Genc, S;
Chamberland, M;
Koller, K;
Tax, CMW;
Zhang, H;
Palombo, M;
Jones, DK;
(2021)
Repeatability of Soma and Neurite Metrics in Cortical and Subcortical Grey Matter.
In: Gyori, N and Hutter, J and Nath, V and Palombo, M and Pizzolato, M and Zhang, F, (eds.)
Computational Diffusion MRI. Mathematics and Visualization.
(pp. 135-145).
Springer: Cham, Switzerland.
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Abstract
Diffusion magnetic resonance imaging is a technique which has long been used to study white matter microstructure in vivo. Recent advancements in hardware and modelling techniques have opened up interest in disentangling tissue compartments in the grey matter. In this study, we evaluate the repeatability of soma and neurite density imaging in a sample of six healthy adults scanned five times on an ultra-strong gradient magnetic resonance scanner (300 mT/m). Repeatability was expressed as an intraclass correlation coefficient (ICC). Our findings reveal that measures of soma density (mean ICC = 0.976), neurite density (mean ICC = 0.959) and apparent soma size (mean ICC = 0.923) are highly reliable across multiple cortical and subcortical networks. Overall, we demonstrate the promise of moving advanced grey matter microstructural imaging towards applications of development, ageing, and disease.
| Type: | Book chapter |
|---|---|
| Title: | Repeatability of Soma and Neurite Metrics in Cortical and Subcortical Grey Matter |
| ISBN-13: | 9783030730178 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-73018-5_11 |
| Publisher version: | https://doi.org/10.1007/978-3-030-73018-5_11 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10138375 |
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