35 results about "Neuroimaging" patented technology

The invention provides an Alzheimer disease auxiliary accurate identification method and system based on a multi-omics marker network and an adaptive support vector machine. Accurate identification of Alzheimer's disease and other types of dementia is realized through a marker network based on five modal data and an adaptive support vector machine model. According to the method, multi-source heterogeneous data such as blood, urine, neuroimaging, electrophysiology and clinical evaluation are fused, specific markers are analyzed and screened by adopting a weighted gene co-expression network, and cross-modal feature interactive learning is realized through a self-attention mechanism. An online learning mechanism is introduced to enable the model to adapt to new data distribution, and the contribution degree of each marker to diagnosis is output in combination with an interpretability module. Finally, multi-center data synchronization and model optimization are realized by means of a cloud platform, the accuracy, specificity and early diagnosis capability of AD diagnosis and identification and the adaptability of the model are remarkably improved, the limitation of the prior art is overcome, and doctors are assisted to diagnose and treat the Alzheimer's disease.

The invention discloses a depression brain function connection data analysis method based on multi-map fusion, is applied to the field of deep learning and nerve imaging, and aims to solve the problem that depression brain function connection cannot be represented accurately and effectively in the prior art. According to the method, brain region level blood oxygen concentration dependency signals are obtained from resting state functional magnetic resonance imaging data based on a plurality of brain maps divided according to different scales or functions, and then a functional connection matrix of each map is obtained through calculation based on a correlation algorithm; and then abstracting the functional connection matrix into graph structure data, carrying out feature extraction by using a multi-scale graph convolutional layer, and finally fusing and analyzing multi-map features by using two different strategies of multi-head cross attention fusion and adaptive map weight learning, so as to execute downstream tasks such as diagnosis or biomarker positioning. According to the method, multi-map information of functional magnetic resonance imaging is well integrated, the reliability of a depression brain function connection analysis result is improved, and an objective reference is favorably provided for clinic.

A system for personalized neuromodulation includes a neuroimaging recording device including sensors for detecting neural activity in a patient. An optimization module determines a personalized neuromodulation treatment protocol for the patient. The optimization module includes a neuromodulation target optimization module for determining neural targets in the patient for the personalized neuromodulation treatment protocol. The optimization module includes a neuromodulation parameters optimization module for determining optimized neural stimulation parameters for the personalized neuromodulation treatment protocol. A personalized neural stimulation device receives the neural targets from the neuromodulation target optimization module and receives the optimized neural stimulation parameters from the neuromodulation parameters optimization module. The personalized neural stimulation device delivers the personalized neuromodulation treatment protocol to the patient. The personalized neural stimulation device includes at least one neural stimulation device for stimulating the targets in the patient. A neuroimaging navigation system determines a placement of the neural stimulation device(s) about the patient.

A method of training a machine learning model for reconstructing video or audio data based on neuroimaging data of a subject is provided. The method includes: training a neuroimaging data encoder based on neuroimaging data from a neuroimaging training dataset for generating neuroimaging data embeddings; and training a diffusion model based on the neuroimaging data embeddings generated by the neuroimaging data encoder as conditions on the diffusion model. In this regard, the diffusion model is trained to reconstruct video or audio data based on neuroimaging data embeddings of neuroimaging data of a subject obtained in response to a visual or audio stimulus. The neuroimaging data encoder includes a masked autoencoder. The above-mentioned training the neuroimaging data encoder includes training an encoder of the masked autoencoder based on the neuroimaging training dataset using unsupervised learning with masked data modeling for generating the neuroimaging data embeddings. The unsupcrviscd learning with masked data modeling includes generating neuroimaging data embeddings from neuroimaging data from the neuroimaging training dataset, masking a portion of the neuroimaging data embeddings into masked neuroimaging data embeddings and training the masked autoencoder to recover the masked neuroimaging data embeddings. There is also provided a method of using the machine learning model trained for reconstructing video or audio data based on neuroimaging data of a subject.

Systems and methods are disclosed for assessing visual function of a user or patient. The systems and methods may contain steps, including: extracting data from an electrode site of a sensor; obtaining an average of the data across successive video presentations; applying a fast-Fourier transform to the average; determining a signal-to-noise ratio (SNR) of results of the fast-Fourier transform at tagged frequencies; determining a steady-state visual evoked potential (SSVEP) amplitude based on the SNR of the results of the fast-Fourier transform at the tagged frequencies; and determining a ratio of lower to higher spatial frequency SSVEPs to assess the vision function of the user.

This invention relates to the field of organic small molecule fluorescent probe technology, specifically to a neuron-specific dual-targeting fluorescent probe targeting the AChR of cholinergic neurons and its applications. The fluorescent probe, whose structure follows general formula I, is characterized by its dual-targeting structure, making it particularly suitable for the identification and labeling of cholinergic neurons. Furthermore, this fluorescent probe exhibits significant advantages in multiple application scenarios, such as cell imaging, protein and organelle labeling, tumor-specific recognition, and photodynamic therapy, with its potential application value in neuroimaging being particularly prominent. This fluorescent probe is expected to advance the development of neurobiology, disease diagnosis, and treatment methods, and has promising application prospects.

The invention discloses a mental disease typing method and system integrating DNA methylation and neuroimaging, and the method is a neuroimaging biological annotation method integrating DNA methylation and brain connection group data, and comprises the steps: recognizing brain network features related to a specific molecular phenotype in a whole brain range through a machine learning model; and mechanism-sensitive layering of the mental disorder heterogeneity group is realized. The method does not need to depend on a prior classification or hypothesis mechanism, can be suitable for different types of mental disorder people, provides technical support for exploring potential biological mechanisms and identifying targeted therapy groups, and has high generalizability and clinical application prospects.

Developmental outcomes of preterm children can be predicted using an automated classifier. An automated classifier can be trained to predict outcomes of a particular developmental assessment using neuroimaging data collected from preterm children that measures anatomical and / or functional features of the nervous system, including cortical and / or subcortical brain structures, and developmental assessment outcomes obtained later in the child's development. The classifier is able to predict unobserved developmental problems based on the structure and / or function of the preterm child's brain, and the predictions can be used to inform decisions about developmental interventions.

The present disclosure provides technologies that allow reduced field of view or fast imaging with reduced image distortion. The first technique capitalizes on the benefit of reduced field of view imaging for full field of view coverage. The second technique allows achieves high resolution 3D images in a focused region. These techniques are expected to have applications for cancer imaging, neuro imaging, and other biomedical imaging areas.

An imaging system comprising: a light source configured to provide wavelength-swept emission of light; a sample delivery channel coupled to the light source and arranged to be coupled to an object to be imaged to direct light from the light source towards said object; a reference channel coupled to the light source for receiving light therefrom; a sample receiving channel arranged to be coupled to the object to be imaged for receiving sample light from the object; a detector coupled to the sample receiving channel for receiving sample light, and coupled to the reference channel for receiving reference light, wherein the detector is arranged to combine the received sample light with the reference light to provide a combined light signal comprising one or more components at a beat frequency between sample light and reference light; and a lock-in amplifier arranged to: (i) receive a detection signal based on the combined light signal and formed of one or more components at different frequencies, (ii) receive a selection signal at a selection frequency, and (iii) to provide one or more output signals indicative of a component of the detection signal at the selection frequency.

The disclosed system provides an integrated neuroimaging analytics platform for comprehensive preoperative neurosurgical planning, intraoperative neurosurgical guidance and post-operative assessment by processing multimodal MRI (structural, diffusion, functional, and angiography) and CT data. The platform performs detailed anatomical characterization by delineating tumor subregions (peritumoral edema, enhancing tumor, and necrotic core), segmenting brain tissues (gray matter, white matter, and CSF), and executing lobe, cortical / subcortical parcellation. Advanced 3D rendering visualizes tumors alongside critical white matter fibre tracts derived from diffusion MRI, while MRA data is used to segment cerebrovascular structures, and functional MRI analysis identifies eloquent cortices associated with motor, speech, and visual functions. All results are integrated within a user-friendly GUI featuring advanced multiplanar slicing and a smart brush for interactive mask editing, complemented by a speech-to-text engine for streamlined analytical reporting. This comprehensive approach facilitates precise and efficient surgical planning, thereby enhancing patient safety and improving clinical outcomes.

The invention relates to a living body capillary dynamic nerve imaging tracking method, system and device and a medium, and the method comprises the steps: collecting the continuous capillary image data of a living body tissue; performing blood vessel segmentation processing on the continuous capillary image data, and identifying a capillary network skeleton for motion analysis; based on the capillary network skeleton, calculating a motion vector of the segmented continuous capillary image data, and obtaining a motion track of red blood cells in the capillary; according to the motion trail, haemodynamic parameters including the flow velocity, the direction and the blood oxygen level are generated; synchronously acquiring local neural activity data of the continuous capillary image data under the same space-time coordinate system; and according to the haemodynamic parameters and the local neural activity data, generating a tracking result of the in-vivo microvascular haemodynamics and neural activity, so as to achieve the purpose of performing synchronous tracking and dynamic correlation analysis on the microvascular haemodynamics and neural activity in the in-vivo environment.

Methods, compositions and kits useful in the detection, assessment, diagnosis, prognosis and / or treatment of brain injuries, especially mild traumatic brain injury (mTBI) or concussion, are based upon detection of changes in levels of certain protein biomarkers in a subject undergoing testing, or upon detection of changes in levels of certain protein biomarkers in conjunction with neuroimaging analyses to detect changes in vascular or blood brain barrier (BBB) permeability in the brain, or to detect damage to fiber tracts in the brain, in which changes in biomarker levels correlate with detection of changes in BBB permeability or in brain fiber tract or white matter damage in a subject with brain injury such as mTBI or concussion.

This application relates to the field of medical neuroimaging processing and computer-aided diagnosis technology, and discloses a method based on normal white matter. 18 An Alzheimer's Disease (AD) Auxiliary Assessment System Based on F-florbetapir Retention Features. This system includes a data acquisition module for acquiring multimodal brain imaging data of subjects; an image preprocessing and segmentation module for impurity removal and target localization of the image data; a feature extraction module for extracting image features from the processed images; and a joint assessment and prediction module for AD auxiliary assessment based on the extracted features, incorporating a subject performance characteristic model and a logistic regression system. This Alzheimer's Disease (AD) auxiliary assessment system can significantly improve the accuracy of early AD diagnosis, accurately predict the rate of cognitive decline, and dynamically monitor the effect of immunotherapy.

The invention discloses a medical image fusion method based on multi-scale cavity convolution and a channel attention mechanism, and the method comprises the steps: obtaining a sample set of fMRI and MRI-T1 image pairs based on an Alzheimer's disease neuroimaging plan database; constructing a medical image fusion model; adopting a residual network combining multi-scale cavity convolution and a channel attention mechanism to extract each modal feature pattern of the sample set; performing weighted fusion on the modal feature map through an adaptive weight mechanism to generate a fused feature map; reconstructing the fusion feature map into a single-channel medical image by adopting a multi-layer convolutional network, and outputting a fusion result map; constructing a joint loss function training fusion model, and optimizing parameters; and performing reasoning processing on the fMRI and MRI-T1 image pair based on the optimized parameters to generate a fused image. The method is suitable for a multi-modal medical image fusion task, and the overall performance of the fusion image in the aspects of structure restoration, detail maintenance and semantic expression can be improved.

System, apparatus, method and computer-accessible medium according to exemplary embodiments of the present disclosure which facilitate accessible, data-driven neuroimaging. Exemplary embodiment of the present disclosure provides for a magnet, a gradient, and spectrometer technology that can be contained in an imaging suite that can be affordably manufactured, delivered, and operated anywhere in the world by a trained field nurse. Exemplary networks of these field-deployed MR suites can be tethered to a remote resource base such as a hospital through satellite links to a cloud platform. In exemplary embodiments, massive amounts of heterogeneous data can be collected from diverse populations in a standardized way and archived for machine learning approaches to permit model-based inference generation.

Neuroimaging and / or brain activity sensing machines, such as MEG and OPM machines use a helmet-like or head covering device that includes sensors for acquiring information from a participant's brain activity. The machine is operated while the participant undergoes tests, which can include a need for sight. People with glasses, therefore, need access to their glasses, but the glasses need to be non-ferromagnetic. In addition, due to the shape of the helmet-like objects and the need to have the sensors as close to the participant's head as possible, having glasses that include a contour that closely matches the shape of the helmet will improve the information obtained by the machine.

The invention discloses a depressive attack suicide risk prediction method and device based on a multi-modal neural image. The method belongs to the crossing field of medical artificial intelligence and neural image analysis, and a multi-level machine learning model is trained by extracting the brain multi-parameter structure-function coupling degree, so that the coupling relationship between the brain region structure and function of a depressive attack patient with suicide risk is visualized. The objective biomarker-structure function coupling degree is provided, clinical doctors are assisted in interviews and questionnaire survey, and besides, brain region changes of patients with suicide risks are found through neuroimaging, so that the doctors are helped to optimize diagnosis and subsequent treatment schemes.

This invention provides a magnetic resonance receiving device suitable for auditory stimulation of awake marmosets, relating to the field of receiving coil design for magnetic resonance imaging. It includes an animal bed, a head fixation assembly, and a body shell. The animal bed has a placement slot; the head fixation assembly includes a mandibular support and a head shell, the mandibular support being detachably disposed in the placement slot, the head shell being detachably disposed above the mandibular support, and the body shell being detachably disposed on the animal bed; the head shell contains multiple radio frequency coils; both the mandibular support and the two side walls of the head shell have through slots for inserting auditory stimulation tubing. This invention, through the integrated design of radio frequency coils, a head fixation assembly, and an auditory stimulation-compatible interface, enables high-quality neuroimaging and precise simultaneous auditory stimulation experiments in an ultra-high field magnetic resonance environment.

A neuroimaging-based approach to predict treatment response in mental disorders by acquiring and analysing brain PET dopamine measures from patients. The method uses a short, simplified protocol for [18F]FDOPA brain PET imaging adapted for clinical practice. Individual [18F]FDOPA brain PET data are then quantified with a fully-automated analysis pipeline to extract information on the dopamine function of the subject. This information coupled with clinical information is run through a prediction algorithm to identify those patients whose illness will not respond to conventional antipsychotics.

This invention discloses a collaborative optimization method and apparatus for three-dimensional tissue segmentation and registration of brain neural images. The method includes the following steps: Step S01. Constructing a segmentation and registration collaborative model, including a shared feature encoder, a segmentation path, and a registration path. The segmentation path is used to generate a segmentation probability distribution map, and the registration path is used to generate a deformation field. Step S02. Obtaining a training set of three-dimensional magnetic resonance imaging (MRI) images of the brain. Step S03. Collaboratively training the segmentation and registration collaborative model according to a multi-task collaborative loss function. The loss function includes segmentation loss, registration loss, and a collaborative regularization term. The collaborative regularization term modulates the deformation field gradient penalty term by using a multi-scale boundary weight map and tissue-specific mechanical weights. Step S04. Receiving the image pairs to be registered in real time and inputting them into the trained segmentation and registration collaborative model to obtain the registration result. This invention can significantly improve computational efficiency while ensuring segmentation and registration accuracy.

In an embodiment, a neuroimager alignment and coupling evaluation (NACE) system, includes a neuroimaging device; a fiducial affixed to the neuroimaging device; an imaging device; and an augmented reality (AR) module coupled to the imaging device and configured to provide alignment data for the neuroimaging device by tracking facial or cranial landmarks and the fiducial.

A method of preparing a neuroimaging system for neuroimaging and analysis of a subject's brain tissue, the neuroimaging system comprising a plurality of optical elements, wherein each optical element comprises one of: (i) a light source for emitting light towards the subject's brain tissue, or (ii) a light detector for detecting scattered light from the subject's brain tissue, the method comprising: placing optical elements on the subject's scalp to provide at least one source-detector pair where a light source on the subject's scalp is arranged to emit light towards the subject's brain tissue and a detector on the subject's scalp is arranged to detect scattered light from the subject's brain tissue which was emitted from that light source; wherein placing an optical element on the subject's scalp comprises: first, arranging said optical element at a selected location on the subject's scalp for providing at least one source-detector pair; and then, performing an iterative optical element arrangement adjustment process comprising: obtaining a measurement signal indicative of a goodness of fit for said optical element as arranged on the subject's scalp; and re-arranging said optical element at, or proximal to, the selected location on the subject's scalp based on the indication of goodness of fit for said optical element; wherein said iterative optical element arrangement adjustment process is repeated until a said measurement signal indicates a goodness of fit for said optical element which satisfies a threshold criterion.

The present invention relates to the field of optical neuroimaging technology, and specifically provides a large-field fluorescence microscopic imaging system, which is mainly used as a miniature head-mounted fluorescence microscope for small animals, and includes: an objective lens, a liquid lens, a tube lens, and a detector. The light emitted by a point on the object passes through the three lenses in the objective lens in sequence and becomes a parallel light beam. The parallel light beam enters the liquid lens, and the radius change of the two curved surfaces of the liquid lens is controlled by voltage to achieve focusing; the light beam then passes through the two lenses in the tube lens to image the light beam on the image plane of the detector. The present invention has a small number of components, light weight, and the lenses are all spherical lenses, which have the advantages of simple structure, light weight, and low cost. The overall field of view can reach 8mm, which reduces the complexity and cost of the system on the basis of improving the imaging performance of the system.

The invention discloses a potential cargo anchor prediction method and device based on neural imaging psychological topic analysis, relates to the technical field of cargo anchor potential prediction, and solves the problems of relatively high subjectivity, long evaluation period and high trial and error cost of cargo anchor potential evaluation in the prior art. The method comprises the following steps: obtaining functional magnetic resonance imaging signals and behavior data of tested audiences, information of goods in a goods carrying video and market performance data of a goods carrying anchor, and analyzing the preprocessed functional magnetic resonance imaging signals to obtain an average brain activation image, individual psychological theme score data are generated and calculated to obtain psychological theme score data of each anchor carrying goods, a data set is constructed and trained to obtain a market performance prediction model and apply the model, and total consumer feedback of the market is predicted through data of laboratory small sample testees. The efficiency, accuracy and scientificity of potential cargo anchor prediction are improved, and the trial and error cost is remarkably reduced.

The present invention provides a method and system for constructing a vascular depression recognition model, including: collecting plasma samples of a person to be tested, preprocessing the plasma samples to obtain the results of the processed plasma samples; performing LC-MS / MS detection on the results of the processed plasma samples to obtain plasma protein quantitative information; screening the plasma protein quantitative information through fold change and T-test P value to obtain plasma differentially expressed proteins; using the XGBoost machine learning algorithm and LASSO analysis to isolate plasma protein markers from the plasma differentially expressed proteins; and adopting Logistic regression analysis to establish a vascular depression recognition model using the plasma protein markers. The vascular depression model construction method proposed by the present invention, which does not rely on clinical scales and neuroimaging, can effectively identify vascular depression according to a specific combination of plasma protein markers, and has good clinical application value and popularization value.

A method for ventriculostomy of the cavity of the anterior horn of the lateral ventricle in children, characterized by the fact that a calculation is carried out before the start of operative correction, based on the data of preliminary neuroimaging of the brain in the frontal and sagittal planes, while aligning the obtained images along the coordinate axes, the first side in determining the key point is selected in the projection of the widest part of the ventricular cavity, retreating from the coronary suture to the ventral side from 0 mm to 10 mm; the second side in determining the key point is selected in the frontal plane in the projection of the widest cavity of the anterior horn of the lateral ventricle of the brain, from the side of catheter placement, its maximum width is determined, the final result obtained is divided in half, the results of the two sides for determining the key point are transferred to the cranial vault;intraoperatively, a ventricular catheter is inserted through the key point parallel to the sagittal suture, and its insertion tip is directed to the imaginary biauricular line.

The application provides an Alzheimer's disease auxiliary precision identification method and system based on a multi-omics marker network and an adaptive support vector machine. Through a marker network based on five modal data and an adaptive support vector machine model, the precise identification of Alzheimer's disease and other types of dementia is realized. The method fuses multi-source heterogeneous data such as blood, urine, neuroimaging, electrophysiology and clinical evaluation, adopts weighted gene co-expression network analysis to screen specific markers, and realizes cross-modal feature interaction learning through a self-attention mechanism. The online learning mechanism is introduced to enable the model to adapt to new data distribution, and the explainability module is combined to output the contribution of each marker to the diagnosis. Finally, with the help of a cloud platform, multi-center data synchronization and model optimization are realized, which significantly improves the accuracy, specificity, early diagnosis ability and adaptability of the AD diagnosis and identification model, overcomes the limitations of the prior art, and assists doctors in diagnosing and treating Alzheimer's disease.

The application provides a free-moving mouse neural imaging method and device based on visual servoing, and relates to the technical fields of neural imaging and optical sensor, and the method comprises the following steps: acquiring the space section line data of a mouse head marker by synchronously scanning the marker with at least two line lasers installed at the end of a tracking mechanism; solving the rotation component and the translation component of the marker step by step according to the space section line data, and combining to obtain a six-degree-of-freedom pose matrix of the marker relative to the end of the tracking mechanism; controlling the movement of the tracking mechanism according to the six-degree-of-freedom pose matrix, so that an imaging system installed at the end of the tracking mechanism follows the marker; in the process of the movement of the tracking mechanism, a fluorescent image sequence of the mouse brain is collected by the imaging system, and non-rigid displacement correction and light intensity equalization correction are performed. The application realizes high-precision, high-throughput, non-contact and long-term stable imaging of the neural activity of a free-moving mouse, and is compatible with various microscopic imaging paradigms.

The invention relates to the technical field of neuro-endoscope transnasal approach preoperative planning, provides a neuro-endoscope transnasal approach preoperative planning method and system based on multi-modal image fusion, and solves the problem that a planning path safety boundary is difficult to accurately control. The method comprises the following steps: acquiring intracranial nerve imaging, angiography and distance evaluation data of a patient; fusing the two types of imaging data by adopting a multi-modal image fusion algorithm to obtain an initial three-dimensional fusion image, performing virtual ultrasonic scanning simulation on the anatomical structure based on a preset acoustic tissue characteristic model to generate acoustic characteristic data, and fusing the acoustic characteristic data with the initial three-dimensional fusion image to obtain a target three-dimensional fusion image; on the basis of the target three-dimensional fusion image, the preliminary planning path is adjusted in combination with a preset avoidance rule and a protection strategy, a preoperative planning scheme is output, multi-modal accurate fusion of the nerve nuclei and the blood vessels is achieved, and safe and accurate path planning support matched with a preoperative scene is provided for the neuroendoscopic nasal approach surgery.