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Normalisation of neonatal brain network measures using stochastic approaches

Research output: Chapter in Book/Report/Conference proceedingConference paper

Standard

Normalisation of neonatal brain network measures using stochastic approaches. / Schirmer, Markus; Ball, Gareth; Counsell, Serena J.; Edwards, A. David; Rueckert, Daniel; Hajnal, Joseph V.; Aljabar, Paul.

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 proceedingConference paper

Harvard

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

APA

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

Vancouver

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). https://doi.org/10.1007/978-3-642-40811-3_72

Author

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

Bibtex Download

@inbook{35d517a7fe45416a87faf523169e4e4d,
title = "Normalisation of neonatal brain network measures using stochastic approaches",
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. ",
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",
author = "Markus Schirmer and Gareth Ball and Counsell, {Serena J.} and Edwards, {A. David} and Daniel Rueckert and Hajnal, {Joseph V.} and Paul Aljabar",
year = "2013",
doi = "10.1007/978-3-642-40811-3_72",
language = "English",
isbn = "9783642408106",
volume = "16",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "574--581",
editor = "Kensaku Mori and Ichiro Sakuma and Yoshinobu Sato and Barillot, {Christian } and Nassir Navab",
booktitle = "Medical image computing and computer-assisted intervention",
edition = "1",
note = "16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013; ; Conference date: 22-09-2013 Through 26-09-2013",

}

RIS (suitable for import to EndNote) Download

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

Y1 - 2013

N2 - 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.

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.

KW - Brain connectivity

KW - Brain networks

KW - connectivity

KW - Diffusion mris

KW - Gold standards

KW - neonatal

KW - Network measures

KW - Stochastic approach, Diffusion

KW - Diffusion tensor imaging

KW - Electric network analysis

KW - Magnetic resonance imaging

KW - Tensors, Graph theory

UR - http://www.scopus.com/inward/record.url?scp=84894632769&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-40811-3_72

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 -

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