A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging.

Background: Diagnosis of early mild cognitive impairment (EMCI) as a prodromal stage of Alzheimer's disease (AD) with its delineation from the cognitively normal (CN) group remains a challenging but essential step for the planning of early treatment. Although several studies have focused on the MCI diagnosis, this study introduces the early stage of MCI to assess more thoroughly the earliest signs of disease manifestation and progression.

New Method: We used random forest feature selection model with a Gaussian-based algorithm to perform method evaluation.