61 results about "Functional brain" patented technology

A method of functional brain mapping of a subject is disclosed. The method is effected by (a) illuminating an exposed cortex of a brain or portion thereof of the subject with incident light; (b) acquiring a reflectance spectrum of each picture element of at least a portion of the exposed cortex of the subject; (c) stimulating the brain of the subject; (d) during or after step (c) acquiring at least one additional reflectance spectrum of each picture element of at least the portion of the exposed cortex of the subject; and (e) generating an image highlighting differences among spectra of the exposed cortex acquired in steps (b) and (d), so as to highlight functional brain regions. Algorithms for calculating oxygen saturation and blood volume maps which can be used to practice the method are also disclosed. Systems for practicing the method are also disclosed.

The nonlinear complexity of EEG signals is believed to reflect the scale-free architecture of the neural networks in the brain. Analysis of the complexity and synchronization of EEG signals as described herein provides a quantitative measure for routine monitoring of functional brain development in infants and young children and provide a useful biomarker for detecting functional abnormalities in the brain before the cognitive, behavioral or social manifestations of these brain developments can be observed and measured by standard tests. One or more machine learning algorithms are used to discover relevant patterns in the complexity and synchronization values determined from the EEG data to facilitate risk assessment and / or diagnosis of developmental disorders in infants and young children by predicting cognitive, behavioral and social outcomes of the measured functional brain activity patterns.

Described herein are systems and methods for noninvasive functional brain imaging using low-coherence interferometry (e.g., for the purpose of creating a brain computer interface with higher spatiotemporal resolution). One variation of a system and method comprises optical interference components and techniques using a lock-in camera. The system comprises a light source and a processor configured to rapidly phase-shift the reference light beam across a pre-selected set of phase shifts or offsets, to store a set of interference patterns associated with each of these pre-selected phase shifts, and to process these stored interference patterns to compute an estimate of the number of photons traveling between a light source and the lock-in camera detector for which the path length falls within a user-defined path length range.

This invention relates generally to methods for localization and visualization of implanted electrodes and penetrating probes in the brain in 3D space with consideration of functional brain anatomy. Particularly, this invention relates to precise and sophisticated methods of localizing and visualizing implanted electrodes to the cortical surface and / or topological volumes of a patient's brain using 3D modeling, and more particularly to methods of accurately mapping implanted electrodes to the cortical topology and / or associated topological volumes of a patient's brain, such as, for example, by utilizing recursive grid partitioning on a manipulable virtual replicate of a patient's brain. This invention further relates to methods of surgical intervention utilizing accurate cortical surface modeling and / or topological volume modeling of a patient's brain for targeted placement of electrodes and / or utilization thereof for surgical intervention in the placement of catheters or other probes into it.

System and method for processing, non-concurrently collected, electroencephalogram (EEG) data and resting station functional magnetic resonance imaging (rsfMRI) data, non-invasively, to create a patient-specific three-dimensional (3D) mapping of the patient's functional brain network. The mapping can be used to more precisely identify candidates of resective neurosurgery and to help create a targeted surgical plan for those patients. The methodology automatically maps the patient's unique brain network using non-concurrent EEG and resting state functional MRI (rsfMRI). Generally, the current invention merges non-concurrent EEG data and rsfMRI data to map the patient's epilepsy / seizure network.

The invention relates to an image processing technique and specifically relates to a classifying method for functional magnetic resonance image data based on multi-scale brain network characteristics. The invention solves the problem of low classifying accuracy of the traditional magnetic resonance image data classifying method. The classifying method for functional magnetic resonance image data based on multi-scale brain network characteristics comprises the following steps: S1) pre-processing the resting state functional magnetic resonance image; S2) adopting a dynamic random seed method for performing region segmentation on the image and extracting mean time sequences for the cut brain areas; S3) calculating a relevance degree of each two mean time sequences of the brain areas; S4) performing binarization processing on the incidence matrix; S5) calculating a local property of the resting state functional brain network and an AUC value thereof in a specific threshold space; S6) constructing a classifier; S7) quantizing the importance and redundancy of the selected characteristics in the classifier. The classifying method provided by the invention is fit for the classification of the magnetic resonance image data.

The invention discloses functional brain-invigorating whole bean soybean milk for spaceflight and a preparation method thereof. The invention aims to develop a whole bean soybean milk product which isapplied to spaceflight, has brain-invigorating functions of improving memory and attention, and has no addition of food emulsion stabilizers. According to the functional brain-invigorating whole beansoybean milk for spaceflight and the preparation method thereof, by utilizing a high-pressure spray micro-nano pulverization technology, the effects of superfine pulverization and refinement, homogenization and emulsification are realized on whole beans and phosphatidylserine through a one-step method. The prepared whole bean soybean milk product is fine and smooth in mouthfeel, has no beany flavor, reserves all nutritional ingredients of raw materials, has good stability under the condition of no food emulsion stabilizer, and has the brain-invigorating function of improving the memory.

The invention discloses an electroencephalogram (EEG) channel selection method assisted by functional magnetic resonance imaging, in order to overcome the defect that the spatial resolution is low as EEG channels are selected purely depending on EEG data. The EEG channel selection method comprises the following steps: (1) acquiring the activation conditions of relevant functional brain regions according to fMRI experimental data; (2) establishing an EEG forward model according to a brain standard structure image; (3) calculating the correlation degrees between the channels and specific brain functions according to the EEG forward model; and (4) selecting the EEG channels according to the obtained brain function correlation degrees. Compared with the prior art, the EEG channel selection method has the advantages that the high spatial resolution of the fMRI technology is utilized, and the limitation that the EEG spatial resolution is low in the EEG channel selection is broken through to a certain extent; compared with the traditional method for carrying out channel selection depending on experience or data analysis, the EEG channel selection method has better theoretical basis; and different channel selection methods can be worked out aiming at different people.

A system and method for measuring a functional lateralization of a subject brain is provided. The method includes providing a set of functional magnetic resonance imaging (fMRI) data acquired during a resting state of a subject, and selecting a plurality of seed voxels associated with locations in hemispheres of a brain of the subject. The method also includes determining a degree of within-hemisphere connectivity for each seed voxel using the fMRI data, determining a degree of cross-hemisphere connectivity for each seed voxel using the fMRI data, and computing an autonomy index for each seed voxel using the degree of within-hemisphere connectivity and the degree of cross-hemisphere connectivity, wherein the autonomy index is indicative of a connectivity asymmetry between the hemispheres. The method further includes generating a report indicative of a specialization profile determined for a region of interest in the brain of the subject.

An electroencephalogram signal recognition method based on a brain network and deep learning comprises the steps that motor imagery electroencephalogram data and language imagery electroencephalogram data are obtained and preprocessed, and corresponding labels are obtained; based on the multi-lead electroencephalogram data, respectively calculating the phase synchronism of the multi-narrow-band inter-lead time sequence; setting a threshold value to obtain a plurality of narrowband connection matrixes based on phase synchronization; constructing a functional brain network through the connection matrix; a deep learning model is trained, the multi-narrow-band synchronous brain network serves as input of the convolutional neural network at the same time, and the structure and parameters of the network are optimized; according to the method, training and classification are carried out through a synchronous brain network classification model, the strong feature extraction capability and the time sequence signal processing capability of a deep learning algorithm are fully utilized, and the time sequence information hidden in the electroencephalogram signals is combined, so that the electroencephalogram signal recognition task of the multi-narrow-band brain network is completed; the sizes of a multi-input convolution layer and a convolution kernel are reasonably designed, and the classification effect is improved.

The invention relates to a whole-brain individualized brain function map construction method taking an independent component network as a reference. The method comprises the following steps: utilizingbrain resting state fMRI data of an individual subject; introducing an independent component analysis method to construct a group-level brain function sub-network; then, reversely reconstructing eachtested brain function sub-network and a characteristic time sequence corresponding to the function sub-network by utilizing space-time regression; taking a characteristic time sequence correspondingto the functional sub-network as a reference signal; and introducing an inverse distance weighting coefficient, a sub-network inverse variation coefficient weighting, a correlation factor and an iterative process to obtain a whole-brain individualized function map with an independent component network as a reference. The method has the advantages of pure data driving, complete correspondence of brain regions, whole-brain coverage, more flexible functional brain region subdivision and the like, and a more accurate objective imaging tool is provided for researching a normal human brain operationmechanism and brain function impairment related to diseases.

A method for data recording and / or data analysis in the brain function test of a combined magnetic resonance-PET apparatus is disclosed, wherein the function image data are recorded through two modes according to at least two active states, the recording of the magnetic resonance image data and the PET image data is performed in the interior of the time window determined by the active states, then recording operation and / or the synchronization of image data are performed according to the exciting time sequence determining the active states.

According to the multi-task-based feature selection method for the functional brain network under multiple thresholds, multi-level network features are extracted in a multi-threshold mode, and multi-level features are extracted from the thresholded network in a multi-core multi-task learning mode for further classification processing. The defects of an existing method are overcome, and then the characteristics with discrimination and interpretation are learned. According to the gk-MTFS method, feature learning under each threshold value is taken as a task, structured information of a network is reserved for each task by adopting a graph core (a core constructed on a graph), and internal correlation among tasks is explored by adopting multi-task learning, so that features with higher discrimination and interpretability are learned. Finally, a real brain disease data set is verified, and experimental results show that compared with a method at the present stage, the method provided by the invention has better classification characteristics on the brain diseases.

The invention discloses a functional brain network classification method based on pre-training and a graph neural network, and the method comprises the following steps: 1), obtaining fMRI data, and carrying out the preprocessing of the fMRI data; 2) performing brain region division and feature extraction on the fMRI data, and constructing a functional brain network in a graph form; 3) inputting the functional brain network without labels into a node coding layer for training; 4) aggregating network training through node information; 5) training the outputs of the step 3) and the step 4) through an edge relation prediction network; 6) inputting the functional brain network data with labels into the node coding layer trained in the step 3) for training; 7) performing training in the node information aggregation network trained in the step 4); and 8) performing training and classification through a functional brain network classification model. According to the invention, a large amount of label-free brain network data is utilized, and the graph neural network is pre-trained, so that the pre-trained network only needs to be trained on a small amount of label data to adapt to a downstream functional brain network classification task.

The invention discloses a control system and method for functional brain deep electric stimulation. The system includes a motion signal collector used for collecting real time motion signals of wrist tremble; a functional electric stimulator generating pulse current used for stimulating; a controller in communicational connection with the motion signal collector and the functional electric stimulator separately. The controller stores tremble levels and stimulation parameters corresponding to each tremble level in advance. The controller receives the real time motion signals and judges whether the motion belongs to the tremble levels or not. If the motion belongs to the tremble levels, the controller selects the stimulation parameters corresponding to the corresponding tremble level and sends the stimulation parameters to the functional electric stimulator for stimulating. According to the invention, current attack level is quantified and real time self-adaptive control is performed on functional brain deep electric stimulation. The system and method are especially suitable for real time monitoring of hand tremble of Parkinson disease sufferers and performing self-adaptive control.

The invention relates to an electroencephalogram signal analysis method which comprises the following steps: S1, constructing a functional brain network according to electroencephalogram signals; S2,according to the functional brain network, recognizing different network motifs, wherein the network motifs are in a node connection mode; S3, counting the number of different network motifs, and constructing a network motif characteristic vector; and S4, according to the network motif characteristic vector, completing intelligent classification of electroencephalograms. Compared with the prior art, the electroencephalogram signal analysis method provided by the invention is capable of effectively evaluating global connectivity of a brain function network, depicting function module division ofa bran, and accurately describing characteristics and states of the brain.

The invention provides a tactile stimulation device and a tactile brain atlas measuring system, which relate to the technical field of a functional brain atlas measuring device. The haptic stimulationdevice comprises a stimulation assembly, a driving end fixing member and a terminal fixing member. The stimulation assembly comprises a telescopic driving member, a catheter and a guide wire. One endof the catheter is connected with the driving end fixing part and the other end is connected with the terminal fixing part; the guide wire penetrates into the catheter; the telescopic drive member isconnected to the first end of the guide wire for driving the guide wire to move relative to the catheter so that the second end of the guide wire extends outwardly or retracts inwardly from the catheter. By the tactile stimulation device provided by the invention, the technical problems of slow response speed and low application precision of the tactile stimulation site existing in the device forapplying the tactile stimulation in the prior art are alleviated.

The invention designs an fMRI data classification and recognition method and device based on brain area function connection. The method comprises the steps: acquiring fMRI data of a testee; preprocessing the obtained fMRI data to obtain a brain gray matter image; segmenting the brain gray matter image into a plurality of brain regions with different functions, and extracting an average voxel timesequence of each brain region; based on the fuzzy decision rough set, selecting a part of brain regions with significant differences from the plurality of functional brain regions; calculating Pearsoncorrelation coefficients among different brain regions based on the selected partial brain regions, and performing nonlinear processing on the coefficients by adopting Fisher-z transform to obtain afunctional connection matrix of the partial brain regions; sparsifying the correlation coefficient values in the matrix, reserving the correlation coefficient values above a threshold value, and expanding the matrix into a one-dimensional feature vector; and taking the obtained one-dimensional feature vector as input and sending the one-dimensional feature vector to a trained SVM recognition modelto obtain an output label of the testee and judge the fMRI data category of the testee.

The invention discloses a wireless multi-brain-region brain blood oxygen wearable detection system and method. The system comprises a collector and a plurality of probes, wherein the plurality of probes communicate with the collector through cables; the probes include prefrontal lobe brain region probes covering the left side and the right side of the brain and any brain region probe or a combination of multiple brain region probes of occipital lobe brain region probes, parietal lobe brain region probes and temporal lobe brain region probes covering the left side and the right side of the brain; the plurality of probes are correspondingly attached to divided functional brain regions; the probes are driven and controlled to emit detection light to the corresponding brain regions; the probessimultaneously receive the emitted detection light of the functional brain regions, and collect brain blood oxygen signals of the brain regions; and the collected brain blood oxygen signals of the brain regions are processed to obtain brain blood oxygen collection information of the brain regions. Through simultaneous detection of the multiple brain regions, the brain regions of partial anteriorcirculation cerebral infarction and posterior circulation cerebral infarction can be covered, and the limitation that only the anterior circulation infarction of the brain can be reflected is overcome.

A system and methods for functional brain mapping is provided. The method includes providing a set of time-series functional magnetic resonance imaging (fMRI) data acquired from a brain of a subject while performing a functional task and decomposing the set of time-series fMRI data into a set of task signals and a set of non-task signals using a model related to the functional task performed by the subject. The method also includes generating a task activity map using the set of task signals and generating a non-task activity map using the set of non-task signals. The method further includes producing a combination map by selectively weighting the task activity map and non-task activity map, and combining the selectively weighted maps, using a statistical parameter, in dependence of a threshold.

Disclosed is a health maintenance pillow for preventing senile dementia. Raw materials of a pillow inner of the health maintenance pillow comprise, in weight ratio, 50-70 grams of acorus gramineus, 60-70 grams of ophiopogon roots, 70-80 grams of cistanche, 50-60 grams of ginseng, 40-50 grams of sealwort, 50-60 grams of polygala, 50-60 grams of platycladi seeds, 40-50 grams of tuckahoe and 50-60 grams of spina date seeds. Herbaceous plants are taken as the raw materials to expand blood vessels of brains, oxygen, glucose, amino acid and phospholipid absorption of cerebral cortex cells is enhanced, recovery of brain cells is promoted, functions of functional brain cells are improved, so that a patient has a fine sleep state and sleep quality to ensure normal living conditions and daily performances, and memory can be improved.

The invention discloses a parameter setting method and device for electroencephalogram neural feedback training, and a related medium. The parameter setting method comprises the following steps of: obtaining the resting-state electroencephalogram signal and the resting-state functional magnetic resonance data of a subject; calculating individual alpha oscillation peak frequency corresponding to the resting-state electroencephalogram signal; calculating the standardized clustering coefficient of an individual functional brain network topology attribute corresponding to the resting-state functional magnetic resonance data; and before the electroencephalogram feedback training of frontal lobe alpha asymmetry is carried out, setting an alpha frequency band parameter used for extracting feedback signals on the basis of the individual alpha oscillation peak frequency, and setting the training difficulty parameter of the electroencephalogram neural feedback on the basis of the standardized clustering coefficient of a resting-state functional magnetic resonance brain network. According to the parameter setting method, alpha frequency band parameters and the training difficulty parameters are calculated by collecting the resting-state electroencephalogram signal and the resting-state functional magnetic resonance data of the subject, so that better parameters are set for the electroencephalogram neural feedback training, and the subject can be better helped to achieve a better training effect.

The invention relates to a structure-function brain network bidirectional mapping construction method and a brain network bidirectional mapping model, and the method comprises the steps: constructing a feature preprocessing module, and obtaining a brain structure network and a brain function network; constructing a structural feature extraction module and a functional feature extraction module to obtain structural features and functional features of the brain; constructing a structure classifier module and a function classifier module, and obtaining an illness state classification result based on the structure features and an illness state classification result based on the function features; constructing a structure-function bidirectional mapping network, and performing bidirectional mapping on the brain structure network and the brain function network; and training and learning the constructed structure feature extraction module, the constructed function feature extraction module, the constructed structure classifier module, the constructed function classifier module and the constructed structure-function bidirectional mapping network by using the preprocessed data sets of the brain structure network and the brain function network. And the constructed brain network model is helpful for revealing a complex relationship between a brain structure and functions.

A device and method for assessment of traumatic brain injury (TBI) is described. The device is configured to acquire brain electrical signals from a patient's forehead using one or more neurological electrodes. The acquired brain electrical activity data is subjected to artifact rejection and feature extraction, and a subset of features are then combined in at least one classifier function. The classifier functions statistically place a patient in one of four categories related to the extent of brain dysfunction: 1) normal brain electrical activity; 2) abnormal brain electrical activity consistent with non-structural injury with less severe manifestations of functional brain injury; 3) abnormal brain electrical activity consistent with non-structural injury with more severe manifestations of functional brain injury; and 4) abnormal brain electrical activity consistent with structural brain injury.

Apparatus and methods for modification of the frequency response profile of steady-state free precession (SSFP) type of magnetic resonance imaging (MRI) sequences. Using alternating dephasing moments within succeeding radiofrequency (RF) excitation pulses, the frequency response function of SSFP sequences can be modified to different shapes such as near triangular or bell shaped. The particular response function as produced by alternating dephasing moments can be used, among others, for functional brain MRI, MR spectroscopy or spatial encoding.

The invention discloses a modular feature selection method (MLFS for short) for brain disease classification, and the method comprises the following steps: carrying out the preprocessing of a functional magnetic resonance image, and dividing a brain into a pre-designated brain region; extracting an average time sequence corresponding to all brain regions and constructing a functional brain map; searching modular structure information by using a signed spectral clustering algorithm; and selecting a discriminative feature through a group LASSO method based on modularization, wherein a support vector machine (SVM) is used for classification. According to the embodiment of the invention, the discriminative features in the brain map can be clearly identified by using modular information, and the method is used for brain disease classification, and has a certain reference value for studying cognitive impairment of the brain.