Exploring heritability of functional brain networks with inexact graph matching
File(s)
Accepted version
Author(s)
Ktena, SI
Arslan, S
Parisot, S
Rueckert, D
Type
Conference Paper
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.
Date Issued
2017-06-19
Date Acceptance
2017-06-01
Citation
Proceedings - International Symposium on Biomedical Imaging, 2017, pp.354-357
ISBN
9781509011711
ISSN
1945-7928
Publisher
IEEE
Start Page
354
End Page
357
Journal / Book Title
Proceedings - International Symposium on Biomedical Imaging
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
IEEE 14th International Symposium on Biomedical Imaging
Subjects
q-bio.NC
cs.NE
Publication Status
Published
Start Date
2017-04-18
Finish Date
2017-04-21
Coverage Spatial
Melbourne, VIC, Australia