TY - GEN
T1 - Parcellation-independent multi-scale framework for brain network analysis
AU - Schirmer, M. D.
AU - Ball, G.
AU - Counsell, S. J.
AU - Edwards, A. D.
AU - Rueckert, D.
AU - Hajnal, J. V.
AU - Aljabar, P.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84929466360&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11182-7_3
DO - 10.1007/978-3-319-11182-7_3
M3 - Conference contribution
AN - SCOPUS:84929466360
T3 - Mathematics and Visualization
SP - 23
EP - 32
BT - Computational Diffusion MRI - MICCAI Workshop 2014
A2 - Schneider, Torben
A2 - Reisert, Marco
A2 - O’Donnell, Lauren
A2 - Rathi, Yogesh
A2 - Nedjati-Gilani, Gemma
PB - springer berlin
T2 - 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
Y2 - 18 September 2014 through 18 September 2014
ER -