Introduction: Alzheimer's disease diagnosis requires postmortem visualization of amyloid and tau deposits. As brain atrophy can provide assessment of consequent neurodegeneration, our objective was to predict postmortem neurofibrillary tangles (NFT) from in vivo MRI measurements.
Methods: All participants with neuroimaging and neuropathological data from the Alzheimer's Disease Neuroimaging Initiative, the National Alzheimer's Coordinating Center and the Rush Memory and Aging Project were selected (n = 186). Two hundred and thirty two variables were extracted from last MRI before death using FreeSurfer. Nonparametric correlation analysis and multivariable support vector machine classification were performed to provide a predictive model of Braak NFT staging.
Results: We demonstrated that 59 of our MRI variables, mostly temporal lobe structures, were significantly associated with Braak NFT stages (P < .005). We obtained a 62.4% correct classification rate for discrimination between transentorhinal, limbic, and isocortical groups.
Discussion: Structural neuroimaging may therefore be considered as a potential biomarker for early detection of Alzheimer's disease-associated neurofibrillary degeneration.
Keywords: Alzheimer's disease; Dementia; Early diagnosis; Imaging biomarkers; Machine-learning; Neurofibrillary degeneration; Neuroimaging; Neuropathology; Predictive model; Structural MRI; Tau pathology.