Exploring heritability of functional brain networks with inexact graph matching
Ktena SI, Arslan S, Parisot S, Rueckert D (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: IEEE Computer Society
Pages Range: 354-357
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Melbourne, VIC, AUS
ISBN: 9781509011711
DOI: 10.1109/ISBI.2017.7950536
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.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Ktena, S.I., Arslan, S., Parisot, S., & Rueckert, D. (2017). Exploring heritability of functional brain networks with inexact graph matching. In Proceedings - International Symposium on Biomedical Imaging (pp. 354-357). Melbourne, VIC, AUS: IEEE Computer Society.
MLA:
Ktena, Sofia Ira, et al. "Exploring heritability of functional brain networks with inexact graph matching." Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, VIC, AUS IEEE Computer Society, 2017. 354-357.
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