  The characterisation of the brain as a "connectome", in which the connections
are represented by correlational values across timeseries and as summary
measures derived from graph theory analyses, has been very popular in the last
years. However, although this representation has advanced our understanding of
the brain function, it may represent an oversimplified model. This is because
the typical fMRI datasets are constituted by complex and highly heterogeneous
timeseries that vary across space (i.e., location of brain regions). We compare
various modelling techniques from deep learning and geometric deep learning to
pave the way for future research in effectively leveraging the rich spatial and
temporal domains of typical fMRI datasets, as well as of other similar
datasets. As a proof-of-concept, we compare our approaches in the homogeneous
and publicly available Human Connectome Project (HCP) dataset on a supervised
binary classification task. We hope that our methodological advances relative
to previous "connectomic" measures can ultimately be clinically and
computationally relevant by leading to a more nuanced understanding of the
brain dynamics in health and disease. Such understanding of the brain can
fundamentally reduce the constant specialised clinical expertise in order to
accurately understand brain variability.
