117 results about "Neural imaging" patented technology

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

The invention discloses a depression recurrence risk intervention method, device and equipment and a medium, and belongs to the technical field of medical treatment. The method comprises the following steps: integrating a gene risk score, neuroimaging brain region characteristics and clinical medical history data through a multi-modal fusion neural network, and generating an individual baseline risk score; the baseline score is dynamically corrected based on the self-assessment data, and the real-time performance of risk assessment is enhanced; analyzing time sequence characteristics of the physiological and behavior data by using an LSTM time sequence model, and outputting a short-term recurrence early warning label; and in combination with emotion knowledge graph analysis and dynamic risk grading of real-time voice / text data, a hierarchical intervention strategy is triggered. Through multi-dimensional data fusion and a dynamic calibration mechanism, the problems that a traditional method is single in evaluation dimension and lags in response are solved, closed-loop management from risk early warning to accurate intervention is achieved on the premise that direct clinical diagnosis is avoided, and comprehensiveness and timeliness of prevention and control of depression recurrence are improved.

The invention discloses a multi-modal deep learning traceability method and system fusing magnetoencephalogram and electroencephalography, and relates to the technical field of artificial intelligence and neuroimage.Real magnetoencephalogram signals and electroencephalography signals are preprocessed and then input into a traceability model, the probability of occurrence of a source in a corresponding area is predicted, and the traceability of the source in the corresponding area is obtained by combining an imported source partition distance matrix. A final traceability result is obtained; the training process of the traceability model is as follows: constructing a generative adversarial network, and generating a multi-modal neural electrophysiological data set; inputting the multi-modal neural electrophysiological data set into a residual network of a double-branch structure, and performing stage hierarchical extraction and decoupling on magnetoencephalogram signals and electroencephalogram signals respectively; extracting features in different stages by using a multi-scale convolution module, fusing the extracted features, inputting the fused features into a classifier, defining a loss function, and updating trainable parameters of the traceability model; the traceability method improves the accuracy and generalization ability of traceability positioning.

The invention discloses a multi-mode autism diagnosis method and system based on resting state fMRI and phenotypic text information, a medium and a product. FMRI and phenotypic text information are processed through a pre-trained heterogeneous graph neural network model to output a diagnosis result; the resting state fMRI is utilized to construct a brain function connection graph, and the phenotypic text information is coded into a two-dimensional vector; respectively extracting brain connection feature embedding of the fMRI mode and phenotype information feature embedding of the text mode; fusing the feature embedding in the two modes through a gating fusion network; and constructing a heterogeneous group diagram based on the fusion features and phenotypic information similarity, and obtaining group diagram features on the heterogeneous group diagram by using dual-channel information aggregation and adaptive feature fusion to output a diagnosis result. The invention aims to improve the accuracy of autism diagnosis by using multi-modal information, and can be applied to the fields of neuroimaging analysis, intelligent medical treatment and the like.

The invention provides a brain region grey matter layering method, device and equipment based on diffusion magnetic resonance imaging, and can be applied to the technical field of neural image calculation. The method comprises the following steps: acquiring diffusion magnetic resonance imaging data corresponding to a target brain area of a target object; processing the signal feature data by using an encoder to obtain encoding feature vectors corresponding to the N grey matter areas respectively; for the nth grey matter area of the N grey matter areas, clustering at least one voxel located on the nth grey matter area based on a preset clustering number k and the coding feature vector to obtain an nth clustering result; for the mth cluster of the k clusters, determining sorting information of the mth cluster according to the position information of voxels in the mth cluster, the position information of the first interface and the position information of the second interface; and sorting the k clustering clusters according to the sorting information of the k clustering clusters to obtain a brain region grey matter layering result for the target object.

The present invention provides a method for extracting a neuroimaging biomarker based on an interpretable ensemble three-dimensional convolutional neural network (3DCNN) to address limitations in the prior art. The present invention derives a novel neuroimaging biomarker P-score from prediction results obtained by an ensemble three-dimensional convolutional neural network model. The solution can help researchers to conduct studies on longitudinal trajectory changes of structural magnetic resonance imaging (sMRI) during the progression of Alzheimer's disease, and analyze an association of the longitudinal trajectory changes with neurodegenerative changes of Alzheimer's disease subjects. The extracted neuroimaging biomarker can provide a basis for predicting a sequence of intervention of brain regions in the neurodegenerative changes of Alzheimer's disease patients and upcoming clinical symptoms.

The invention relates to the technical field of image processing, and discloses a neural image data classification method and system based on machine learning, and a storage medium. The method comprises the steps of carrying out artifact removal processing on multi-modal neural image data through an N4 bias field correction algorithm to obtain a standardized data set, constructing a cross-modal feature fusion matrix to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into a three-dimensional residual convolutional network for deep learning to obtain discriminative feature representation, carrying out classification through a hierarchical integrated classifier to obtain a result, and carrying out classification through a three-dimensional neural network. And performing incremental learning on the newly added data to obtain an updated label. The technical problems that in an existing neural image data analysis technology, multi-modal data cannot be effectively fused, single feature representation discrimination is insufficient, deep network training is difficult, and continuous learning ability is lacked are solved.

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 invention provides a schizophrenia core epicenter region identification method based on a multi-mode nerve image, and belongs to the technical field of medical image analysis and neuropsychiatric disease diagnosis. According to the method, a macroscopic-mesoscopic-microscopic covariant network model is established by integrating multi-modal nerve image data (including structural magnetic resonance, functional magnetic resonance imaging and diffusion tensor imaging) and mesoscopic-microscopic scale data (gene expression atlas, neurotransmitter distribution and cell construction characteristics). The method comprises the following steps: firstly, constructing various connection networks, then identifying a core epicenter region with abnormal grey matter thickness based on a graph theory algorithm, carrying out cross-scale matching on the epicenter region and gene expression, neurotransmitter and cell structure characteristics by utilizing spatial correlation analysis, and analyzing a diffusion path of the epicenter region through connection omics. By fusing the multi-scale biomarkers, the limitation of single modal analysis is broken through, and systematic analysis of the schizophrenia pathological network is realized.

The invention belongs to the technical field of medical image processing and neural image analysis, and particularly discloses a cerebral apoplexy prognosis prediction method which comprises the following steps: step 1, performing whole cerebral vessel segmentation and morphological feature extraction on a CTA image to obtain 40 blood vessel morphological features; 2, extracting high-throughput omics characteristics from an infarction core and a half-dark band region in the DWI image, and obtaining 1026 image omics characteristics; 3, screening high-resolution features based on minimum absolute contraction and a selection operator, establishing a support vector machine classification model, and predicting a prognosis classification result of a three-month improved Rankin scale of the stroke patient; the deep learning network is used for automatically segmenting the whole brain blood vessel, so that subjective difference of manual recognition is reduced; starting from pathophysiology of occurrence and development of cerebral apoplexy, vascular morphological characteristics and infarction region imageomics characteristics are comprehensively incorporated to carry out prognosis prediction on cerebral apoplexy.

The invention provides an EEG function connection prediction method based on fMRI deep cross-modal representation learning, and relates to the crossing field of neuroiconography and artificial intelligence. According to the method, a sample pair is constructed through time alignment of EEG and a blood oxygen level dependent signal, an end-to-end deep neural network architecture composed of a time projection unit, a cross-attention fusion encoder and a connection synthesis head is designed, and direct mapping from a BOLD signal to EEG function connection is achieved. The model adopts a composite loss function, and gives consideration to the consistency of the prediction connection matrix with the real EEG function connection in numerical precision and topological mode, thereby achieving the high-fidelity reconstruction of the EEG function connection map at the frequency domain level. According to the invention, the topological structure stability of the brain function network can be effectively maintained. Under the conditions of incomplete EEG data, serious noise interference or complete loss, the frequency domain function connection characteristics can still be stably recovered, and a reliable analysis substitution path is provided for brain function network research.

The invention relates to the technical field of medical image processing, in particular to a multi-time-point neural image comparative analysis method and system.The method comprises the following steps that a first three-dimensional neural image matrix and a second three-dimensional neural image matrix are obtained, rigid transformation matrix registration is calculated, and a third three-dimensional neural image matrix is generated; constructing a three-dimensional elastic grid model and adjusting node coordinates based on a mutual information criterion; generating a three-dimensional displacement vector field; constructing a Jacobian matrix and calculating determinant values; executing natural logarithmic transformation to generate a microscopic volume change rate map; and calculating a logarithm Jacobian mean value and determining a volume change state. According to the method, pose deviation is eliminated by calculating a rigid transformation matrix, a vector field reflecting fine displacement is constructed by utilizing a mutual information criterion, voxel-level quantitative analysis is realized in combination with a Jacobian matrix and logarithmic transformation, and minimal lesion recognition precision and diagnosis efficiency are improved in cooperation with a standard brain anatomy template.

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

The invention belongs to the technical field of small animal mental disease diagnosis, and provides a neuroimaging-based small animal mental disease diagnosis system, which comprises an image data acquisition and preprocessing module for performing multi-modal imaging on a small animal by using high-precision neuroimaging equipment, acquiring neuroimaging data, preprocessing the acquired image data, and acquiring neuroimaging data; comprising the steps of denoising, registration, segmentation and the like so as to improve data quality; a feature extraction and analysis module; according to the invention, the resolution and the signal-to-noise ratio of the image data are improved through the multi-modal neural imaging technology, and a reliable data basis is provided for accurate diagnosis; image features are automatically extracted and analyzed through a deep learning algorithm, human intervention is reduced, and the accuracy and efficiency of diagnosis are improved; a personalized diagnosis model is constructed, individual differences of small animals are considered, and more accurate diagnosis is realized; through introduction of the cloud data management platform, centralized storage, sharing and cooperative analysis of data are realized.

The invention relates to a method for determining an individualized nerve regulation target spot, and the method comprises the steps: obtaining nerve image data, which are repeatedly measured for multiple times, of a subject and corresponding clinical psychological assessment data in a preset time period; calculating a brain activity index of each voxel in a preset brain region; establishing a plurality of candidate mathematical models to describe the relationship between the brain activity indexes and the clinical scores; selecting an optimal model through statistical test of fitting residual errors; generating a parameter weight map representing'symptom-activity 'association strength based on the optimal model; and finally, determining an individualized nerve regulation target according to the numerical distribution of the map. The invention also relates to a corresponding device, a processor and a computer readable storage medium thereof, and solves the problem of inaccurate target positioning caused by neglecting symptom heterogeneity and individual brain function difference in the prior art through a core methodology of ''longitudinal tracking-multi-model fitting-residual optimal selection''. The curative effect of nerve regulation and control treatment is obviously improved.

The invention relates to the technical field of medical health, and provides an AD early warning model construction method and system based on a multi-dimensional test, and the method comprises the steps: collecting nerve image data, biomarker data and clinical feature data of a plurality of subjects, carrying out the abnormal data screening of a nerve image data set, carrying out the data cleaning of a biomarker data set, and carrying out the data cleaning of the biomarker data set; carrying out data coding on the clinical feature data set, respectively carrying out feature extraction on the preprocessed neural image data set and the preprocessed biomarker data set to obtain a neural image feature set and a biomarker feature set, and constructing a multi-dimensional feature matrix set; and obtaining a weighted multi-dimensional feature matrix set by using the training set matrix subset and the test set matrix subset, training the clinical prediction model by using the weighted multi-dimensional feature matrix set to obtain an early warning experiment model, and testing the early warning experiment model to obtain an early warning model. The problems of low diagnosis precision and abnormal data interference of a single data source can be solved.

The invention discloses a neural signal extraction self-supervised learning method and system based on sparse decomposition, belongs to the technical field of deep learning computer image processing, and solves the problems that in an existing traditional neural signal extraction method, calculation time is long, and in an existing deep learning neural signal extraction method, calculation time is short. Signals need to be labeled manually; and the extraction accuracy is low. Comprising the following steps: obtaining a denoised neural microscopic image as a data set; constructing a sparse decomposition network, and training the sparse decomposition network by adopting a loss function; inputting the denoised neural microscopic image into the trained sparse decomposition network for reasoning to obtain a sparse part image representing neuron information; drawing an F / F image, performing threshold segmentation processing, and extracting spatial positions of neurons; according to the spatial positions of the neurons, F / F time sequence signals of each neuron are extracted, and the spatial position and starting and ending time of each neuron event are recorded; the method is suitable for a neural imaging technical scene.

The invention provides a neural image analysis method and system based on deep learning. The method comprises the following steps: receiving neural image original data; carrying out multi-modal feature collaborative extraction on the original data of the neural image to generate an initial feature map; inputting the initial characteristic spectrum into a deep characteristic fusion network to carry out enhanced recognition on the lesion area to obtain a lesion area characteristic spectrum; and performing three-dimensional topological structure analysis on the lesion area characteristic spectrum, and constructing a lesion area three-dimensional structure model. According to the method, the spatial form, the volume and the surface topological relation of the lesion area can be accurately represented, a concrete structural basis is provided for surgical path planning, and the reliability of surgical path planning is improved.

The invention relates to the technical field of neural images, and discloses a brain health level evaluation system and method based on neural image.The brain health level evaluation system comprises an imaging unit, a preprocessing unit, a segmentation unit, an analysis unit, a center unit, a treatment unit and an evaluation unit, the preprocessing unit preprocesses the neural image, the segmentation unit segments the preprocessed neural image, the central unit receives an area difference value and a graying deviation value and calculates a form value XT and an activity value HD, and the evaluation unit calculates an evaluation value P according to the form value XT and the activity value HD. According to the method, the generated nerve image is processed, the accuracy of the nerve image is ensured, the evaluation value P calculated by adopting the form value XT and the activity value H can accurately reflect the self condition of the examiner, and the smaller the evaluation value P is, the higher the brain health level is.

The invention relates to the technical field of artificial intelligence and computer vision, in particular to an Alzheimer's disease diagnosis method and system based on multiple views and a convolutional neural network and a storage medium. The method comprises the following steps: acquiring diffusion tensor imaging data, preprocessing the diffusion tensor imaging data to produce an FA image and an MD image, registering the FA image and the MD image to the same space, extracting voxel features, deep learning features, fiber features and radiomics features from the FA image and the MD image registered to the same space, and performing fusion and dimensionality reduction on the extracted features to obtain a fusion image; and the final classification result is determined by adopting integrated learning for classification, so that the biomarker of AD can be effectively identified, and comprehensive pathological insights are provided.

The invention discloses a sparse electroencephalogram signal source positioning method based on functional magnetic resonance guidance, and relates to the crossing field of electroencephalogram signal processing and neuroimaging technologies. The method mainly comprises three parts of fMRI-guided sparse source space construction, subject specificity forward model construction and multi-target regularization inverse problem solving. The method comprises the following steps: firstly, screening high-activation voxels through BOLD signal intensity of fMRI, and constructing a sparse source space through connected component analysis and representative point selection; then, a subject specific boundary element (BEM) head model is constructed based on the structural MRI, and electrode registration and lead field matrix calculation are completed; and then a multi-target regularization model fusing data fidelity, space compactness and intensity consistency is constructed, optimal estimation of source current intensity is obtained through analysis and solution, and finally a positioning result of brain power activation is output. According to the method, the advantages of high time resolution of the EEG and high spatial resolution of the fMRI are fully played, the limitations of inverse problem morbidity, low spatial resolution and insufficient multi-mode fusion in traditional brain power supply positioning are effectively solved, and brain power supply positioning with high precision, noise resistance and high interpretability is realized; and a new scheme is provided for cognitive neuroscience research, nervous system disease diagnosis and brain-computer interface development.

This invention relates to the field of intelligent brain disease diagnosis technology, specifically providing an auxiliary diagnostic method and system based on multimodal decoupled dynamic graph learning. The method includes: acquiring and preprocessing multimodal data (such as neuroimaging, genetic markers, etc.) of the subject; extracting common pathological information and modality-specific features through a shared encoder and modality-specific encoders respectively, and optimizing the separation process using a decoupling loss function; furthermore, fusing all modality embeddings using a multi-head self-attention mechanism with a masked matrix to generate initial node representations, where the mask is used to suppress modality self-attention; subsequently, performing hierarchical dynamic graph convolution based on the node representations: in each layer, dynamically updating the graph adjacency matrix by combining the current node representation with the original features, and iteratively optimizing the node representations through message passing; finally, inputting the optimized representations into a classifier to obtain disease prediction results. This invention improves the automation performance and reliability of diagnosis.

The invention discloses an ADHD child food addiction neural mechanism evaluation method based on fMRI, and the evaluation method mainly comprises the following steps: obtaining the test results of the attention, execution function, intelligence quotient and food addiction of to-be-evaluated children, and dividing the to-be-evaluated children into an ADHD with food addiction group, an ADHD without food addiction group and a health control group according to the results; performing fMRI stimulation normal form execution after grouping, and performing brain function data acquisition; and finally, constructing a mathematical model of neural image data analysis, so that a specific neural circuit of ADHD children food addiction and a mechanism modulated by an emotional state can be revealed non-invasively and objectively, and an important scientific basis and tool are provided for early recognition, pathological mechanism research and targeted intervention strategy development of ADHD co-disease food addiction.

This invention relates to a multimodal data fusion method for modality missing, wherein the method includes the following steps: (1) acquiring multimodal data, including at least two of neuroimaging data, text data, and numerical data, wherein modality missing exists in the multimodal data; (2) inputting the multimodal data into a pre-trained multimodal data fusion model for feature encoding and cross-modal feature fusion processing to obtain fused features containing multimodal information; (3) outputting personalized risk prediction results through a risk prediction module using the acquired multimodal fusion features. This invention also relates to a corresponding system, device, processor, and computer-readable storage medium thereof. Using the multimodal data fusion method, system, device, processor, and computer-readable storage medium of this invention for modality missing, cross-modal adaptive modeling of patient multimodal data is performed to achieve accurate efficacy prediction.

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

The application discloses a multi-modal deep learning source tracing method and system combining magnetoencephalogram and electroencephalogram, relates to the field of artificial intelligence and neuroimaging technology, and inputs real magnetoencephalogram signals and electroencephalogram signals into a source tracing model after preprocessing, predicts the probability of the occurrence of corresponding regional sources, combines an imported source partition distance matrix, and obtains a final source tracing result; the training process of the source tracing model is as follows: a generative adversarial network is constructed to generate a multi-modal neuroelectrophysiological data set; the multi-modal neuroelectrophysiological data set is input into a double-branch structure residual network to perform stage hierarchical extraction and decoupling on the magnetoencephalogram signals and the electroencephalogram signals respectively; a multi-scale convolution module is used to extract features at different stages, the extracted features are input into a classifier after being fused, and a loss function is defined to update trainable parameters of the source tracing model; and the source tracing method improves the accuracy and generalization ability of source positioning.

The invention provides an electronic medical record feature extraction method based on natural language processing, and the method comprises the steps: obtaining an electronic medical record of a brain injury patient, the electronic medical record comprising preoperative brain map data, medical record text records, and postoperative brain map data; extracting medical record character records by using a natural language processing model, and determining text features; on the basis of the preoperative brain image data and the postoperative brain image data, brain region features are determined; based on data time nodes in the electronic medical record of the brain injury patient, performing feature fusion on the text features by using the preoperative brain map data and the postoperative brain map data; and based on the brain region features and the text features, determining electronic medical record features of the brain injury patient. According to the scheme, an electronic medical record is processed by using a natural language through a multi-modal data processing scheme, and the electronic medical record is further combined with the brain map features of the neural image data, so that organically combined multi-modal data features capable of being applied by an artificial intelligence technology are formed, and a foundation is laid for subsequent application.

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 invention provides a cerebral cortex thickness measurement method and system based on a level set image segmentation algorithm, and belongs to the technical field of neural image analysis, and the method comprises the steps: obtaining a brain magnetic resonance imaging image, and carrying out the skull stripping processing; performing three-dimensional visualization on the stripped image through volume rendering, and acquiring three-dimensional coordinates of points of interest and a region of interest on the surface of the cortex by using a preset interaction means; using a level set method to carry out brain grey matter and brain white matter segmentation on the region of interest to obtain a brain grey matter segmentation layer and a brain white matter segmentation layer, and storing three-dimensional coordinate data of points in the segmentation layers; calculating the minimum Euclidean distance according to the three-dimensional coordinate data of the points in the segmentation layer and the three-dimensional coordinates of the points of interest to obtain the cortex thickness of the points of interest; the method solves the problems that the existing cerebral cortex thickness measurement lacks a visual means, is not supported by a mathematical model, cannot be accurate to the thickness of an interest point, and cannot display and retain comprehensive information.

The present invention relates to a multivariate pattern analysis and generalized representation analysis method for a brain signal, comprising the following steps: a) preprocessing brain representation data; b) performing multivariate pattern analysis on the preprocessed data; and c) performing generalized representation analysis on the data that has been subjected to multivariate pattern analysis. The present invention can more comprehensively mine information in a neural image and other forms of brain representation data, break through the limitation of single-modal analysis, and better perform data analysis.