114 results about "Entire brain" patented technology

The invention discloses a Parkinson's dyskinesia individualized SCAN network positioning method based on a multi-modal image and deep learning. The method comprises the steps of obtaining multi-modal medical image data, preprocessing the multi-modal medical image data, obtaining a multi-modal structure image and functional connection data, and calculating a spontaneous neural activity index of a whole-brain voxel level; taking a priori brain region related to the spontaneous neural activity index and dyskinesia as a seed point, constructing a seed point voxel function connection graph representing individual brain function connection, and performing nonlinear feature fusion and extraction through the deep learning network model; the bilinear attention network is adopted to capture the interaction information of the feature data and the individual dyskinesia symptom which is significantly related, an individualized SCAN network positioning result is obtained, the structure-function coupling characteristics of the individual brain are comprehensively described, the cross-modal pathological features related to the dyskinesia can be more sensitively recognized, and the accuracy and accuracy of the diagnosis and treatment of the dyskinesia can be improved. And the accuracy and robustness of abnormal brain region detection are obviously improved.

The invention discloses a disturbance of consciousness stimulation regulation and control system and method fused with electroencephalogram connection recognition. The system comprises a simulated electroencephalogram signal data acquisition stage, a connection recognition analysis stage, a stimulation parameter optimization stage and an executable stimulation instruction conversion stage. The method has the following advantages and effects that a whole-electroencephalogram activity distribution diagram is generated by simulating an electroencephalogram signal data acquisition stage, a key connection area is identified, and a brain function connection map is generated by utilizing a function connection analysis network and a phase synchronization algorithm in a connection identification analysis stage; in the stimulation parameter optimization stage, space-time correlation between a whole electroencephalogram activity distribution map and a brain function connection map is established, a multi-objective optimization algorithm is adopted to generate an optimized stimulation parameter set through a fusion network, and finally, in the executable stimulation instruction conversion stage, the optimized stimulation parameter set is converted into an executable stimulation instruction based on a self-adaptive control model. Therefore, the accuracy and the self-adaptive capability of electroencephalogram signal stimulation regulation and control are remarkably improved.

The application discloses a kind of nnUNet zebra fish juvenile whole brain vascular system segmentation methods based on autonomous data set training, it is related to high-resolution imaging technology, image processing and medical image segmentation field, the method makes full use of zebra fish live transparency and fluorescent label advantage, obtains high-resolution whole brain three-dimensional vascular image data, and constructs high-quality segmentation truth value database by semi-automatic segmentation and artificial correction, training is carried out using nnU-Net deep learning model, realize the three-dimensional automatic segmentation of zebra fish brain vascular system signal.The application method significantly improves the degree of automation and precision of image segmentation, effectively solves the problems of low efficiency, high artificial dependence and poor repeatability of traditional brain vascular segmentation.The method is suitable for large-scale high-throughput data processing, can provide efficient, standardized image processing scheme for zebra fish brain vascular development mechanism and brain vascular disease model research, and has wide application prospect.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The invention provides a multi-element emotion recognition method and device based on electroencephalogram space-time dynamic representation, and relates to the technical field of biomedical engineering and artificial intelligence crossing. The multi-element emotion recognition method comprises the following steps: acquiring an electroencephalogram signal generated when a user watches a target video; performing frequency decomposition on the electroencephalogram signal to obtain a time domain feature map representing time sequence dynamic characteristics of the electroencephalogram signal on a plurality of frequency scales; according to the time domain feature map, obtaining electroencephalogram space-time dynamic representation representing a dynamic evolution mode of the electroencephalogram signal in a whole brain space; determining a target emotion subgroup type to which the user belongs in the plurality of emotion subgroup types, and obtaining a target decoding strategy corresponding to the target emotion subgroup type; and decoding the electroencephalogram space-time dynamic representation through a target decoding strategy to obtain the multi-element emotion of the user. By adopting the method provided by the invention, the defects existing in the traditional emotion recognition method can be effectively overcome, and the accurate recognition of the multi-element emotion of the user is realized.

Systems and methods to allow for generating a simulated humanoid. The simulated humanoid operates in a simulation space with simulated humanoid having a whole brain emulation module. The whole brain emulation module includes a virtual stimuli input module that is configured to receive or capture stimuli input data. The whole brain emulation module includes an encoder that is configured to translate the stimuli input data into a simulated functional neurodata frame. The whole brain emulation module also includes a brain state module that maintains a current brain state corresponding to a current functional neurodata frame of the simulated humanoid.

The invention relates to a brain anomaly network positioning method and system based on functional connection network mapping in the technical field of neural image data processing. The brain abnormal network positioning method comprises the following steps: calculating a whole brain function network diagram connected with each abnormal site by using resting state function connection data of large-scale health subjects based on a plurality of dispersed abnormal sites reported in previous literatures; then superposing the function network diagrams to obtain a network probability graph; and finally, filtering the probability graph through a threshold value to obtain a final core anomaly network graph. According to the method, the inherent functional connection architecture of the brain is used as a reference system, abnormal sites which seem to be uncorrelated in different researches are successfully traced and unified to a common and stable functional network, and compared with a single brain region marker, the generated network-level biomarker integrates more source evidences, so that the network-level biomarker has the advantages that the network-level biomarker can be widely applied to the field of biomarkers of the brain region. Therefore, the problems of result heterogeneity and inconsistency in brain abnormality discovery are solved.

This invention belongs to the technical field of monitoring methods, specifically disclosing a method for clinical monitoring of craniocerebral diseases based on electromagnetic field biodetection technology. The method includes preparing four electrode pads, which are respectively fixed to the left and right sides of the patient's brain, the forehead, and the occipital region. The electrode pads are then connected to a monitor. Based on the detection data from the four electrode pads, the monitor obtains detection parameters at each time point: R1 represents the perturbation coefficient generated by the left and forehead electrode pads, R2 represents the perturbation coefficient generated by the right and forehead electrode pads, R3 represents the perturbation coefficient generated by the left and occipital region electrode pads, R4 represents the perturbation coefficient generated by the right and left sides electrode pads and the occipital region electrode pads, and R5 represents the perturbation coefficient generated by the left and right sides electrode pads or the perturbation coefficient of the entire brain. The method assesses the degree of edema, the presence of hemorrhage, or fluid accumulation. Using four electrodes positioned at different locations in the brain allows for targeted, three-dimensional, multi-parameter detection of lesions in different parts of the brain.

The invention relates to the technical field of brain disease target positioning, in particular to a brain disease target positioning system and method based on functional magnetic resonance imaging. The method comprises the following steps: calculating a whole brain function connection map of an individual patient based on 4D rs-fMRI data, calculating a Z score of each voxel function connection strength in the whole brain function connection map of the individual patient, and screening abnormal voxels based on a preset threshold value; aggregating the abnormal voxels through a clustering algorithm so as to determine a cluster; the therapeutic effect scores of the multiple clusters can be predicted through the machine learning model, the clusters and the candidate therapeutic targets can be screened based on the therapeutic effect scores, the positions of the candidate targets can be accurately determined, and multiple selections of the candidate targets can be provided; the target spot is converted into an accurate target spot suitable for an individual through a nonlinear registration algorithm; and determining a target positioning state according to the deviation between the accurate target and the actual treatment target, and adjusting corresponding parameters based on the target positioning state. The invention provides a plurality of targets and effectively determines the positions of the targets.

Embodiments of the present application provide a whole brain and whole spinal cord automatic delineation method and device, electronic equipment and medium, the method comprises: acquiring a CT image, segmenting the CT image according to a first adaptive threshold to obtain a human body contour region of interest, segmenting the human body contour region of interest according to a second adaptive threshold to obtain a human body skeleton contour region of interest, performing inflation operation and corrosion operation on the human body skeleton contour region of interest to obtain a brain tissue region of interest, performing inflation translation superposition subtraction processing and corrosion operation on the human body skeleton contour region of interest to obtain a spinal cord target contour, and combining the brain tissue region of interest and the spinal cord target contour to obtain a whole brain and whole spinal cord region of interest. By using the method, the whole brain and whole spinal cord region of interest can be automatically, quickly and accurately delineated, which greatly helps image analysis, accurate treatment and reduction of the workload of clinicians for patients.

The application discloses a new use of Leonurine or a pharmaceutically acceptable salt thereof in the preparation of a medicament for treating radiation-induced brain injury, specifically, providing a safe and effective therapeutic drug for iron death, neuroinflammation, neuron damage and lipid metabolism disorder accompanying the radiation-induced brain injury, solving the technical problem of lack of specific treatment means in the prior art, and having a wide clinical application prospect. Experiments adopt a C57BL / c mouse 30 Gy whole brain X-ray radiation model, and results show that Leonurine can significantly improve the body weight decrease and survival rate of the model mice, reduce the serum IL-6 and IFN-alpha levels, inhibit the activation of microglial cells in the cerebral cortex and hippocampus, protect and repair damaged neurons, and improve the ultrastructure of mitochondria and synapses; meanwhile, the expression of GPX4 and SLC7A11 proteins in the brain tissue is up-regulated, the GSH content is increased to inhibit iron death, and the levels of GPE, GPS and GPC related to lipid metabolism are restored.

The present application relates to medical image analysis and brain network research technical field, specifically to glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises obtaining patient fMRI and structural MRI data and preprocessing, excluding tumor area by lesion mask registration strategy, reducing quality effect interference; dividing tumor core area, peritumoral abnormal area and normal brain area; registering Yeo-17 network template to individual brain area to realize mapping; defining tumor core area as independent network unit, and 17 normal networks to form a new set; calculating whole brain voxel and network functional connection strength, and determining functional connection voxel according to threshold; quantifying intratumoral function proportion RIFR and peritumoral connection proportion RPTR, and generating visualization atlas. The present application accurately maps individual brain function network, overcomes tumor heterogeneity interference, provides repeatable quantitative index, and provides reliable imaging analysis tool for brain glioma function protection and clinical research.

The invention discloses a whole brain positioning and health state evaluation method and system based on an adaptive attention mechanism, electronic equipment and a computer readable storage medium. The whole-brain positioning and health state evaluation method based on the adaptive attention mechanism comprises the following steps: collecting whole-brain MRI data, and marking the structure of each part of the region of the whole brain; performing data preprocessing on the whole brain MRI data; on the basis of the whole-brain MRI data after data preprocessing, detecting each partial region of the whole brain by using a whole-brain positioning network based on an adaptive weight and residual error reconstruction mechanism; and according to the regional form of each part of the whole brain in the detection result, performing comprehensive health form evaluation. The method can solve the problems that the edge detection precision of each region of the whole brain is low and each partial region of the whole brain is difficult to accurately distinguish at present; the generalization ability is improved; the health state is accurately evaluated; manual intervention is reduced, one-key rapid whole-brain positioning and accurate health state evaluation are achieved, and simplicity and high efficiency are achieved.

The invention discloses a non-degreasing biological tissue dyeing method and device. The dyeing method of the non-degreasing biological tissue comprises the following steps: a sample fixing step: fixing a biological tissue sample by adopting paraformaldehyde and / or glutaraldehyde; and a dyeing step: carrying out electrophoresis dyeing on the fixed biological tissue sample which is not degreased by using a nucleic acid dye and a beta-amyloid protein small molecule dye. According to the non-degreasing biological tissue dyeing method disclosed by the invention, the non-degreasing biological tissue is directly dyed, so that the damage of degreasing operation to a sample is avoided; moreover, the nucleic acid dye is combined with the beta-amyloid protein small molecule dye, so that the whole brain sample can be quickly and efficiently dyed, and high-resolution fluorescence labeling and three-dimensional space distribution imaging of beta-amyloid protein plaques in the whole brain range can be realized; the method is of great significance in revealing the spatial distribution pattern of beta-amyloid protein and explaining the pathogenesis of Alzheimer's disease.

The invention provides a method and device for generating information based on brain image data, and the method comprises the steps: carrying out the longitudinal brain image analysis of the brain image data, collected at multiple time points, of a target individual, carrying out regional division on the brain of the target individual at each time point and extracting morphological measurement values of each region based on a longitudinal brain image analysis result and a cortex partition map; based on the brain form norm, the deviation degree of the morphological measurement value of each region of the brain of the target individual at each time point relative to the brain form norm is determined, and the individual brain form deviation vector corresponding to the target individual at each time point is obtained; performing correlation calculation on the individual brain form deviation vector corresponding to the target individual at each time point and the statistical data of the plurality of mental diseases in the whole brain range to obtain cross-disease brain form similarity features; and inputting the cross-disease brain form similarity features into a pre-trained machine learning model, and outputting classification information about whether the target individual is a schizophrenia patient or not, and / or prediction information about clinical indexes.

The application relates to the field of intelligent medical treatment, in particular to a double-encoder comparison method, equipment and program product based on whole-brain voxel levels. The method comprises the following steps: acquiring a three-dimensional whole-brain image dataset; constructing a double-encoder comparison decoding model by using the three-dimensional whole-brain image dataset; the construction process of the double-encoder comparison decoding model comprises the following steps: grouping normal or diseased three-dimensional whole-brain images, selecting an arbitrary normal three-dimensional whole-brain image as a reference image, inputting the reference image and other normal or diseased three-dimensional whole-brain images into a to-be-trained encoder model in parallel to obtain output features, comparing the output features to obtain a whole-brain difference classification result; repeating the steps of updating the to-be-trained encoder model and comparison calculation according to the reference image and other normal or diseased three-dimensional whole-brain images until a preset stop condition is reached, and obtaining a double-encoder comparison decoding model. The application has good clinical application value.

The invention relates to the technical field of neurological rehabilitation, in particular to a transcranial direct current closed-loop regulation and control method based on near-infrared brain oxygen real-time feedback, which comprises the following steps: collecting brain oxygen baseline signals of each region of the whole brain, calculating the mean value and the standard deviation of each brain region, and establishing a whole brain blood oxygen regulation and control threshold matrix of sub-regions and sub-parameters; dynamically adjusting the core target brain region, and adjusting the sampling frequency and the single detection duration according to the fluctuation frequency of the oxyhemoglobin concentration of the core target brain region; according to the signal fluctuation frequency, continuously adapting the detection parameters, and combining the comparison result of the core characteristic parameters and the whole brain blood oxygen regulation threshold matrix, optimizing the transcranial direct current regulation parameters, and outputting optimized data; by means of the mode, dynamic adaptation of the detection parameters and the signal fluctuation characteristics is carried out, and regulation and control collaboration is improved.

The application discloses a kind of whole brain segmentation method, system, equipment and medium, method includes obtaining CT image data, and standard image data is obtained by preprocessing;Standard image data is input into whole brain segmentation model, and whole brain segmentation result is obtained.Wherein, whole brain segmentation model is obtained based on sample annotation data training, and whole brain segmentation model includes CNN encoder, Transformer encoder, multiple cross-domain fusion modules and the feature enhancement module corresponding to cross-domain fusion module one by one, and decoder.The application has the advantages that the soft tissue contrast of CT image data is low, it is difficult to distinguish cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue problem, by CNN encoder extraction local feature and Transformer encoder extraction global semantic feature, and local feature and global semantic feature dynamic interaction and fusion, accurately segmented advantage is carried out to whole brain cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue.

The application discloses an Alzheimer's disease pathological region positioning and classification prediction method, comprising the following steps: constructing an Alzheimer's disease classification model; constructing a visual explanation model based on counterfactual reasoning; introducing a three-dimensional coordinate attention mechanism guided by a counterfactual graph to further enhance the classification model; and iterating the explanation model and the Alzheimer's disease classification model. The counterfactual reasoning explanation model divides fine pathological regions by using a counterfactual reasoning method, uses the position information of the generated pathological regions to guide the classification model, and enables the classification model to focus on learning the discriminant region related to the disease and to have higher sensitivity to the pathological region; the three-dimensional coordinate attention mechanism is used to obtain the dependency relationship between three-dimensional image regions and to retain the accurate position information in the three-dimensional space, the model is more likely to obtain the interested region, the whole brain structure is analyzed, and thus the training efficiency and the accuracy of the classification model are improved, and the model has higher robustness.

The invention discloses a method for calculating a quantitative magnetic susceptibility image through total field inversion based on a Green function. The method is used for separating a background magnetic field and reconstructing a whole-brain quantitative magnetic susceptibility image in magnetic resonance imaging. According to the boundary element method full-field inversion method, filtering and regularization processing on tissue magnetic susceptibility are prevented from being additionally introduced when background field interference is removed, the background field can be effectively separated, a whole-brain quantitative magnetic susceptibility image can be reconstructed, the boundary erosion phenomenon cannot occur, and the boundary element method full-field inversion method particularly shows excellent performance in a cortex area. According to the method, a fast multipole method is adopted to efficiently process storage and calculation of a dense matrix generated by a boundary integral equation.

The invention provides an electroencephalogram rhythm disturbance detection and sleep-aiding regulation and control method based on a coherent network map, and relates to the technical field of sleep-aiding regulation and control. According to the method, electroencephalogram time-space synchronization is coded into graph topology features, early rhythm topology disturbance before micro-awakening can be captured, compared with single-channel feature detection, the sensitivity is higher, the anti-interference capacity is higher, and advanced early warning of rhythm collapse risks is achieved. A whole-brain coupled oscillator phase equation is constructed based on a generalized Kuleramotort model, phase prediction is optimized in combination with the communication weight of a slow wave coherent network, and compared with single-channel phase detection, the uplink phase time delay calculation precision is higher, it is ensured that an optimal regulation window of a slow wave uplink phase is accurately locked through exogenous stimulation, and the optimal regulation window of the slow wave uplink phase is accurately locked. And the enhancement effect of slow wave synchronism is greatly improved. The slow-wave steady-state manifold memory baseline is updated by adopting an exponential moving average method, natural dynamic changes of slow-wave rhythms in a sleep cycle can be adapted, detection errors caused by a fixed baseline are eliminated, and the accuracy of disturbance detection is improved.

The invention relates to the technical field of medical image analysis and brain network research, in particular to a glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises the steps of obtaining and preprocessing fMRI and structural MRI data of a patient, eliminating a tumor area through a focus shielding registration strategy, and reducing mass effect interference; dividing a tumor core region, a peritumoral abnormal region and a normal brain region; a Yeo-17 network template is registered to an individual brain region to realize mapping; defining a tumor core area as an independent network unit, and forming a new set with 17 normal networks; calculating the connection strength of whole brain voxels and network functions, and judging functional connection voxels according to a threshold value; and quantifying the intratumoral function ratio RIFR and the peritumoral ligation ratio RPTR, and generating a visual map. According to the method, an individual brain function network is accurately mapped, tumor heterogeneity interference is overcome, repeatable quantitative indexes are provided, and a reliable imaging analysis tool is provided for brain glioma function protection and clinical research.

The invention discloses a brain age prediction method based on spatial heterogeneity and time continuity modeling. The method comprises the following steps: 1, acquiring brain magnetic resonance imaging data and a real age label thereof; 2, the brain magnetic resonance imaging data passes through a brain age prediction backbone network to obtain whole brain feature representation and an age prediction result of a brain region unit; 3, inputting the whole-brain feature representation, the age prediction result and the real age tag into a comparative learning module of regional perception to obtain comparative learning loss; 4, training the brain age prediction backbone network according to the contrast learning loss, and 5, initializing the brain age prediction backbone network into a teacher model and a student model in a model reasoning stage, and obtaining more accurate brain age through the processing of the teacher model and the student model. According to the method, spatial heterogeneity and time continuity in the brain aging process can be modeled, and the accuracy of brain age prediction is improved.

The present application belongs to the technical field of medical signal processing and neural electrophysiological diagnosis, and particularly relates to a method for establishing an epilepsy diagnosis system based on time-varying brain network and evoked potential regulation. The present application aims to solve the problems that the prior art is difficult to capture the dynamic causal flow of neural activity, cannot accurately depict the transient reorganization characteristics of brain regions of juvenile myoclonic epilepsy (JME) patients, and lacks multi-modal feature integration analysis. The present application comprises the following steps: collecting whole brain electroencephalogram signals; extracting TMS evoked potential waveform after preprocessing; constructing a time-varying adaptive directional transfer function matrix; screening a time-varying brain network visualization atlas; performing baseline correction and superimposed averaging on the evoked potential; identifying the differences in connection and potential between JME patients and healthy controls and different clinical subgroups; and finally integrating the whole process to establish a standardized automated analysis system and a structured database. Through the method, the joint quantification of cortical excitability and time-varying causal connection is realized, and the objectivity, sensitivity and mechanism analysis ability of epilepsy diagnosis are significantly improved.

The invention discloses a cerebrovascular reactivity evaluation method for non-invasive calculation of a breath-holding index, and belongs to the field of hemodynamics numerical calculation and multi-scale modeling. The method comprises the following steps: constructing a one-dimensional (1D) arteriole hemodynamic model based on an allotactic scale law; constructing a main artery three-dimensional (3D) model, and coupling the main artery three-dimensional (3D) model with the arteriole one-dimensional model and the microcirculation zero-dimensional (0D) model to form a whole-brain 3D-1D-0D geometric multi-scale hemodynamic model; based on blood flow related data obtained in the experiment process, parameterization description is conducted on arteriole radius changes and microcirculation resistance changes; a breath holding index (BHI) is calculated by numerical solution of the geometric multi-scale model. According to the method, the non-invasive numerical calculation of the breath-holding index can be realized without directly implementing a breath-holding experiment or an external stimulation condition, and a stable and repeatable calculation means is provided for quantitative analysis and related engineering application of cerebrovascular hemodynamic behaviors.

The invention discloses a track type nerve regulation and control positioning navigation system and method, and particularly relates to the field of medical equipment, and the track type nerve regulation and control positioning navigation system comprises a treatment chair, a track, a mechanical arm, a coil adapter, a transcranial magnetic coil, an upper computer support, an upper computer, an optical locator support and an optical locator; the transcranial magnetic coil is fixedly installed on the coil adapter, the coil adapter is fixedly installed on the mechanical arm, the track is fixedly installed on the treatment chair, the mechanical arm is installed on the track in a sliding mode, the upper computer is fixedly installed on the upper computer support, and the optical locator is fixedly installed on the optical locator support. Whole-brain multi-target automatic and high-precision nerve regulation and control treatment is realized, the treatment effect and the positioning accuracy are greatly improved, the working intensity of doctors is reduced, and personal errors and off-target risks are avoided.

Disclosed in the present invention are a 3D navigator-based 3D gradient spin-echo diffusion imaging method, and a device. The method comprises: first, using a global saturation module to destroy previous residual transverse magnetization; second, by means of a diffusion encoding module, embedding a pair of trapezoidal cosine oscillation gradients or pulse gradients into radio frequency pulses of 90°x-180°y-90°-x, and separating diffusion encoding from signal acquisition; then using a fat saturation module to suppress a fat signal; using a 3D gradient spin-echo readout method to acquire an image signal; and finally, acquiring a 3D navigation echo signal in the last spin echo for correcting a phase error between multiple excitations. The present invention can effectively eliminate phase errors between multiple acquisitions and realize high-resolution whole-brain diffusion imaging. Compared with 2D echo planar imaging, the present invention increases the signal-to-noise ratio of diffusion magnetic resonance imaging, thereby supporting higher-resolution whole-brain 3D diffusion magnetic resonance imaging.

The invention provides a whole-brain organoid based on human induced pluripotent stem cells as well as a construction method and application thereof, and belongs to the technical field of organoid construction. The method comprises the following steps: culturing human induced pluripotent stem cells in a complete culture medium to obtain a cell aggregate; culturing in an embryoid body forming culture medium to obtain a spherical embryoid body; culturing in a nerve induction culture medium tissue to form a nerve epithelium structure; embedding the single embryoid body with the nerve epithelium structure in a Matrigel gel drop, culturing in a nerve expansion culture medium to form a nerve epithelium-like structure, and replacing a mature culture medium for culturing to obtain the whole-brain organ. According to the method, by dynamically and accurately regulating and controlling culture parameters, the control ability of a cell differentiation area is improved, and interference of non-target cell types is reduced, so that the efficiency and consistency of embryoid formation and neural differentiation are directly improved, and finally, the obtained mature organoid has a more complete epithelial structure and cell polarity.

Resting-state magnetic resonance imaging (fMRI) technology records brain activity by measuring the global blood oxygen saturation of the subject's brain, providing high spatial resolution data. Although the temporal resolution is lower, in elderly individuals at rest, the introduction of time series data still allows for good observation of the hemodynamic direction of individual pixels, making it one of the important evaluation indicators for intervention and treatment of brain diseases. The neural circuit features constructed based on a single brain region towards the whole brain are also an important evaluation method for fMRI data, namely, the quantification of the physiological interaction of the brain region towards the whole brain in a certain state. This invention constructs a method for quantifying the treatment effect of Alzheimer's disease (AD) based on resting-state magnetic resonance imaging (fMRI) technology. This method mainly addresses: (1) the difficulty in quantifying the rehabilitation effect during the treatment of AD patients; and (2) the quantification of the rehabilitation effect of different treatment modes during the treatment of AD patients.

The present application relates to the technical field of risk assessment, and particularly relates to a neurodegenerative disease risk assessment method based on brain imaging technology, comprising obtaining brain imaging data of a subject in a resting state, a controlled neural disturbance state and a recovery state after the disturbance is removed, and performing same-region registration and time sequence alignment processing on the brain imaging data to generate a baseline brain network atlas, a disturbance response offset sequence and a recovery rebound sequence. The present application constructs a multi-level evaluation mechanism of fragile nodes-unstable propagation path-whole brain vulnerability cumulative value, jointly calculates the node vulnerability weight and the brain network propagation intensity, quantifies the overall stability of the brain network, and outputs the risk level and the priority degenerative brain area, realizes systematic evaluation from local brain area abnormalities to whole brain network risk, and improves the network-level analysis capability of neurodegenerative disease risk prediction.