  Numerous studies have established that estimated brain age, as derived from
statistical models trained on healthy populations, constitutes a valuable
biomarker that is predictive of cognitive decline and various neurological
diseases. In this work, we curate a large-scale heterogeneous dataset (N =
10,158, age range 18 - 97) of structural brain MRIs in a healthy population
from multiple publicly-available sources, upon which we train a deep learning
model for brain age estimation. The availability of the large-scale dataset
enables a more uniform age distribution across adult life-span for effective
age estimation with no bias toward certain age groups. We demonstrate that the
age estimation accuracy, evaluated with mean absolute error (MAE) and
correlation coefficient (r), outperforms previously reported methods in both a
hold-out test set reflective of the custom population (MAE = 4.06 years, r =
0.970) and an independent life-span evaluation dataset (MAE = 4.21 years, r =
0.960) on which a previous study has evaluated. We further demonstrate the
utility of the estimated age in life-span aging analysis of cognitive
functions. Furthermore, we conduct extensive ablation tests and employ
feature-attribution techniques to analyze which regions contribute the most
predictive value, demonstrating the prominence of the frontal lobe as well as
pattern shift across life-span. In summary, we achieve superior age estimation
performance confirming the efficacy of deep learning and the added utility of
training with data both in larger number and more uniformly distributed than in
previous studies. We demonstrate the regional contribution to our brain age
predictions through multiple routes and confirm the association of divergence
between estimated and chronological brain age with neuropsychological measures.

Computer Vision and Pattern Recognition;Image and Video Processing;Quantitative Methods