Mar 6, 2020

Integrated morphometric, molecular, and clinical characterization of Parkinson's disease pathology

bioRxiv
Ross D. MarkelloBratislav Misic

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 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, demo...Continue Reading

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Mentioned in this Paper

Sleep
Dopamine transporter
Progressive Disease
Subgroup
Morphometric Analysis
Multimodal Imaging
Learning
Parkinson Disease
Binding (Molecular Function)
Neuroimaging

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