ArticleNPJ Parkinson's disease2021
Multimodal phenotypic axes of Parkinson's disease.
Article in NPJ Parkinson's disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Biological heterogeneity in Parkinson's disease: implications for MRI-based patient stratification.NPJ Parkinson's disease · 2026Review
- Integrating imaging and omics for enhanced subtyping of mild cognitive impairment associated with Alzheimer's disease.Journal of translational medicine · 2025Article
- DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson's disease subtypes.BMC bioinformatics · 2025Article
- Brain-clinical biotyping in patients with idiopathic REM sleep behavior disorder.NPJ Parkinson's disease · 2025Article
- Deciphering shared molecular dysregulation across Parkinson's disease variants using a multi-modal network-based data integration and analysis.NPJ Parkinson's disease · 2025Article
- A novel integrative multimodal classifier to enhance the diagnosis of Parkinson's disease.Briefings in bioinformatics · 2025Article
- Mapping of catecholaminergic denervation, neurodegeneration, and inflammation in 6-OHDA-treated Parkinson's disease mice.NPJ Parkinson's disease · 2025Article
- Decision tree-based approach to robust Parkinson's disease subtyping using clinical data of the Michael J. Fox Foundation LRRK2 cross-sectional study.Frontiers in artificial intelligence · 2025Article
- Mapping of catecholaminergic denervation, neurodegeneration, and inflammation in 6-OHDA-treated Parkinson's disease mice.Research square · 2024Article
- Genetic-based patient stratification in Alzheimer's disease.Scientific reports · 2024Article
- covSTATIS: a multi-table technique for network neuroscience.Aperture neuro · 2024Article
- Multivariate investigation of aging in mouse models expressing the Alzheimer's protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics.Brain structure & function · 2024Article
- Domain adapted brain network fusion captures variance related to pubertal brain development and mental health.Nature communications · 2023Article
- Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses.iScience · 2023Article
- Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease.PLoS biology · 2023Article
- Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response.Vaccines · 2023Article
- Targeting Nrf2 signaling pathway and oxidative stress by resveratrol for Parkinson's disease: an overview and update on new developments.Molecular biology reports · 2023Review
- JAC4 Alleviates Rotenone-Induced Parkinson's Disease through the Inactivation of the NLRP3 Signal Pathway.Antioxidants (Basel, Switzerland) · 2023Article
- Article
- Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks.Translational psychiatry · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.