Abstract
Parkinson’s disease is clinically and biologically heterogeneous,yetitsspatiotemporalprogressionremainspoorlycharacterized.We present a connectome-constrained disease progression model that jointly estimates subject-specific disease time and data-driven subtypes from longitudinalmorphometry.Appliedto85imagingandclinicalbiomarkers from the Parkinson’s Progressive Markers Initiative (PPMI) cohort, the model recovers four morphologically distinct progression subtypes. We validatethemodelonahold-outcross-sectionaldatasetandbenchmarkit 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.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/SASHIMI_063.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=7CmM2lNknV
BibTex
@InProceedings{SemDan_Discovering_MICCAISAT2026,
author = { Semchin, Daniel AND d’Angremont, Emile AND Ding, Hao AND Antar, Alan AND Lorenzi, Marco AND Arfanakis, Konstantinos AND van der Werf, Ysbrand D. AND Thompson, Paul M. AND Gutman, Boris A.},
title = { { Discovering Subtypes of Neurodegenerative Progression with a Scalable Connectome-Constrained Dynamic Model } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17258},
month = {pending},
page = {pending}
}