GAT-LSTM Based Predictive Model for Developmental Dyslexia Diagnosis: A Graph-Attention and Time-Series Learning Approach

Abstract

Developmental dyslexia is a common neurodevelopmental learning disorder that severely impacts children's reading abilities and social adaptation. In recent years, brain network analysis based on functional magnetic resonance imaging has provided new insights into its neural mechanisms, yet it struggles to capture the temporal characteristics of dynamic brain interactions. To address this, this paper proposes a GAT-LSTM framework for high-precision classification of DD. This method first constructs a dynamic functional connectivity network based on the AAL90 brain atlas. It then employs GAT to adaptively learn spatial dependencies between brain regions within each time window, followed by LSTM to model the temporal evolution patterns of node embedding sequences. To further enhance the model's temporal consistency and discriminative power, dynamic graph stability constraints are introduced during training. Experimental results demonstrate that the proposed method achieves an 85.36% classification accuracy, significantly outperforming baseline models. This study not only provides a novel computational paradigm for the objective diagnosis of DD but also offers robust support for the application of brain network modeling in neurodevelopmental disorder research.