66 results about "Brain functioning" patented technology

The present invention provides systems and methods for providing music based cognitive skills development. More particularly the present invention may provide a music based cognitive skills development platform. The platform is provided using computer implemented systems and methods for training a human subject so as to enhance his/her intelligence, attention, language skills and brain functioning. The present invention provides these benefits using exercises, one or more of which are based on musical training aspects, however, in addition to musical training other cognitive skills development exercises may be used, as described herein. The invention may be utilized by users of varying musical skills and may be presented at a level corresponding to the prior musical training (if any) of a user, and in a form corresponding to the cognitive capacity, interests and attention span of a user.

The invention discloses a novel genotype analysis method based on multimodal brain images. With utilization of information, including a structural image and a functional image, of two kinds of modes, regression on the rs429358 locus genotype linked to the Alzheimer's disease closely is realized. The method comprises: extracting voxel features from a brain structure images and extracting connecting network features from a brain function image; carrying out feature extraction on the two kinds of features by using an elastic network and keeping brain area and network connection with a high association degree; and then carrying out fusion and genotype regression on selected nodes and side attributes by using a multi-kernel support vector. The method has the following advantages: for the brain image with the complicated structure and functions and genotype data, a novel regression analysis method for a single locus genotype based on structural and functional multimodal brain images is put forward; the analysis effect is good; and complementary information of the two modes is utilized fully.

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 relates to a method for classifying electroencephalogram (EEG) signals based on a multi-scale brain function network. The method comprises the steps that data are acquired, specifically,the EEG signals in a resting state are collected and preprocessed; multi-scale time series are calculated, specifically, multi-scale processing is conducted on preprocessed time series of all leads,and thus the generalized multi-scale coarse-grained time series are obtained; the multi-scale brain function network is built, specifically, the amplitude correlation degree and the phase correlationdegree of all the leads of the EEG signals are taken as the quantitative standards to calculate multi-scale weighted brain function networks; a multi-scale convolutional neural network capable of learning the multi-scale brain function network is built; and neural network training is conducted, specifically, the cross-correlation degree of decision errors generated by all convolutional neural network paths is taken as a penalty term and a penalty loss function to accelerate decision-making of the neural network and reduce the generalization error.

Compositions, foods, and medicaments for enhancing memory and learning function and/or cognitive function are provided. Compositions, foods, and medicaments for improving brain function and particularly enhancing memory and learning function and/or cognitive function can be provided by including a peptide including an amino acid sequence set forth in any one of SEQ ID NOs 1 to 16 as an active ingredient.

The invention relates to the field of online auxiliary evaluation, in particular to a cognitive and brain image data integration evaluation method for Alzheimer's disease. According to the method, firstly, multi-field screening and evaluation of the cognitive function are carried out, the purposes of rapid screening and detailed evaluation of the cognitive function are achieved, then multi-mode magnetic resonance imaging data analysis is carried out, and the multi-mode magnetic resonance imaging data analysis comprises the steps of extracting hippocampus volume and shape analysis, calculating a function connection value of a cognitive-related brain function network, and calculating the cognitive-related brain function network. As an index in the aspect of neuroimaging, the brain function is evaluated. Carrying out cognitive and neural Bayesian joint modeling; and finally, classifying and identifying the specific change mode of the Alzheimer's disease by using supervised learning.

A method for modifying cranial electrical potentials utilizes an electrical lead having first and second electrodes. The electrical lead may also include either a resistive component for increasing the resistance of the electrical lead, or a diode for restricting the flow of electricity through the lead to a single direction. The electrical lead is attached between skin surface areas of a human head, such as between ears, or an ear and an area of the scalp or forehead. Preferably, two electrical leads are utilized, one lead extending from a left ear to a right portion of the head, and the other electrical lead extending from the right ear to the left portion of the head. Scalp electrical potentials are altered in a passive manner resulting in beneficial effects on a variety of psychiatric disorders and brain functioning.

The invention belongs to the technical field of information, and specifically relates to a brain imaging intelligent test analysis method for network information recognition and a system thereof. Themethod of the invention comprises four steps of network information cognition test preprocessing, network information cognition task testing, brain imaging data cognition calculation, and network information cognition result output. The test analysis system of the invention comprises four software modules corresponding to four steps, and four hardware modules of a network information multimedia presentation module, a cognitive response signal acquisition module, a functional nuclear magnetic resonance brain imaging module and an intelligent analysis module. The method has the advantages of designing a network information test signal by using a virtual reality technology, modeling a brain function network based on a neural network model, and performing fusion analysis of a brain rest state,DTI and f-MRI test data; and realizing intelligent calculation through an artificial intelligence technology. More accurate test analysis results can be obtained by the method.

The invention discloses an individual cognitive ability prediction method and system based on dynamic function connection. The method comprises the following steps: acquiring functional magnetic resonance imaging data in a resting state; extracting an average time sequence signal of each brain region through a brain template after preprocessing of the functional magnetic resonance imaging data; calculating and constructing a dynamic function connection matrix by using a sliding time window; with the dynamic function connection matrix as input high-dimensional dynamic brain description originaldata, constructing a model combining convolution and a long-short-term memory network; and substituting a dynamic function matrix and corresponding cognitive behavior data into the model for trainingand testing so as to realize prediction of cognitive competence. According to the invention, dynamic information of brain function connection is fully utilized, the advantages of a deep learning method in big data processing are utilized, calculation is rapid, prediction effect is ideal, various behavior data of a population can be predicted, dependence on the absolute value of the amplitude of imaging signals is avoided, migrations among different MRI machines can be realized, and prediction performance is excellent.

The invention provides a virtual digital brain construction method and system and an intelligent robot control system. The virtual digital brain construction method comprises the following steps of: constructing a virtual digital brain to obtain a brain function causal connection network in a resting state and a task state; constructing a brain structure network by using a hybrid fiber bundle tracking method combining a deterministic algorithm and a probabilistic algorithm; comparing the structure network with the brain function causal connection network, and deleting causal connection withoutdirect structure connection in the brain interval to obtain an improved virtual digital brain; and taking the resting state as a base line, acquiring an activation state of a brain region under tasksignal stimulation, and establishing a node neural activity signal prediction model, namely a final virtual digital brain. According to the invention, the brain function causal connection network is improved through the structure network, and the causal connection without direct structure connection in the brain interval is deleted and the influence of indirect connection is removed by jointly using the function and structure information, so that the establishment of a more effective node neural activity signal prediction model in subsequent processing is facilitated.

The invention discloses a directed dynamic brain function network multiclass emotion recognition construction method and device. The method comprises the following steps: preprocessing acquired electroencephalogram data, splitting the electroencephalogram data into four standard sub-bands; performing time window partitioning on electroencephalogram signals of each standard sub-band; and calculating a directed brain function network matrix through a GPDC (general purpose digital computer), and further performing dynamic planning on the directed brain function network matrix according to time sequences, so as to obtain a dynamic network on a certain time scale. Performance parameters of information communication of the obtained dynamic network can be calculated, the performance parameters comprise overall efficiency and partial efficiency, and parameters of different emotions can be statistically analyzed.

The invention discloses a brain function registration method based on a graph model, and the method comprises the following steps: mapping high-dimensional brain function image data to a two-dimensional time sequence matrix by taking a brain function activity signal of a subject in a specific cognitive function state as input and taking a brain graph model as a basis; constructing a graph convolutional neural network model to distinguish different cognitive function states, and utilizing a meta analysis method to generate a brain activation distribution prior graph to assist in predicting a brain function activation mode of each subject specificity; combining the two sides to map brain function image data of each subject to a shared representation space suitable for a large-scale group, and finally achieving the accurate brain function alignment between individuals. According to the method, the effect dose of statistical test on a group can be enhanced, the number of tested samples required in brain cognitive function research is reduced, the clinical research cost is saved, and meanwhile, the graph representation information generated in the shared representation space can also be used for accurately predicting the tested brain function state and behavioral indexes.

The invention discloses a sensorineural deafness brain function network evolution modeling method, which comprises the following steps: S01, extracting brain network state data, taking a BOLD signal of a resting state fMRI as an object, analyzing and extracting the state expression of a brain network through a sliding window technology, and obtaining the high-dimensional vector expression of the brain network state; s02, acquiring a low-dimensional mapping and clustering result by using the state observation matrix; s03, analyzing a conversion mode of the brain network state, constructing a state set according to a clustering result of the state, analyzing a relationship between state switching and time, and obtaining a time sequence diagram of brain network state evolution on a time axis; s04, on the basis of the brain network evolution model of the timed automaton, according to the brain network state evolution sequence diagram, establishing an evolution process model through the timed automaton theory, a quantitative brain network dynamic description model is provided, and the state transition rule and the evolution process of the human brain network can be effectively described. The method has universality for different tested data, and abnormal evolution process of the subject can be identified.

The present invention discloses a task-state electroencephalogram signal analysis method based on algebraic topology and belongs to specific applications of a complex network analysis technology in the field of neural signal processing. The method comprises the following steps: using electroencephalogram signals as a data source, constructing distance relationship of electrodes at different spatial positions by calculating coherence between the electrodes, using the algebraic topology method to dynamically construct a simplex-based brain functional network, characterizing task-state electroencephalogram signal neural characteristics, further analyzing nature of the brain functional network by calculating Betti number and Euler's characteristic number, and realizing a quantitative study ofa brain function model of subjects under a task state. The task-state electroencephalogram signal analysis method is verified to perform well in the task-state electroencephalogram signal analysis, provides the new method for measuring neural responses in the task state, explores new rules and evidence for brain-like computing, thus can inspire artificial intelligence frameworks and specific algorithms design, etc., and promotes development of a new generation of artificial intelligence.

The invention relates to a brain function connection network analysis method based on a resting state functional magnetic resonance image, and the method is characterized in that the method comprises the following steps: S1, obtaining an rs-fMRI image, and carrying out the preprocessing of the rs-fMRI image; s2, segmenting the rs-fMRI image by adopting three brain templates with different scales, and constructing three graphs G1, G2 and G3; s3, according to the obtained three graphs G1, G2 and G3, hidden feature representation of the graphs is learned by adopting a GNN module, and preliminary prediction labels L1, L2 and L3 are obtained; obtaining a final prediction label after voting; s4, performing significance analysis on a pooling result of the GNN module in the step S3, and obtaining a brain region with significant difference in the functional network; and S5, mapping the prominent brain region of the functional network to the Yeo 7 brain functional network map, and obtaining the mapped functional network connection, namely the individual difference functional sub-network. According to the method, the brain network is constructed by using the three different scales of brain templates, the integrated learning strategy is adopted, the multi-scale information of the multiple brain templates is fully fused, and the model performance is improved.

The invention discloses an automatic sketching method and sketching system for a brain function region of an MRI head image, a computing device and a storage medium, and the method comprises the steps: obtaining a preset number of T1MRI brain images, and making 116 brain function region segmentation tags of each image; performing same preprocessing and 2.5 D block processing on the MRI brain image and the brain function region segmentation tag to obtain respective 2.5 D data blocks; inputting the 2.5 D data blocks into the established semantic segmentation neural network for training until the model is stably converged, and obtaining an optimal brain functional region segmentation neural network model; inputting the to-be-segmented image subjected to the same preprocessing and 2.5 D block processing into the trained brain function region segmentation neural network model to obtain a brain function region segmentation result; and performing post-processing and edge detection on the brain function region segmentation result to obtain a brain function region contour sketching result. The method can achieve the automatic segmentation of the human brain function region, improves the speed and accuracy of the segmentation of the brain function region, and also improves the robustness and adaptability of the segmentation of the brain function region.

The invention relates to an automatic rapid visualization method for resting state brain function connection. According to the invention, through analysis of brain functional magnetic resonance imaging data, functional images are automatically acquired, and data analysis is automatically completed; and brain network connection strength information obtained through analysis is automatically returned to PACS, so a user can conveniently retrieve and read the brain network connection strength information at any time. The automatic rapid visualization method provided by the invention makes up a technical support blind area of conventional medical magnetic resonance scanning equipment, and enriches biological information indexes for medical technicians to refer to, so the comprehensiveness and objectivity of a clinical diagnosis or intervention evaluation link are improved.