  Brain age estimation is clinically important as it can provide valuable
information in the context of neurodegenerative diseases such as Alzheimer's.
Population graphs, which include multimodal imaging information of the subjects
along with the relationships among the population, have been used in literature
along with Graph Convolutional Networks (GCNs) and have proved beneficial for a
variety of medical imaging tasks. A population graph is usually static and
constructed manually using non-imaging information. However, graph construction
is not a trivial task and might significantly affect the performance of the
GCN, which is inherently very sensitive to the graph structure. In this work,
we propose a framework that learns a population graph structure optimized for
the downstream task. An attention mechanism assigns weights to a set of imaging
and non-imaging features (phenotypes), which are then used for edge extraction.
The resulting graph is used to train the GCN. The entire pipeline can be
trained end-to-end. Additionally, by visualizing the attention weights that
were the most important for the graph construction, we increase the
interpretability of the graph. We use the UK Biobank, which provides a large
variety of neuroimaging and non-imaging phenotypes, to evaluate our method on
brain age regression and classification. The proposed method outperforms
competing static graph approaches and other state-of-the-art adaptive methods.
We further show that the assigned attention scores indicate that there are both
imaging and non-imaging phenotypes that are informative for brain age
estimation and are in agreement with the relevant literature.
