512 results about "Brain region" 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 belongs to the field of artificial intelligence and electroencephalogram emotion recognition, and provides a double-branch electroencephalogram emotion recognition method and system based on brain region topology and time-space, and the method comprises the steps: preprocessing a to-be-recognized electroencephalogram signal to obtain a plurality of electroencephalogram fragments, and extracting a difference entropy sequence of each electroencephalogram fragment and a Spearman correlation coefficient matrix between channels; based on the Spearman correlation coefficient matrix, utilizing a bridging dynamic graph attention network module to extract topological features of a brain region; processing the differential entropy sequence by using a multi-scale space-time mixed attention module to obtain multi-scale space-time features; carrying out residual mutual cross attention fusion on the topological features of the brain region and the multi-scale spatial-temporal features to obtain fusion features; and performing classification based on the fusion features, and determining an emotion recognition result corresponding to the electroencephalogram signal. According to the method, the accuracy and robustness of emotion recognition are improved by utilizing the spatial topology characteristics and the multi-topology time dynamic characteristics of the electroencephalogram signals, and the defects of modeling spatial dependence and time dynamic are overcome.

The invention discloses a medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

The invention discloses an electroencephalogram epilepsy detection method and system based on a node adaptive graph neural network, relates to a computer system based on a biological model, and provides the scheme for solving the problems of graph structure immobilization and the like in the prior art. The method comprises the following steps: an electroencephalogram signal acquisition and preprocessing step; constructing a hybrid EEG graph; optimizing a self-adaptive residual image; node specific diffusion convolution is carried out; modeling time sequence characteristics; and performing classified output. The system comprises a data acquisition module, a mixed graph construction module, a self-adaptive mapping module, a node specific convolution module, a time sequence modeling module and a classification output module. When the system runs, the steps of the method are executed, so that the electroencephalogram epilepsy detection function based on the node adaptive graph neural network is realized. The method has the technical advantages that (1) graph structure self-learning is carried out; (2) carrying out brain region personalized modeling, and strengthening region feature expression; and (3) combining space-time dependence modeling, and completely depicting the epilepsy dynamic process.

The invention discloses an epilepsy abnormal brain network identification method based on a multi-scale static-dynamic fusion network, and belongs to the field of brain image analysis. The method comprises the following steps: firstly, constructing a static function connection weighted graph and a dynamic function connection graph; and fusing the static and dynamic representations by adopting a cross attention module. In order to describe a multi-scale spatial relationship, performing lexical meta-processing on brain connection according to anatomical partition and a functional network; and the local-global fusion module is used for integrating the fine granularity and the macroscopic relationship, so that the brain region with diagnostic significance is highlighted. In the training stage, cross entropy, reverse contrast loss and sparse regularization based on contrast graph adjacency matrix entropy are jointly used. The method is verified on multi-center functional magnetic resonance data, compared with other mainstream depth models, the classification accuracy, generalization and interpretability are remarkably improved, an abnormal brain region consistent with an epilepsy network can be positioned, and brain image markers with biological significance can be connected and recognized.

The invention discloses a brain heuristic multi-expert multi-mode emotion recognition method and system, equipment and a medium, and belongs to the technical field of artificial intelligence and biomedical signal processing. The method comprises the following steps: by simulating a brain function partitioning mechanism, dividing an electroencephalogram signal into a plurality of brain regions according to neuroanatomy prior, and designing a special expert network for each region; a global-local double-current encoder is adopted to cooperatively extract spatial-temporal characteristics of each brain region signal, and meanwhile, a multi-scale large-kernel convolution module is utilized to extract peripheral physiological signal characteristics; and finally, dynamically fusing multi-expert features through an adaptive routing network to realize sentiment classification. Expert load balancing and bifurcation regularization joint loss are introduced into the model in training, and effective cooperation and feature diversity of experts are ensured. According to the method, excellent recognition precision is obtained in practice, it is verified through interpretability analysis that the decision-making process conforms to neuroscience cognition, and a high-precision and high-reliability solution is provided for application of brain-computer interfaces, mental health monitoring and the like.

The invention discloses an electroencephalogram emotion recognition method based on multi-scale convolution and an attention mechanism. The method comprises the steps that firstly, original electroencephalogram signals are processed; secondly, extracting space and frequency features of the electroencephalogram signals by utilizing a feature pyramid network, and capturing multi-level information of local and global brain regions in a multi-scale convolution structure; a multi-scale attention aggregation module is further introduced, and brain region feature self-adaptive weighting is achieved through a parallel space and channel attention mechanism; secondly, global modeling and long-range dependence capture of time domain features are achieved through a Transform coding structure; and finally, outputting an emotion recognition result through a full-connection classifier. According to the method, the time domain, frequency domain and space domain features of the electroencephalogram signals can be extracted at the same time, efficient and accurate emotion classification is achieved, the robustness and universality of electroencephalogram emotion recognition are remarkably improved, and the method can be widely applied to the fields of intelligent human-computer interaction, mental health monitoring, emotion regulation and control and the like.

The invention belongs to the technical field of image processing, and relates to a deep learning framework for enhancing Alzheimer's disease classification, which comprises a convolutional backbone network for extracting multi-scale semantic features stage by stage and a convolutional feature extraction structure comprising five stages, and the convolutional feature extraction structure comprises a convolutional multilayer perceptron module; the interpretable expansion large convolution kernel convolution module is used for simultaneously capturing a local fine-grained spatial relationship and the importance of global position information and comprises a three-dimensional convolution structure used for extracting spatial enhancement features and a weight distribution mechanism used for focusing semantic information at a spatial block level; the graph attention enhancement module is used for modeling a topological dependency relationship between brain intervals and comprises a screening strategy, a multi-head attention mechanism, a graph convolution and a self-adaptive graph neural network; according to the method, the receptive field is expanded, the multi-scale features and the context information are fused, and the robustness is improved.

The invention relates to the technical field of visual search, in particular to a visual search experiment method for head fixing brain imaging. Comprising the following steps: fixing a target object on an experiment table, and training the target object through a display screen to obtain a trained target object; obtaining a target stimulus for an experiment and a target display strategy of the target stimulus in a display screen, the target stimulus being an icon for identification; and executing the target display strategy in the display screen, and obtaining behavioral statistical data and brain region microscopic imaging data of the trained target object based on the execution action of the trained target object. According to the method, the brain region microscopic imaging data of the trained target object can be obtained by executing the target display strategy corresponding to the target stimulation in the display screen, the display strategy of the target stimulation in the display screen is optimized, the motion noise interference of the target object is reduced, the brain region microscopic imaging data with higher quality is obtained, and the accuracy of the brain region microscopic imaging data is improved. And the comprehensiveness and the reliability of an experimental result are improved.

The invention provides a mood disorder assessment system based on multi-level feature fusion, and the system comprises a data collection unit which is used for collecting electroencephalogram signals of a plurality of brain regions of a to-be-assessed patient; the electroencephalogram feature extraction unit is used for extracting electroencephalogram features corresponding to the electroencephalogram signals of the brain regions; the multi-level feature extraction unit is constructed on the basis of the symptom features of the multiple testees and the corresponding electroencephalogram features, and is used for performing multi-level electroencephalogram feature latent variable extraction on the electroencephalogram features of the brain regions of the patient to be evaluated to obtain multi-level electroencephalogram feature latent variables; and the feature fusion and classification unit is used for carrying out classification prediction based on the electroencephalogram feature latent variables to obtain a mood disorder assessment result of the patient to be assessed. The method solves the problem that a mood disorder assessment system in the prior art adopts a single feature extraction and learning strategy and has no constraint of symptom information, so that the recognition capability of a model for mood disorders of different functional abnormality types is limited.

The invention discloses an electroencephalogram cap for meditation training and a control method thereof, and relates to the technical field of intelligent wearable equipment, the electroencephalogram cap comprises a cap body, an information acquisition module, a signal processing module, a wireless transmission module, a power supply module and a control host; the information acquisition module, the signal processing module, the wireless transmission module and the power supply module are respectively arranged on the helmet body, and the control host is respectively and electrically connected with the information acquisition module, the signal processing module, the wireless transmission module and the power supply module; the information acquisition module is used for acquiring microvolt-level electroencephalogram signals of a corresponding brain region and providing a data source for state recognition, the signal processing module is used for real-time denoising, power frequency interference suppression and artifact preliminary recognition, and the wireless transmission module is used for outputting processed electroencephalogram data streams in real time and transmitting the processed electroencephalogram data streams to the corresponding brain region. The power supply module supplies power to the information acquisition module, the signal processing module and the wireless transmission module. The electroencephalogram cap is convenient to wear, multi-dimensional analysis is achieved, feedback is timely, and the comfort level of mind calming training experience and the training effect can be improved.

The invention relates to the field of medical image analysis and neuropsychiatric disease diagnosis, and particularly discloses a brain network typing diagnosis system and method for attention deficit hyperactivity disorder. Comprising a data acquisition and preprocessing module, a topological feature extraction module, a supervised manifold learning module, a network reconstruction and diagnosis module, a parameter optimization module and a result verification module, continuous homology analysis is performed on a brain region function connection matrix based on an algebraic topology theory, and a brain region topological feature matrix is generated; utilizing supervised manifold learning to map the brain region topological feature matrix to a Riemannian manifold, and calculating a brain region importance weight map based on ADHD phenotypic features as supervised signals; according to the method, the brain network is subjected to subtype specific reconstruction through the curvature flow theory of Riemannian geometry, the topological difference between different subtypes is calculated, accurate typing diagnosis of ADHD is achieved, and the diagnosis accuracy is improved by 15%-20%.

The embodiment of the invention discloses an ultrasonic nerve regulation and control system. The system comprises an ultrasonic transducer array, a signal generation module and a power amplification module, wherein the signal generation module is used for determining a target nerve rhythm in response to a nerve regulation instruction for a target brain region, and determining a target ultrasonic parameter matched with the target nerve rhythm from each candidate ultrasonic parameter; the signal generation module is also used for generating an ultrasonic signal according to the target ultrasonic parameter; the power amplification module is used for amplifying the ultrasonic signal to obtain an amplified ultrasonic signal; and the ultrasonic transducer array is used for outputting ultrasonic waves to the target brain region according to the amplified ultrasonic signals so as to induce neural activity matched with the target neural rhythm in the target brain region. According to the technical scheme provided by the embodiment of the invention, the fineness of nerve regulation and control can be improved.

The invention relates to the technical field of medical large models, and discloses a feature propagation method and system of a brain region-gene network in a medical large model so as to improve the overall performance of the model. The method comprises the following steps: determining a point-edge neighborhood matrix, an edge-surface neighborhood matrix, an initial node feature information matrix, an initial edge feature information matrix and an initial surface feature information matrix of a brain region-gene network; then, the point-edge neighborhood matrix and the edge-surface neighborhood matrix are kept unchanged, and iteration processing is sequentially carried out on the node feature information matrix, the edge feature information matrix and the surface feature information matrix according to the total number of target iterations; the feature propagation process of single iteration comprises a point-edge and edge-surface dimension raising process and a reverse surface-edge and edge-point dimension reduction process; and finally, performing feature splicing and classification processing according to the node feature information matrix, the edge feature information matrix and the surface feature information matrix after iteration is terminated.

The invention belongs to the technical field related to data processing, and provides a brain network analysis method and system based on multi-network collaborative topology analysis in order to solve the problem that an analysis result is unstable due to threshold selection subjectivity in existing Alzheimer disease brain network analysis. Extracting brain regions corresponding to the default mode network, the significance network and the execution control network, calculating correlation coefficients among different brain regions to construct a function connection matrix, and converting the function connection matrix into a distance matrix meeting complex construction requirements; constructing a Vietories-Rips complex based on the distance matrix, calculating a coherence group under each scale of the filtering sequence, and extracting topological features of a 0-dimensional Betti number and a 1-dimensional Betti number; according to the method, the Betti number is extracted, a curve that the Betti number changes along with the threshold value is drawn, the difference of the two groups in topological characteristics is compared, the specific topological biomarker related to the Alzheimer's disease is identified, and richer biomarker information is provided for early diagnosis of the Alzheimer's disease.

The invention relates to an autism classification method based on a multi-scale residual image neural network, and aims to cope with the challenge of crowd autism classification in multi-modal medical data. The method comprises the following steps: firstly, providing a new function connection feature construction method, extracting second-order function connection features by using tangent Pearson embedding to capture a high-order interaction relationship between brain intervals, and then adopting a maximum independent domain to adaptively minimize statistical dependence between the features and acquisition sites, and combining F-score to screen the features with the most discriminative ability, so as to obtain the feature with the most discriminative ability. And redundancy is effectively removed. Secondly, a multi-modal edge weight calculation method fusing imaging information and non-imaging information is provided, so that noise interference is effectively suppressed while a key discriminant relation is reserved. And finally, expanding a node receptive field layer by layer by stacking multiple layers of Chebyshev convolutions with residual errors on the subject graph so as to capture multi-level relation characteristics, and performing weighted modeling and adaptive fusion on convolution output of each layer by using a multi-head self-attention mechanism, so that effective integration of multi-scale information is realized, and accurate classification of autism is realized. The method has excellent performance in the aspect of autism classification, and an innovative, feasible and effective solution is provided for solving the autism classification task in the multi-modal medical data.

The invention relates to an epilepsy electroencephalogram detection method adopting multi-modal feature fusion and an attention mechanism, and belongs to the technical field of medical signal processing and artificial intelligence. According to the method, automatic detection of epilepsy is realized through three core modules: firstly, preprocessing and time-frequency transformation are performed on electroencephalogram signals, a time-frequency graph is generated, and power spectrum characteristics of different frequency bands are extracted; the method comprises the steps that firstly, a time-frequency diagram and a function connection diagram are processed at the same time through a multi-modal feature combined extraction network, and finally fusion enhancement and classification decision making of multi-modal features are achieved through a self-adaptive feature fusion and classification network in combination with a learnable frequency band attention mechanism and a Transform encoder. According to the method, the time-frequency characteristics, the brain region connection relation and the long-range dependency relation of the electroencephalogram signals can be captured at the same time, the limitation of a traditional method in the aspects of feature expression and mode recognition is solved, and the accuracy of epilepsy detection is remarkably improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting automatically a machine learning model that locates region(s) of the brain of a subject that is / are associated with a clinically relevant outcome. One of the methods includes: receiving a brain image dataset of a subject; receiving, from a user, an indication of a patient outcome of interest; selecting, based on the indication of a patient outcome of interest, a model from a plurality of models to produce a selected model; determining brain data of interest for the patient outcome of interest; determining, using the selected model, subject specific brain data of interest based on the brain image dataset of the subject and on the brain data of interest; and taking an action based on the subject specific brain data of interest.

The invention discloses a light field reconstruction brain image brain region boundary extraction method based on convex hull fitting. The method comprises the steps that the p percentile of a brain image is calculated, and image cutting, contrast stretching and Gaussian filtering smooth denoising are carried out; carrying out Canny edge detection on the filtered image to obtain a binary edge image; boundary enhancement and connection are carried out through expansion and closed operation, all connected regions are extracted, and the contour with the maximum area is selected as the contour of the brain region boundary; a datum point is determined based on the contour of the brain region boundary, the polar angle and distance of each point except the datum point are calculated and sorted, and convex hull fitting is carried out through Graham Scan scanning based on the datum point and the sorted point set; converting the convex hull into a closed boundary region, and obtaining a mask image of the brain image after light field reconstruction through a mask function; according to the method, the special boundary form of the light field reconstruction image can be accurately adapted, deep learning and data training are not needed, and the generated mask has high geometric consistency.

The invention discloses a hand motion FNIRS signal classification method based on t distribution optimization feature extraction, and the method specifically comprises the steps: employing a portable functional near-infrared collection device to collect an optical density signal of a forehead cortex brain region during the motion execution period during the operation of the device; after pretreatment, the concentration of oxyhemoglobin HBO and the concentration of deoxidized hemoglobin HBR are calculated according to the corrected Beer-Lambert law; according to motion execution and resting state segmentation data, time domain features and time-frequency domain features of oxyhemoglobin concentration data signals of each channel of each experiment test are extracted, and a first feature matrix is formed; performing kernel density estimation and kurtosis and skewness test on the first feature matrix to obtain a second feature matrix, performing t distribution on each corresponding feature satisfying normal distribution in the second feature matrix of the motion execution and resting states, and optimizing feature selection on the basis to obtain an optimized third feature matrix; the optimized third feature matrix is used as classifier input, a brain-computer interface system model training result is obtained, and effective feature selection and extraction are achieved; according to the method, the brain-computer interface system feature extraction of the hand motion FNIRS signals can be optimized, so that the classification performance is improved.

The invention discloses a brain abnormal development trajectory identification method and system, and belongs to the technical field of magnetic resonance image analysis, and the method comprises the following steps: obtaining T1 weighted magnetic resonance images of a plurality of healthy individuals, and carrying out the offset correction of the structure index of each brain region in the images, a generalized additive position-scale-shape model of the healthy crowd is constructed; generating a norm development trajectory curve and a normal change range thereof; obtaining a T1 weighted magnetic resonance image of a to-be-evaluated individual, and selecting a target brain region of the to-be-evaluated individual; obtaining various offset corrected structure indexes of the target brain region of the individual to be evaluated; obtaining a mean value and a standard deviation of each structure index at the position of the brain region corresponding to the same-age healthy population of the individual to be evaluated; and generating a recognition report of the abnormal brain development trajectory of the to-be-evaluated individual. The problem that it is difficult to accurately, clearly and visually reflect the abnormal recognition condition of the brain trajectory of the individual to be evaluated and the position of the corresponding abnormal brain region is solved.

In embodiments, a method for configuring an intelligent agent to do a task based on spatial-temporal magnetic imaging data of the brain of a worker is disclosed. The method includes generating a brain region parameter indicating an active neocortex region associated with visual processing during performance of the task based on the spatial-temporal magnetic imaging data. The method further includes selecting a convolutional neural network (CNN) component type in response to a match between the brain region parameter and an associated CNN component type. The method includes configuring the intelligent agent based on the selected CNN component and a neocortical processing flow parameter derived using the spatial-temporal magnetic imaging data, wherein the intelligent agent is configured to process image data using the CNN component and provide an output of the CNN to another AI component via a data connection created based on the neocortical processing flow parameter.

The invention provides a method and system for evaluating the instant curative effect of acupuncture head acupoints on cerebral stroke cognitive impairment based on electroencephalogram signals, and the method comprises the steps: collecting the electroencephalogram signals before, during and after acupuncture in stages, and synchronously combining a MoCA / MMSE scale and a cognitive task test to obtain subjective curative effect data; an insulating acupuncture needle is adopted to reduce interference on electroencephalogram signals, and 4-8-conduction portable dry electrode electroencephalogram equipment is matched to collect signals in an area avoiding acupuncture points of the head, so that acupuncture operation and electroencephalogram collection are ensured not to influence each other. A condition vector is generated by using acupuncture point parameters and acupuncture manipulation, and a space-time Transform-GNN model is driven to dynamically focus alpha / theta / beta frequency band time sequence characteristics and a brain network connection mode of cognitive related brain regions such as a prefrontal lobe, a parietal lobe and a temporal lobe. According to the method, a multi-modal feature vector containing electroencephalogram frequency domain features, time domain components and clinical scale scores is constructed through an electroencephalogram-acupoint-curative effect triple data training model of at least 100 patients, and an obvious / effective / invalid level is output.

The invention relates to a driver brain-controlled vehicle intention recognition method and system based on neural mechanism guidance. Recording EEG signals of the driver under different vehicle control motor imagery tasks; analyzing the collected EEG data, and identifying a key cortex activation area related to vehicle control; deploying the positions of an fNIRS emission light source and a receiving detector, and obtaining hemodynamic signals; brain region activation analysis is carried out on the collected fNIRS signals, the difference response condition of a key cortex activation region in a motor imagery task is verified, and a motor imagery data set about driver vehicle control is made; constructing a deep learning model, automatically extracting time domain, frequency domain, space and time sequence characteristics of the fNIRS signal, and training the model; the motor imagery brain signals are classified, and the vehicle control intention of the driver is output; according to the method, the physiological interpretability and the model structure rationality of the brain-controlled vehicle system are remarkably improved, and the problem of insufficient generalization caused by blind modeling is avoided.

The invention discloses application of an Hspa5 inhibitor in preparation of a medicine for preventing or treating anxiety disorder. An anxiety mouse model is constructed through chronic constraint stress (CRS), and in combination with medial amygdala kernel (MeA) transcriptome sequencing and qPCR verification, it is found that the endoplasmic reticulum molecular chaperone Hspa5 is remarkably up-regulated in the anxiety state. Furthermore, an Hspa5 specific inhibitor HA15 is locally injected into a MeA brain region, so that the anxiety-like behavior induced by the CRS is remarkably improved, and the exploration time of an open field experiment central region, the exploration time of an open arm of an elevated cross labyrinth and the exploration time of a bright box of a bright-dark box experiment are prolonged. The invention discloses the function of Hspa5 as a novel anti-anxiety target for the first time, and provides a direct experimental basis and a transformation direction for developing a novel anti-anxiety drug which is non-monoamine and targets an endoplasmic reticulum homeostasis.

The invention discloses a brain development characteristic evolution analysis system and method based on multi-mode brain image data. The system comprises a brain development map construction module, a data acquisition module, a data processing module and an evolution matching evaluation module. The method comprises the following steps: collecting demographic information and multi-modal brain image data of multi-age healthy people, constructing a structure, and connecting and activating a three-dimensional group brain development map; the method comprises the following steps: processing individual data by adopting the same process, obtaining a multi-modal brain feature vector in a unified brain region space, mapping the multi-modal brain feature vector to a corresponding group map space, calculating a deviation amount and combining a stability weight to obtain a fusion deviation index, identifying an abnormal brain region according to the fusion deviation index, calculating an abnormal proportion and a risk change rate, and realizing brain development feature evolution analysis. According to the method, group evolution priori and individual multi-modal data are fused, brain development can be dynamically and accurately evaluated, and the problems of static detection, group individual disjunction, insufficient multi-modal fusion and the like are solved.

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 discloses an emotion intervention system and device based on a reminiscence therapy, and belongs to the technical field of mental health intervention and intelligent medical equipment. The device comprises a stimulation presentation subsystem, a physiological signal collection subsystem, a behavior response recognition subsystem, a neural response modeling subsystem, an intervention effect evaluation subsystem and a central control and feedback regulation subsystem, a closed-loop intervention framework is formed through real-time communication bus interconnection, and by presenting an individualized life event image set, multi-modal physiological signals and behavior responses are synchronously collected, so that the intervention effect is evaluated. A neural response model is constructed based on a brain region abnormal activation mode disclosed by functional magnetic resonance imaging, the activation level of a target brain region is predicted, the intervention effect is evaluated by integrating multiple indexes, and a central control system dynamically adjusts a stimulation sequence according to an evaluation result and a neural response predicted value by applying a hierarchical reinforcement learning algorithm. Personalized and precise emotion intervention aiming at the subclinical depression state is realized, and the intervention effect and the nerve regulation pertinence are effectively improved.

The invention relates to the technical field of psychiatric disease diagnosis, and discloses a defective schizophrenia prediction and analysis system based on multi-modal brain network characteristics, which comprises an image data acquisition module, an image data processing module, a deep learning model module and a prediction module which are connected in sequence, the image data acquisition module acquires a brain MRI image; the image data processing module carries out cortical and subcortical reconstruction on the brain MRI image to obtain a model input index; the deep learning model module performs training by using a training data set composed of a plurality of brain MRI images and defective schizophrenia diagnosis results thereof to obtain a target prediction model; and the prediction module inputs the real-time brain MRI image into the target prediction model to obtain a prediction result. According to the method, the multi-dimensional brain structure image features are integrated, diagnosis information contained in brain region changes is fully mined, prediction results of defective schizophrenia and non-defective schizophrenia are improved, and a more comprehensive biological basis is provided for clinical diagnosis.

The application provides a multi-modal brain network classification method based on feature decoupling and dynamic graph construction, and relates to the technical field of artificial intelligence, and the method comprises the following steps: acquiring functional magnetic resonance imaging (fMRI) data and structural magnetic resonance imaging (sMRI) data of a to-be-tested person, obtaining multi-modal brain network data according to the fMRI data and the sMRI data, processing the multi-modal brain network data based on a preset multi-modal brain network classification model, and obtaining the brain network state of the to-be-tested person. The application can fully utilize the advantages of the two modalities by combining the fMRI data and the sMRI data, thereby improving the accuracy and comprehensiveness of the brain network state classification, and the dynamic graph attention module based on a graph attention network (GAT) can capture the dynamic change characteristics of the brain network, construct a dynamic graph representation, and enhance the sensitivity of the model to the dynamic connection relationship between brain regions.