MAN-GNN: An interpretable biomarker architecture for neurodevelopmental disorders

Abstract

Neurodevelopmental disorders exhibit highly similar behavioral characteristics in clinical assessments, heavily relying on subjective behavioral reports, leading to insufficient understanding of the neurobiological mechanisms behind inter-patient heterogeneity and symptom overlap between diseases. To address this issue, this study proposes a graph neural network framework that integrates neuroimaging data, focusing on three key problems: Firstly, enhance the nonlinear features in brain neural activity by introducing the Neurodynamics Rössler system. Transform raw static neural signals into simulated signals with nonlinear, temporal, and dynamic features, thereby more accurately reflecting the process of brain neural activity. Secondly, improve feature discrimination by integrating the spatial adjacency characteristics of local brain regions with the topological structure information of the global brain network to highlight key features. Thirdly, improve noise resistance and generalization ability. Introducing adaptive controllers and cross-site adversarial learning mechanisms, the interference of heterogeneous noise is effectively reduced. This study conducted experimental validation on data from neurodevelopmental disorders such as ADHD and ASD. The results indicate that this framework not only has advantages in classification accuracy but also possesses good interpretability, making it a promising tool for imaging biomarker research and auxiliary diagnosis.