Computational analysis and modeling of graph-structured neuroimaging data

Graph representations are often used to model structured data at an
individual or population level and have numerous applications in
pattern recognition problems. In the field of neuroscience, where such
representations are commonly used to model structural or functional
connectivity between a set of brain regions, graphs have proven to be
of great importance to reveal patterns related to brain development
and disease, which were previously unknown. This talk is going to
cover the evaluation of similarity between brain connectivity networks
in a manner that accounts for the graph structure and is tailored for
a particular application. At the same time, exploiting the wealth of
imaging and non-imaging information for disease prediction tasks
requires models capable of simultaneously representing individual
features and data associations between subjects from potentially large
populations. The latter can be particularly beneficial in large-scale
studies and graphs provide a natural framework for such
tasks. Concepts from signal processing on graphs allow convolutions on
a population graph incorporating both imaging and non-imaging
information and this talk will demonstrate their importance for
semi-supervised classification tasks, inferring subject specific
properties from their imaging features and interactions within a
population.