Brain Atlas Selection for Developmental Dyslexia: A Comparative Study of GCN Classification Based on Graph-Theoretical and Topological Feature Fusion

Abstract

Developmental dyslexia (DD) is a common neurodevelopmental disorder, and abnormalities in the functional brain network structure are suggested to be closely related to dysregulation of connections between brain regions. However, the applicability of different brain atlas segmentation strategies in DD brain network modeling has not been systematically evaluated. To this end, this study constructed brain functional networks using three brain atlases (AAL, Craddock, and Power) for comparative analysis and built a Graph Convolutional Network (GCN) classification framework that integrates traditional graph-theoretic features and higher-order global topological features extracted using Persistent Homology. The results of the experiment show that the AAL template performs best in DD recognition, with a classification accuracy rate of 83.31%. Additionally, by converging cross-threshold topological feature stability analysis with Grad-CAM model attribution, we identified a set of brain regions that exhibited both robust intergroup differences across multiple sparsity levels and high importance in the GCN’s decision-making process, thereby jointly pinpointing the core neural substrates of DD. Our results provide a theoretical basis for brain atlas selection and functional connectivity modeling, and offer a new path for optimizing DD-assisted recognition methods.