Predicting future cognitive impairment in preclinical Alzheimer's disease using amyloid PET and MRI: A multisite machine learning study.

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Title: Predicting future cognitive impairment in preclinical Alzheimer's disease using amyloid PET and MRI: A multisite machine learning study.
Authors: Yang B; Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: b.y.yang@wustl.edu., Earnest T; Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA., Bilgel M; Laboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA., Albert MS; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA., Johnson SC; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53726, USA., Davatzikos C; Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA., Erus G; Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA., Masters CL; Florey Institute of Neuroscience and Mental Health, Parkville, VIC 3052, Australia., Resnick SM; Laboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA., Miller MI; Center of Imaging Science and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA., Bakker A; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA., Morris JC; Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA., Benzinger TLS; Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA., Gordon BA; Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA., Sotiras A; Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA. Electronic address: aristeidis.sotiras@pennmedicine.upenn.edu.
Corporate Authors: Alzheimer’s Disease Neuroimaging Initiative, Preclinical Alzheimer’s Disease Consortium
Source: Neurobiology of aging [Neurobiol Aging] 2026 Sep; Vol. 165, pp. 8-23. Date of Electronic Publication: 2026 Apr 19.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 8100437 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1558-1497 (Electronic) Linking ISSN: 01974580 NLM ISO Abbreviation: Neurobiol Aging Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1558-1497
DOI:10.1016/j.neurobiolaging.2026.04.005