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Multimodal phenotypic axes of Parkinson’s disease by Markello, Ross D. ;Shafiei, Golia ;Tremblay, Christina ;Postuma, Ronald B. ;Dagher, Alain ;Misic, Bratislav is a Medicine article available to read on EtoBox.
What is Multimodal phenotypic axes of Parkinson’s disease about?
## 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
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- Author
- Markello, Ross D. ;Shafiei, Golia ;Tremblay, Christina ;Postuma, Ronald B. ;Dagher, Alain ;Misic, Bratislav
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Medicine (Health Sciences)