36 results about "Neural imaging" patented technology

The invention relates to an intracranial puncture locating method based on mixed reality and belongs to the field of neural imaging navigation. The method includes the steps of firstly, collecting CTscanning data of a patient with a marker, and creating three-dimensional models of the head and intracranial lesions of the patient according to the CT data; determining puncture targets, a puncture direction and puncture depth on the basis of the three-dimensional models, and creating an intracranial puncture path; finally, through rigid registration, overlapping the three-dimensional model of the head and the puncture path on a surgical part of the patient through a mixed reality technology, so that a surgeon can conduct puncture surgery under the guidance of the virtual three-dimensional models and correct puncture operation in real time according to the puncture path. Since the virtual three-dimensional modules are totally matched with a real scene, the virtual puncture patch is highlymatched with an actual situation, and therefore by conducting the puncture operation along the virtual puncture path, deviations or faults existing in traditional empirical operation are effectivelyavoided so that the purpose of improving the intracranial puncture precision and puncture efficiency can be achieved.

An encephalic electrode individualization locating method based on multimode medical image data fusion relates to the fields of medical instruments and medical image processing and neuroimaging. By means of multimode medical image data including magnetic resonance images, X-ray images and intranperative optical photos, the space position relation of an encephalic electrode and a brain tissue structure is comprehensively measured, a relation between the position of the encephalic electrode and a brain anatomical structure is established, and accurate and fast individualization locating of the encephalic electrode is achieved. The encephalic electrode individualization locating method based on multimode medical image data fusion can be used in the fields of cognitive neuroscience basic research, clinic neurosciences and the like, provide accurate brain space position information for an encephalic electroencephalogram signal, and enhance application value in the aspect of electrophysiological basis research.

The invention discloses a flexible implantable neural photoelectrode. The flexible implantable neural photoelectrode comprises a recording electrode layer, a metal interconnection layer and an opticaldevice layer which are sequentially arranged, wherein a plurality of electrode sites are arranged on the recording electrode layer, the metal interconnection layer is used for connecting the electrode sites with the rear end of a neural imaging system, and the optical device layer is used for transmitting laser from the rear end of the neural imaging system to the flexible implantable neural photoelectrode and emitting the laser. The invention further discloses a method for preparing the flexible implantable neural photoelectrode. The flexible implantable neural photoelectrode provided by theinvention is high in integration level, and multi-channel and high-density signal recording can be realized; the size of the part implanted into the brain is small, so that the damage of device implantation to the brain tissue can be reduced; a flexible material is adopted to be matched with the Young modulus of the brain tissue, so that the nerve scar caused in the in-vivo device implantation process can be reduced, and long-term in-vivo stable recording is realized; and accurate stimulation, in-situ recording and cross-brain-region synchronous recording can be realized through photoelectricsignal interconnection.

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.

Cerebral cortex thickness estimation in brain magnetic resonance imaging (MRI) is an important technical means for researching brain development and neurodegenerative diseases in neuroimaging. The invention provides a cerebral cortex thickness estimation algorithm based on a three-dimensional Laplace operator, which can accurately capture geometrical morphological characteristics in brain nuclearmagnetic resonance imaging. The method comprises the following steps: firstly, starting from the elimination of a cross overlapping region generated on the surface of a gray matter layer and the surface of a white matter layer, constructing a tetrahedral mesh which reflects the inherent geometrical characteristics of the brain and is matched with MRI; secondly, constructing a three-dimensional Laplace operator by utilizing a geometric constraint relationship of a tetrahedral mesh, and calculating the distribution of a cerebral cortex internal temperature field under a Diels boundary by utilizing a finite element method; then, determining a local isothermal surface, obtaining the gradient line direction of the isothermal surface in the temperature field through a calculation geometry method, and a tetrahedral mesh unit where internal points on a gradient line are located is rapidly locked through a half-half surface data storage structure; and finally obtaining the thickness characteristic information of the cerebral cortex according to the direction and the step length of each gradient line by combining the set gradient step length. According to the method, the morphological structure detection capability of the cerebral cortex can be effectively improved by constructing a high-quality cerebral cortex tetrahedral mesh and determining a high-precision temperature field gradientline.

The invention discloses a method for mining biomarkers based on multi-map neuroimaging data, relates to a method for identifying graphs, and can consider a high-order complementary relationship amongmultiple maps and sample weight information at the same time. Feature analysis is carried out on the neural image data by adopting a multi-map feature selection method based on weight-induced low-ranklearning. According to the method, a first-order neighborhood aggregation mode is adopted, the sum of all connection strengths of each brain region serves as the characteristics of the brain region,the selected characteristics are more stable in a loop iteration mode, finally, the selected characteristics are fused and classified through a multinuclear support vector machine, and thus the Alzheimer's disease diagnosis precision is improved. The method overcomes the defects that in an existing Alzheimer's disease classification technology, sample weight information and multi-map information cannot be considered, and Alzheimer's disease diagnosis and classification errors are prone to occurring.

The present disclosure includes provides methods for assessing resting-state fMRI functional connectivity, resting-state MEGI functional connectivity, and/or task-based spatiotemporal auditory cortical activity latency in a subject to detect, monitor, and/or diagnose Tinnitus, with or without hearing impairment. The present disclosure also provides systems, devices, and methods for diagnosing Tinnitus and/or hearing impairment in a subject. Also provided are systems configured for performing the disclosed methods and computer readable medium storing instructions for performing steps of the disclosed methods.

The invention discloses a neuropathology hub node identification method combining pathology and topological information. The method comprises the following steps: firstly, determining a basic mathematical model of a brain network; then defining a neuropathologic potential and a neuropathologic potential difference, and then defining a neuropathologic potential difference of the whole brain network; constructing an energy function of the neuropathology hub recognition, and determining an optimization method of the neuropathology hub recognition; finally, real nerve image data are preprocessed, and an optimization algorithm is executed to obtain a neuropathology hub; according to the method, the effect of the neuropathology hub in the brain network topological structure and the characteristic expression of the neuropathology load distributed at the neuropathology hub are analyzed in a combined mode, the hub node with the high neuropathology potential is recognized, and the limitation that only topological characteristics of the hub node in the network structure are considered in a traditional hub recognition method is solved.

The invention relates to a construction method and application of a morphological fusion classification index of a neural image marker, and the construction method comprises the following steps: obtaining the structural MRI data of M centers, and extracting the feature data of a brain structure image; independently training the data of each center, respectively establishing a classification model of each center, and obtaining classification models of M centers; for any sample, calculating a classification weight value of the sample in each feature of each model in classification models of all centers, namely an SHAP matrix; and then taking the sample size used during training of each model as a weight, and calculating according to a formula (1) to obtain a single morphological fusion classification index MICI value: Si represents the sample size of the model i, B represents the total number of the features, ai represents the SHAP value of the feature a in the model i, and i = 1-M. The MICI value can well realize identification between mental disease patients and normal people, and has good interpretability, evolutionary property and expandability.

The invention discloses a brain resting state functional magnetic resonance imaging graph classification method, which adopts a mode of combining a traditional machine learning method and a deep learning method to analyze a neuroimaging result of a user. The problem that in the prior art, a resting state functional magnetic resonance imaging graph analysis method cannot have high classification performance and low training data volume at the same time is solved.

The invention provides a Web-based disturbance of consciousness electroencephalogram data analysis system. A platform mainly comprises two parts, namely an electroencephalogram acquisition experiment platform and a data analysis platform. Firstly, a resting state experiment normal form is selected, electroencephalogram data is collected by using an electroencephalogram cap, and then the data is imported into a rear-end database of the platform for data analysis. The front end performs algorithm selection of electroencephalogram data analysis and presentation of a result interface, and the rear end receives a request of the front end, calls electroencephalogram data in an algorithm database of data analysis, processes the data and returns a result to the front end. According to the disturbance of consciousness (DOC) electroencephalogram data analysis system provided by the invention, a consciousness level evaluation and scientific research platform integrating requirements of medical staff and requirements of scientific researchers can be realized, and rapid conversion of a computer-aided analysis technology based on neural image data on DOC clinical evaluation can be promoted.

The invention provides a psychological physical test method and equipment for negative emotion of a drug addiction patient and a medium, and belongs to the technical field of addiction evaluation. The problem that existing test standards are not uniform is solved. The psychological physical test method for the negative emotion of the drug addiction patient comprises the following steps: S1, inputting external stimulation; S2, testing negative emotion; S3, outputting behavior reaction; wherein the step S2 comprises the following steps: S2.1, testing negative emotion behavior; S2.2, carrying out a negative emotion scale test; and S2.3, carrying out a nerve image test. The method has the advantage of unified test standard.

The invention belongs to the technical field of neuroimaging, and particularly relates to a research method for a teenager muscular spasm epilepsy multi-brain-region co-activation mode. The method comprises the following steps: firstly, analyzing the grey matter volume of a JME patient by utilizing voxel-based morphological analysis (VBM); it is assumed that a brain region with gray matter volume atrophy is a key brain region causing disease generation, so that the brain region with gray matter volume atrophy is used as a region of interest, and brain dynamic activity changes of a JME patient and a healthy control group in the selected region of interest are analyzed through a co-activation mode. Therefore, the difference between the dynamic brain activities of the JME patient and the normal person in the resting state can be searched.

The present invention relates to a system and method for training an artificial intelligence (AI) based neural imaging system for evaluating and replicating actuarial calculation patterns of known valuation systems. In particular, present inventions disclose evaluating, using neural imager, output from a data generation unit and output from the actuarial assumption generation unit with the output from a valuation system to generate at least an image model replicating the actuarial calculations of the valuation system using neural networks.