16 results about "Cerebral structure" patented technology

Examples of the presently disclosed technology provide new systems and methods for real-time temperature propagation and tissue damage visualization during laser interstitial thermal therapy (LITT) procedures that do not rely on real-time MR imaging. Accordingly, examples enable performance of LITT procedures in regular operating rooms lacking MR-equipment-thereby reducing costs and improving availability for LITT procedures. Examples achieve these advantages by leveraging “discretized” patient-specific 3D brain structure representations to perform numerical methods for solving partial differential equations that estimate real-time (or close to real-time) temperature propagation within a patient's brain during a LITT procedure.

Provided herein are targeting peptides and vectors containing a sequence that encodes the targeting peptides that deliver agents to specific substructures in the brain. Specifically, the targeting peptide is a component of a modified, sequence-specified adeno-associated virus (AAV) capsid protein further wherein the brain substructure may be the globus pallidus, putamen, internal capsule, caudate, claustrum, substantial nigra, motor cortex, insula,.temporal cortex, thalamus, hippocampus, subiculum, and deep cerebellar nuclei.

The application relates to the technical field of medical health early warning, in particular to an intracranial pressure monitoring and early warning system based on imaging features, which comprises the following modules: a brain structure twin construction module, which is used for acquiring multi-modal image data and constructing brain structure twins; a brain imaging feature extraction module, which is used for obtaining brain imaging features; an intracranial pressure inversion atlas generation module, which is used for acquiring the distribution response relationship of the brain imaging features and generating an intracranial pressure inversion atlas; an intracranial blood flow regulation capacity quantification module, which is used for acquiring transcranial Doppler ultrasound data and quantifying an intracranial blood flow regulation capacity index; an intracranial pressure state evolution module, which is used for obtaining an intracranial pressure state evolution curve; and an intracranial pressure monitoring and early warning module, which is used for generating graded early warning thresholds and triggering monitoring and early warning. The application realizes dynamic quantitative early warning suitable for individual physiological characteristics by acquiring the evolution trend of intracranial pressure.

A biomechanically realistic brain model for impact testing comprises white matter simulant materials and gray matter simulant materials positioned to correspond with anatomical brain structure. The white matter simulant comprises anisotropic hydrogels with embedded magnetically-responsive, electrically-responsive, thermally-responsive, and / or mechanically-responsive particles that exhibit directionally-dependent stress-strain responses. The gray matter simulant comprises isotropic hydrogels or silicones, particularly siloxanes, that exhibit uniform stress-strain responses to applied forces. The materials are cast, injected, printed, or formed in anatomically correct positions and share realistic interfaces. The brain model accurately imitates physical brain responses during impact tests, particularly angular impacts, providing realistic testing results for biomechanical analysis.

This invention discloses an on-orbit intelligent processing method for spaceborne images based on brain-like computing, belonging to the field of on-orbit intelligent processing of spaceborne images. This invention utilizes a spiking neural network to simulate the structure of the human brain: including a feature extraction layer, a pulse coding layer, an STDP learning layer, a lateral inhibition layer, and a decision output layer; and improves model accuracy through on-orbit updates and federated learning. This invention reduces computational energy consumption and improves efficiency. Simultaneously, this invention leverages the synaptic plasticity of neurons to construct a SNN with excellent spatial and temporal resolution.

The present disclosure discloses a model construction method and device, a storage medium and an electronic device, which are used for constructing a brain-like model of a pulse neural network based on biological brain topology constraints, and relate to the technical field of computational neuroscience. The present disclosure solves the problem of lack of biological rationality of the brain-like model of the pulse neural network. The model construction method comprises: dividing brain regions of to-be-processed functional magnetic resonance imaging data to obtain M brain region image data; generating M model nodes based on the M brain region image data; generating N model edges based on a correlation coefficient matrix between the M model nodes; screening the N model edges based on a preset network topology threshold to obtain S model edges meeting a preset condition; generating a topology constraint of a brain-like model based on a biological brain function network based on the M model nodes and the S model edges; and constructing the brain-like model based on the topology constraint. The present disclosure can improve the biological rationality of the brain-like model constructed based on the pulse neural network.

The application relates to the technical field of medical big data processing and artificial intelligence auxiliary diagnosis, in particular to an early Alzheimer's disease diagnosis method, which comprises the following steps: preprocessing and registering structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) image data of a to-be-diagnosed object, constructing a mixed feature pyramid to extract multi-scale anatomical features, adopting a space-time manifold embedding module to extract dynamic functional features, strengthening pathological correlation features through a cross-dimension double attention mechanism, and finally realizing diagnosis classification through multi-modal feature adaptive fusion. The application can accurately capture the deep correlation between brain structure microlesions and functional network abnormalities, and significantly improve the diagnosis accuracy of Alzheimer's disease and early mild cognitive impairment.

The application belongs to the technical field of medical information and neuroscientific technology, and particularly relates to an early screening method and system for Alzheimer's disease based on eye movement tracking, comprising the following steps: collecting human eye movement data of a subject during the execution of a cognitive task; constructing a personalized eye movement dynamics model based on the human eye movement data, extracting an eye movement behavior feature vector; utilizing a neural-eye movement isomorphic mapping relationship; identifying a neurodegenerative attenuation feature related to Alzheimer's disease; and detecting whether an epilepsy-like discharge feature exists in the virtual neural function signal. Through the construction of the neural-eye movement isomorphic mapping relationship, the virtual neural function signal of the deep brain structure can be reconstructed by using the eye movement data, the screening cost is effectively reduced, the evaluation accuracy of the function state of the hippocampus and amygdala is improved, and through the comorbidity correction of the epilepsy-like discharge feature, the accuracy and reliability of the early screening of Alzheimer's disease are significantly improved.

The present application relates to the field of medical magnetic resonance image reconstruction and intraoperative navigation, and particularly relates to an intraoperative magnetic resonance image reconstruction method and system based on medical registration guidance, comprising: acquiring preoperative high-field magnetic resonance images and intraoperative low-field magnetic resonance images of the same patient; inputting the preoperative high-field magnetic resonance images and the intraoperative low-field magnetic resonance images into a trained image processing model; performing registration by taking the preoperative images as the moving images and the intraoperative images as the fixed images through a registration network, generating a deformation field from the preoperative to the intraoperative, and correcting the brain structure displacement caused by the surgery; performing spatial transformation on the preoperative images based on the deformation field to obtain high-field images aligned with the intraoperative images; and fusing the aligned high-field prior information and the intraoperative low-field image features by using a reconstruction network to reconstruct high-quality target images. The present application effectively improves the definition and detail resolution of the intraoperative images by introducing individualized preoperative prior, and provides more accurate image support for surgical navigation.

This disclosure relates to organisms genetically engineered to have reduced higher brain structure, tissues obtained from such organisms, and methods for creating organisms in which the capacity to experience pain is severely reduced or completely absent, for the purpose of supplying tissues and organs free from cruelty. This approach ensures a lack of sensory perception in the animal from which the tissue is intended, thereby eliminating pain and suffering in the animal involved. This strategy provides an approach to generating tissues (including organs for transplantation) through the creation of organisms incapable of developing higher brain function.

This invention relates to the field of artificial intelligence-assisted diagnostic technology, and discloses a method and system for early auxiliary diagnosis and prediction of neurodegenerative diseases using clinical magnetic resonance imaging (MRI). This method addresses the problem of achieving accurate and automated identification and progression prediction of neurodegenerative diseases in their early stages through routine clinical MRI examinations. The method includes: acquiring multimodal MRI images as training samples; dimensionality reduction and simplification to extract quantitative values ​​of the first brain structure morphology; labeling diagnostic categories and training a first classification model; and extracting a feature coefficient matrix. Based on the feature weight coefficients contained in the feature coefficient matrix, core driving brain regions corresponding to different diagnostic categories are selected, and a second classification model is constructed. Clinical MRI images of patients awaiting early or prodromal stages are acquired; quantitative values ​​of the second brain structure morphology are extracted; the second classification model is used to calculate probability scores for different diagnostic categories; and auxiliary diagnostic or progression prediction results are output based on the probability scores.

The application discloses a brain-computer interface electrode implantation surgery planning adaptive system and belongs to the field of diagnosis. The system comprises a data acquisition and storage module, a source domain model construction module, a target domain feature extraction module, a transfer learning module and a path planning and evaluation module. The data acquisition and storage module is used for collecting and storing historical case data. The source domain model construction module is used for training a source domain electroencephalogram decoding experience model and establishing a mapping relationship between brain structure characteristics and expected neural signal quality. The target domain feature extraction module is used for extracting features of preoperative multi-modal brain images of a new patient to obtain target domain brain structure characteristics. The transfer learning module is used for adaptively transferring the source domain electroencephalogram decoding experience model to the target domain to generate a target domain adaptive model. The path planning and evaluation module is used for generating a personalized implantation path planning scheme. The intraoperative feedback and postoperative backflow module is used for triggering online re-planning according to changes in a surgical environment, feeding actual implantation parameters and signal quality data back into the data acquisition and storage module and realizing continuous updating of a knowledge base. The application promotes the standardization and intelligentization of surgery.

The application discloses a brain age prediction method fusing integrated learning and reinforcement learning, and relates to the technical field of medical artificial intelligence and neuroimaging analysis. The method comprises the following steps: step a: acquiring structural magnetic resonance imaging data and corresponding age information, performing standardization preprocessing to eliminate non-biological signal variation, and generating a unified specification of brain structure feature images. The application changes the static and rigid nature of the traditional brain age prediction integrated strategy by introducing a reinforcement learning mechanism. Compared with the existing integrated method using fixed weights or pre-set grouping, the application forms the division of the age interval and the distribution of the model weight into a sequence decision problem that can be learned by an intelligent agent. The reinforcement learning intelligent agent can autonomously find and lock the optimal age grouping boundary and the best model fusion ratio in each group that can minimize the overall prediction error through continuous trial and error and exploration in the simulation environment.

The present application relates to the technical field of artificial intelligence assisted diagnosis, and discloses a method and system for early auxiliary diagnosis and prediction of neurodegenerative diseases in clinical magnetic resonance imaging, which is used to solve the problem that it is difficult to realize precise and automatic identification and progress prediction of neurodegenerative diseases in the early stage through clinical conventional magnetic resonance imaging examination. The method comprises the following steps: collecting multi-modal magnetic resonance imaging as training samples, extracting first brain structure morphological quantitative values by dimension reduction and simplification, labeling diagnosis category labels and training a first classification model, and extracting a feature coefficient matrix; based on the feature weight coefficients contained in the feature coefficient matrix, screening out core driven brain areas corresponding to different diagnosis categories, and constructing a second classification model; obtaining clinical magnetic resonance images of early or precursor expectant examinees, extracting second brain structure morphological quantitative values, calling the second classification model to calculate probability scores of different diagnosis categories, and outputting auxiliary diagnosis or progress prediction results based on the probability scores.