Exploring heritability of functional brain networks with inexact graph matching

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 mo...

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Bibliographic Details
Published inProceedings (International Symposium on Biomedical Imaging) pp. 354 - 357
Main Authors Ktena, Sofia Ira, Arslan, Salim, Parisot, Sarah, Rueckert, Daniel
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Diseases
functional brain connectivity
Genetics
graph matching
Histograms
Indexes
Network topology
Simulated annealing
Time series analysis
twin study
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Summary: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.
ISSN:1945-8452
DOI:10.1109/ISBI.2017.7950536