The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans

IntroductionRelationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). Methods: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. Results: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an approximate to 10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (A beta) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. Discussion: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Automated summary

The relationship between white matter hyperintensities and various factors such as brain aging, AD neuropathology, cognitive decline, and Aβ positivity after age 65 have been observed in a large MRI database. The Brain Chart established can quantify brain-aging trajectories and provide systematic evaluation of brain-aging patterns relative to a large consortium. Brain aging, AD-like atrophy, and WMHs were shown to be better predictors of cognition than chronological age in MCI/AD subjects.