Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer’s disease
Abstract The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer’s disease (AD) has created a pressing need for the ability to accurately identify optimal candidates for anti-amyloid therapy, specifically those with evidence for incipient cognitive decline, since patients...
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| Format: | Article |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02039-2 |
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| author | Jarrad Perron Carly Scramstad Ji Hyun Ko |
| author_facet | Jarrad Perron Carly Scramstad Ji Hyun Ko |
| author_sort | Jarrad Perron |
| collection | DOAJ |
| description | Abstract The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer’s disease (AD) has created a pressing need for the ability to accurately identify optimal candidates for anti-amyloid therapy, specifically those with evidence for incipient cognitive decline, since patients with mild cognitive impairment (MCI) may remain stable for several years even with positive AD biomarkers. Using fluorodeoxyglucose PET and biomarker data from 594 ADNI patients, a neural network ensemble was trained to forecast cognition from MCI diagnostic baseline. Training data comprised PET studies of patients with biological AD. The ensemble discriminated between progressive and stable prodromal subjects (MCI with positive amyloid and tau) at baseline with 88.6% area-under-curve, 88.6% (39/44) accuracy, 73.7% (14/19) sensitivity and 100% (25/25) specificity in the test set. It also correctly classified all other test subjects (healthy or AD continuum subjects across the cognitive spectrum) with 86.4% accuracy (206/239), 77.4% sensitivity (33/42) and 88.23% (165/197) specificity. By identifying patients with prodromal AD who will not progress to dementia, our model could significantly reduce overall societal burden and cost if implemented as a screening tool. The model’s high positive predictive value in the prodromal test set makes it a practical means for selecting candidates for anti-amyloid therapy and trials. |
| format | Article |
| id | doaj-art-c8f235551b7d46cdb9109f64dffa8bf6 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c8f235551b7d46cdb9109f64dffa8bf62025-08-20T01:51:38ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-02039-2Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer’s diseaseJarrad Perron0Carly Scramstad1Ji Hyun Ko2Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of ManitobaSection of Neurology, Department of Internal Medicine, Rady Faculty of Health Sciences, University of ManitobaGraduate Program in Biomedical Engineering, Price Faculty of Engineering, University of ManitobaAbstract The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer’s disease (AD) has created a pressing need for the ability to accurately identify optimal candidates for anti-amyloid therapy, specifically those with evidence for incipient cognitive decline, since patients with mild cognitive impairment (MCI) may remain stable for several years even with positive AD biomarkers. Using fluorodeoxyglucose PET and biomarker data from 594 ADNI patients, a neural network ensemble was trained to forecast cognition from MCI diagnostic baseline. Training data comprised PET studies of patients with biological AD. The ensemble discriminated between progressive and stable prodromal subjects (MCI with positive amyloid and tau) at baseline with 88.6% area-under-curve, 88.6% (39/44) accuracy, 73.7% (14/19) sensitivity and 100% (25/25) specificity in the test set. It also correctly classified all other test subjects (healthy or AD continuum subjects across the cognitive spectrum) with 86.4% accuracy (206/239), 77.4% sensitivity (33/42) and 88.23% (165/197) specificity. By identifying patients with prodromal AD who will not progress to dementia, our model could significantly reduce overall societal burden and cost if implemented as a screening tool. The model’s high positive predictive value in the prodromal test set makes it a practical means for selecting candidates for anti-amyloid therapy and trials.https://doi.org/10.1038/s41598-025-02039-2Deep learningNeuroimagingProdromalFDG PETMCIAlzheimer’s disease |
| spellingShingle | Jarrad Perron Carly Scramstad Ji Hyun Ko Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer’s disease Scientific Reports Deep learning Neuroimaging Prodromal FDG PET MCI Alzheimer’s disease |
| title | Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer’s disease |
| title_full | Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer’s disease |
| title_fullStr | Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer’s disease |
| title_full_unstemmed | Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer’s disease |
| title_short | Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer’s disease |
| title_sort | brain metabolic imaging based model identifies cognitive stability in prodromal alzheimer s disease |
| topic | Deep learning Neuroimaging Prodromal FDG PET MCI Alzheimer’s disease |
| url | https://doi.org/10.1038/s41598-025-02039-2 |
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