Fetal brain surface atlas, weeks 21-36 of gestation, constructed using 242 in-utero scans from fetal dHCP cohort
slavakarolis
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Fetal brain surface atlas, weeks 21-36 of gestation, constructed using 242 in-utero scans from the fetal dHCP cohort.
The following surfaces were generated: pial, mid-thickness, white matter and very inflated. The maps of changes in cortical morphology - sulcal depth
, curvature
, and thickness
- are also available
Pictures of the white matter surface (right hemisphere) are here:
The template was generated by adapting the procedure developed for the neonatal surface atlas in Bozek et al. (2018 NeuroImage, 179, 11–29), which iteratively refines templates through a repeated alignment of individuals to a common space using the MSM algorithm. The first stage was to generate a common reference space, which was initialised via affine sulcal-depth-based registration to the dHCP neonatal GW36 template. From this, an initial set of age-specific templates was then generated through adaptive kernel-weighted averaging of co-registered surfaces, where weights were given by a Gaussian kernel, centered at each GW, with width adapted for each template to compensate for differences in the number of available scans. At subsequent 5 iterations, age-specific templates were obtained through non-rigid MSM alignment of all examples to the template from the previous iteration. This was driven by sulcal depth (for the 2nd iteration) and mean curvature thereafter.
For any issues please write to: slava.karolis@kcl.ac.uk
Please cite this work as: Karolis V, Williams L, Kyriakopoulou V, Bozek J, Uus A, Makropoulos A, Schuh A, Cordero Grande L, Hughes E, Price A, Deprez M, Rutherford M, Edwards AD, Rueckert D, Smith S, Hajnal J, Arichi T, Robinson E (2023) Developing Human Connectome Project spatio-temporal surface atlas of the fetal brain. G-Node. https://doi.org/10.12751/g-node.qj5hs7
| datacite.yml | |
|---|---|
| Title | Developing Human Connectome Project spatio-temporal surface atlas of the fetal brain |
| Authors |
Karolis,Vycheslav;Centre for the Developing Brain, King’s College London, London, UK
Williams,Logan;School of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK Kyriakopoulou,Vanessa;Centre for the Developing Brain, King’s College London, London, UK Bozek,Jelena;University of Zagreb, Zagreb, Croatia Uus,Alena;School of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK Makropoulos,Antonios;ThinkSono, London, United Kingdom Schuh,Andreas;Department of Computing, Imperial College London, London, UK Cordero Grande,Lucilio;Centre for the Developing Brain, King’s College London, London, UK Hughes,Emer;Centre for the Developing Brain, King’s College London, London, UK Price,Anthony;Centre for the Developing Brain, King’s College London, London, UK Deprez,Maria;Centre for the Developing Brain, King’s College London, London, UK Rutherford,Mary;Centre for the Developing Brain, King’s College London, London, UK Edwards,A. David;Centre for the Developing Brain, King’s College London, London, UK Rueckert,Daniel;Klinikum rechts der Isar, Technical University of Munich, Munich, Germany Smith,Stephen;FMRIB, University of Oxford, Oxford, UK Hajnal,Joseph;School of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK Arichi,Tomoki;Centre for the Developing Brain, King’s College London, London, UK Robinson,Emma;School of Imaging Sciences & Biomedical Engineering, King’s College London, London, UK |
| Description | This repository contains spatio-temporal surface atlas, spanning 21-36 weeks of gestation, generated by adapting the procedure developed for the neonatal surface atlas in Bozek et al., 2018, which iteratively refines templates through a repeated alignment of individuals to a common space using MSM algorithm to the fetal data. At each iteration, the age-specific templates are obtained through weighted averaging of co-registered surfaces, whereby weights are defined by the Gaussians centred on the gestational weeks for which templates are calculated, and are subsequently used as a target space for the following iteration. Adaptive kernel regression, compensating for a difference in the number of scans available for different ages, was used to parameterise the width of the Gaussians. The atlas was generated in three stages: first, a common reference space was initialised via affine sulcal-depth-based registration to the dHCP neonatal GW36 template. At the next iteration, the template was refined using sulcal-depth-based nonlinear alignment, followed by 4 iterations of curvature-based alignment (a more fine-grained feature than the sulcal depth). |
| License | Creative Commons CC0 1.0 Public Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/) |
| References |
J. Bozek, A. Makropoulos, A. Schuh, S. Fitzgibbon, R. Wright, M. F. Glasser, T. S. Coalson, J. O’Muircheartaigh, J. Hutter, A. N. Price, L. Cordero-Grande, R. P. A. G. Teixeira, E. Hughes, N. Tusor, K. P. Baruteau, M. A. Rutherford, A. D. Edwards, J. V. Hajnal, S. M. Smith, D. Rueckert, M. Jenkinson, E. C. Robinson. (2018). Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project. NeuroImage. 179, 11–29 [doi:10.1016/j.neuroimage.2018.06.018] (IsSupplementTo)
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| Funding |
European Union's Seventh Framework Programme (FP/2007-2013), 319456
|
| Keywords |
Surface atlas
Fetal MRI Template Brain development |
| Resource Type |
Dataset |
