Automated MRI measures predict progression to Alzheimer's disease

The prediction of individuals with mild cognitive impairment (MCI) destined to develop Alzheimer's disease (AD) is of increasing clinical importance. In this study, using baseline T1-weighted MRI scans of 324 MCI individuals from two cohorts and automated software tools, we employed factor analyses and Cox proportional hazards models to identify a set of neuroanatomic measures that best predicted the time to progress from MCI to AD. For comparison, cerebrospinal fluid (CSF) assessments of cellular pathology and positron emission tomography (PET) measures of metabolic activity were additionally examined. By 3 years follow-up, 60 MCI individuals from the first cohort and 58 MCI individuals from the second cohort had progressed to a diagnosis of AD. Cox models on the first cohort demonstrated significant effects for the medial temporal factor [Hazards Ratio (HR) = 0.43{95% confidence interval (CI), 0.32-0.55}, p