Journal
JOURNAL OF ALZHEIMERS DISEASE
Volume 89, Issue 4, Pages 1493-1502Publisher
IOS PRESS
DOI: 10.3233/JAD-220585
Keywords
Alzheimer's disease; brain metabolism; FDG PET; machine learning
Categories
Funding
- Natural Science and Engineering Research Council of Canada (NSERC) [RGPIN2016-05964]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Cogstate
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. HoffmannLa Roche Ltd
- company Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Lumosity
- Lundbeck
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- Canadian Institutes of Health Research
- St. Boniface Hospital Research Foundation [1406-3216, 1410-3216]
- Canadian Institute of Health Research (CIHR) [PJT162144]
- Honourable Douglas and Patricia Everett
- Royal Canadian Properties Limited Endowment Fund [1403-3131]
- Alzheimer's Society of Manitoba
- Research Manitoba
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The study assessed the efficacy of the MAD framework in tracking brain metabolic changes in the prodromal stage of Alzheimer's disease, with the GLM algorithm showing excellent performance in distinguishing between sMCI and pAD patients two years prior to diagnosis. Significant increases in MAD scores were identified in pAD, particularly close to diagnosis, and were associated with cognitive decline.
Background: We previously introduced a machine learning-based Alzheimer's Disease Designation (MAD) framework for identifying AD-related metabolic patterns among neurodegenerative subjects. Objective: We sought to assess the efficiency of our MAD framework for tracing the longitudinal brain metabolic changes in the prodromal stage of AD. Methods: MAD produces subject scores using five different machine-learning algorithms, which include a general linear model (GLM), two different approaches of scaled subprofile modeling, and two different approaches of a support vector machine. We used our pre-trained MAD framework, which was trained based on metabolic brain features of 94 patients with AD and 111 age-matched cognitively healthy (CH) individuals. The MAD framework was applied on longitudinal independent test sets including 54 CHs, 51 stable mild cognitive impairment (sMCI), and 39 prodromal AD (pAD) patients at the time of the clinical diagnosis of AD, and two years prior. Results: The GLM showed excellent performance with area under curve (AUC) of 0.96 in distinguishing sMCI from pAD patients at two years prior to the time of the clinical diagnosis of AD while other methods showed moderate performance (AUC: 0.7-0.8). Significant annual increment of MAD scores were identified using all five algorithms in pAD especially when it got closer to the time of diagnosis (p < 0.001), but not in sMCI. The increased MAD scores were also significantly associated with cognitive decline measured by Mini-Mental State Examination in pAD (q < 0.01). Conclusion: These results suggest that MAD may be a relevant tool for monitoring disease progression in the prodromal stage of AD.
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