PAPER / ARXIV:2609.10890
Daniel Semchin , Emile d'Angremont , Hao Ding , Alan Antar , Marco Lorenzi , Konstantinos Arfanakis , Ysbrand van der Werf , Paul Thompson , Boris Gutman
RESUMO
Parkinson's disease is clinically and biologically heterogeneous, yet its spatiotemporal progression remains poorly characterized. We present a connectome-constrained disease progression model that jointly estimates subject-specific disease time and data-driven subtypes from longitudinal morphometry. Applied to 85 imaging and clinical biomarkers from the Parkinson's Progressive Markers Initiative (PPMI) cohort, the model recovers four morphologically distinct progression subtypes. We validate the model on a hold-out cross-sectional dataset and benchmark it against SuStaIn under a matched training and validation protocol. Only our method recovers subtypes that correspond significantly to clinical motor subtypes and genetic variants of Parkinson's Disease.
NO MESMO MAPA