Cortical atrophy pattern–based subtyping predicts prognosis of amnestic MCI: an individual-level analysis

Abstract

We categorized patients with amnestic mild cognitive impairment (aMCI) based on cortical atrophy patterns and evaluated whether the prognosis differed across the subtypes. Furthermore, we developed a classifier that learns the cortical atrophy pattern and predicts subtypes at an individual level. A total of 662 patients with aMCI were clustered into 3 subtypes based on cortical atrophy patterns. Of these, 467 patients were followed up for more than 12\xa0months, and the median follow-up duration was 43\xa0months. To predict individual-level subtype, we used a machine learning-based classifier with a 10-fold cross-validation scheme. Patients with aMCI were clustered into 3 subtypes: medial temporal atrophy, minimal atrophy (Min), and parietotemporal atrophy (PT) subtypes. The PT subtype had higher prevalence of APOE ε4 carriers, amyloid PET positivity, and greater risk of dementia conversion than the Min subtype. The accuracy for binary classification was 89.3% (MT vs. Rest), 92.6% (PT vs. Rest), and 86.6% (Min vs. Rest). When we used ensemble model of 3 binary classifiers, the accuracy for predicting the aMCI subtype at an individual level was 89.6%. Patients with aMCI with the PT subtype were more likely to have underlying Alzheimer s disease pathology and showed the worst prognosis. Our classifier may be useful for predicting the prognosis of individual aMCI patients.