Exploring Heritability of Functional Brain Networks with Inexact Graph Matching
This work addresses the challenge of tracking local functional connectivity changes in brain networks for neuroscience and medical applications, but it is incremental as it builds on existing graph matching techniques.
The researchers tackled the problem of comparing individual brain connectivity networks when correspondences between network elements are not preserved, by proposing a novel graph edit distance method that accurately reflects similarities and provides element correspondences, validated on 116 twin subjects from the Human Connectome Project.
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.