution diagram is generated by simulating an electroencephalogram signal data acquisition stage, a key connection area is identified, and a brain function connection map is generated by utilizing a function connection analysis network and a phase synchronization algorithm in a connection identification analysis stage; in the stimulation parameter optimization stage, space-time correlation between a whole electroencephalogram activity distribution map and a brain function connection map is established, a multi-objective optimization algorithm is adopted to generate an optimized stimulation parameter set through a fusion network, and finally, in the executable stimulation instruction conversion stage, the optimized stimulation parameter set is converted into an executable stimulation instruction based on a self-adaptive control model. Therefore, the accuracy and the self-adaptive capability of electroencephalogram signal stimulation regulation and control are remarkably 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 a brain region correlation analysis system and method for autistic children. The system comprises a brain region division module used for dividing the cerebral cortex into a plurality of brain regions; the training data set construction module is used for constructing a training data set, and each training sample comprises a sensor space function connection matrix and a source space function connection matrix corresponding to the sensor space function connection matrix; the signal preprocessing module is used for acquiring a real electroencephalogram signal and calculating a real value of a corresponding sensor space function connection matrix; the deep learning mapping module is used for learning a mapping relation from the sensor space function connection matrix to the source space function connection matrix and outputting a predicted value of the source space function connection matrix; and the brain region correlation analysis module is used for calculating the correlation between the brain regions. The method can be used for accurately analyzing the correlation between the brain areas of the autism children.

The invention belongs to the technical field of deep learning and brain science, and particularly relates to an ADHD graph convolution model construction method based on fMRI spatial-temporal characteristics, and the method comprises the following steps: processing resting state functional magnetic resonance data; fMRI sequence processing: randomly cutting fMRI sequences from different sites to obtain fMRI sequences with consistent sequence lengths, and obtaining a functional connection matrix according to Pearson's correlation; model input data: the model input data is composed of an fMRI sequence, ADHD phenotype information and a functional connection matrix, and the input data is divided to obtain a training set, a verification set and a test set; constructing a model; and model training: model evaluation. According to the method, the gated feature fusion module is proposed to fuse the captured fMRI features to obtain the spatial-temporal features, the phenotypic information of the subject is considered, the ADHD graph convolution model based on the fMRI spatial-temporal features is constructed, and the classification accuracy of ADHD diseases is improved.

The invention is applicable to the technical field of brain discipline, and provides a brain function connection modeling and coding method, device and equipment and a storage medium, the method comprises the following steps: constructing a first brain function connection graph according to rs-fMRI data, performing graph enhancement processing on the first brain function connection graph by adopting a preset adaptive graph enhancement strategy to obtain a second brain function connection graph, performing feature coding on the second brain function connection graph by adopting a preset topological attention coding strategy to obtain corresponding graph features, and performing feature projection on the graph features by adopting a preset feature projection strategy to obtain brain function discrimination features for brain function connection classification, therefore, the generated brain function discrimination features can accurately identify subtle differences of different types of brain function connections, and high-accuracy discrimination can be realized whether normal and abnormal brain function states or brain connection modes corresponding to different diseases, so that powerful support is provided for clinical aid decision making, and the accuracy of brain function discrimination is improved. And the accuracy and scientificity of brain disease diagnosis and treatment can be improved.

The invention provides an individualized TMS target positioning method and system for spastic cerebral palsy children, and aims to realize TMS precise nerve regulation and control for different types of SCP children. According to the method, the structural features of the healthy child are obtained, and after image processing, accurate division of the SCP child brain region is achieved, the defects of a current brain injury child brain region spectrogram are overcome, and a basis is provided for seed point selection of different SCP symptoms; multi-modal image parameter features are further scanned, whole-brain voxel correlation analysis is performed based on seed points to construct functional connection diagrams, the crossed and overlapped functional connection diagrams are searched to serve as a liability focus network of different symptoms, a voxel set close to the scalp surface serves as a TMS target, and SCP child treatment targets of different clinical symptoms are accurately positioned. Based on a focus network mapping technology, functional connection characteristics related to different symptoms or symptom complexes are better positioned, a specific brain network is identified, and an accurate intervention target is provided for TMS treatment.

The invention discloses a multi-mode autism diagnosis method and system based on resting state fMRI and phenotypic text information, a medium and a product. FMRI and phenotypic text information are processed through a pre-trained heterogeneous graph neural network model to output a diagnosis result; the resting state fMRI is utilized to construct a brain function connection graph, and the phenotypic text information is coded into a two-dimensional vector; respectively extracting brain connection feature embedding of the fMRI mode and phenotype information feature embedding of the text mode; fusing the feature embedding in the two modes through a gating fusion network; and constructing a heterogeneous group diagram based on the fusion features and phenotypic information similarity, and obtaining group diagram features on the heterogeneous group diagram by using dual-channel information aggregation and adaptive feature fusion to output a diagnosis result. The invention aims to improve the accuracy of autism diagnosis by using multi-modal information, and can be applied to the fields of neuroimaging analysis, intelligent medical treatment and the like.

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 belongs to the technical field of artificial intelligence and physiological signal processing, and discloses an electroencephalogram emotion recognition method and system based on a multi-task self-supervision and dynamic graph fusion network, and the method comprises the steps: obtaining and preprocessing electroencephalogram and other physiological signals, and extracting multi-band energy features; constructing a dynamic graph structure, taking electrodes as nodes, taking frequency band energy as characteristics, and fusing spatial distance and functional connectivity to generate a dynamic adjacency matrix; designing multi-task self-supervised pre-training, including spatial jigsaw, frequency jigsaw and cross-modal contrast learning tasks, to learn general characterization; a dynamic graph fusion network is adopted to carry out end-to-end training, and a shared feature extraction module of the dynamic graph fusion network realizes adaptive fusion of multi-modal features by utilizing Chebyshev graph convolution and embedding a cross-modal attention mechanism; the classification module sets an independent classification head for each task, and optimization is carried out through a joint loss function. According to the method, the accuracy and generalization ability of electroencephalogram emotion recognition are remarkably improved.

The invention discloses a brain function magnetic resonance imaging data analysis method based on a contrast graph neural network, and the method comprises the steps: firstly carrying out the data enhancement of a brain function connection graph, and simulating the heterogeneity of brain function magnetic resonance data, so as to improve the diversity of a data set; secondly, the hidden space embedding features of the brain function connection diagram are efficiently learned by fusing a double-Hough-Laplacian diagram convolutional network and a contraction incentive mechanism; the embedded features are mapped to a group of prototype vectors, and prototype allocation codes corresponding to the embedded features are calculated by adopting a Sinkhorn-Knopp algorithm; performing exchange optimization on prototype codes between different enhanced brain connection diagrams of the same subject through a contrast learning strategy, and compelling codes of homologous subjects to be aligned at the minimum cost in combination with a cross entropy loss function; and finally, applying the pre-training model to a functional magnetic resonance imaging data set of the Alzheimer's disease, and carrying out interpretability analysis on learning features to improve the classification efficiency and pathological analysis of the Alzheimer's disease under a limited tag condition.

The present invention belongs to the field of rehabilitation therapy technology and discloses a longitudinal analysis method and system for magnetic resonance imaging data of mild brain injury. The method extracts BOLD signals from the magnetic resonance imaging data of subjects; constructs a symmetric positive definite sparse brain functional connectivity network set of subjects based on sparse inverse covariance matrix estimation; determines the brain functional connectivity network dictionary and sparse coefficient matrix in kernel space based on Riemannian manifold sparse coding; performs spatial distribution analysis of brain functional connectivity atomic networks; and performs longitudinal analysis of magnetic resonance imaging data of mild brain injury. By analyzing the differences in the spatial distribution of these highly present brain functional connectivity atomic networks in the brain, the present invention digs out brain functional connectivity imaging markers for distinguishing the three mild brain injury rehabilitation treatment stages: acute phase, subacute phase, and complete recovery, thereby realizing longitudinal analysis of the mild brain injury rehabilitation process.

The invention discloses a nerve regulation and control method, device and equipment based on electrical stimulation and a storage medium. The method comprises the steps that metabolism image data, functional image data and anatomical image data of a detected person are acquired; based on the metabolism image data and the functional image data, respectively calculating the standardized uptake value ratio of each brain region of the detected person and the functional connection strength between each brain region and all other brain regions; on the basis of the standardized uptake value ratio of each brain region of the detected person and the functional connection strength between each brain region and all other brain regions, determining the abnormal characteristics of the brain of the detected person and the position data of a target spot; and time domain interference electrical stimulation parameters are set based on the position data of the target spot, the brain abnormal characteristics of the detected person and the anatomical image data. According to the method, the PET image data and the rs-fMRI image data can be combined and analyzed, the metabolism and function state of the brain of the detected person can be evaluated more comprehensively, and corresponding nerve regulation and control can be carried out based on the time domain interference electrical stimulation parameters.

The invention discloses an electroencephalogram sleep staging method and device based on time-space relation learning and a medium. The method comprises the following steps: acquiring electroencephalogram data; constructing a context sequence of adjacent sleep periods according to the electroencephalogram data; inputting the sequence obtained by construction into a sleep staging model, and outputting a staging result; wherein the working mode of the sleep staging model is as follows: generating a space-time diagram by splicing an instance adaptive brain function connection matrix of a single sleep period and a space-time transition matrix of an adjacent sleep period; each adjacent sleep period is directly spliced with a to-be-tested sleep period sample, the adjacent sleep periods and the corresponding time-space diagrams are input into a gated time-space diagram convolution module together, the time-space dependency relationship between brain channels of the adjacent sleep periods is learned, and characteristics used for staging are obtained. According to the method, the global space-time transition relation of the sleep context sequence is captured by combining the construction of the space-time diagram and the design of the gated space-time diagram convolutional network, and the method has relatively good reliability and accuracy in electroencephalogram sleep staging tasks.

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 relates to the technical field of transcranial magnetic stimulation target spot positioning, and provides a transcranial magnetic stimulation individual target spot positioning method and system and a program product, and the method comprises the steps: obtaining the magnetic resonance brain imaging data of a tested individual; preprocessing the magnetic resonance brain imaging data to obtain preprocessed magnetic resonance brain imaging data, and performing superficial brain region division according to the preprocessed magnetic resonance brain imaging data to obtain a plurality of functional sub-regions; individual target stimulation targets of transcranial magnetic stimulation are determined according to the multiple functional sub-regions, fluctuation of functional connection strength between different voxels can be reduced, instability of resting state functional connection in a tested individual is effectively counteracted, and even if the connection strength of some voxels changes along with time, the tested individual cannot be affected. And the functional characteristics of the whole functional sub-region are still kept relatively consistent, so that the robustness of target spot positioning is 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 brain function dynamic prediction device and equipment based on rTMS and a medium, and relates to the technical field of brain data processing.The device is used for obtaining multi-mode magnetic resonance imaging data and conducting preprocessing to generate a BOLD time sequence; carrying out Hilbert transform on the BOLD time sequence, and determining a phase coherence matrix of different brain regions at the same time point; performing weighted eigenvector dynamic analysis on the phase coherence matrix to generate a weighted eigenvector; performing clustering analysis on the weighted feature vectors to generate functional connection brain state clustering data; determining the similarity between the function connection brain state clustering data and the Yeo resting state network, and generating a function connection brain network state; state change data between functional connection brain network states is determined, and a state transition probability matrix is generated; and generating an rTMS prediction result according to the transition probability matrix and the brain index. The prediction efficiency can be improved.