Braak neurofibrillary tangle staging prediction from in vivo MRI metrics

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
Highlights • Several regional MRI metrics are significantly associated with neurofibrillary tangles pathology. • Braak staging can be predicted with 62.4% accuracy using machine-learning techniques. • Structural MRI is a potential biomarker to support early diagnosis of Alzheimer's disease.