Fig 1.
Workflow for Data Analysis Pipeline:
(A) Data preprocessing steps. (B) Extraction of the SUVR for each cerebellar region, construction of connectivity matrices, and subsequent graph theory analysis. (C) Statistical analysis based on graph theory features and visualization of differential nodes. (D) Identification of the most relevant features through least absolute shrinkage and selection operator regression, using graph theory analysis features, with performance assessed via 5-fold cross-validation. The potential of cerebellar graph theory features for early prediction of AD was evaluated.
Table 1.
Presents the baseline demographic and clinical features of study participants.
Table 2.
Comparison of amyloid accumulation connectivity alterations between CN, AD, EMCI, and LMCI.
Fig 2.
Cerebellar Graph Theory Analysis of CN, EMCI, LMCI, and AD Individuals.
Cerebellar graph theory analysis reveals significant differences in amyloid plaque deposition connectivity between the CN, EMCI, LMCI, and AD groups: (A) AD vs. CN group; (B) CN vs. EMCI group; (C) EMCI vs. LMCI group; (D) LMCI vs. AD group in terms of significant differences in betweenness centrality (p < 0.05, Bonferroni correction). The nodes represent 26 cerebellar lobules, with red and blue indicating regions with significantly increased and decreased intermediary centrality. L,left; R, right.
Fig 3.
Inter-group comparison of cerebellar amyloid plaque deposition connectivity in the study groups.
The data illustrate the distribution of changes in cerebellar network connectivity, including small-world properties, average degree, clustering coefficient, and centrality. P-values were obtained after correcting for multiple comparisons across the 26 cerebellar regions using false discovery rate methods (* p < 0.05; ** p < 0.01; *** p < 0.001). CN: cognitively normal, EMCI: early mild cognitive impairment, LMCI: late mild cognitive impairment, AD: Alzheimer’s disease, CC: clustering coefficient, BC: betweenness centrality.
Table 3.
Classification performance of cerebellar graph-theoretical features for CN vs.AD, CN vs. EMCI, EMCI vs. LMCI, and LMCI vs. AD in the training and testing sets.
Fig 4.
Performance of the cerebellum model and cortical brain model on the training and testing sets for binary classifications: CN vs. AD, CN vs. EMCI, EMCI vs. LMCI, and LMCI vs. AD.
Fig 5.
SHAP summary dot plots of the top 10 features of the best-performing ML models in different groups.
Table 4.
Comparative experiment results.