Graphs are widely used as a natural framework that captures interactions
between individual elements represented as nodes in a graph. In medical
applications, specifically, nodes can represent individuals within a
potentially large population (patients or healthy controls) accompanied by a
set of features, while the graph edges incorporate associations between
subjects in an intuitive manner. This representation allows to incorporate the
wealth of imaging and non-imaging information as well as individual subject
features simultaneously in disease classification tasks. Previous graph-based
approaches for supervised or unsupervised learning in the context of disease
prediction solely focus on pairwise similarities between subjects, disregarding
individual characteristics and features, or rather rely on subject-specific
imaging feature vectors and fail to model interactions between them. In this
paper, we present a thorough evaluation of a generic framework that leverages
both imaging and non-imaging information and can be used for brain analysis in
large populations. This framework exploits Graph Convolutional Networks (GCNs)
and involves representing populations as a sparse graph, where its nodes are
associated with imaging-based feature vectors, while phenotypic information is
integrated as edge weights. The extensive evaluation explores the effect of
each individual component of this framework on disease prediction performance
and further compares it to different baselines. The framework performance is
tested on two large datasets with diverse underlying data, ABIDE and ADNI, for
the prediction of Autism Spectrum Disorder and conversion to Alzheimer's
disease, respectively. Our analysis shows that our novel framework can improve
over state-of-the-art results on both databases, with 70.4% classification
accuracy for ABIDE and 80.0% for ADNI.