  Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the
healthy brain aging pattern are associated to the accelerated brain aging and
brain abnormalities. Hence, efficient and accurate diagnosis techniques are
required for eliciting accurate brain age estimations. Several contributions
have been reported in the past for this purpose, resorting to different
data-driven modeling methods. Recently, deep neural networks (also referred to
as deep learning) have become prevalent in manifold neuroimaging studies,
including brain age estimation. In this review, we offer a comprehensive
analysis of the literature related to the adoption of deep learning for brain
age estimation with neuroimaging data. We detail and analyze different deep
learning architectures used for this application, pausing at research works
published to date quantitatively exploring their application. We also examine
different brain age estimation frameworks, comparatively exposing their
advantages and weaknesses. Finally, the review concludes with an outlook
towards future directions that should be followed by prospective studies. The
ultimate goal of this paper is to establish a common and informed reference for
newcomers and experienced researchers willing to approach brain age estimation
by using deep learning models
