12 results about "Brain activation" patented technology

The invention discloses a calculation method for integrating task induction and intrinsic spontaneous brain function activity. The calculation method comprises the following steps: calculating a brain activation mode when an individual executes a corresponding cognitive task based on task state functional magnetic resonance imaging data and a general linear model; identifying individual large-scale nerve avalanche with spatial continuity based on resting state functional magnetic resonance imaging data; the method comprises the following steps: performing principal component analysis on resting state functional magnetic resonance data of an individual to construct a low-dimensional state space; a task-induced brain activation mode and intrinsic spontaneous nerve avalanche are projected to an individual low-dimensional state space; calculating the Euclidean distance between the task-induced brain activity and the intrinsic spontaneous nerve avalanche in the low-dimensional state space; and detecting the prediction effect of the geometric distance on the performance of the tested task through the regression model. The method is verified on a real data set, and experimental results show that the method not only can integrate two basic brain function activities, but also can significantly predict individual cognitive performance differences.

The invention discloses a sacral marrow magnetic stimulation urination disorder rehabilitation system and method based on an electroencephalogram closed loop, and relates to the technical field of biomedical engineering, the system comprises an electroencephalogram signal acquisition module, a signal processing module, a task guiding module, a control module and a sacral marrow magnetic stimulation module; the electroencephalogram signal acquisition module is used for acquiring an electroencephalogram signal when a user executes a urination-related motor imagery task; the signal processing module carries out preprocessing, feature extraction and intention recognition on the collected electroencephalogram signals. According to the urination disorder rehabilitation system and method provided by the invention, accurate rehabilitation treatment of urination disorder is realized by fusing electroencephalogram signal recognition and sacral marrow magnetic stimulation technologies, the activity characteristics of the brain urination center can be captured in real time, the opportunity and intensity of sacral marrow magnetic stimulation can be dynamically adjusted, high matching with the brain activation rhythm is ensured, and the rehabilitation effect of urination disorder is improved. The recovery of the urination function of the patient is effectively promoted, and the recovery effect is improved.

The invention discloses a child learning disorder detection and evaluation system based on a near-infrared N-back task normal form. The system comprises a task stimulation presentation device, a near-infrared brain function imaging device, a data processing module and a result interpretation module. Key brain activation and behavior indexes are extracted by presenting an interesting N-back task and synchronously collecting brain blood oxygen signals and behavior data. The core of the method is that a result interpretation module compares indexes with threshold groups and grades the indexes and the threshold groups according to a preset quantitative interpretation rule, and then automatically outputs four types of refined evaluation results of high, moderate, low cognitive load type and task insensitive type according to a determined mapping relation between brain activation and behavior grades. According to the method, the problems that a traditional method is high in subjectivity, brain behavior data are disjointed and cognitive strategies cannot be distinguished are solved, and objective and automatic accurate evaluation is achieved.

This invention provides an apparatus, method, and storage medium for assessing schizophrenic tendency based on near-infrared data. The apparatus includes a processor configured to: acquire near-infrared data of a subject; process the near-infrared data and convert the processed near-infrared data into a test image; input the test image into a classification model to obtain a classification result for the subject, wherein the classification result indicates the subject's tendency to be either healthy or schizophrenic. This invention normalizes near-infrared data based on a preset data set formed by fusing second data sequences from multiple healthy subjects and multiple schizophrenic individuals. This avoids the loss of brain activation information caused by normalization at different scales, resulting in higher accuracy when the classification model classifies the test image converted from the normalized near-infrared data.

A method for mapping functions of a brain includes receiving an MRI data set for a subject comprising fMRI data acquired with the subject lying in MRI scanning equipment in a state of rest, generating a voxel-wise correlation map that identifies, for each of a plurality of voxels of the brain, a measure of the degree of time correlation between spontaneous brain activations at one voxel of the brain as revealed in the resting-state fMRI data and spontaneous brain activations at each of the other voxels of the plurality of bran voxels as revealed in the resting-state fMRI data. The voxel-wise correlation map is input to a trained 3D convolutional neural network based machine learning algorithm to generate a functional connectivity map identifying a location where a predefined brain function is performed within the subject's brain by identifying the voxels involved in performing that predefined brain function.

To provide a sensory transmission system and a sensory transmission method that can appropriately protect personal information of a subject.SOLUTION: A sensory transfer system includes: a first device that detects, when perceived by the first subject, brain activation information of a first subject; an estimation device that estimates, on the basis of the detected brain activation information of the first subject, recollected sensory information that a second subject different from the first subject recalls in response to a perception and encodes the recollected sensory information using the brain activation information of the first subject; and a second device that decodes the encoded recalled sensory information and provides a stimulus to the second subject so as to recall the decoded recalled sensory information.SELECTED DRAWING: Figure 2

The application discloses a kind of about dysphagia magnetic resonance intelligent image diagnosis system, it is related to medical image processing and diagnostic technique field, including: swallowing state functional magnetic resonance acquisition module;Swallowing movement timing decomposition module;Virtual swallowing contrast parameter quantification module;Aspiration pneumonia risk prediction module;Diagnostic report generation module.The application will be obtained by traditional dependence X ray swallowing contrast leakage-Aspiration degree, pharyngeal residual state and pharyngeal transport time such as key quantitative index, by the brain activation timing and functional connection characteristics of functional magnetic resonance imaging Equivalent mapping, so that patients do not need to bear ionizing radiation can obtain and contrast gold standard highly comparable quantitative evaluation parameters, the application not only can realize no radiation equivalent quantitative evaluation, also can create new "functional survival brain area" evaluation and rehabilitation target positioning ability, establish the model for preventing aspiration pneumonia to occur, provide reliable quantitative standard for clinical rehabilitation.

The application discloses a transient co-activation pattern analysis method and system, and relates to the field of functional magnetic resonance technology. fMRI data of natural stimulus processing of a plurality of subjects is acquired; the fMRI data of each subject is preprocessed to obtain preprocessed fMRI data; a subject-to-subject analysis method is used to calculate the preprocessed fMRI data to obtain stimulus-induced signals; spontaneous activity signals and non-neural signals in the stimulus-induced signals are filtered out by a linear regression method to obtain task-induced signals; a co-activation pattern analysis method is used to analyze the task-induced signals frame by frame to obtain brain co-activation patterns; a K-means clustering method is used to identify and extract repeatedly appearing subject-to-subject co-activation states in the brain co-activation patterns; the subject-to-subject co-activation states are reordered and visually displayed to obtain visualized transient brain activation patterns. The application improves the accuracy and sensitivity of a dynamic co-activation pattern analysis method of the brain under continuous stimulation.

The emotion recognition method based on brain activation area multi-view contrast learning provided by the application comprises the following steps: obtaining an emotion signal to be recognized; performing space-time transformation on the emotion signal to be recognized to obtain a space-time signal to be recognized; inputting the space-time signal to be recognized into a pre-trained dual-path decoupling multi-view model to obtain an emotion recognition result; by setting two encoders for extracting time features and space features in the final dual-path decoupling multi-view encoder, multi-dimensional information in the space-time signal to be recognized can be fully mined and utilized, and the decoupling of corresponding space-time features is realized; by dividing the space-time signal sample into a high activation area and a low activation area according to the activation degree of the brain area, a targeted data enhancement strategy can be performed according to the different activation degrees of the brain area, and the pre-trained dual-path decoupling multi-view model is trained by using the data-enhanced sample, thereby improving the accuracy and reliability of the pre-trained dual-path decoupling multi-view model for emotion recognition.

An object of a system according to an embodiment is to generate a dialogue by utilizing a user's memory, reduce a sense of loneliness, and promote activation of the brain.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a photograph or a memory from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a dialogue on the basis of the information analyzed by the analysis unit. The providing unit provides the user with the dialogue generated by the generating unit.SELECTED DRAWING: Figure 1

The invention discloses a method for improving fMRI analysis performance by applying a phase generated by Hilbert transform, and belongs to the field of biomedical signal processing. First, phase data is generated from fMRI amplitude data using a Hilbert transform method. The complex data is then constructed along with the fMRI magnitude data. Furthermore, a data driving method, such as a complex independent component analysis method of blind source separation, is adopted to extract interested space brain activation components. According to the method, an extraction result which is similar to complex data constructed by MRI scanning phase data but is obviously superior to that of amplitude data is obtained. Taking default network component extraction as an example, the total number of effective voxels is increased by 90% compared with an amplitude data result, and is reduced by less than 0.6% compared with an extraction result of complex data constructed by scanning phase data. Therefore, the method can widely improve the analysis performance of the existing amplitude-only fMRI data, and has an important value in the aspects of promoting brain function research and objective marker development and pathogenesis research of neuropsychiatric brain diseases.

A method for mapping functionally related brain regions of a subject. The method includes receiving a structural brain image and a dataset of resting-state functional MRI (rs-fMRI) three-dimensional (3D) image frames of a brain of the subject includes 3D image frames of the subject's brain over time. A functional connectivity map identifying groupings of functionally connected voxels is overlaid on the structural brain image. Spontaneous brain activations associated with each voxel in a grouping of functionally connected voxels are time-correlated with spontaneous brain activations in one or more other voxels in the grouping of functionally connected voxels. A reference location map for a pre-defined resting state network is generated, including reference information output. The reference location map includes brain reference locations that are informative of the one or more resting-state networks to assist a user in identifying the resting state network of the functional connectivity map.