The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated with MoCA Score

Purpose The aim of this study was to evaluate random forests (RFs) to identify ROIs on 18F-florbetapir and 18F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score. Materials and Methods Fifty-seven subjects with significant white matter disease presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a multicenter prospective observational trial, had MoCA and 18F-florbetapir PET; 55 had 18F-FDG PET. Scans were processed using the MINC toolkit to generate SUV ratios, normalized to cerebellar gray matter (18F-florbetapir PET), or pons (18F-FDG PET). SUV ratio data and MoCA score were used for supervised training of RFs programmed in MATLAB. Results 18F-Florbetapir PETs were randomly divided into 40 training and 17 testing scans; 100 RFs of 1000 trees, constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: right posterior cingulate gyrus, right anterior cingulate gyrus, left precuneus, left posterior cingulate gyrus, and right precuneus. Amyloid increased with decreasing MoCA score. 18F-FDG PETs were randomly divided into 40 training and 15 testing scans; 100 RFs of 1000 trees, each tree constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: left fusiform gyrus, left precuneus, left posterior cingulate gyrus, right precuneus, and left middle orbitofrontal gyrus. 18F-FDG decreased with decreasing MoCA score. Conclusions Random forests help pinpoint clinically relevant ROIs associated with MoCA score; amyloid increased and 18F-FDG decreased with decreasing MoCA score, most significantly in the posterior cingulate gyrus.