To analyze whether data-driven MoCA score-based subtypes from the ADNI cohort can predict dementia conversion better than conventional MCI classifications and identify the neuroimaging features that differentiate these subtypes in the ADNI population.

Mild cognitive impairment (MCI) is a cognitive state between normal aging and dementia. However, early detection of cognitive decline is still a challenge. MoCA is widely used for screening, yet the domain-based classification often overlooks the heterogeneity of cognitive decline. These hidden patterns could be found in a large cohort, hence the need to explore data-driven subtypes. This may better capture individual variability, improve precision in prognosis, and link cognitive domains to underlying neuroimaging features.

Preliminary results showed that data-driven MoCA subtypes exhibit greater heterogeneity in dementia conversion, while performing similarly to the Petersen/Winblad criteria. The median score of diffusion tensor imaging (DTI) metrics within the regions of interest showed that subtypes with a higher proportion of dementia cases exhibited increased mean diffusivity (MD) and radial diffusivity (RD). While axial diffusivity (AD) and fractional anisotropy (FA) were comparable on all subtypes. 

Our data-driven MoCA subtypes can exhibit greater heterogeneity in dementia conversion with neuroimaging correlations, while performing similarly to the traditional Petersen/Winblad classification.