Distinguishing early from late mild cognitive impairment: a multi-level analysis of regional morphometry and KLS-derived network topology.

Introduction: Distinguishing between early Mild Cognitive Impairment (EMCI) and late mild cognitive impairment (LMCI) is crucial for clinical trials, but objective biomarkers are lacking. We therefore examined regional morphometry and network topology across cognitively normal (CN), EMCI, and LMCI groups to address this gap. We also evaluated whether combining these features could effectively classify mild cognitive impairment (MCI) subtypes. Methods: We analyzed T1-weighted magnetic resonance imaging (MRI) data from 208 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (67 CN, 83 EMCI, 58 LMCI). We used both voxel- and surface-based morphometry to measure local atrophy and combined this with graph analysis of individual structural covariance networks (SCNs). We also performed correlation and machine learning analyses. Results: We found that cortical thickness (CT) in EMCI was not significantly different from CN, but it was significantly reduced in the LMCI group. The right hippocampus and the left thalamus, however, showed a significant difference between CN and EMCI. In the Kullback-Leibler (KL) divergence-based similarity (KLS) network analysis, the EMCI group showed a greater randomization when compared to the LMCI group, while LMCI was accompanied by elevated nodal centrality in the left hippocampus and orbital frontal region. Correlation analysis confirmed this was a maladaptive phenomenon, as higher centrality was linked to poorer cognitive performance. Finally, a classifier combining these structural and network features successfully differentiated the MCI subtypes. Conclusion: Our findings suggest that differences in Gray matter volume (GMV) may be more easily observed in the EMCI group. We identified a corresponding non-linear pattern of network topology, characterized by randomization in the EMCI group than in the LMCI. These multi-faceted biomarkers enabled the accurate machine-learning-based differentiation of MCI subtypes, offering a powerful framework for improving patient stratification in clinical trials.