Discovering Hidden Links: Harnessing Similarity Network Fusion to Reveal Common Clusters in Healthy Aging, Mild Cognitive Impairment, and Dementia

Cognitively Unimpaired (CU), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD) are diagnostic categories used to categorize degrees of cognitive impairment. Older adults experience brain changes associated with aging and cognitive decline at different ages and progress at varying rates, leading to heterogeneous patterns of cognitive decline. As a result, people within the same diagnostic category often exhibit significant differences in cognitive abilities and brain functions. The present study aimed to investigate how data-driven categories map onto diagnostic categories. Therefore, using data from 515 participants in the Alzheimer’s Disease Neuroimaging Initiative database and a multivariate clustering approach, Similarity Network Fusion, we combined brain imaging features (cortical thickness average, surface area, and volume) with cognitive measures (Alzheimer’s Disease Assessment Scale-Cognitive, Mini-Mental State Exam, Montreal Cognitive Assessment, Clinical Dementia Rating) in older adults who had a clinical diagnosis of CU, MCI, or AD. We identified four data-driven groups spanning a gradient of cognitive and neural severity. Group 1 (87% diagnosed with dementia) showed the most significant impairment, while Group 4 (96% cognitively unimpaired) showed minimal impairment. Groups 2 and 3 captured transitional stages, including an “at-risk” group with early neural and cognitive decline. The current study provides evidence that more nuanced, data-driven approaches can reveal commonalities in individuals’ etiology and underlying neurobiology across traditional diagnoses. These results may lead to more focal therapies and a better understanding of who is at risk for converting to dementia, allowing for earlier detection and treatment of cognitive decline.