Multimodal phenotypic axes of Parkinson’s disease

Abstract Individuals with Parkinson’s disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the “average” patient, ignoring patient heterogeneity and obscuring important individual...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ross D. Markello, Golia Shafiei, Christina Tremblay, Ronald B. Postuma, Alain Dagher, Bratislav Misic
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/6dd08d7bb13342ac8d5db6a5e717d2e8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6dd08d7bb13342ac8d5db6a5e717d2e8
record_format	dspace
spelling	oai:doaj.org-article:6dd08d7bb13342ac8d5db6a5e717d2e82021-12-02T15:13:31ZMultimodal phenotypic axes of Parkinson’s disease10.1038/s41531-020-00144-92373-8057https://doaj.org/article/6dd08d7bb13342ac8d5db6a5e717d2e82021-01-01T00:00:00Zhttps://doi.org/10.1038/s41531-020-00144-9https://doaj.org/toc/2373-8057Abstract Individuals with Parkinson’s disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the “average” patient, ignoring patient heterogeneity and obscuring important individual differences. Modern large-scale data sharing efforts provide a unique opportunity to precisely investigate individual patient characteristics, but there exists no analytic framework for comprehensively integrating data modalities. Here we apply an unsupervised learning method—similarity network fusion—to objectively integrate MRI morphometry, dopamine active transporter binding, protein assays, and clinical measurements from n = 186 individuals with de novo Parkinson’s disease from the Parkinson’s Progression Markers Initiative. We show that multimodal fusion captures inter-dependencies among data modalities that would otherwise be overlooked by field standard techniques like data concatenation. We then examine how patient subgroups derived from the fused data map onto clinical phenotypes, and how neuroimaging data is critical to this delineation. Finally, we identify a compact set of phenotypic axes that span the patient population, demonstrating that this continuous, low-dimensional projection of individual patients presents a more parsimonious representation of heterogeneity in the sample compared to discrete biotypes. Altogether, these findings showcase the potential of similarity network fusion for combining multimodal data in heterogeneous patient populations.Ross D. MarkelloGolia ShafieiChristina TremblayRonald B. PostumaAlain DagherBratislav MisicNature PortfolioarticleNeurology. Diseases of the nervous systemRC346-429ENnpj Parkinson's Disease, Vol 7, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neurology. Diseases of the nervous system RC346-429
spellingShingle	Neurology. Diseases of the nervous system RC346-429 Ross D. Markello Golia Shafiei Christina Tremblay Ronald B. Postuma Alain Dagher Bratislav Misic Multimodal phenotypic axes of Parkinson’s disease
description	Abstract Individuals with Parkinson’s disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the “average” patient, ignoring patient heterogeneity and obscuring important individual differences. Modern large-scale data sharing efforts provide a unique opportunity to precisely investigate individual patient characteristics, but there exists no analytic framework for comprehensively integrating data modalities. Here we apply an unsupervised learning method—similarity network fusion—to objectively integrate MRI morphometry, dopamine active transporter binding, protein assays, and clinical measurements from n = 186 individuals with de novo Parkinson’s disease from the Parkinson’s Progression Markers Initiative. We show that multimodal fusion captures inter-dependencies among data modalities that would otherwise be overlooked by field standard techniques like data concatenation. We then examine how patient subgroups derived from the fused data map onto clinical phenotypes, and how neuroimaging data is critical to this delineation. Finally, we identify a compact set of phenotypic axes that span the patient population, demonstrating that this continuous, low-dimensional projection of individual patients presents a more parsimonious representation of heterogeneity in the sample compared to discrete biotypes. Altogether, these findings showcase the potential of similarity network fusion for combining multimodal data in heterogeneous patient populations.
format	article
author	Ross D. Markello Golia Shafiei Christina Tremblay Ronald B. Postuma Alain Dagher Bratislav Misic
author_facet	Ross D. Markello Golia Shafiei Christina Tremblay Ronald B. Postuma Alain Dagher Bratislav Misic
author_sort	Ross D. Markello
title	Multimodal phenotypic axes of Parkinson’s disease
title_short	Multimodal phenotypic axes of Parkinson’s disease
title_full	Multimodal phenotypic axes of Parkinson’s disease
title_fullStr	Multimodal phenotypic axes of Parkinson’s disease
title_full_unstemmed	Multimodal phenotypic axes of Parkinson’s disease
title_sort	multimodal phenotypic axes of parkinson’s disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6dd08d7bb13342ac8d5db6a5e717d2e8
work_keys_str_mv	AT rossdmarkello multimodalphenotypicaxesofparkinsonsdisease AT goliashafiei multimodalphenotypicaxesofparkinsonsdisease AT christinatremblay multimodalphenotypicaxesofparkinsonsdisease AT ronaldbpostuma multimodalphenotypicaxesofparkinsonsdisease AT alaindagher multimodalphenotypicaxesofparkinsonsdisease AT bratislavmisic multimodalphenotypicaxesofparkinsonsdisease
_version_	1718387574726197248

Multimodal phenotypic axes of Parkinson’s disease

Ejemplares similares