Deep learning-driven risk-based subtyping of cognitively impaired individuals

Quantifying heterogeneity in Alzheimer’s disease (AD) risk is critical for individualized care and management. Recent attempts to assess AD heterogeneity have used structural (magnetic resonance imaging (MRI)-based) or functional (Aβ or tau) imaging, which focused on generating quartets of atrophy patterns and protein spreading, respectively. Here we present a computational framework that facilitated the identification of subtypes based on their risk of progression to AD. We used cerebrospinal fluid (CSF) measures of Aβ from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n=544, discovery cohort) as well as the National Alzheimer’s Coordinating Center (NACC) (n=508, validation cohort), and risk-stratified individuals with mild cognitive impairment (MCI) into quartiles (high-risk (H), intermediate-high risk (IH), intermediate-low risk (IL), and low-risk (L)). Patients were divided into subgroups utilizing patterns of brain atrophy found in each of these risk-stratified quartiles. We found H subjects to have a greater risk of AD progression compared to the other subtypes at 2- and 4-years in both the discovery and validation cohorts (ADNI: H subtype versus all others, p < 0.05 at 2 and 4 years; NACC: H vs. IL and LR at 2 years, p < 0.05, and a trend toward higher risk vs. IH, and p < 0.05 vs. IH, and L risk groups at 48 months with a trend toward lower survival vs. IL). Using MRI-based neural models that fused various deep neural networks with survival analysis, we then predicted MCI to AD conversion. We used these models to identify subtype-specific regions that demonstrate the largest levels of atrophy-related importance, which had minimal overlap (Average pairwise Jaccard Similarity in regions between the top 5 subtypes, 0.25±0.05 (± std)). Neuropathologic changes characteristic of AD were present across all subtypes in comparable proportions (Chi-square test, p>0.05 for differences in ADNC, n=31). Our risk-based approach to subtyping individuals provides an objective means to intervene and tailor care management strategies at early stages of cognitive decline.