85 results about "Brain aging" patented technology

The invention discloses a brain age prediction method based on structure and functional MRI image multi-modal fusion, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, extracting spatial structure characteristics of a structural magnetic resonance image by using DenseNet121; meanwhile, a function connection matrix is constructed according to the time sequence of the functions, a graph structure is constructed on the basis of the matrix, the characteristic of each node is the connection strength between the node and other nodes, and the edge is converted into sparse graph representation from the absolute value of the connection strength; then extracting functional features by using a graph attention network, and fusing the structure and the functional features by using a cross attention mechanism; and applying a gating mechanism fusion result to a brain age prediction regression task. According to the brain age prediction method, complementary information of multi-modal data is fully utilized, and biological markers of brain aging can be accurately captured.

According to the multi-scale brain age prediction model construction method based on the magnetic resonance image and the application, the constructed brain age prediction model is higher in generalization and robustness, higher prediction precision is kept, the whole brain-sub-network-voxel brain age can be predicted, the predicted brain age has better interpretability in the physiological sense, and the brain age prediction accuracy is improved. The difference of brain ages among different sub-networks and a specific mode of PAD and cognition association are explored, the specific sub-network for regulating cognition is found, the difference mode of aging of different brain regions is seen from the voxel level, and the prediction performance of the model is superior to that of a current mainstream neural network model. The method comprises the following steps: (1) data collection; (2) data preprocessing; (3) constructing a whole-brain and functional sub-network brain age prediction model based on a simple full convolutional neural network SFCN method; (4) constructing a voxel level brain age prediction model based on a ScaledDense U-Net method; and (5) carrying out offset correction on the brain age deviation.

The invention provides a brain age estimation method based on a dynamic fuzzy learnable brain network, and belongs to the technical field of medical image processing and artificial intelligence. According to the technical scheme, the method comprises the following steps that S1, brain nuclear magnetic resonance imaging of a subject is collected, and preprocessing and data division are carried out; s2, constructing graph structure data, and performing feature extraction and position information embedding on the data; s3, constructing a dynamic fuzzy learnable brain network model comprising a main branch and a local branch, and respectively extracting global and local connection features; s4, introducing a dynamic fuzzy multi-head self-attention module into the main branch to realize effective modeling of global features; s5, a local branch dynamically models a dependency relationship between channels through a convolution filter and a learnable graph attention module; s6, after the features of the main branches and the local branches are fused, brain age prediction is carried out through a multi-layer perceptron. According to the method, the modeling capability of the brain function connection mode is improved, and the brain age prediction task can be more effectively completed.

The invention discloses application of a plant lactobacillus CCFM1357 to conversion of anthocyanin to generate various active metabolites, inhibition of a cGAS-STING pathway and enhancement of improvement of brain aging, and belongs to the technical field of microorganisms and the technical field of medicines. The phytobacterium plantarum CCFM1357 fermented anthocyanin and the compound preparation thereof provided by the invention have the following effects: (1) a marker for relieving D-gal induced mouse brain senescence and a marker for relieving D-gal induced mouse brain senescence; (2) promoting and relieving D-gal induced mouse histopathologic characterization; (3) relieving D-gal induced mouse neuroinflammation; (4) relieving behavioral characterization of the D-gal induced aging mouse; and (5) a cGAS-STING signal channel is inhibited. The phytobacterium plantarum CCFM1357 fermented anthocyanin and the compound preparation thereof provided by the invention can be used for a product for relieving brain aging, and have a huge application prospect.

The invention provides a brain age prediction method based on a twinborn pruning attention neural network, and belongs to the technical field of medical image intelligent diagnosis. While the accuracy of brain age prediction is ensured, the calculation overhead of the model for high-dimensional rs-fMRI image data is reduced, and the generalization ability of the model in a small sample and individual difference significant scene is improved. According to the technical scheme, the method comprises the following steps that S1, resting state functional magnetic resonance imaging of a subject is collected; s2, constructing a pruning module; s3, constructing a twin neural network model; s4, designing a joint loss function to comprehensively consider structural similarity and label similarity; and S5, after model training is completed, inputting test set samples into the trained twin network structure one by one for prediction analysis. The method has the beneficial effect that the accuracy and generalization ability of brain age prediction are improved.

The invention belongs to the technical field of fetal brain age prediction, and relates to an earthworm cerebellar fetal brain age prediction method and system based on multi-modal feature fusion, an MST-Mamba segmentation network is adopted, and local-global aggregators are embedded in each level of an encoder, so that the cooperation of local detail capture and global semantic modeling is realized; meanwhile, a dynamic channel fusion device is deployed at the jump connection part of the encoder and the decoder, so that the problems of fuzzy boundary, missed division, wrong division and the like are avoided; through three parallel branches of a multi-granularity form-texture collaborative perception architecture, two types of explicit features of macroscopic geometry and topological form and implicit features of microscopic texture are synchronously extracted, and comprehensive characterization of the development features of the earthworm cerebellar part is realized; the explicit features are subjected to standardized calibration and then spliced and fused with the implicit features in the channel dimension, the problems that multi-modal feature fusion is insufficient and calibration lacks are solved, finally prediction is conducted through a multi-layer perceptron regression head, and the accuracy and stability of the brain age prediction result are guaranteed from the source.

Methods for treating a neuropsychiatric disorder by administering an iboga alkaloid and a cardioprotective agent in conjunction with analysis of brain image data is described. Also described are methods to improve brain health and to slow or reverse brain aging by disorder by administering an iboga alkaloid and a cardioprotective agent, where analysis of brain image data is used to monitor and / or evaluate treatment effectiveness.

The invention provides isolated Ackermansiella muciniphila, a composition containing the same and an anti-aging and / or anti-oxidation application of the Ackermansiella muciniphila. The Akkermansia muciniphila has the advantages that the average life and the longest life of the Akkermansia muciniphila in a nematode model can be prolonged, the movement activity, the swallowing function and the reproductive capacity of the Akkermansia muciniphila can be improved, the expression of insulin / IGF-1 pathway genes (daf-16, daf-2 and age-1), the expression of antioxidant genes (gst-4 and sod-3) and the expression of longevity related genes (sir-2.1) of the Akkermansia muciniphila can be up-regulated, and the antioxidant capacity and the heat stress tolerance of the Akkermansia muciniphila can be enhanced. It is shown that the Akkermansia muciniphila provided by the invention can be used for preparing or developing the anti-oxidation and anti-aging products, and the anti-oxidation and anti-aging products can be prepared from the Akkermansia muciniphila. The invention provides an efficient and safe solution for anti-aging and / or anti-oxidation.

The embodiment of the invention discloses an autism spectrum disorder subtype division method and device, a medium and a program product. The method comprises the following steps: constructing a connection brain map based on neuroimaging data and a functional brain region division template of an ASD individual; constructing a brain age regression model, predicting the social brain age of the ASD individual based on the connection brain map and the brain age regression model, and obtaining the brain age difference of the ASD individual in combination with the actual brain age of the ASD individual; obtaining an ADOS social score of the ASD individual, and carrying out clustering analysis on the ADOS social score and the brain age difference to obtain a clustering subtype; and carrying out behavioral verification and neural dimension verification on the clustering subtypes, and constructing a combined portrait among the subtypes, the behavior features and the neural features based on a verification result. According to the method, the social brain age can be predicted by constructing the connection brain map, the clustering subtypes are divided, the combined portrait is constructed, a doctor can be accurately assisted to detect ASD subtype neural development differences, and discovery and application of subtype specific biomarkers are assisted.

The present application discloses a method, device, medium and product for determining a comprehensive brain aging index, relating to the field of image data processing. The method includes: preprocessing images such as T1 and TOF-MRA to obtain brain tissue segmentation images; post-processing TOF-MRA magnetic resonance images to extract cerebrovascular imaging features; using a method combining modules such as asymmetric convolution to perform convolution feature extraction on T1 and T2-FLAIR images, and compressing the features to generate a convolution feature group; after connecting demographic indicators, cerebrovascular imaging features and the convolution feature group, using a biological age prediction model to obtain a brain aging age prediction result, and combining with the actual age to obtain a comprehensive brain aging index. The present application can identify changes in large intracranial blood vessels, and can also reflect the state of earlier microvascular aging, improve the sensitivity to microvascular aging, solve the limitations such as low efficiency and poor consistency of manual feature extraction, and achieve comprehensive quantitative evaluation of brain aging.

The invention discloses a brain age prediction method, system and device and a medium, and relates to the technical field of biomedical image analys.The method comprises the steps that firstly, precise local detail features representing the cerebral cortex and brain tissue are precisely captured, then the dependency relationship between local areas of different images is obtained based on a windowed multi-head self-attention mechanism, and the brain age prediction result is obtained; a channel attention mechanism is introduced, importance weights of different feature channels are learned, and the importance weights are applied to feature fusion, so that long-distance dependency relationships and fine structure features are accurately captured; and then based on a cross attention mechanism, guiding global detail features to focus on a region with rich local features and guiding the local detail features to focus on a most relevant local region so as to mine deeper detail features, and finally dynamically adjusting the contribution proportion of the local detail features and global context detail features in a final decision. And the features are fused into final features so as to perceive local detail features and global detail features in a deeper level.

A system for estimating a subject's functional brain age using non-invasive electroencephalography (EEG), the system includes: an EEG acquisition module (101) configured to record multichannel EEG signals from the subject; a stimulation control module (102) configured to present a predefined sequence of cognitive and sensory tasks that specifically target the frontal, temporal, parietal and occipital brain regions, and to generate synchronized event markers; a data processing module (103) configured to prepare the recorded EEG signals by filtering, artifact removal, normalization and segmentation into task-oriented time windows; a machine learning module (104) comprising one or more self-monitoring contrastive learning encoders configured to transform the preprocessed EEG signals into latent feature representations; and a brain age estimation module (105) configured to process the latent feature representations to generate a BrainAge Score indicating a difference between a predicted biological brain age and the subject's chronological age.

The invention provides a fetal brain age prediction network training method, an application method and electronic equipment, and belongs to the technical field of medical image processing, and the training method comprises the steps: carrying out the similarity sorting conversion of a fetal brain magnetic resonance image and a corresponding brain age label, and obtaining a similarity sorting matrix, performing vector embedding reconstruction on the similarity sorting matrix to obtain an embedded vector, and determining an initial permutation entropy; performing median absolute deviation weighting on the initial permutation entropy to obtain a weighted permutation entropy, performing multi-scale ordinal number information reconstruction on the weighted permutation entropy to obtain a multi-scale permutation entropy, performing time sequence enhancement reconstruction on the multi-scale permutation entropy to obtain a time sequence multi-scale permutation entropy, and constructing permutation entropy regularization loss according to the time sequence multi-scale permutation entropy; and obtaining a fetal brain age prediction network according to permutation entropy regularization loss iterative training. According to the method, the loss function is constructed through the permutation entropy regularizer, so that the continuous ordered relation in regression can be captured, and the distinguishing ability and generalization ability of the fetal brain age prediction network are remarkably improved.

The application discloses a brain age estimation method based on adversarial learning, extracts corresponding image features specific to age based on whole brain structural T1 magnetic resonance imaging, and realizes prediction of brain age, and comprises the following steps: step one: data collection and pretreatment, more than 2000 cases of data from 5 sites are collected, the age span is 5-94, the collected data is pretreated, all brain image data is registered to MNI standard space, and the size is standardized; step two: based on the adversarial learning deep neural network, the network is trained and optimized, and prediction of brain age is realized; step three: performance evaluation of brain age prediction task, optimization of prediction results, model feature extraction capability and generalization capability evaluation. Compared with the prior art, the application reduces the phenomenon that the model training result is unstable and the prediction effect is not ideal due to uneven age distribution of samples.

The invention discloses a multi-parameter MRI brain age prediction method and device based on uncertainty perception, equipment and a medium, and the method comprises the steps: determining a first feature sequence of an sMRI image, a second feature sequence of a DTI image, and a third feature sequence of an fMRI image through a feature extraction module; and determining a GM sampling vector, a DTI sampling vector and an fMRI sampling vector based on the first feature sequence, the second feature sequence and the third feature sequence by using a probability distribution encoder, and performing brain age prediction by using the GM sampling vector, the DTI sampling vector and the fMRI sampling vector. According to the method and the device, the fusion feature vector determined by fusing the sMRI, DTI and fMRI information is utilized to perform brain age prediction, so that multi-dimensional information of the brain structure and function can be more comprehensively acquired, and the accuracy of brain age prediction is improved. Meanwhile, a probability distribution encoder is introduced in the process of fusing the multi-parameter MRI images, multi-parameter MRI feature mapping is converted into Gaussian distribution through the probability distribution encoder to model feature uncertainty, richer potential information in the multi-parameter MRI images is captured, and the accuracy of brain age prediction is further improved.

The invention relates to the technical field of image processing, in particular to a brain age prediction method, system and device based on federal learning. According to the method, a three-dimensional brain MRI image is taken as input data, and multi-center collaborative modeling is realized by adopting a federated learning framework aiming at the problems of medical data privacy protection and data islands; each medical institution does not need to share original image data, and only completes feature extraction, model training and local parameter updating locally based on private data; after the central server receives the local parameters uploaded by the mechanisms, data distribution differences are considered, and a unified global model is generated through fusion of a preset aggregation rule. According to the method, privacy leakage and compliance risks of cross-mechanism transmission of original image data are effectively avoided, common characteristics of multi-center data can be integrated, the brain age prediction precision and generalization ability of a global model are improved, and the method is suitable for brain age evaluation research and clinical auxiliary diagnosis scenes jointly developed by multiple mechanisms.

The application relates to the technical field of brain image analysis, and discloses a fine-grained brain age prediction method, a system, a terminal and a storage medium.The method comprises the following steps: a plurality of brain tissue structures are segmented from a brain structure nuclear magnetic resonance image of a target object after preprocessing; a plurality of feature maps are generated according to the brain tissue structures and are mapped to a plurality of positive faces; a four-stage network model is used to process the mapped samples; and dynamic adaptive lateral attention is added after each stage to predict the brain age of each brain region.The application predicts the brain age by using the fine-grained brain cortex region level, and introduces a dynamic adaptive lateral attention mechanism to simulate the real brain structure lateral relationship, so that the topological structure and local features of the cortex can be effectively expressed, and the error caused by the brain morphology difference is reduced.

The invention belongs to the technical field of brain science disease early screening, prevention and mechanism analysis, and particularly relates to a brain age prediction and brain disease risk assessment method and a related system thereof. According to preprocessing of human tissue transcriptome gene expression data and crowd plasma proteome data, it is guaranteed that input data is coordinated and consistent; establishing brain age prediction models of different brain tissue areas on the basis of LASSO regression of bootstrap sampling; and taking the selected brain region model with high correlation between the predicted age and the actual age as input, and evaluating the risk of poor brain age of the brain region model on brain diseases. According to the method, transcriptome information and plasma proteome information are combined, and the onset risk of the brain diseases is predicted through Cox risk regression, so that the accuracy of model prediction is improved, and a potential target is provided for subsequent early intervention of the brain diseases.

A method of predicting brain age for a subject having bipolar disorder (BD) comprises obtaining at least one medical image of a brain of a subject; producing a brain map based on the at least one medical image; segmenting the brain map into more than one brain regions; and calculating a brain age prediction of the subject based on a predetermined set of key features for each of the brain regions; wherein the subject has BD.

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.

We describe a computer-implemented method for determining a patient's brain age and optionally stratifying patients into dementia risk groups based on the determined brain age. The methods comprise extracting at least one volumetric feature from an image of a brain by: obtaining at least one volume value for at least part of the patient's brain, and normalising the at least one obtained volume value to obtain the at least one volumetric feature. Brain age is predicted by inputting the at least one extracted volumetric feature into a pre-trained brain age model, wherein the brain age model is a linear regression model. Bias of the linear regression model may also be corrected. A classification model, such as a logistic regression binary classifier, may be used to stratify patients into dementia risk groups.

The invention discloses a plateau population brain aging comprehensive index determination method and device, a medium and a computer program product, which adopt a 3D-CNN architecture to process nerve image data, improve brain age prediction precision, apply a longitudinal comparative analysis method, realize individualized brain aging trajectory tracking, generate a saliency map, and improve brain age prediction accuracy. And visual abnormal brain region positioning information is provided. And a multi-modal weighted fusion algorithm is adopted, so that data isomerism challenges are overcome, and information complementation and result verification are realized. Brain oxygen saturation parameters are incorporated into a brain aging evaluation system, and the key problem that hypoxia affects brain aging in a plateau environment is solved. A plateau population brain aging norm is established, the problem that plain standards are not applicable is solved, and a plateau brain aging specific mode is disclosed. The integrated evaluation process simplifies the operation steps, reduces the professional threshold, improves the prevention and treatment effect through personalized intervention suggestions, and is beneficial to delaying the brain aging process.

The present invention addresses the problem of providing an agent for preventing or treating amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), Huntington's disease (HD), spinocerebellar ataxia (SCA), aging-related degenerative or neurological disease, brain aging, or diseases associated with brain aging, as well as a more stable antibody that exhibits an effect of preventing or treating these diseases, Alzheimer's disease (AD), or frontotemporal lobar degeneration (FTLD). A human monoclonal antibody that specifically binds to human HMGB1, wherein the human monoclonal antibody (anti-human HMGB1 antibody) comprises a heavy chain CDR1, heavy chain CDR2, and heavy chain CDR3 each consisting of a specific amino acid sequence and a light chain CDR1, light chain CDR2, and light chain CDR3 each consisting of a specific amino acid sequence, is used as an agent for preventing or treating ALS, PD, HD, SCA, aging-related degenerative or neurological disease, brain aging, or diseases associated with brain aging. An antibody in which the light chain complementarity determining region (CDR) 3 of the anti-human HMGB1 antibody has been modified is used.

The method and application of constructing a multi-scale brain age prediction model based on magnetic resonance imaging (MRI) have enhanced generalization and robustness, maintained high prediction accuracy, and can predict brain age at the whole brain, subnetwork, and voxel levels, making the predicted brain age more interpretable in a physiological sense. The method explores the differences in brain age between different subnetworks and the specific patterns of their PAD and cognitive associations, finds specific subnetworks that regulate cognition, and observes the differential patterns of aging in different brain regions at the voxel level. The model's prediction performance is superior to the current mainstream neural network model. The method includes: (1) data collection; (2) data preprocessing; (3) constructing a whole brain and functional subnetwork brain age prediction model based on the simple fully convolutional neural network (SFCN) method; (4) constructing a voxel-level brain age prediction model based on the ScaledDense U-Net method; and (5) bias correction for brain age deviation.

The invention discloses a lactobacillus reuteri source extracellular vesicle for preventing and relieving senescence syndrome by relieving immune cell senescence, and belongs to the technical field of microorganisms and the technical field of medicines. The extracellular vesicle provided by the invention can improve learning, memory and cognitive abilities; the muscle strength capability is enhanced; the exercise coordination ability is improved; the motion balance capability is enhanced; the blood inflammatory factor level is obviously reduced, and the anti-inflammatory factor level is improved; the enzyme activity of the senescence-related enzyme SA-beta-Gal in the brain tissue is obviously reduced; the expression levels of brain-derived neurotrophic factors and neurotransmitters are obviously up-regulated; the collagen content and the collagen synthesis capability in skin tissues are obviously improved. The lactobacillus reuteri CCFM1471 source extracellular vesicles do not generate toxic and side effects in a host body, can be used for preparing functional foods and / or dietary supplements and related beauty products with the effects of preventing and relieving senescence syndromes, and have huge application prospects.

This application belongs to the field of fetal brain age prediction technology, and relates to a method and system for predicting fetal brain age of the cerebellar vermis based on multimodal feature fusion. It employs the MST-Mamba segmentation network, and achieves synergy between local detail capture and global semantic modeling by embedding local-global aggregators at each level of the encoder. Simultaneously, a dynamic channel fusion unit is deployed at the jump connection between the encoder and decoder to avoid problems such as boundary ambiguity, missed classification, and misclassification. Through three parallel branches of a multi-granularity morphology-texture collaborative perception architecture, it simultaneously extracts two types of explicit features (macro-geometric and topological morphology) and two types of implicit features (micro-texture), achieving a comprehensive representation of the developmental features of the cerebellar vermis. After standardizing and calibrating the explicit features, they are spliced ​​and fused with the implicit features along the channel dimension to solve the problems of insufficient multimodal feature fusion and lack of calibration. Finally, prediction is performed using a multilayer perceptron regression head, ensuring the accuracy and stability of the brain age prediction results from the source.

A method of predicting brain age for a subject having major depressive disorder (MDD) comprises obtaining at least one medical image of a brain of a subject; producing a brain map based on the at least one medical image; segmenting the brain map into more than one brain regions; and calculating a brain age prediction of the subject based on a predetermined set of key features for each of the brain regions; wherein the subject has MDD.

The invention discloses application of an A2aR-GZMK inhibitor in preparation of an anti-aging product, and belongs to the technical field of biological medicines. The invention provides a treatment scheme for delaying brain senescence by targeting GZMK and adenosine-A2aR signal channels so as to improve body senescence for the first time. According to the present invention, the activity of GZMK is specifically inhibited by using a GZMK inhibitor (such as PPACK), and / or the immunosuppression signal of adenosine is blocked by using an adenosine A2a receptor inhibitor (such as Istradex), such that the accumulation and the activation of Gzmk + CD8 T cells in the brain can be effectively reduced, and the senescence-related neuroinflammation can be reduced so as to improve the neuronal function and delay the brain senescence process. And no matter whether the activity of the GZMK is directly inhibited or the expression of the GZMK is indirectly regulated and controlled by targeting an upstream A2a receptor, a solid biological basis and a clinical safety basis are provided.

A method of predicting brain age for a subject having bipolar disorder (BD) comprises obtaining at least one medical image of a brain of a subject; producing a brain map based on the at least one medical image; segmenting the brain map into more than one brain regions; and calculating a brain age prediction of the subject based on a predetermined set of key features for each of the brain regions; wherein the subject has BD.

The application discloses an AD characteristic information analysis method based on brain medical images, which comprises the following steps: data preprocessing is performed on brain MRI images to be analyzed to obtain standard images of brain regions; spatial characteristic analysis of the whole brain and key brain regions; whole brain image characteristic analysis; cognitive characteristic analysis of the whole brain and key brain regions; and aggregation analysis results are obtained to form corresponding explanation data. The application further discloses an AD characteristic information analysis system based on brain medical images. The method for analyzing cognitive index characteristics such as brain age, logical memory score, visual memory score and long-time delay memory score from brain MRI images by using deep learning technology can provide analysis results of brain MRI images and other important medical indexes for doctors, and can provide effective arguments and explanations for subsequent scientific research analysis and interpretation.