Parcellation-independent multi-scale framework for brain network analysis
Schirmer MD, Ball G, Counsell SJ, Edwards AD, Rueckert D, Hajnal JV, Aljabar P (2014)
Publication Type: Conference contribution
Publication year: 2014
Publisher: springer berlin
Book Volume: 39
Pages Range: 23-32
Conference Proceedings Title: Mathematics and Visualization
Event location: Boston, MA, USA
ISBN: 9783319111810
DOI: 10.1007/978-3-319-11182-7_3
Abstract
Structural brain connectivity can be characterised by studies employing diffusion MR, tractography and the derivation of network measures. However, in some subject populations, such as neonates, the lack of a generally accepted paradigm for how the brain should be segmented or parcellated leads to the application of a variety of atlas- and random-based parcellation methods. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences has yet to be resolved, in order to enable more meaningful intraand inter-subject comparisons. This work proposes a parcellation-independent multi-scale analysis of commonly used network measures to describe changes in the brain. As an illustration, we apply our framework to a neonatal serial diffusion MRI data set and show its potential in characterising developmental changes. Furthermore, we use the measures provided by the framework to investigate the inter-dependence between network measures and apply an hierarchical clustering algorithm to determine a subset of measures for characterising the brain.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Schirmer, M.D., Ball, G., Counsell, S.J., Edwards, A.D., Rueckert, D., Hajnal, J.V., & Aljabar, P. (2014). Parcellation-independent multi-scale framework for brain network analysis. In Torben Schneider, Marco Reisert, Lauren O’Donnell, Yogesh Rathi, Gemma Nedjati-Gilani (Eds.), Mathematics and Visualization (pp. 23-32). Boston, MA, USA: springer berlin.
MLA:
Schirmer, M. D., et al. "Parcellation-independent multi-scale framework for brain network analysis." Proceedings of the MICCAI Workshop on Computational Diffusion MRI, CDMRI 2014 held under the auspices of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, Boston, MA, USA Ed. Torben Schneider, Marco Reisert, Lauren O’Donnell, Yogesh Rathi, Gemma Nedjati-Gilani, springer berlin, 2014. 23-32.
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