286 results about "Brain network" patented technology

Means is provided for generating a vibration or a vibration signal, which contains audible range components that are vibration components in the audible frequency range and super-highfrequency components within a range exceeding the audible frequency range up to a predetermined maximum frequency, and which has an autocorrelation order represented by at least either one of a first property and a second property. By applying the vibration or an actual vibration generated from the vibration signal to a human being, a fundamental brain activation effect for activating the fundamental brain network system constituted of a fundamental brain including the brain stem, thalamus and hypothalamus that are regions to bear the fundamental function of the human being and the fundamental brain network of neuronal projection from the fundamental brain to various brain regions is introduced.

The invention relates to an electroencephalogram feature extracting method based on brain function network adjacent matrix decomposition. The current motion image electroencephalogram signal feature extraction algorithm mostly focuses on partially activating the qualitative and quantitative analysis of brain areas, and ignores the interrelation of the bran areas and the overall coordination. In light of a brain function network, and on the basis of complex brain network theory based on atlas analysis, the method comprises the steps of: firstly, establishing the brain function network through a multi-channel motion image electroencephalogram signal, secondly, carrying out singular value decomposition on the network adjacent matrix, thirdly, identifying a group of feature parameters based on the singular value obtained by the decomposition for showing the feature vector of the electroencephalogram signal, and fourthly, inputting the feature vector into a classifier of a supporting vector machine to complete the classification and identification of various motion image tasks. The method has a wide application prospect in the identification of a motion image task in the field of brain-machine interfaces.

The invention discloses a multimodal brain network feature fusion method based on multi-task learning, and the multimodal brain network feature fusion method based on the multi-task learning includes the steps of preprocessing the obtained functional magnetic resonance imaging (fMRI) images and diffusion tensor imaging (DTI) images, registrating the preprocessed fMRI image to the standard AAL template, carrying out a fiber tracking for preprocessed DTI images, calculating fiber anisotropy (FA) value, and constructing structure connection matrix through the AAL template. Clustering coefficient of each brain area in a function connection matrix and the structure connection matrix is calculated to be regarded as function features and structure features. As two different tasks, the function features and the structure features assess an optimal feature set by solving the problem of multi-task learning optimization. The method uses information with multiple modalities complementing each other to learn simultaneously and to classify, improves the classification accuracy, solves the problems that a single task feature does not consider the correlation between features, and the fact that only one modality feature is used for pattern classification can bring to insufficient amount of information.

The invention discloses an early and late mild cognitive impairment classification method and device based on brain function network characteristics and belongs to the medical image processing technology field. According to the method, firstly, sample data is pre-processed, multiple brain region time series are extracted, the brain function network is constructed by utilizing a correlation coefficient between the Pearson's correlation calculation brain area time series, and brain network parameters are calculated; secondly, the stepwise analysis method is utilized to extract characteristics, abinary classifier is trained, corresponding characteristic vectors are extracted from to-be-classified resting state functional magnetic resonance data and are inputted to the trained binary classifier, and the medical image classification result is acquired. Compared with a method in the prior art, the method is advantaged in that classification accuracy, sensitivity and specificity are better.

The invention discloses a brain network classification method based on a graph convolutional neural network. The brain network classification method comprises the following steps that firstly, blood oxygenation level dependent signals of various brain regions are extracted from a brain function magnetic resonance image; secondly, a brain mapping capable of reflecting the topological structure features of functional connections between the brain regions is established; and finally, the established brain mapping and an actual diagnostic label are input into the graph convolutional neural networkfor feature learning and model training. The brain network classification method is used for brain network classification.

Brain stimulation methods and devices in which at least two separate magnetic pulse sources with interposed space between them are placed over two distinct brain regions. The coils are pulsed at between 1 and 100 milliseconds apart (e.g., between 5 ms and 40 ms), thereby producing neuroplastic effects upon a third brain region that is network-connected to said first and second regions.

A method of estimating the likelihood of brain concussion from neurophysiological data acquired from the brain of the subject is disclosed. The method comprises: identifying activity-related features in the data; constructing a subject-specific brain network activity (BNA) pattern having a plurality of nodes, wherein each node represents a feature of the activity-related features, and each pair of nodes is assigned with a connectivity weight; calculating a BNA pattern similarity describing a comparison between the constructed BNA pattern and a baseline BNA pattern being specific to the subject; and assessing the likelihood of brain concussion responsively to the BNA pattern similarity.

The invention discloses a brain function magnetic resonance image classification method based on network centrality. The method comprises the following steps of: preprocessing a brain function magnetic resonance image, performing brain region segmentation, and extracting an average time sequence of each brain region; calculating a partial correlation coefficient between each average time sequence, and obtaining a partial correlation coefficient matrix; performing binarization on the partial correlation coefficient matrix to obtain a brain network model; calculating the network centrality of each node in the network; and classifying the brain function magnetic resonance image by utilizing an adaptive improvement classifier, and checking the adaptive improvement classifier by employing a leave-one-out cross validation testing method. The brain function network is established by utilizing the brain function magnetic resonance image, the brain function magnetic resonance image is classified by utilizing the network topology information, and the brain function magnetic resonance image can be accurately classified.

The invention discloses a mild cognitive impairment disease classifying method based on a brain network and brain structure information, and belongs to the field of medical image disease classification. The method includes the steps that individual cortex characteristics are calculated first, and difference between individual cortices is calculated with a specific kernel function to construct the structural brain network; any number of parameter types are selected from network characteristics and original MRI data to be combined, characteristics are selected from each combined datum through a stepwise judgment method, and the top ten characteristics appearing the most frequently are used as classification characteristics; a support vector machine based on a radial basis function is adopted for classification. The interference on characteristic selection by different combining modes of the cortex characteristics and network characteristics is fully considered, and robustness and stationarity of characteristic selection are ensured. The characteristics with high sensitivity can be selected fast and effectively, and classification precision is improved.

The invention provides a modeling method for a group brain network. The modeling method comprises the following steps that (1), nodes of the group brain network are defined according to group brain imaging data; (2), connecting matrixes between the different nodes are calculated according to the nodes defined in the step (1) and adopted as edges; (3), a group brain network model is constructed according to the nodes defined in the step (1) and the edges defined in the step (2). Meanwhile, the invention provides a topological attribute analytical method for the group brain network. The topological attribute analytical method is achieved based on the modeling method for the group brain network. According to the modeling method and topological attribute analytical method for the group brain network, the interactive mode features of a studied group can be judged, and whether key members exist in the group or not is further analyzed. Therefore, group behaviors can be predicated by utilizing a group neural activity mode result.

The invention provides a depression recognition and analysis system based on a resting state brain network. The depression recognition and analysis system comprises (a) a resting state electroencephalogram data acquisition and preprocessing module used for collecting resting state electroencephalogram data of subjects and preprocessing the collected resting state electroencephalogram data, (b) a brain network metric extraction module used for constructing a personalized brain network structure, respectively finding out common active brain regions of a depression group and a normal control group from the personalized brain network structure, finding out different brain regions based on the common active brain regions of the two groups and extracting brain network metrics, and (c) a classification recognition module used for conducting feature selection on the extracted brain network metrics and functional connection features and classifying the data of which features are already screened to achieve recognition of depression patients and normal subjects. The depression recognition and analysis system has the advantages that feature dimension is effectively reduced, calculation efficiency is improved, and depression recognition can be effectively achieved.

The invention provides a fatigue classification method for constructing a brain function network and a correlation vector machine based on generalized consistency. Compared with the prior art, the method has higher reliability and accuracy, an effective fatigue classification network is constructed through information flowing direction and a causal relationship to classify connection characteristics of a brain network under different mental states, thereby effectively verifying results of topological structure research and improving the detection ability of driving fatigue. The fatigue classification method has the advantages that the brain network is constructed by using a generalized consistency algorithm method, the brain is regarded as a multi-regional cooperative network, informationcirculation direction and causal relationship between nodes of the brain are researched, topological structure changes of the brain network are analyzed under different mental states, the fatigue generation mechanism is disclosed, and a new perspective is provided for fatigue-related research; the correlation vector machine is used for classifying the connection characteristics, 90% or above classification accuracy can be achieved, the reliability of topological structure analysis is verified, and a new method is provided for fatigue detection.

The invention discloses a weighted graph regularization sparse brain network construction method. According to the method, on the basis of analysis of keeping similar local manifold characteristics ofsimilar data in original data space after projection, constrained modeling is carried out on association between brain connection by using a graph regularization item; with consideration of correlation analysis, the similarity degree of essences in a time sequence signal sequence of a brain area can be measured, sparse modeling is constrained by using measured functional connection strength priorinformation, and a weighted sparse regularization item is established; and then combined modeling is carried out on the whole brain function network and a brain network with the biological significance is constructed. According to the invention, on the basis of integration of correlation analysis, sparsity and graph regularization constraint into the unified modeling frame, a brain function network is constructed effectively by utilizing the similarity and locality of functional magnetic resonance brain image data fully; and the method is also used for neurological disease identification andbiological marker analysis.

The invention discloses a method for extracting a brain function network of an individual based on the analysis of multiple tested brain function data. The method comprises the following steps of: calculating the tested independent components of the individual, having correspondence among the different tested, based on the tested brain function data of the individual; using a provided algorithm for analyzing the independent components with reference signals based on a multi-target function optimization framework, meanwhile, optimizing the correspondence between the tested independent components of the individual and the reference signals and the independence among the different tested components of the individual, wherein the reference signals are obtained by jointly analyzing the independent components of the tested brain function data of the individual, and can also be obtained from a brain network pattern and the like obtained through the brain network analysis or meta analysis of other modal imaging data; after the tested independent components of the individual are obtained, using a provided time sequence calculation method to calculate a time sequence corresponding to each independent component; and judging the obtained independent components to obtain a brain function network, wherein the time sequence corresponding to the independent component is an activating mode corresponding to the brain function network.

The invention discloses a mathematical talent teenager brain network analysis method based on a weighted phase lag index, and the method comprises the steps: constructing a phase synchronization network based on a task state EEG signal through employing an algorithm based on the weighted phase lag index, and converting the phase synchronization network into a functional connectivity network; and measuring the connection network by utilizing graph theory indexes, and describing connection network characteristics of the mathematical talent teenagers, including active brain regions and the graphtheory indexes. Compared with the prior art, the method provided by the invention has the advantages that the connection network characteristics of the mathematical exceptional teenagers are describedbased on the task state EEG signals, and the phase synchronization network is constructed by adopting the weighted phase lag index and is converted into the functional connection network, which reduces the probability of detecting false positives when the phase difference between volumetrically conducted noise sources is zero, and increases the sensitivity of detecting phase synchronization.

The invention provides a method for detecting fMRI brain network dynamic covariations. The method comprises the following steps of: obtaining a sample; Firstly, constructing a brain functional networkby utilizing functional magnetic resonance data; according to a certain basis (factors such as age, disease course length, education degree and the like), different network matrixes are arranged in sequence;obtaining a cross-tested network matrix sequence; then, a sliding window method is used for researching a covariant relationship between ROI brain regions which cross a tested object; and obtaining a series of covariant matrixes, and depicting the difference of the brain interval collaborative activity modes under the trans-tested scale is by calculating mathematical indexes for describing covariant relationship variability or through statistical inspection, so that the interactive relationship between brain regions in the network can be better understood.

The invention discloses a system for early detection and risk prediction of schizophrenia. The technical problem that currently, it is hard to accurately and objectively carry out early detection andrisk prediction on schizophrenia can be solved. The system comprises a test module, a collecting and preprocessing module, a data analysis module, a learning classification module and a classificationperformance evaluation method. According to the system for early detection and risk prediction of the schizophrenia, high-risk individuals, ultra-high-risk individuals and initially-attacked people of the schizophrenia are subjected to quantitative and objective detection recognition and risk prediction by comprehensively utilizing cognitive tests, clinical detection, P50 sensory gating electrophysiological characteristics and an XGBoost(the Extreme Gradient Boosting) machine learning method; specifically, by combinedly using cognition, clinic and P50 sensory gating experiments, and adoptingsource location, brain networks, machine learning and other analysis methods, initially-attacked people, high-risk people, ultra-high-risk people and normal contrast people are classified, and the classification accuracy, sensitivity and specificity are improved.

The invention provides a multi-modal brain image depression identification method and system based on graph node embedding. Deep learning is applied to the depression recognition of a multi-modal brain image, and a bridge is built between a multi-modal brain network and a convolutional neural network (CNN) through graph node embedding, so that the CNN can be used for the depression recognition ofthe multi-modal brain image, and therefore, depression recognition accuracy is improved. The method comprises the following steps of: 1) acquiring the resting state fMRI and DTI image data of a depressive patient and a normal control group; 2) preprocessing the acquired fMRI and DTI image data; 3) respectively constructing a brain function network and a brain structure network according to the preprocessed fMRI and DTI image data to obtain a brain network adjacency matrix, and 4) adopting graph node embedding to express the adjacency matrix as an image, inputting the image into a convolutionalneural network for classifying the image, and establishing a classification model for identifying depressive patients and normal subjects.

The invention provides an auxiliary diagnosis device and a method for Alzheimer's disease of a brain network multi-frequency fusion map core, which relates to the technical field of computer-aided diagnosis. The device comprises an image preprocessing module, an image frequency dividing module, a graph kernel generation module, a graph kernel fusion module and an auxiliary diagnosis module. The image dividing module matches the fMRI image with the AAL template and divides the fMRI image. The image kernel generation module constructs a multi-frequency brain network for the divided image and forms a matrix. The graph kernel fusion module fuses all the graph kernels into one graph kernel. The auxiliary diagnostic module combines the fused graph kernel with the kernel limit learning machine torealize the diagnosis of Alzheimer's disease. The invention also provides a method for diagnosing by using the device. The brain network multi-frequency fusion image core auxiliary diagnosis device and method for Alzheimer's disease provided by the invention can sufficiently express the difference of brain activity information under multi-frequency bands, so that the signal information of the functional nuclear magnetic resonance image can be fully exerted.