Early Mild Cognitive Impairment Diagnosis via Resting-State fMRI Brain Networks Using a Region-Specific Hierarchical Fusion Graph Neural Network

Early mild cognitive impairment (EMCI) is the earliest intervenable stage of Alzheimer’s disease (AD). Although graph neural networks (GNNs) have begun to exploit brain network topology, traditional fMRI-based diagnostic methods often neglect these structural patterns by relying on vectorized features. Furthermore, existing GNNs frequently disregard inter-regional functional heterogeneity and group-level discriminative patterns, leading to limited accuracy and biomarker interpretability. To address these challenges, we propose HF-BrainGNN, an end-to-end hierarchical graph learning framework for EMCI identification. Our method introduces a functional affinity region convolution (FAR-Conv) layer to learn region-adaptive kernels, a Differential Focus Pooling (DF-Pool) module to identify disease-salient brain regions by maximizing inter-group distinctiveness, and a hierarchical integration classifier (HIC) to fuse multi-level graph representations. The framework is optimized using classification, focus separation, and consistency regularization losses. Experiments on the ADNI dataset (104 EMCI, 114 Cognitively Normal) show that HF-BrainGNN achieves 86.78% accuracy, outperforming the best baseline (Hi-GCN) by 4.64%. Furthermore, the automatically identified regions, such as the bilateral hippocampus and default mode network hubs, align with established EMCI biomarkers. Ultimately, HF-BrainGNN provides an efficient, interpretable artificial intelligence tool for precise brain network characterization and early AD intervention.