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Method, system and device for identifying brain nerve development time-varying function connection difference and storage medium

A neurodevelopmental and functional technology, applied in the field of data processing, can solve the problem of only being suitable for and unable to effectively obtain distinguishable features of nonlinear latent structure identification, etc., to achieve convenient operation, improve adaptive learning ability, and strong practicability. Effect

Pending Publication Date: 2021-10-15
XI AN JIAOTONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, they are either based on multi-layer linear models or only suitable for classification, unable to efficiently capture nonlinear latent structures or identify distinguishable features from data

Method used

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  • Method, system and device for identifying brain nerve development time-varying function connection difference and storage medium
  • Method, system and device for identifying brain nerve development time-varying function connection difference and storage medium
  • Method, system and device for identifying brain nerve development time-varying function connection difference and storage medium

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Embodiment 1

[0055] refer to figure 1 , the method for analyzing brain development data based on sparse deep dictionary learning SDDL according to the present invention comprises the following steps:

[0056] 1) Obtain the recorded brain development data;

[0057] 2) In the brain development data, each individual and its corresponding data features and their change values ​​are aggregated into a piece of unit data, and a data matrix is ​​constructed using the unit data corresponding to each individual, and the data matrix includes the sample size N and the sample feature p;

[0058] 3) dividing the data matrix obtained in step 2) into a training set and a test set;

[0059] 4) Establish a sparse deep dictionary learning model;

[0060] 5) using the training set and the test set to train the step-by-step sparse deep dictionary learning model to obtain the trained sparse deep dictionary learning model;

[0061] 6) Using the trained sparse deep dictionary learning model to analyze the time...

Embodiment 2

[0076] The system for identifying the time-varying functional connectivity differences in brain neurodevelopment according to the present invention includes:

[0077] Building blocks for building sparse deep dictionary learning models;

[0078] The training module is used to train the sparse deep dictionary learning model, wherein, during the training process, the sparse deep auto-encoder learns the dictionary from the raw data in the latent space, while using Norm and KL divergence perform sparse regularization terms;

[0079] An analysis module for analyzing differences in brain neurodevelopmental time-varying functional connectivity using a trained sparse deep dictionary learning model.

Embodiment 3

[0081] A computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implements the time-varying function of recognizing brain neurodevelopment when the processor executes the computer program The steps of the method of connecting the differences.

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Abstract

The invention discloses a method, a system and equipment for identifying brain nerve development time-varying function connection differences, and a storage medium. The method comprises the following steps: constructing a sparse deep dictionary learning model; a sparse depth dictionary learning model is trained, in the training process, a sparse depth automatic encoder learns a dictionary from original data of a potential space, and meanwhile, a Tl1 norm and a KL divergence are used for executing a sparse regularization item; the trained sparse deep dictionary learning model is utilized to analyze the brain neural development time-varying function connection difference, the method, system and device and the storage medium can identify the brain neural development time-varying function connection difference, and meanwhile, the advantage of deep learning in advanced nonlinear feature extraction and the interpretability of dictionary learning are combined.

Description

technical field [0001] The invention belongs to the field of data processing, and relates to a method, a system, a device and a storage medium for recognizing the time-varying functional connection difference of brain nerve development. Background technique [0002] Most dictionary learning analysis functional connectivity (FC) / dynamic functional connectivity (dFC) is based on linear dictionary learning, which ignores the nonlinear structure or higher-level features of the data. To solve this problem, kernel tricks are introduced to obtain non-linear mappings and then perform dictionary learning in the transform space, but the interpretability of this method is challenging. Dictionary learning based on deep models has also been proposed, and Sulam et al. proposed a multi-layer convolutional sparse coding model to learn a global dictionary from multi-layer linear combinations of serial convolution dictionaries. Tariyal et al. used a greedy deep dictionary learning with a lin...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06N3/08G06N3/063
CPCG06F30/27G06N3/08G06N3/063G06F18/214
Inventor 乔琛杨岚李佳嘉吴娇于爱菊龚若林
Owner XI AN JIAOTONG UNIV
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