Normalisation of neonatal brain network measures using stochastic approaches

Markus Schirmer; Gareth Ball; Serena J. Counsell; A. David Edwards; Daniel Rueckert; Joseph V. Hajnal; Paul Aljabar

doi:10.1007/978-3-642-40811-3_72

Normalisation of neonatal brain network measures using stochastic approaches

Markus Schirmer^*, Gareth Ball, Serena J. Counsell, A. David Edwards, Daniel Rueckert, Joseph V. Hajnal, Paul Aljabar

^*Corresponding author for this work

Imperial College London

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

4 Citations (Scopus)

Abstract

Diffusion tensor imaging, tractography and the subsequent derivation of network measures are becoming an established approach in the exploration of brain connectivity. However, no gold standard exists in respect to how the brain should be parcellated and therefore a variety of atlas- and random-based parcellation methods are used. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences necessitates the use of normalisation schemes to enable meaningful intra- and inter-subject comparisons. This work proposes methods for normalising brain network measures using random graphs. We show that the normalised measures are locally stable over distinct random parcellations of the same subject and, applying it to a neonatal serial diffusion MRI data set, we demonstrate their potential in characterising changes in brain connectivity during early development.

Original language	English
Title of host publication	Medical image computing and computer-assisted intervention
Subtitle of host publication	Proceedings of the 16th International Conference, Nagoya, Japan, September 22-26, 2013, Part 1
Editors	Kensaku Mori, Ichiro Sakuma, Yoshinobu Sato, Christian Barillot, Nassir Navab
Publisher	Springer Berlin Heidelberg
Pages	574-581
Number of pages	8
Volume	16
Edition	1
ISBN (Print)	9783642408106
DOIs	https://doi.org/10.1007/978-3-642-40811-3_72
Publication status	Published - 2013
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013; - Japan, Nagoya, United Kingdom Duration: 22 Sept 2013 → 26 Sept 2013

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin Heidelberg
Volume	8149
ISSN (Print)	0302-9743

Conference

Conference	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013;
Country/Territory	United Kingdom
City	Nagoya
Period	22/09/2013 → 26/09/2013

Keywords

Brain connectivity
Brain networks
connectivity
Diffusion mris
Gold standards
neonatal
Network measures
Stochastic approach, Diffusion
Diffusion tensor imaging
Electric network analysis
Magnetic resonance imaging
Tensors, Graph theory

Access to Document

10.1007/978-3-642-40811-3_72

Cite this

Schirmer, M., Ball, G., Counsell, S. J., Edwards, A. D., Rueckert, D., Hajnal, J. V., & Aljabar, P. (2013). Normalisation of neonatal brain network measures using stochastic approaches. In K. Mori, I. Sakuma, Y. Sato, C. Barillot, & N. Navab (Eds.), Medical image computing and computer-assisted intervention: Proceedings of the 16th International Conference, Nagoya, Japan, September 22-26, 2013, Part 1 (1 ed., Vol. 16, pp. 574-581). (Lecture Notes in Computer Science; Vol. 8149). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-40811-3_72

Schirmer, Markus ; Ball, Gareth ; Counsell, Serena J. et al. / Normalisation of neonatal brain network measures using stochastic approaches. Medical image computing and computer-assisted intervention: Proceedings of the 16th International Conference, Nagoya, Japan, September 22-26, 2013, Part 1. editor / Kensaku Mori ; Ichiro Sakuma ; Yoshinobu Sato ; Christian Barillot ; Nassir Navab. Vol. 16 1. ed. Springer Berlin Heidelberg, 2013. pp. 574-581 (Lecture Notes in Computer Science).

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abstract = "Diffusion tensor imaging, tractography and the subsequent derivation of network measures are becoming an established approach in the exploration of brain connectivity. However, no gold standard exists in respect to how the brain should be parcellated and therefore a variety of atlas- and random-based parcellation methods are used. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences necessitates the use of normalisation schemes to enable meaningful intra- and inter-subject comparisons. This work proposes methods for normalising brain network measures using random graphs. We show that the normalised measures are locally stable over distinct random parcellations of the same subject and, applying it to a neonatal serial diffusion MRI data set, we demonstrate their potential in characterising changes in brain connectivity during early development. ",

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Schirmer, M , Ball, G , Counsell, SJ , Edwards, AD, Rueckert, D, Hajnal, JV & Aljabar, P 2013, Normalisation of neonatal brain network measures using stochastic approaches. in K Mori, I Sakuma, Y Sato, C Barillot & N Navab (eds), Medical image computing and computer-assisted intervention: Proceedings of the 16th International Conference, Nagoya, Japan, September 22-26, 2013, Part 1. 1 edn, vol. 16, Lecture Notes in Computer Science, vol. 8149, Springer Berlin Heidelberg, pp. 574-581, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013; , Nagoya, United Kingdom, 22/09/2013. https://doi.org/10.1007/978-3-642-40811-3_72

Normalisation of neonatal brain network measures using stochastic approaches. / Schirmer, Markus ; Ball, Gareth ; Counsell, Serena J. et al.
Medical image computing and computer-assisted intervention: Proceedings of the 16th International Conference, Nagoya, Japan, September 22-26, 2013, Part 1. ed. / Kensaku Mori; Ichiro Sakuma; Yoshinobu Sato; Christian Barillot; Nassir Navab. Vol. 16 1. ed. Springer Berlin Heidelberg, 2013. p. 574-581 (Lecture Notes in Computer Science; Vol. 8149).

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

TY - CHAP

T1 - Normalisation of neonatal brain network measures using stochastic approaches

AU - Schirmer, Markus

AU - Ball, Gareth

AU - Counsell, Serena J.

AU - Edwards, A. David

AU - Rueckert, Daniel

AU - Hajnal, Joseph V.

AU - Aljabar, Paul

PY - 2013

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AB - Diffusion tensor imaging, tractography and the subsequent derivation of network measures are becoming an established approach in the exploration of brain connectivity. However, no gold standard exists in respect to how the brain should be parcellated and therefore a variety of atlas- and random-based parcellation methods are used. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences necessitates the use of normalisation schemes to enable meaningful intra- and inter-subject comparisons. This work proposes methods for normalising brain network measures using random graphs. We show that the normalised measures are locally stable over distinct random parcellations of the same subject and, applying it to a neonatal serial diffusion MRI data set, we demonstrate their potential in characterising changes in brain connectivity during early development.

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KW - connectivity

KW - Diffusion mris

KW - Gold standards

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KW - Network measures

KW - Stochastic approach, Diffusion

KW - Diffusion tensor imaging

KW - Electric network analysis

KW - Magnetic resonance imaging

KW - Tensors, Graph theory

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DO - 10.1007/978-3-642-40811-3_72

M3 - Conference paper

C2 - 24505713

SN - 9783642408106

VL - 16

T3 - Lecture Notes in Computer Science

SP - 574

EP - 581

BT - Medical image computing and computer-assisted intervention

A2 - Mori, Kensaku

A2 - Sakuma, Ichiro

A2 - Sato, Yoshinobu

A2 - Barillot, Christian

A2 - Navab, Nassir

PB - Springer Berlin Heidelberg

T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013;

Y2 - 22 September 2013 through 26 September 2013

ER -

Schirmer M , Ball G , Counsell SJ , Edwards AD, Rueckert D, Hajnal JV et al. Normalisation of neonatal brain network measures using stochastic approaches. In Mori K, Sakuma I, Sato Y, Barillot C, Navab N, editors, Medical image computing and computer-assisted intervention: Proceedings of the 16th International Conference, Nagoya, Japan, September 22-26, 2013, Part 1. 1 ed. Vol. 16. Springer Berlin Heidelberg. 2013. p. 574-581. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-40811-3_72

Normalisation of neonatal brain network measures using stochastic approaches

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