Machine Learning Models of Voxel‐Level [<sup>18</sup>F] Fluorodeoxyglucose Positron Emission Tomography Data Excel at Predicting Progressive Supranuclear Palsy Pathology
The results suggest that glucose hypometabolism in the thalamus and cerebellar dentate have the highest potential for predicting PSP pathology. Our optimized machine learning model outperformed the best currently available biomarker to predict PSP pathology. ANN NEUROL 2025;98:410-420.
Authors
- Val J. Lowe (ORCID: https://orcid.org/0000-0002-5612-1667)
- Farwa Ali (ORCID: https://orcid.org/0000-0001-6060-3995)
- Keith A. Josephs (ORCID: https://orcid.org/0000-0003-2930-8634)
- Dennis W. Dickson (ORCID: https://orcid.org/0000-0001-7189-7917)
- Nha Trang Thu Pham
- Addison S. Braun
- Neha Atulkumar Singh (ORCID: https://orcid.org/0000-0002-3614-9179)
- Jennifer L. Whitwell (ORCID: https://orcid.org/0000-0001-6914-1563)
- Ryota Satoh (ORCID: https://orcid.org/0000-0002-3418-2987)
Institutions
- Jacksonville College (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Annals of Neurology
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1002/ana.27265
- Citations
- 1
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Institutes of Health