82 results about "Functional brain" patented technology

The invention discloses a multi-modal dynamic structure-function multilayer brain network calculation method and terminal, and the method comprises the steps: obtaining a multi-modal brain image, and carrying out the preprocessing of the multi-modal brain image, and obtaining a target multi-modal brain image; constructing a dynamic functional brain network, a structural brain network and a grey matter morphological similar brain network according to the target multi-modal brain image; and calculating according to the dynamic functional brain network, the structural brain network and the grey matter morphological similar brain network through a network topology similarity principle to obtain structural-functional coupling. According to the method, after MRI data is preprocessed, a morphological brain network is obtained through sMRI, a functional connection brain network is obtained through fMRI, a structural connection brain network is obtained through DTI, a multi-layer brain network coupling index is calculated, while structural and functional connection is considered, morphological information is also considered, then exploration of the model on the brain structure and function is improved, and the method has the advantages of being high in practicability and the like. And more comprehensive and accurate brain structure and function information can be provided.

The invention relates to the field of deep learning and emotion recognition, in particular to an EEG emotion recognition system and method based on multi-scale space-time diagram convolution and comparative learning, and the method comprises the steps: carrying out the preprocessing of an EEG signal; constructing positive and negative sample pairs based on the emotion categories and the identities of the subjects; extracting hidden space representation of the electroencephalogram signal by adopting a stack auto-encoder, and dividing the electroencephalogram signal into a plurality of functional brain regions to obtain a brain source signal after dimension reduction; constructing an undirected graph structure of the brain source signal; constructing a multi-scale space-time diagram convolutional network as a feature extraction network, inputting an undirected graph structure, dynamically learning and updating an adjacent matrix, expressing features of brain source signals in the undirected graph structure, and extracting emotion related features; based on a multi-scale space-time diagram convolution and comparative learning framework, an EEG emotion recognition model is built and trained to eliminate individual differences. According to the method, the multi-dimensional information of the EEG signals is fully utilized, and the accuracy, robustness and generalization performance of emotion recognition are remarkably improved.

The invention discloses a construction method of a dyskinesia phenotype classification model of a Parkinson's disease patient and a diagnosis system, and belongs to the field of medical auxiliary diagnosis devices. The system comprises an image acquisition module and a phenotype classification module, wherein the classification module is composed of a space-time diagram convolution feature extraction module, a hierarchical node fusion module, a functional brain network construction and feature extraction module, a local brain region feature screening module, a hierarchical brain feature pairing fusion module and an output module. According to the method, multi-modal brain image data are fused, space-time and complex relation characteristics of a brain region are deeply mined, a key lesion brain region is positioned, and dyskinesia phenotypes are accurately classified by training a neural network. The system can significantly improve the diagnosis accuracy of the Parkinson's disease dyskinesia phenotype, has good expansibility and adaptability, and provides a scientific basis for early diagnosis and personalized intervention. The invention further relates to an operation method of the system, an auxiliary diagnosis device and a computer readable storage medium.

The invention discloses a Parkinson subtype diagnosis model based on balance multi-mode brain network fusion, which is characterized in that functional magnetic resonance data and diffusion tensor imaging data are preprocessed respectively to obtain fMRI signals and a structural brain network, and then a functional brain network is constructed by using a Pearson's correlation coefficient; thresholding the functional brain network and the structural brain network, respectively extracting functional and structural topological features in the functional brain network and the structural brain network, and performing modal alignment on the functional brain network and the structural brain network; then fusing multi-modal brain network features, and splicing topological features and multi-modal fusion features of functions and structures so as to reserve modal specific information and multi-modal fusion information; secondly, the spliced features are added to an adaptive weight learning module, so that contribution degrees of different modes are dynamically adjusted, weights are multiplied by the two features respectively, so that balanced multi-mode features of each subject are obtained, and the balanced multi-mode fusion features are sent to a multi-layer perceptron for classification.

The invention relates to the technical field of determination of nerve regulation and control precise stimulation targets, and discloses an individualized multi-frequency regulation and control stimulation system for Rett regulation and control. The individualized multi-frequency regulation and control stimulation system comprises a data acquisition module, a structure and function brain image analysis module and a multi-frequency electroencephalogram self-adaptive regulation and control module, the data acquisition module is used for acquiring resting state functional magnetic resonance imaging data rs-fMRI, structural magnetic resonance imaging data sMRI and EEG data, and the structural and functional brain image analysis module is used for identifying an abnormal brain area of an RTT patient and determining an optimal stimulation target. The multi-frequency electroencephalogram self-adaptive regulation and control module comprises two sub-modules including an electroencephalogram state recognition module and an FUS self-adaptive stimulation module, and the electroencephalogram state recognition module is used for analyzing EEG signals, extracting frequency band power change and instantaneous phase information corresponding to a target brain region and providing real-time feedback signals for self-adaptive regulation and control. According to the system, the target spot is accurately determined, and the individualized accurate stimulation target spot is determined by combining the structure and function image and the disease exposure model.

Methods, systems, and devices are described for reconstructing spatially resolved electrical activity in the brain. In some example embodiments, EEG and MRI data are used to estimate volumetric distribution of electrostatic potential inside the MRI domain throughout the entire brain. Spatially and temporally varying field estimates can be generated using a brain wave model which is based on weakly evanescent transverse cortical wave propagation and constrained using the tissue properties gained from the MRI data. The disclosed techniques enable brain activity imaging with high spatial and temporal resolution, thereby providing a tool for assessing functional brain states and monitoring changes in those states in relation to various normal and pathological conditions.

The invention discloses a mental disease brain dysfunction detection method based on dynamic causal modeling, and belongs to the field of biomedical engineering. The dynamic causal relationship of information transmission among regions of the brain can be accurately captured by adopting dynamic causal modeling based on electroencephalogram, and the method has unique advantages in the aspect of revealing abnormal modes of mental disease information transmission. Different from a traditional functional brain network construction mode, the system pays more attention to capturing the causal relationship and dynamic change of brain intervals when processing and explaining data, the modeling method based on the causal relationship can provide more detailed and personalized information transmission analysis to a certain extent, a more accurate result is provided, and the method is suitable for popularization and application. Furthermore, the functional disorder in the mental disease brain neural network can be sensitively recognized, the pathological mechanisms of bipolar affective disorder, schizophrenia and split affective disorder can be revealed, and support is provided for early diagnosis and personalized treatment.

The invention provides a brain disease structure-function coupling analysis method based on a dynamic spiking neural network, belongs to the technical field of intelligent auxiliary medical diagnosis, and effectively solves the technical problem that an existing structure-function coupling analysis method is insufficient in the aspects of capturing brain dynamic changes and nonlinear coupling characteristics and integrating multi-modal data. According to the technical scheme, firstly, a dynamic pulse neural network model is constructed; extracting a pulse structure connection matrix from the original fMRI time sequence; then extracting a pulse function connection matrix from the function brain network, and inputting the pulse function connection matrix into a pulse coupling pooling module to obtain pulse structure-function coupling; and finally, inputting the obtained coupling information into a classification layer to obtain a disease recognition result. The method has the advantages that a brand new analysis tool and view angle are provided for brain disease research, deep understanding of brain disease neurological mechanisms is promoted, and the method has wide application potential in clinical application.

The embodiment of the invention discloses an autism spectrum disorder subtype division method and device, a medium and a program product. The method comprises the following steps: constructing a connection brain map based on neuroimaging data and a functional brain region division template of an ASD individual; constructing a brain age regression model, predicting the social brain age of the ASD individual based on the connection brain map and the brain age regression model, and obtaining the brain age difference of the ASD individual in combination with the actual brain age of the ASD individual; obtaining an ADOS social score of the ASD individual, and carrying out clustering analysis on the ADOS social score and the brain age difference to obtain a clustering subtype; and carrying out behavioral verification and neural dimension verification on the clustering subtypes, and constructing a combined portrait among the subtypes, the behavior features and the neural features based on a verification result. According to the method, the social brain age can be predicted by constructing the connection brain map, the clustering subtypes are divided, the combined portrait is constructed, a doctor can be accurately assisted to detect ASD subtype neural development differences, and discovery and application of subtype specific biomarkers are assisted.

The invention discloses a visual evoked brain signal decoding method and system based on a multi-modal diffusion model, and the method can reconstruct a high-resolution image from an fMRI signal and generate a descriptive text. According to the method, an fMRI signal is mapped to an image-text detail potential feature space and an image-text advanced semantic feature space of a CLIP model through a lightweight regression model, and an image and a text are generated under the guidance of a joint condition by using a multi-modal diffusion model. According to the method, multi-condition semantic information of image and text features is fused, high-fidelity image and text description are generated from brain signals at the same time by using a multi-mode potential diffusion model for the first time, and the superior ability of functional brain region analysis in the aspect of specific semantic content decoding is revealed. The invention provides a solution for brain-computer interface, neuroscience research and medical auxiliary diagnosis.

The invention discloses a schizophrenia auxiliary diagnosis method based on spatio-temporal dynamics, and belongs to the technical field of biomedical image mode recognition. A functional brain network image is obtained by f-MRI, and a space-time dynamic model is constructed by means of a Markov chain, so that an individual functional brain network convergence mode and a maximum information flow transmission mode between brain networks can be extracted. Three corresponding index quantization function brain network spatio-temporal dynamic modes are provided, and the three corresponding index quantization function brain network spatio-temporal dynamic modes are a network stationary probability vector, a network optimal step number mean value matrix and a network direct connection strength matrix. According to the method, an effective auxiliary diagnosis model is constructed for three proposed functional brain network spatio-temporal dynamic indexes by means of a statistical test method and a logistic regression algorithm, so that accurate quantitative calculation of the probability of suffering from schizophrenia of an individual is realized, and a powerful technical support is provided for early diagnosis and intervention of schizophrenia; the kit fills the blank of the existing diagnosis technology in the field, and has remarkable clinical application value and scientific significance.

A system for estimating a subject's functional brain age using non-invasive electroencephalography (EEG), the system includes: an EEG acquisition module (101) configured to record multichannel EEG signals from the subject; a stimulation control module (102) configured to present a predefined sequence of cognitive and sensory tasks that specifically target the frontal, temporal, parietal and occipital brain regions, and to generate synchronized event markers; a data processing module (103) configured to prepare the recorded EEG signals by filtering, artifact removal, normalization and segmentation into task-oriented time windows; a machine learning module (104) comprising one or more self-monitoring contrastive learning encoders configured to transform the preprocessed EEG signals into latent feature representations; and a brain age estimation module (105) configured to process the latent feature representations to generate a BrainAge Score indicating a difference between a predicted biological brain age and the subject's chronological age.

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 discloses a brain disease classification method, system and device based on an SPD matrix and a storage medium. The method comprises the steps that test imaging data sets of the brain of a functional brain disease patient and the brain of a normal person and training imaging data sets of the brain of the functional brain disease patient and the brain of the normal person are acquired; the training imaging data set comprises a first time sequence; each test imaging data set comprises a second time sequence; according to the first SPD matrixes, determining a first distance matrix between any two first SPD matrixes in the test imaging data set; determining a first kernel matrix in the training imaging data set according to the first distance matrix, and determining a second kernel matrix in the test imaging data set according to the second distance matrix; and obtaining a brain disease classification result according to the first kernel matrix, the second kernel matrix and the classification label. The method can be widely applied to the technical field of data processing.

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 application discloses a complex action-based spatio-temporal frequency domain feature fusion action recognition method and system. The application preprocesses the electroencephalogram signal of the complex action motor imagery paradigm, extracts the global spatio-temporal feature by using a time-space partition branch network TG-Net, extracts the frequency domain feature by using a frequency branch network F-Net for the power spectrum density of the preprocessed signal, and performs action recognition after flattening, splicing and the like of the frequency domain feature and the global spatio-temporal domain feature. The application constructs a time-space frequency domain double branch network, deeply fuses and splices the global spatio-temporal feature and the power feature, breaks through the limitation of the traditional method which is limited in the time-space domain, realizes the complementary feature extraction of the motor imagery electroencephalogram signal in the time, space and frequency dimensions, and adopts the partition weighting splicing mode of the functional brain area in the space dimension to highlight the important channel action recognition method, so that the comprehensive feature is acquired, and the classification precision is improved.

This invention relates to the field of brain function image classification and processing technology, specifically to an adaptive high-order fusion learning method for brain function image classification, comprising the following steps: S1, acquiring the brain function image to be classified and performing image data preprocessing; S2, constructing a high-order functional brain network matrix sequence; S3, adaptively learning the weights of different-order functional brain networks, weighted summing them to further output features; S4, capturing the contextual dependencies of the high-order functional brain network matrix sequence; S5, further extracting features and performing weighted fusion of features; S6, predicting and outputting the classification result; S7, parameter tuning. This invention, through end-to-end adaptive high-order functional brain network fusion learning, utilizes adaptive learning of different-order functional brain network weights to enhance the interpretability of the inference process between input features and prediction results, and captures temporal information before and after the generation of the high-order functional brain network matrix through a self-attention mechanism, accurately and efficiently classifying and identifying images of normal brain function and brain disorders.

The invention relates to the technical field of biological medicines, and relates to a small molecule peptide composition for targeted repair of stroke brain cells based on stem cells. The invention designs small molecule peptides with targeted homing and differentiation regulation functions, the small molecule peptides are reasonably combined, and a drug delivery system is also constructed. The composition can guide the stem cells to accurately home to the damaged brain area, regulate and control the stem cells to be differentiated into functional brain cells, and can break through the blood brain barrier. Through testing, the treatment effect of the stem cells is remarkably improved, and the safety and effectiveness of treatment are enhanced.

An apparatus includes an intraosseous appliance formed to have hollow channels open to a proximal extradermal surface of the appliance. Optical emitters and detectors reside within channels of the intraosseous appliance having distal openings in a distal base of the intraosseous appliance, in a lateral surface of the intraosseous appliance, and in an extradermal surface of the intraosseous appliance, so as to establish optical paths between the optical emitters and detectors and, respectively, the dura, bone wall of a bun hole and skin of the subject. Intracerebral and extracerebral electrodes reside within respective channels of the intraosseous appliance having distal openings in the distal base of the intraosseous appliance and in the extradermal surface of the intraosseous appliance, respectively.

A method for achieving super-resolution in localizing electrical fields measured at the head surface with electroencephalography through a generative model of the cerebral cortex that has a very high resolution of cortical surface dipoles constructed from the known properties of human cerebral cortex and adapted to optimize the Bayesian explanation the individual's cortical surface electrical fields. The iterative optimization of the prior (generative) with the posterior (observed) fields with extensive data from extended recordings provides a probabilistic estimation of the individual's functional brain activity that can be used to train artificial neural network approximations of the individual's mental activity.

The invention discloses a brain network analysis method based on a multi-scale kernel attention mechanism, and the method comprises the following steps: carrying out the preprocessing of functional magnetic resonance imaging data, and obtaining a time sequence of each brain region; utilizing Pearson correlation to construct an initial functional brain network of each subject as an initial input feature of the model; performing feature extraction and analysis on the initial functional brain network based on an encoder of a multi-scale kernel attention mechanism; based on the prior knowledge of brain modularity, the output of the encoder based on the multi-scale kernel attention mechanism is aggregated and read by using an orthogonal clustering mode; and the classification layer outputs a final identification result. According to the method, a complex interaction relationship between brain regions is captured in a deep learning model in a flexible and interpretive manner, a high-level functional brain network can be effectively constructed, valuable information is provided for a recognition task, a biological marker can be effectively explored on the basis of improving the recognition performance, and the recognition efficiency is improved. The method has important theoretical significance and practical value.

A method for analyzing a user's behavioral, self-reported, and neural data to derive said user's neurobehavioral traits is disclosed. The method comprises: obtaining data associated with said user from a plurality of sources, wherein said data associated with said user comprises electroencephalography (EEG) data and said user's responses to one or more questionnaires; processing said data associated with said user to assess a range of functional brain measures or metrics by generating a model that is specific to said user's neurotype, wherein said neurotype comprises time-domain and time-frequency representations of said user's functional neural activity; and using said model to generate one or more predictive insights indicative of said user's neurobehavioral traits.

The present invention provides a method for classifying mental illness based on the local coupling pattern of the structural-functional brain network, which belongs to the field of medical information intelligent diagnosis technology; its technical solution is: frequent subgraph mining is performed on the connection matrix of the structural brain network to obtain all frequently connected subgraphs; the coupling coefficient between the structural brain network and the functional brain network is calculated through all the frequently connected subgraphs mined; then the coupling coefficient obtained is feature selected using the lasso model through five-fold cross validation; the coupling coefficient obtained after attribute simplification is classified using random forests to evaluate the accuracy. The beneficial effects of the present invention are: the present invention can better capture the key features of the structural-brain network, significantly improve the accuracy and efficiency of mental illness classification, and help doctors formulate more effective treatment plans for patients.

The invention is applicable to the technical field of neuroscience, and provides a functional brain network determination method, device and equipment for nicotine addiction intervention, and the method comprises the following steps: respectively collecting resting-state electroencephalogram signals of two groups of subjects under the guidance of a smoking normal form, preprocessing the collected resting-state electroencephalogram signals, and determining the resting-state electroencephalogram signals of the two groups of subjects according to the preprocessed resting-state electroencephalogram signals; electroencephalogram micro-state analysis is carried out on all the obtained processed electroencephalogram signals, a group micro-state template and corresponding micro-state features are obtained, and micro-state conversion pairs with statistical significance are screened out through bidirectional interaction analysis of a linear mixing effect model constructed according to the micro-state features; and performing brain power analysis on the group micro-state template corresponding to the micro-state transition pair, and determining a functional brain network related to neural feedback training for nicotine addiction intervention, thereby realizing more precise capture of subtle changes of brain function states before and after neural feedback treatment, and improving the accuracy of the neural feedback treatment. And a more objective and quantitative nerve marker is provided for revealing an intervention mechanism and optimizing a treatment strategy.

The present invention discloses identity recognition of multi-task functional near-infrared spectroscopy signals based on group averaging. The main steps are as follows: preprocess the original optical signals output by the near-infrared spectroscopy instrument, and extract the preprocessed signals according to different tasks and modalities; construct a functional brain network based on the extracted signals using Pearson correlation; divide the brain network into different groups according to different ratios, and combine the averaging operation to obtain a representative brain network within the group. This method extracts features with individual specificity in a simple way, and at the same time provides a benchmark method for the identity recognition problem based on near-infrared spectroscopy data, having important theoretical significance and practical application value.

The invention relates to the technical field of medical image diagnosis, and particularly discloses a brain network analysis method based on time-space two dimensions and multiple scales, which comprises the following steps: obtaining a functional magnetic resonance image and extracting a time sequence of each brain region; on the basis of Pearson's correlation, functional homogeneity weighting is combined, and a functional brain map is constructed on fine, medium and coarse scales; multi-scale spatial features are extracted through a mixed structure of a U-Net encoder and a multi-scale graph isomorphic network; for a brain region BOLD signal, an internal and external dual-branch selective state space model architecture is adopted to respectively capture short-term instantaneous fluctuation and long-term trend dependence, and a time sequence fragment related to a pathological time sequence attention mechanism enhanced disease is extracted through a multi-scale context to obtain time features; and performing residual gating fusion on the spatio-temporal characteristics and then inputting the spatio-temporal characteristics into a classifier to obtain a classification result. According to the method, the limitation of single scale and single time sequence modeling is broken through, the refinement and functional fusion of the brain spatial-temporal characteristics are realized, and the accuracy and interpretability of brain disease auxiliary diagnosis are improved.

Disclosed herein are methods of determining a therapeutic for a patient with a psychiatric or neurological disorder, based upon the patient's functional brain imaging data through use of a generative model that predicts brain activity.

The invention discloses a time-space frequency domain feature fusion action recognition method and system based on complex actions. According to the method, electroencephalogram signals of a complex action motor imagery normal form are preprocessed, a space-time partition branch network TG-Net is used for extracting global space-time features, meanwhile, a frequency domain feature is extracted from the power spectrum density of the preprocessed signals through a frequency branch network F-Net, and the frequency domain feature and the global space-time domain feature are flattened, spliced and then subjected to action recognition. According to the method, the global spatio-temporal features and the power features are deeply fused and spliced by constructing the spatio-temporal frequency domain double-branch network, the design breaks through the limitation that the traditional method is mostly limited to the time-space domain, complementarity feature extraction of the motor imagery electroencephalogram signals in the three dimensions of time, space and frequency is achieved, and the accuracy of the motor imagery electroencephalogram signals is improved. And the spatial dimension adopts a partition weighted splicing mode of a functional brain region to highlight an action recognition method of an important channel, so that comprehensive features are obtained, and the classification precision is improved.

This invention relates to the field of brain network analysis technology, specifically to a multimodal brain network fusion method based on tensor spectral clustering. The method includes: acquiring multimodal brain network data, including structural magnetic resonance imaging (SMRI) data, functional magnetic resonance imaging (fMRI) data, and diffusion tensor imaging (DTI) data; preprocessing the multimodal brain network data; constructing individual morphological brain networks, functional brain networks, and structural brain networks based on the preprocessed multimodal brain network data, and performing normalization processing; fusing the normalized multimodal brain networks using a tensor spectral clustering algorithm to obtain a fused brain network; acquiring the subject's behavioral data, inputting the behavioral data into the fused brain network, and calculating the subject's behavioral prediction values. This invention not only advances research in neuroscience, cognitive science, and psychology but also provides strong support for related clinical applications and personalized medicine.

The invention discloses a large model semantic prompt guided dynamic brain network analysis method and system, and belongs to the application technology of a large model in a dynamic functional brain network. According to the method, semantic priori is introduced, brain region function semantic representations are constructed and aligned with node-level features, and subnet function semantic representations are constructed and aligned with subnet-level structures. On the basis, a time convolution module is constructed to simulate the time change process of the dynamic network. Furthermore, the subnet strength change semantics are used as prompt information to be injected into the model for adjusting the sensitivity of the time features to the stage change. Next, a spatial structure feature extraction module is constructed to further extract a stable topological structure relationship, and graph paper semantic information is introduced to assist in fusing topological features, so that the modeling capability of the model for the importance of a network structure is enhanced; and finally, inputting fusion features from a plurality of semantic hierarchies into a multi-layer perceptron to realize disease state classification, and based on semantic weight and feature contribution analysis, revealing the explanation ability of semantic priori on disease-related brain network changes.