基于图强化学习的模态解耦脑龄预测模型OA

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.