Title: Exploring Heritability of Functional Brain Networks with Inexact Graph Matching,
Abstract: Data-driven brain parcellations aim to provide a more accurate representation
of an individual's functional connectivity, since they are able to capture
individual variability that arises due to development or disease. This renders
comparisons between the emerging brain connectivity networks more challenging,
since correspondences between their elements are not preserved. Unveiling these
correspondences is of major importance to keep track of local functional
connectivity changes. We propose a novel method based on graph edit distance
for the comparison of brain graphs directly in their domain, that can
accurately reflect similarities between individual networks while providing the
network element correspondences. This method is validated on a dataset of 116
twin subjects provided by the Human Connectome Project.