现代信息科技2026，Vol.10Issue(3)：63-69,75,8.DOI:10.19850/j.cnki.2096-4706.2026.03.013
基于图强化学习的模态解耦脑龄预测模型
Modal Decoupling Brain Age Prediction Model Based on Graph Reinforcement Learning
摘要
Abstract
In order to improve the precision and generalization ability of the brain age prediction model,and to support the early identification of neurodegenerative diseases and the research on brain aging mechanisms,this paper proposes a modal decoupling brain age prediction model based on graph reinforcement learning.First,this study constructs individual brain networks based on fMRI and sMRI data,and uses Graph Neural Network to model the topological features of brain regions.Second,the model adaptively adjusts the number of graph convolution layers through the dynamic graph convolution mechanism(AC framework),and adopts the Double Deep Q-Network(DDQN)to optimize the GraphSAGE strategy to adapt to brain network patterns of different modalities.Experimental results indicate that the performance of the proposed model in metrics such as MAE is superior to existing deep convolutional networks and traditional Graph Neural Network methods.This study not only reflects the advantages of dynamic graph convolution and reinforcement learning strategies in brain age prediction,but also provides a new technical approach for further exploring the mechanism of brain aging.关键词
脑龄预测/图神经网络/强化学习/图强化学习/静息态fMRI/结构MRIKey words
brain age prediction/Graph Neural Network/reinforcement learning/graph reinforcement learning/resting-state fMRI/structural MRI分类
信息技术与安全科学引用本文复制引用
宋佳颖,马慧彬..基于图强化学习的模态解耦脑龄预测模型[J].现代信息科技,2026,10(3):63-69,75,8.基金项目
黑龙江省省属高等学校基本科研业务费科研项目(2021-KYYWF-0579) （2021-KYYWF-0579）
