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- Title
Measurement of neurodegeneration using a multivariate early frame amyloid PET classifier.
- Authors
Matthews, Dawn C.; Lukic, Ana S.; Andrews, Randolph D.; Wernick, Miles N.; Strother, Stephen C.; Schmidt, Mark E.
- Abstract
Introduction:Amyloidmeasurement provides important confirmation of pathology for Alzheimer's disease (AD) clinical trials. However, many amyloid positive (Am+) earlystage subjects do not worsen clinically during a clinical trial, and a neurodegenerative measure predictive of decline could provide critical information. Studies have shown correspondence between perfusion measured by early amyloid frames post-tracer injection and fluorodeoxyglucose (FDG) positron emission tomography (PET), but with limitations in sensitivity. Multivariate machine learning approaches may offer a more sensitivemeans for detection of disease related changes aswehave demonstrated with FDG. Methods: Using summed dynamic florbetapir image frames acquired during the first 6 minutes post-injection for 107 Alzheimer's Disease Neuroimaging Initiative subjects, we applied optimized machine learning to develop and test image classifiers aimed at measuring AD progression. Early frame amyloid (EFA) classification was compared to that of an independently developed FDG PET AD progression classifier by scoring the FDG scans of the same subjects at the same time point. Score distributions and correlation with clinical endpoints were compared to those obtained from FDG. Region of interest measures were compared between EFA and FDG to further understand discrimination performance. Results: The EFA classifier produced a primary pattern similar to that of the FDG classifier whose expression correlated highly with the FDG pattern (R-squared 0.71), discriminated cognitively normal (NL) amyloid negative (Am-) subjects from all Am+ groups, and that correlated in Am+ subjects with Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale (R = 0.59, 0.63, 0.73) and with subsequent 24-month changes in these measures (R = 0.67, 0.73, 0.50). Discussion: Our results support the ability to use EFA with a multivariate machine learning-derived classifier to obtain a sensitivemeasure of AD-related loss in neuronal function that correlateswith FDGPET in preclinical and early prodromal stages aswell as in late mild cognitive impairment and dementia.
- Subjects
AMYLOID; ALZHEIMER'S disease; POSITRON emission tomography; MILD cognitive impairment; MINI-Mental State Examination
- Publication
Alzheimer's & Dementia: Translational Research & Clinical Interventions, 2022, Vol 8, Issue 1, p1
- ISSN
2352-8737
- Publication type
Article
- DOI
10.1002/trc2.12325