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

## TL;DR

This study uses PET imaging and network analysis to detect early Alzheimer’s changes in the cerebellum, showing potential for early diagnosis.

## Contribution

The study introduces cerebellar amyloid network features for early Alzheimer’s detection using graph theory and machine learning.

## Key findings

- Cerebellar network connectivity changes were observed across different cognitive stages of Alzheimer’s.
- Machine learning models achieved high accuracy in distinguishing Alzheimer’s from normal cognition.
- Betweenness centrality changes in cerebellar regions indicate early amyloid plaque deposition.

## Abstract

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.