A method and apparatus for predicting early alzheimer's disease based on sfc features

By constructing a neural network model based on brain region sets and brain atlases, and using big data training with multimodal MRI images of the brain, the problem of insufficient accuracy in early Alzheimer's disease prediction in existing technologies has been solved, and in-depth mining and accurate prediction of the relationship between SC and FC networks have been achieved.

CN121439233BActive Publication Date: 2026-07-24SECOND MEDICAL CENT OF CHINESE PLA GENERAL HOSPITAL
2 Cites 0 Cited by

Patent Information

Application Number
CN202511697795.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-19
Publication Date
2026-07-24
Estimated Expiration
2045-11-19

AI Technical Summary

Technical Problem

Existing technologies cannot effectively uncover the structural-functional coupling relationship between the brain's structural and functional connectivity networks, resulting in insufficient accuracy in early Alzheimer's disease prediction.

Method used

Based on brain region sets and brain atlases of multiple brain regions of interest, a structural matrix, a functional matrix, and a structural-functional coupling feature vector are constructed. A neural network model is designed, and big data acquisition and model training are carried out using multimodal MRI images of the brain to perform binary classification prediction.

Benefits of technology

By deeply exploring the SFC relationship between SC and FC networks, the accuracy of early Alzheimer's disease prediction was improved.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The embodiment of the present application relates to a kind of method and device based on SFC feature prediction early Alzheimer's disease, the method comprises: setting first brain region set and its corresponding first brain atlas, design first prediction model;The brain multi-modal MRI image of early Alzheimer's disease population and healthy population is carried out big data acquisition to obtain original sample set;According to first brain atlas and original sample set, generate first data set;First prediction model is trained based on first data set;After training, the brain multi-modal MRI image of any subject is received, and first SFC feature vector is calculated according to first brain atlas and current multi-modal image, and first SFC feature vector is input into first prediction model and is predicted to obtain current prediction result.The present application can mine SFC feature, and can be based on SFC feature early Alzheimer's disease and be classified and predicted.
Need to check novelty before this filing date? Find Prior Art