A Novel Alzheimer's Disease Diagnosis Method Based on Adaptive Fine-Grained Causal Brain Network

Alzheimer's Disease (AD) identification through brain network analysis enhances understandings of the intrinsic neural dynamics within the brain, aiding in the clinical treatment and mechanism research of the brain disease. Current research utilizing functional magnetic resonance imaging (fMRI) time series to study brain effective connectivity networks between different brain regions represents an advanced approach. However, these studies are limited by the brain regions of interest (ROIs) predefined by experts due to the complexity in neuroinformatics on the overly macroscopic scale. To address this issue, we propose a novel method based on adaptive fine-grained causal brain network AFGCBN, which automatically segments ROIs into periodic, micro-level time snippets. In concrete terms, this method consists of two primary components: 1) a fine-grained causal variables learning module that splits the entire time series of each ROI from the brain-region level to the time-snippet level vertices, thereby yielding a fine-grained causal brain network for every subject, and 2) a causal graph fusion model based on Graph Isomorphism Network (GIN) is used to further learn the connectivity of the fine-grained brain network, which is achieved through causal interventions on the coarse-grained brain network. The experimental findings demonstrate that our AFGCBN model excels in constructing causal brain networks for AD diagnosis and delivers superior identification accuracy compared to various current techniques.