ArticleNPJ Parkinson's disease2015
Classification of symptom-side predominance in idiopathic Parkinson's disease.
Article in NPJ Parkinson's disease, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Clustering of Parkinson subtypes reveals strong influence of DRD2 polymorphism and gender.Scientific reports · 2022Article
- Topological patterns of motor networks in Parkinson's disease with different sides of onset: A resting-state-informed structural connectome study.Frontiers in aging neuroscience · 2022Article
- Lexical-semantic search related to side of onset and putamen volume in Parkinson's disease.Brain and language · 2020Article
- Article
- Marked brain asymmetry with intact cognitive functioning in idiopathic Parkinson's disease: a longitudinal analysis.The Clinical neuropsychologist · 2017Article
- Striatal and Hippocampal Atrophy in Idiopathic Parkinson's Disease Patients without Dementia: A Morphometric Analysis.Frontiers in neurology · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Asymmetry of symptom onset in Parkinson's disease (PD) is strongly linked to differential diagnosis, progression of disease, and clinical manifestation, suggesting its importance in terms of specifying a therapeutic strategy for each individual patient. To scrutinize the predictive value of this consequential clinical phenomenon as a neuromarker supporting a personalized therapeutic approach, we modeled symptom-side predominance at disease onset based on brain morphology assessed with magnetic resonance (MR) images by utilizing machine learning classification. The integration of multimodal MR imaging data into a multivariate statistical model led to predict left- and right-sided symptom onset with an above-chance accuracy of 96%. By absolute numbers, all but one patient were correctly classified. Interestingly, mainly hippocampal morphology supports this prediction. Considering a different disease formation of this single outlier and the strikingly high classification, this approach proves a reliable predictive model for symptom-side diagnostics in PD. In brief, this work hints toward individualized disease-modifying therapies rather than symptom-alleviating treatments.
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Registered trials
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