Chapter title |
Normalisation of neonatal brain network measures using stochastic approaches.
|
---|---|
Chapter number | 72 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, February 2014
|
DOI | 10.1007/978-3-642-40811-3_72 |
Pubmed ID | |
Book ISBNs |
978-3-64-240810-6, 978-3-64-240811-3
|
Authors |
Schirmer M, Ball G, Counsell SJ, Edwards AD, Rueckert D, Hajnal JV, Aljabar P, Markus Schirmer, Gareth Ball, Serena J. Counsell, A. David Edwards, Daniel Rueckert, Joseph V. Hajnal, Paul Aljabar, Schirmer, Markus, Ball, Gareth, Counsell, Serena J., Edwards, A. David, Rueckert, Daniel, Hajnal, Joseph V., Aljabar, Paul |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 4% |
Unknown | 27 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 39% |
Researcher | 6 | 21% |
Professor | 3 | 11% |
Student > Bachelor | 1 | 4% |
Student > Doctoral Student | 1 | 4% |
Other | 5 | 18% |
Unknown | 1 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 9 | 32% |
Medicine and Dentistry | 6 | 21% |
Agricultural and Biological Sciences | 2 | 7% |
Psychology | 2 | 7% |
Engineering | 2 | 7% |
Other | 3 | 11% |
Unknown | 4 | 14% |