Over the next twenty-five years, the proportion of the population over age 65 will increase 76%; therefore understanding both the normal and pathological processes involved in the aging of the human brain is of the highest public health priority. We report here the use of a computational method that provides estimates of the “brain age” of individuals that is based solely on a high resolution Magnetic Resonance Image (MRI) of the brain of the individual, and is blinded to his or her true chronological age. The method proceeds in two phases: first, a statistical learning algorithm is used to determine the numerical MRI-based features that predict true age on a training set of 198 healthy elderly individuals; second, these features are used to predict the true age of previously-unseen individuals. In cross-validation experiments, the brain age estimates differed from true age by a mean absolute error of 5.35 years in an elderly cohort, reflecting the broad heterogeneity in structural integrity of the elderly brain. The “brain age” of female subjects was significantly lower than that of male subjects who had the same true age (3.0 years younger for 50-year-olds and 1.6 years younger for 79 year olds), reflecting the longer life expectancy of females. Across the elderly age spectrum, the “brain age” of individuals with Alzheimer's Disease (AD) was significantly higher than that of cognitively-healthy elderly subjects with equivalent true age; however, this was not the case for the subjects with mild cognitive impairment (MCI), a possible AD prodrome
