Mapping the multidimensional geometric landscape of graded phenotypic variation and progression in neurodegenerative syndromes

Abstract

AbstractClinical variants of Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual-/group-level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer’s Coordinating Center database to derive a unified geometric framework of graded AD/FTLD longitudinal phenotypic variation. Using three time-point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate AD/FTLD variants, we apply advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning AD/FTLD clinical progression. Importantly, these geometries enable the assimilation and inter-relation of paradigmatic and mixed cases, capturing dynamic individual trajectories, and linking syndromic variability to neuropathology and key clinical end-points such as survival. This resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in AD/FTLD.