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 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, 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.

Oculomotor deficits in Parkinson's disease: Increasing sensitivity using multivariate approaches

“…These results were in keeping with a small cross sectional study from 1998 (13). Recent evidence suggests that PD comprises a more heterogeneous set of phenotypes than previously hypothesized (33). Metrics such as the damping ratio have the potential not only to help differentiate between PD and HC with greater accuracy, but additionally to allow for more precise definitions of differing clinical phenotypes amongst those with PD.…”

Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information

“…To our knowledge, it was the first attempt using multimodal MRI data to uncover putative subtypes of OCD. Although we only combined structural and functional data in this study, this framework could integrate more modal data sources, such as transporter binding and protein assays (Markello et al, 2021). Avoiding an arbitrary setting, our framework determined the number of subtypes automatically.…”

“…Inspired by one previous study (Markello et al, 2021), we proposed an strategy to determine the optimal combination of K and μ avoiding an arbitrary decision. We assumed that the optimal combinations should generate stable subtyping results.…”

“…Recent progress in techniques such as multi‐view learning methods makes it possible that exploiting the complementary information from different data sources (S. Fan et al, 2020; Rai et al, 2017). As one of these methods, similarity network fusion (SNF) (Wang et al, 2014) is proved to yield a balanced representation of multiview data sources (Markello et al, 2021). Using SNF, Ross et al integrate multimodal data sources and successfully uncover putative subtypes of Parkinson's disease, suggesting the potential of SNF for integrating multimodal data in heterogeneous disorders (Markello et al, 2021).…”

Technology-based therapy-response and prognostic biomarkers in a prospective study of a de novo Parkinson’s disease cohort

“…In brief, a multimodality characterization of PD patients in the very early stages, including neuroimaging data, CSF and/or blood biomarkers, clinical assessment, demographics, and an objective technology-based characterization of motor performance is a promising approach to stratify patients with different progression rates, as also previously discussed 37 . However, in order to render this approach feasible in the clinical practice, further work is required to make these measures as affordable and quick as possible.…”