Verifying and refining early statuses in Alzheimer’s disease progression: a possibility from deep feature comparison

Abstract

Defining the early status of Alzheimer’s disease is challenging. Theoretically, the statuses in the Alzheimer’s disease continuum are expected to share common features. Here, we explore to verify and refine candidature early statuses of Alzheimer’s disease with features learned from deep learning. We train models on brain functional networks to accurately classify between amnestic and non-amnestic mild cognitive impairments and between healthy controls and mild cognitive impairments. The trained models are applied to Alzheimer’s disease and subjective cognitive decline groups to suggest feature similarities among the statuses and identify informative subpopulations. The amnestic mild cognitive impairment vs non-amnestic mild cognitive impairments classifier believes that 71.8% of Alzheimer’s disease are amnestic mild cognitive impairment. And 73.5% of subjective cognitive declines are labeled as mild cognitive impairments, 88.8% of which are further suggested as “amnestic mild cognitive impairment.” Further multimodal analyses suggest that the amnestic mild cognitive impairment-like Alzheimer’s disease, mild cognitive impairment-like subjective cognitive decline, and amnestic mild cognitive impairment-like subjective cognitive decline exhibit more Alzheimer’s disease -related pathological changes (elaborated β-amyloid depositions, reduced glucose metabolism, and gray matter atrophy) than non-amnestic mild cognitive impairments -like Alzheimer’s disease, healthy control-like subjective cognitive decline, and non-amnestic mild cognitive impairments -like subjective cognitive decline. The test–retest reliability of the subpopulation identification is fair to good in general. The study indicates overall similarity among subjective cognitive decline, amnestic mild cognitive impairment, and Alzheimer’s disease and implies their progression relationships. The results support “deep feature comparison” as a potential beneficial framework to verify and refine early Alzheimer’s disease status.