A gray matter volume network in pathologically confirmed cases of argyrophilic grain disease: An exploratory analysis

Background Argyrophilic grain disease (AGD) is a common yet underrecognized tauopathy that often mimics Alzheimer's disease (AD) in clinical and imaging presentations. While regional atrophy in AGD has been reported on magnetic resonance imaging (MRI), network-level structural changes remain poorly understood. Objective We aimed to explore a gray matter volume network related to AGD. Methods Structural MRI data were collected from 12 patients with pathologically confirmed AGD (age at MRI, 87.7 ± 5.5 years; male, 4), 12 patients with pathologically confirmed AD (83.4 ± 10.0 years; male, 4), and 9 healthy controls (HCs; 82.4 ± 1.9 years; male, 2) at Fukushimura Hospital in Japan. Scaled Subprofile Model with principal component analysis was applied to preprocessed gray matter volume data of AGD and HCs to identify an AGD-related network. Results An AGD-related network involving relative reduction in the ambient gyrus, entorhinal cortex, hippocampus, amygdala, and thalamus was identified. Represented by principal components 1, 2, and 3, this network showed significantly higher expression in patients with AGD than HCs (p < 0.0001, permutation test). The expression of the network was also higher in patients with AD than HCs (p < 0.0001, t-test). Conclusions This exploratory study identified a gray matter volume network related to AGD, providing a basis for future research of network-based imaging approaches.