Neuroimaging-based data-driven subtypes of spatiotemporal atrophy due to Parkinson’s disease

Parkinson's disease is the second most common neurodegenerative disease. Despite this, there are no robust biomarkers to predict progression, and understanding of disease mechanisms is limited. We used the Subtype and Stage Inference algorithm to characterize Parkinson's disease heterogeneity in terms of spatiotemporal subtypes of macroscopic atrophy detectable on T1-weighted MRI-a successful approach used in other neurodegenerative diseases. We trained the model on covariate-adjusted cortical thicknesses and subcortical volumes from the largest known T1-weighted MRI dataset in Parkinson's disease, Enhancing Neuroimaging through Meta-Analysis consortium Parkinson's Disease dataset (<i>n</i> = 1100 cases). We tested the model by analyzing clinical progression over up to 9 years in openly-available data from people with Parkinson's disease from the Parkinson's Progression Markers Initiative (<i>n</i> = 584 cases). Under cross-validation, our analysis supported three spatiotemporal atrophy subtypes, named for the location of the earliest affected regions as: '<i>Subcortical</i>' (<i>n</i> = 359, 33%), '<i>Limbic</i>' (<i>n</i> = 237, 22%) and '<i>Cortical</i>' (<i>n</i> = 187, 17%). A fourth subgroup having sub-threshold/no atrophy was named '<i>Sub-threshold atrophy</i>' (<i>n</i> = 317, 29%). Statistical differences in clinical scores existed between the no-atrophy subgroup and the atrophy subtypes, but not among the atrophy subtypes. This suggests that the prime T1-weighted MRI delineator of clinical differences in Parkinson's disease is atrophy severity, rather than atrophy location. Future work on unravelling the biological and clinical heterogeneity of Parkinson's disease should leverage more sensitive neuroimaging modalities and multimodal data.

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Publication Details

Journal
Brain Communications
Published
2025-01-01
DOI
https://doi.org/10.1093/braincomms/fcaf146
Citations
6
Primary Topic
Parkinson's Disease Mechanisms and Treatments
Type
article
Field-Weighted Citation Impact
8.34

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article

Neuroimaging-based data-driven subtypes of spatiotemporal atrophy due to Parkinson’s disease

Tim D. van Balkom, Tracy R. Melzer, Tim Anderson, Letizia Squarcina et al.
6 citations
Brain Communications
Parkinson's Disease Mechanisms and Treatments
8.34
article

Neuroimaging-based data-driven subtypes of spatiotemporal atrophy due to Parkinson’s disease

Tim D. van Balkom, Tracy R. Melzer, Tim Anderson, Letizia Squarcina, Christian Rummel, Rachel Guimarães, Conor Owens‐Walton, Chencheng Zhang, Duygu Tosun, Max A. Laansma, Ines Debove, Gianfranco Spalletta, Gaëtan Garraux, Ysbrand D. van der Werf, Chris Vriend, Clare E. Mackay, Neda Jahanshad, Petra Schwingenschuh, Jiun‐Jie Wang, Rick C. G. Helmich, Hedley Emsley, Henk W. Berendse, Johannes Klein, Benjamin T. Newman, Sarah Al–Bachari, Corey T. McMillan, Daniel Weintraub, Tim Anderson, Fernando Cendes, Emile d’Angremont, Lucas S. R. Santos, Cristiane S. Rocha, Rob M. A. de Bie, Kathleen L. Poston, Beatrice Taylor, John C. Dalrymple‐Alford, Daniela Vecchio, Letícia Ribeiro, Tyler Ard, Michiel F. Dirkx, Roland Wiest, Laura M. Parkes, Clelia Pellicano, Toni L. Pitcher, Eva M. van Heese, Philip Mosley, André Altmann, Odile A. van den Heuvel, Neil P. Oxtoby, Rimona Sharon Weil, Clarissa Lin Yasuda, Jason Druzgal, Boris A. Gutman, Cameron Shand, Mario Rango, Katherine Baquero, Joanna K. Bright, Elnaz Ghasemi, Chih‐Chien Tsai, Paul M. Thompson, Francesca Assogna, Beth Newman, Reinhold Schmidt, Fabrizio Piras, Zeena Shawa, Melanie Suette
article en
6 citations

Abstract

Parkinson's disease is the second most common neurodegenerative disease. Despite this, there are no robust biomarkers to predict progression, and understanding of disease mechanisms is limited. We used the Subtype and Stage Inference algorithm to characterize Parkinson's disease heterogeneity in terms of spatiotemporal subtypes of macroscopic atrophy detectable on T1-weighted MRI-a successful approach used in other neurodegenerative diseases. We trained the model on covariate-adjusted cortical thicknesses and subcortical volumes from the largest known T1-weighted MRI dataset in Parkinson's disease, Enhancing Neuroimaging through Meta-Analysis consortium Parkinson's Disease dataset (<i>n</i> = 1100 cases). We tested the model by analyzing clinical progression over up to 9 years in openly-available data from people with Parkinson's disease from the Parkinson's Progression Markers Initiative (<i>n</i> = 584 cases). Under cross-validation, our analysis supported three spatiotemporal atrophy subtypes, named for the location of the earliest affected regions as: '<i>Subcortical</i>' (<i>n</i> = 359, 33%), '<i>Limbic</i>' (<i>n</i> = 237, 22%) and '<i>Cortical</i>' (<i>n</i> = 187, 17%). A fourth subgroup having sub-threshold/no atrophy was named '<i>Sub-threshold atrophy</i>' (<i>n</i> = 317, 29%). Statistical differences in clinical scores existed between the no-atrophy subgroup and the atrophy subtypes, but not among the atrophy subtypes. This suggests that the prime T1-weighted MRI delineator of clinical differences in Parkinson's disease is atrophy severity, rather than atrophy location. Future work on unravelling the biological and clinical heterogeneity of Parkinson's disease should leverage more sensitive neuroimaging modalities and multimodal data.

Brain CommunicationsVol. 7(2)
University of Southern California (US), University of Liège (BE), Chang Gung University (TW), University of California, San Francisco (US), Universidade Estadual de Campinas (UNICAMP) (BR), University of Canterbury (NZ), Medical University of Graz (AT), QIMR Berghofer Medical Research Institute (AU), Ruijin Hospital (CN), Christchurch Hospital (NZ), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT), University of California System (US), University of Manchester (GB), Chang Gung Memorial Hospital (TW), University of Oxford (GB), New Zealand Brain Research Institute (NZ), Amsterdam Neuroscience (NL), UK Dementia Research Institute (GB), Biomedical Research Institute (US), Brazilian Institute of Neuroscience and Neurotechnology (BR), Fondazione Santa Lucia (IT), National Hospital for Neurology and Neurosurgery (GB), Amsterdam University Medical Centers (NL), University College London (GB), University of Virginia (US), University of Pennsylvania (US), University of Otago (NZ), Vrije Universiteit Amsterdam (NL), Stanford University (US)
Michael J. Fox Foundation for Parkinson's Research, Parkinson Research Foundation, Biogen, University of Otago, UK Research and Innovation, National Institute for Health and Care Research, University College London, Canterbury Medical Research Foundation, Neurological Foundation of New Zealand, Ministero della Salute, Brain Research New Zealand, National Institutes of Health, Allergan, Avid Radiopharmaceuticals, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health Research Council of New Zealand, NIHR Oxford Biomedical Research Centre, National Institute on Aging, National Institute of Neurological Disorders and Stroke
Openalex Percentile: Top 2%
Parkinson's Disease Mechanisms and Treatments
8.34
DOIOpen Access PDFOpenAlex
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