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

Abstract

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 mul- ticenter prospective observational trial, had MoCA and 18F-florbetapir PET; 55 had 18F-FDG PET. Scans were processed using the MINC toolkit to gen- erate SUV ratios, normalized to cerebellar gray matter (18F-florbetapir PET), or pons (18F-FDG PET). SUV ratio data and MoCA score were used for su- pervised 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 in- creased with decreasing MoCA score. 18F-FDG PETs were randomly di- vided 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 associ- ated with MoCA score; amyloid increased and 18F-FDG decreased with de- creasing MoCA score, most significantly in the posterior cingulate gyrus.