29 results about "Functional magnetic resonance imaging" patented technology

A magnetic resonance-compatible programmable synchronized photostimulation system includes a main control unit and a stimulation execution unit. The main control unit is located outside the magnetic resonance scanning room, while the stimulation execution unit is located inside the scanning room and consists only of an LED light panel shielded by a magnetic field. The main control unit is configured to: receive photostimulation sequence parameters configured by the user in a modular manner through a graphical interface; receive a scan trigger signal from the magnetic resonance equipment, and automatically control the start and stop of the photostimulation sequence based on the trigger signal using hardware triggering; thereby achieving high-precision time synchronization between photostimulation and magnetic resonance scanning while maintaining magnetic resonance compatibility. This invention solves electromagnetic compatibility issues through a split architecture, achieves millisecond-level synchronization accuracy through hardware TTL triggering, and enhances experimental paradigm flexibility through modular programmable sequences, making it suitable for neuroscience research fields such as functional magnetic resonance imaging.

The embodiment of the application discloses a kind of Alzheimer's disease classification and key brain area determination method based on random attention and counterfactual contrast learning, it is related to medical image analysis technical field;Alzheimer's disease classification and key brain area are determined by the functional magnetic resonance imaging data of subject;The accuracy, overall performance and stability of the graph convolution network model are significantly better than the existing GNN baseline model in the AD vs.NC task of ADNI real data set;The output key brain area node is highly consistent with clinical medicine priori;Specific brain area and functional connection leading to abnormal classification can be intuitively presented;It conforms to the real pathological mechanism, and whether the verification model in the same framework is really highly dependent on the selected key brain area structure, so that the explanation result can be accepted by clinician in the medical scene with extremely high safety requirement.

This invention provides a method and system for predicting brain diseases by fusing amplitude and phase information with graph-aware hybrid expert graph neural networks. It relates to the field of neuroimaging analysis technology. The method includes: acquiring raw resting-state functional magnetic resonance imaging (fMRI) data of a subject and corresponding brain disease category labels; preprocessing the raw resting-state fMRI data; extracting the mean oxygenation level dependent signal time series of multiple brain regions of the subject and using the mean oxygenation level dependent signal time series as the signal time series; constructing a functional connectivity matrix and a phase adjacency matrix based on the signal time series; constructing a training dataset; constructing a brain disease prediction model based on a two-branch hybrid expert graph neural network; training the brain disease prediction model using the training dataset; acquiring resting-state fMRI data; inputting the resting-state fMRI data into the trained brain disease prediction model for prediction, and outputting the brain disease prediction result.

This invention discloses a brain disease classification and prediction method and system based on a multimodal topological sensing graph network, relating to the field of medical image processing technology. By fusing temporal signals from functional magnetic resonance imaging (fMRI) and spatial structure signals from diffusion tensor imaging (DTI), a spatial structure prior matrix is ​​introduced as a topology modulator to impose physical connectivity constraints on the constructed multi-view functional interaction network. Furthermore, a spatiotemporally coupled global fusion graph is generated through a cross-modal attention mechanism. Graph neural networks are used to extract local nodes, mesoscale subnetworks, and global topological statistical features to construct a global state feature vector. Finally, a two-stage optimization learning framework is employed, combining multi-objective self-supervised pre-training and supervised fine-tuning to improve the model's classification performance and robustness under small sample conditions. This invention effectively solves the problems of functional connectivity redundancy, lack of structural constraints, and loss of topological information in traditional methods, significantly improving the accuracy and interpretability of brain disease classification.

The present disclosure provides an AI-based clinical decision support system comprising an input module configured to receive clinical information comprising brain scan data, an analysis module configured to parse the clinical information using statistical measures from functional connectivity analysis with counterfactual explanations to identify brain connectivity patterns associated with brain disorders, and an output module configured to present a recommended diagnosis and explanation comprising attribution information identifying connectivity features contributing to the diagnosis. The brain scan data comprises functional magnetic resonance imaging, electroencephalography, and magnetoencephalography data. The statistical measures comprise functional connectivity analysis and graph theory metrics including degree metrics, betweenness centrality measures, and clustering coefficients. The analysis module comprises a functional connectivity engine configured to process brain scan data and generate connectivity features, a feature bank configured to store connectivity features, and modeling backbones configured to analyze connectivity features using machine learning techniques.

This application relates to the fields of medical image processing and AI-assisted diagnosis, and specifically to a collaborative perception method based on multi-scale brain network features. The method includes: acquiring resting-state functional magnetic resonance imaging (fMRI) data of a subject and constructing a brain functional connectivity matrix after preprocessing; collaboratively extracting multi-scale brain network features from the brain functional connectivity matrix through parallel global and local perception flows; fusing the global and local feature representations across scales to generate a collaborative feature representation; and outputting classification results based on the collaborative feature representation using a classifier. This method can address the technical problems of existing single-scale models, such as high risk of missed diagnoses due to perceptual blind spots and insufficient discriminative power for complex pathological patterns, thereby improving the overall classification performance of the model.

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.

This invention provides a multimodal signal fusion method for brain-computer interfaces, belonging to the field of data processing technology. Specifically, it includes: acquiring electroencephalogram (EEG) signals, functional magnetic resonance imaging (fMRI) signals, and blood test data; performing time alignment based on a unified time reference; performing modality-specific preprocessing and quality control; constructing a five-level brain topology hierarchy based on brain physiological priors to achieve spatial-physiological alignment of electrode space, brain region space, and biochemical indicators; extracting multi-scale features of each modality based on the five-level brain topology hierarchy; projecting the multi-scale features onto a unified semantic space and achieving semantic alignment through cross-modal comparative learning based on physiological perception; constructing a physiologically guided cross-modal hierarchical fusion network to fuse features of each modality layer by layer from coarse to fine scales, and performing global aggregation based on physiological weights to generate multimodal fusion features. This invention improves fusion efficiency, accuracy, and adaptability.

This invention discloses a method and system for autism diagnosis based on interpretable brain graph neural networks (GCNs) with multi-teacher distillation in the field of medical image processing technology. The method includes: preprocessing functional magnetic resonance imaging (fMRI) data of subjects in a resting state to obtain time-series data of blood oxygenation level-dependent signals in various brain regions of interest (ROIs); constructing a brain network BN matrix by calculating the Pearson correlation coefficient between any two ROIs; constructing a brain network graph based on the BN matrix; inputting the brain network graph into a trained autism diagnosis model for autism diagnosis to obtain a raw score for the predicted category; and converting the raw score for the predicted category into a diagnostic result. This invention uses multiple GCN teacher models to capture BN topological knowledge at different scales to guide the brain graph neural network, and combines an attention mechanism to adaptively fuse information from different subgraphs, enhancing the model's representation learning ability and effectively achieving autism diagnosis.

This invention discloses a non-invasive identification model for endometriosis staging based on brain functional connectivity. By integrating brain functional connectivity phenotypes derived from resting-state functional magnetic resonance imaging (fMRI) data with endometriosis genetic data, and through site matching, screening of effective genetic instrument variables, and analysis of staging-related features, the method accurately identifies staging-specific brain functional connectivity markers, achieving non-invasive and accurate identification of EMs staging. This enables non-invasive auxiliary identification of EMs staging and preoperative risk stratification assessment, reducing the impact of confounding factors and reverse causality on staging judgment, and providing quantifiable central nervous system indicators for clinical preoperative staging.

The application provides a multi-modal covariant network discrimination method fusing electroencephalogram and functional magnetic resonance imaging, aims to reveal specific brain function connection modes corresponding to different motor imagery tasks, and predict motor imagery ability of subjects. The method realizes deep fusion and complementary representation of multi-source neural signals by jointly extracting degree centrality, betweenness centrality, closeness centrality and eigenvector centrality of EEG and fMRI, and constructing a cross-modal covariant network by calculating a Pearson correlation coefficient. The method effectively identifies potential brain function connection modes corresponding to different instructions of motor imagery, selects significant edges based on the correlation between the function connection matrix characteristics and the accuracy of the subjects, uses LASSO (Lasso) regression for leave-one-out prediction, and outputs the classification accuracy and key connection modes of the model under the optimal parameters, so as to realize accurate prediction of different motor imagery tasks. The method provides a new multi-modal neural information fusion strategy for brain-computer interface, neural rehabilitation and clinical function reconstruction, and has important application prospect and academic research value.

This invention relates to the field of medical image processing technology, specifically a method for constructing a brain functional network based on a large language model. First, the functional magnetic resonance imaging (fMRI) data of the subject is preprocessed, brain regions are divided based on a pre-defined atlas, and average time series data are extracted. Then, a large language model is used, referencing a neuroscience knowledge base, to generate prior knowledge text descriptions for each brain region. Simultaneously, instance-level text descriptions are generated based on the activation state and correlation of the time series data. The prior knowledge text and instance-level text are fused and converted into node feature vectors using a professional text encoder. Furthermore, this method constructs a brain map containing node features and edge connections, which is then input into a graph neural network for feature learning and classification training. By deeply integrating prior textual knowledge from the medical field with image data, a brain functional network rich in semantic information is constructed, significantly improving the accuracy of brain disease identification and the model's generalization ability.

This invention discloses a method for identifying neuropsychiatric disorders based on resting-state functional magnetic resonance imaging (fMRI). The method includes the following steps: acquiring an fMRI training dataset; selecting a set of data from the fMRI training dataset as the first data; preprocessing the first data to obtain preprocessed fMRI data; inputting the preprocessed fMRI data into a random inactivation training model to obtain fMRI features and fMRI features for each viewpoint; inputting the fMRI features into a neuropsychiatric disorder identification model to obtain predicted neuropsychiatric disorder results; calculating the total loss value and optimizing the random inactivation training model and the neuropsychiatric disorder identification model; selecting another set of data and repeating the process until preset conditions are met; obtaining a trained neuropsychiatric disorder identification model; acquiring fMRI data to be identified; preprocessing the fMRI data to be identified to obtain preprocessed fMRI data to be identified; inputting the preprocessed fMRI data to be identified into the neuropsychiatric disorder identification model to obtain the final neuropsychiatric disorder result. This invention features high identification accuracy and strong stability of the identification results.

The application discloses a brain representative functional tissue exploration method based on multi-view function gradient density, comprising the following steps: obtaining resting-state functional magnetic resonance imaging data and preprocessing to obtain standardized resting-state image data; processing the standardized resting-state image data based on resting-state functional connectivity to generate a function gradient density map; adopting a spherical wavelet transform method to perform multi-scale decomposition on the function gradient density map to obtain view data corresponding to multiple spatial frequency scales; calculating cross-individual differences of function gradient density features of different individuals under each view based on a Wasserstein distance, and constructing a similarity matrix corresponding to each view; adopting a multi-view spectral clustering method to perform fusion analysis on the similarity matrix to identify a brain representative functional tissue mode; and visually displaying the representative functional tissue mode; and fully considering the cerebral cortex geometric structure and multi-scale functional features, realizing stable identification and individual difference analysis of the brain functional tissue mode.

This invention proposes a diagnostic method for autism spectrum disorder (ASD) based on adaptive fusion of multiple graph Transformers, targeting resting-state functional magnetic resonance imaging (rs-fMRI) brain network analysis scenarios. This method constructs a hybrid GCN-Transformer architecture, simultaneously modeling local topological features and global long-range dependencies in the brain network, and introduces bidirectional attention and a two-stream adaptive fusion mechanism to dynamically integrate complementary information from multiple functional connectivity patterns, such as Pearson correlation, sparse representation, and Granger causality, achieving efficient identification of complex neuropathological features of ASD. Experimental results show that the method achieves a classification accuracy of 94.7% on the ABIDE public dataset, a 3.6% improvement over existing state-of-the-art methods. Its innovation lies in: the first application of hybrid GCN-Transformers to brain network analysis, the proposal of a data-driven dynamic fusion strategy to replace traditional static fusion, and the systematic integration of multiple brain connectivity patterns, providing a more comprehensive and effective solution for intelligent diagnosis of brain diseases.

This invention discloses a brain network representation learning method and system based on multi-view diffusion. First, a shared adjacency matrix is ​​constructed from the same resting-state functional magnetic resonance imaging (fMRI) data based on the Pearson correlation coefficient. Under this shared topology, both the FC (front-end view) and LA (back-end view) views are built. Second, the commonality strength scores of node features in the FC and LA views are calculated. Singular value decomposition is used to identify and discard common noise features across subjects. Then, intra-view topology enhancement and inter-view feature propagation are performed synchronously on a unified graph topology, and information interaction is achieved through a cross-attention mechanism. Finally, the dual-view representations are adaptively fused using an attention mechanism to complete disease diagnosis. This invention solves the problems of incomplete brain functional state representation and insufficient information utilization caused by the reliance on a single perspective in existing brain network analysis methods. It can effectively suppress noise and significantly improve the classification performance of brain diseases in complex scenarios such as class imbalance.

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

Disclosed is a method for determining a treatment to be applied to a brain of a patient, such as determining a transcranial magnetic stimulation (TMS) treatment location(s) and treatment strength to be applied to a brain of a patient suffering from depression. A search of the brain, based on coarse-scale parcellations, treatment metric and one or more anatomical metrics, is conducted to select the treatment location(s). In one embodiment, this involves progressively varying a value of a parameter associated with a gyrus threshold and for each value, identifying candidate treatment locations based on a functional connectivity of a subgenual anterior cingulate cortex during resting-state functional magnetic resonance imaging (fMRI) recorded over a plurality of multimodal brain imaging sessions, and selecting as the respective treatment location the candidate location that is closest to all other candidate treatment locations.

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

This invention relates to an automatic identification method for mild cognitive impairment based on heterogeneous graph neural networks. The method includes the following steps: S1, acquiring resting-state functional magnetic resonance imaging (fMRI) images and diffusion tensor imaging (DTI) images, constructing a heterogeneous graph based on the images, and calculating node features; S2, constructing adjacency matrices for four types of meta-paths based on structure-function coupling common community search, obtaining the adjacency matrix and node feature matrix of the heterogeneous graph; S3, reconstructing the meta-paths to form a new heterogeneous graph; S4, inputting the heterogeneous graph, the new heterogeneous graph, and their corresponding adjacency matrix and node feature matrix into a mild cognitive impairment identification model, and the mild cognitive impairment identification model outputs the identification result. Compared with the prior art, this invention has the advantages of fully considering the heterogeneity of the heterogeneous graph in the neural network and improving the classification performance of the mild cognitive impairment identification model.

The present application relates to the technical field of medical devices and neuromodulation, and discloses an anxiety disorder transcranial electrical stimulation intervention system and method based on functional magnetic resonance, which comprises: a brain function image analysis module, which is used for acquiring functional magnetic resonance imaging data of a subject when performing a threatening stimulus attention bias task, extracting BOLD time series of the dorsolateral prefrontal cortex and the amygdala, calculating effective connection strength values from the dorsolateral prefrontal cortex to the amygdala, functional connection values between the dorsolateral prefrontal cortex and the amygdala, recording behavioral response time indicators of the subject to the threatening stimulus, and generating an individualized stimulation scheme according to the above three indicators through a dynamic grading control algorithm; a transcranial direct current stimulation intervention module, which applies electrical stimulation to the target brain area according to the individualized stimulation scheme; and a central control and data processing module, which is connected with the above two modules respectively and is used for coordinating the work flow. The present application improves the accuracy and individualization level of intervention.

The invention discloses a multi-modal brain function connection brain tumor postoperative post-traumatic stress disorder classification system and method. The system comprises a multi-modal data acquisition module, a multi-map brain network construction module, a multi-kernel map convolution feature extraction module, a dynamic prediction model construction module and an interpretability visualization module. Structural magnetic resonance, functional magnetic resonance and electroencephalogram multi-modal data are integrated, a functional connection network is constructed based on multi-graph division, and multi-scale features are extracted by using a multi-kernel graph convolutional network, so that high-precision PTSD classification and prediction are realized. The problems that existing brain tumor postoperative PTSD diagnosis depends on subjective evaluation and lacks objective biological markers are solved, an integrated solution of early recognition, individualized diagnosis and interpretable analysis is provided, and the diagnosis precision and clinical effectiveness of brain tumor postoperative mental complications are remarkably improved.

A metabolic imaging method based on 19F-MRI, comprising the steps of:administering one or more 3-fluoro-3-deoxy-sugar (s) to a subject; applying 19F-magnetic resonance imaging (19F-MRI) to identify generation, localization and / or distribution of one or more fluorinated compounds in internal organs of the subject; andassessing and / or diagnosing and / or monitoring a disease state associated with a pattern of abnormal activity of oxidizing and reducing metabolic enzymes.

This invention relates to the fields of medical devices and neuromodulation technology, and discloses a transcranial electrical stimulation intervention system and method for anxiety disorders based on functional magnetic resonance imaging (fMRI). The system includes: a brain functional imaging analysis module, used to acquire fMRI data of subjects performing a task involving attentional bias towards threatening stimuli, extracting BOLD time series of the dorsolateral prefrontal cortex and amygdala, calculating the effective connectivity strength value from the dorsolateral prefrontal cortex to the amygdala, the functional connectivity value between the dorsolateral prefrontal cortex and the amygdala, recording the subject's behavioral response time index to threatening stimuli, and generating an individualized stimulation plan based on the above three indicators using a dynamic hierarchical control algorithm; a transcranial direct current stimulation intervention module, which applies electrical stimulation to the target brain region according to the individualized stimulation plan; and a central control and data processing module, connected to the first two modules respectively, for coordinating the workflow. This invention improves the accuracy and individualization of intervention.

The present invention discloses a data classification method and system based on a deep multi-path attention adaptive graph convolutional network, belonging to the field of medical image processing technology. It involves acquiring and preprocessing rs-fMRI data to obtain BOLD sequences, constructing functional connectivity feature vectors as input to the DMAGCN model, and finally obtaining the optimal model for classification through five-fold cross-validation. The present invention ensures data quality through data preprocessing, laying the foundation for accurate analysis. The functional connectivity feature vectors effectively represent the data, and after inputting them into the model, the Transformer backbone network and MLP branch network can extract multi-source domain features. Combining this with the graph network utilizing non-imaging data, the model can learn rich features, enhancing its generalization and adaptability.

This invention relates to a brain region localization magnetic resonance imaging (fMRI) data processing method based on neural stimulation target optimization. It acquires functional magnetic resonance imaging (fMRI) data from subjects, extracts signal response sequences of each brain region before and after neural stimulation through time-domain analysis, and identifies brain regions with characteristic response time windows based on differences in response delay. Based on the neural feedback relationship between the cortex and deep structures, it detects delayed oscillation features in cortical signals to identify potential regulatory pathways in the thalamus and other deep brain regions, thereby locating indirect stimulation targets that cannot be directly imaged. It performs decoupling analysis of cerebral blood flow signals and neural activation signals on candidate brain regions; when blood flow fluctuations are detected in a region but synchronous neural activation is lacking, it is identified as a spurious activation area and excluded. This invention calculates the time-delay correlation between the stimulation sequence and brain region signals using an improved cross-correlation integral function, and combines a nonlinear attenuation term to suppress high-frequency noise interference, making the estimation of brain region response delay more stable and repeatable.

This invention discloses a transcranial magnetic stimulation (TMS) target localization system and method for sleep disorders based on the functional connectivity of the dorsomedial nucleus of the thalamus. The system comprises a data acquisition module that acquires structural magnetic resonance imaging (SMRI) images and resting-state functional magnetic resonance imaging (fMRI) images of the subject; an image preprocessing module that preprocesses the image data; a seed point definition module that segments the dorsomedial nucleus of the thalamus as a seed point in the subject's brain space; a search space definition module that defines the dorsolateral prefrontal cortex as the target search region in the subject's cerebral cortex; a functional connectivity calculation module that extracts the average time series of the seed point and calculates the correlation coefficient between the average time series and the time series of each voxel in the target search region to construct a functional connectivity map; and a target determination module that identifies the voxel coordinates with the strongest functional connectivity to the seed point in the functional connectivity map as the stimulation target for repetitive transcranial magnetic stimulation. This invention uses the dorsomedial nucleus of the thalamus as an anchor point, which is more consistent with the neuropathological mechanism of insomnia and provides stronger mechanism targeting.

The present invention provides a brain information display device and a brain information display method that can generate a hologram as a three-dimensional image showing brain information that forms a three-dimensional curved surface from brain image information, etc. [Solution] The brain information display device 1 comprises a blade, an LED device arranged on the blade, a rotation drive unit, and a control unit 20. The control unit 20 includes an information acquisition unit 41 that acquires brain image information, video information, and / or functional magnetic resonance imaging information obtained by functional magnetic resonance imaging; a coordinate information generation unit 42 that generates three-dimensional coordinate information of the brain information displayed in the image information etc. acquired by the information acquisition unit; a polar coordinate conversion unit 43 that converts the three-dimensional coordinate information generated by the coordinate information generation unit into polar coordinates for a blade-type hologram display; and a hologram generation unit 44 that controls the blade and LED device to generate a hologram as a three-dimensional image showing brain information.

The application belongs to the technical field of image processing, and provides a brain function magnetic resonance imaging head motion correction method, device, equipment and medium, wherein the method comprises: acquiring a brain function magnetic resonance image, processing the brain function magnetic resonance image through a head motion correction model to obtain a head motion correction result; wherein the head motion correction model generates a target deformation image through a smoothing deformation field, and then performs sampling and time sequence enhancement processing to obtain a training sample, adopts a visual cortex-like topological feature space information distribution mechanism and a prefrontal cortex-like working memory characteristic time sequence information coherence mechanism to perform spatial perception logic and time sequence context association on the training sample, and adopts a bidirectional optical flow network and a back-like attention network to select a characteristic weight adaptive distribution mechanism, and performs self-supervised fine-tuning on real data. Through the above scheme, the precision and robustness of brain function magnetic resonance imaging head motion correction are improved.