Multi-Level Adaptive Graph Networks for Brain MRI Classification in Neurodegenerative Disorders

We present an innovative methodology for the classification of Presenile dementia (Pd) using the Hierarchical Adaptive Multi-Domain Analysis Network (HAMDA-Net). This architecture effectively combines sophisticated mathematical theories with cutting-edge domain adaptation techniques, transforming the analysis of functional magnetic resonance imaging (fMRI) data. Within the scope of domain adaptation, we introduce a novel intra-group regularization component that significantly enhances the model’s capacity to generalize across diverse patient populations and varying imaging protocols. This advancement addresses a critical challenge in medical imaging analysis, where inter-site variability often impedes the broad applicability of machine learning models. We provide theoretical assurances regarding the convergence properties of our domain adaptation strategy, demonstrating its effectiveness in minimizing dataset bias while preserving the discriminative features essential for accurate Pd classification. In conclusion, HAMDA-Net represents a transformative advancement in the application of deep learning to neuroimaging analysis. By seamlessly integrating advanced mathematical frameworks with innovative domain adaptation methodologies, our work not only extends the frontiers of Pd classification but also establishes a robust framework adaptable to a wide range of neurological disorders. The theoretical foundations and empirical validations presented herein pave the way for the development of more accurate, interpretable, and generalizable models in medical imaging analysis, with profound implications for personalized healthcare and neuroscientific research.