Exploring heritability of functional brain networks with inexact graph matching

Sofia Ira Ktena; Salim Arslan; Sarah Parisot; Daniel Rueckert

doi:10.1109/ISBI.2017.7950536

Exploring heritability of functional brain networks with inexact graph matching

Sofia Ira Ktena, Salim Arslan, Sarah Parisot, Daniel Rueckert

Imperial College London

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
Publisher	IEEE Computer Society
Pages	354-357
Number of pages	4
ISBN (Electronic)	9781509011711
DOIs	https://doi.org/10.1109/ISBI.2017.7950536
State	Published - 15 Jun 2017
Externally published	Yes
Event	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia Duration: 18 Apr 2017 → 21 Apr 2017

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Country/Territory	Australia
City	Melbourne
Period	18/04/17 → 21/04/17

Keywords

Functional brain connectivity
Graph matching
Twin study

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ISBI.2017.7950536

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Ktena, S. I., Arslan, S., Parisot, S., & Rueckert, D. (2017). Exploring heritability of functional brain networks with inexact graph matching. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 354-357). Article 7950536 (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950536

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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.",

keywords = "Functional brain connectivity, Graph matching, Twin study",

author = "Ktena, {Sofia Ira} and Salim Arslan and Sarah Parisot and Daniel Rueckert",

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year = "2017",

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doi = "10.1109/ISBI.2017.7950536",

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Ktena, SI, Arslan, S, Parisot, S & Rueckert, D 2017, Exploring heritability of functional brain networks with inexact graph matching. in 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017., 7950536, Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, pp. 354-357, 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, 18/04/17. https://doi.org/10.1109/ISBI.2017.7950536

Exploring heritability of functional brain networks with inexact graph matching. / Ktena, Sofia Ira; Arslan, Salim; Parisot, Sarah et al.
2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. p. 354-357 7950536 (Proceedings - International Symposium on Biomedical Imaging).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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KW - Twin study

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M3 - Conference contribution

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ER -

Exploring heritability of functional brain networks with inexact graph matching

Abstract

Publication series

Conference

Keywords

UN SDGs

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