ArticleFrontiers in human neuroscience2022
Identifying and validating subtypes of Parkinson's disease based on multimodal MRI data
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Altered regional spontaneous brain activity in Parkinson's disease: a meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Gray matter structural and functional brain abnormalities in Parkinson's disease: a meta-analysis of VBM and ALFF data.Journal of neurology · 2025Pooled it
- Disease-Duration-Specific Percentiles for Prospective Subtyping of Parkinson's Disease: A PPMI-Based Study.Movement disorders clinical practice · 2026Article
- Data-driven subtyping of Parkinson's disease using MRI: current insights, challenges, and future directions.Frontiers in aging neuroscience · 2026Review
- Novel applications of machine learning and computational neuroscience models to neuroimaging in Parkinson's disease and related disorders.Frontiers in aging neuroscience · 2026Review
- Analysis of Two Neuroanatomical Subtypes of Parkinson's Disease and Their Motor Progression Based on Semi-Supervised Machine Learning.CNS neuroscience & therapeutics · 2025Article
- Altered functional activity and connectivity in Parkinson's disease with chronic pain: a resting-state fMRI study.Frontiers in aging neuroscience · 2025Article
- MRI subtypes in Parkinson's disease across diverse populations and clustering approaches.NPJ Parkinson's disease · 2024Article
- Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease.Journal of big data · 2024Article
- Olfactory functional covariance connectivity in Parkinson's disease: Evidence from a Chinese population.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: We wished to explore Parkinson's disease (PD) subtypes by clustering analysis based on the multimodal magnetic resonance imaging (MRI) indices amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV). Then, we analyzed the differences between PD subtypes. Methods: Eighty-six PD patients and 44 healthy controls (HCs) were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling (AAL) partition using Data Processing and Analysis for Brain Imaging (DPABI) software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF and GMV between groups. Results: Two subtypes of PD were identified. The "diffuse malignant subtype" was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function. The "mild subtype" was characterized by increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV with mild impairment of motor function and cognitive function. Conclusion: Hierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes. These two PD subtypes showed different neurodegenerative patterns upon imaging.
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