Early diagnosis of Alzheimer’s Disease: Graph theoretical analysis of cerebellar network features based on ¹⁸F-AV45 PET

by Ruyi Li, Shaoping Jiang, Zhaoke Pi, Guisu Chen

Pathological and neuroimaging changes in the cerebellum of Alzheimer’s disease (AD) patients have been well documented. However, the changes in cerebellar amyloid plaque deposition connectivity networks during AD progression based on positron emission tomography (PET) imaging remain unclear. We selected ¹⁸F-florbetapir PET (¹⁸F-AV45 PET) imaging data from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset (n = 612) and employed graph theoretical analysis to examine amyloid plaque deposition connectivity, comparing the connectivity differences across cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD groups. In addition, we combined graph theoretical features with the standardized uptake value ratio (SUVR) of regions of interest and applied them to machine learning models for the early diagnosis of AD. As cognitive decline progressed, significant changes in cerebellar network connectivity were observed across groups. Regarding local connectivity, changes in betweenness centrality were evident in multiple cerebellar regions at different cognitive stages. Cerebellar amyloid networks revealed early changes in amyloid plaque deposition connectivity. The machine learning model achieved an area under the curve (AUC) of 0.950 for distinguishing AD from CN, 0.995 for CN vs. EMCI, 0.964 for EMCI vs. LMCI and 0.632 for LMCI vs. AD. These findings provide new insights into the cerebellar pathological features of AD and highlight the potential of this approach for early identification and prediction of AD progression.