ArticleFrontiers in neuroscience2017
Disease-Specific Regions Outperform Whole-Brain Approaches in Identifying Progressive Supranuclear Palsy: A Multicentric MRI Study.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Atrophy in midbrain & cerebral/cerebellar pedunculi is characteristic for progressive supranuclear palsy - A double-validation whole-brain meta-analysis.NeuroImage. Clinical · 2019Pooled it
- Brainstem and Cerebellar Volume Loss and Associated Clinical Features in Progressive Supranuclear Palsy.Annals of clinical and translational neurology · 2026Observational
- Subregional thalamic atrophy in Progressive Supranuclear Palsy: A machine learning study.NeuroImage. Clinical · 2026Article
- Brainstem and cerebellar radiological findings in progressive supranuclear palsy.Brain communications · 2025Review
- Article
- Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes.Alzheimer's research & therapy · 2022Article
- Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes.Frontiers in neurology · 2020Review
- Localizing parkinsonism based on focal brain lesions.Brain : a journal of neurology · 2018Article
- Social Cognition Deficits Are Pervasive across Both Classical and Overlap Frontotemporal Dementia Syndromes.Dementia and geriatric cognitive disorders extraArticle
Corrections and comments
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Authors and funding
13 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To identify progressive supranuclear palsy (PSP), we combined voxel-based morphometry (VBM) and support vector machine (SVM) classification using disease-specific features in multicentric magnetic resonance imaging (MRI) data. Structural brain differences were investigated at four centers between 20 patients with PSP and 20 age-matched healthy controls with T1-weighted MRI at 3T. To pave the way for future application in personalized medicine, we applied SVM classification to identify PSP on an individual level besides group analyses based on VBM. We found a major decline in gray matter density in the brainstem, insula, and striatum, and also in frontomedian regions, which is in line with current literature. Moreover, SVM classification yielded high accuracy rates above 80% for disease identification in imaging data. Focusing analyses on disease-specific regions-of-interest (ROI) led to higher accuracy rates compared to a whole-brain approach. Using a polynomial kernel (instead of a linear kernel) led to an increased sensitivity and a higher specificity of disease detection. Our study supports the application of MRI for individual diagnosis of PSP, if combined with SVM approaches. We demonstrate that SVM classification provides high accuracy rates in multicentric data-a prerequisite for potential application in diagnostic routine.
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