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A framework for multi-component analysis of diffusion MRI data over the neonatal period

We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed...

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Autores principales: Pietsch, Maximilian, Christiaens, Daan, Hutter, Jana, Cordero-Grande, Lucilio, Price, Anthony N., Hughes, Emer, Edwards, A. David, Hajnal, Joseph V., Counsell, Serena J., Tournier, J-Donald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347572/
https://www.ncbi.nlm.nih.gov/pubmed/30391562
http://dx.doi.org/10.1016/j.neuroimage.2018.10.060
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author Pietsch, Maximilian
Christiaens, Daan
Hutter, Jana
Cordero-Grande, Lucilio
Price, Anthony N.
Hughes, Emer
Edwards, A. David
Hajnal, Joseph V.
Counsell, Serena J.
Tournier, J-Donald
author_facet Pietsch, Maximilian
Christiaens, Daan
Hutter, Jana
Cordero-Grande, Lucilio
Price, Anthony N.
Hughes, Emer
Edwards, A. David
Hajnal, Joseph V.
Counsell, Serena J.
Tournier, J-Donald
author_sort Pietsch, Maximilian
collection PubMed
description We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.
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spelling pubmed-63475722019-02-01 A framework for multi-component analysis of diffusion MRI data over the neonatal period Pietsch, Maximilian Christiaens, Daan Hutter, Jana Cordero-Grande, Lucilio Price, Anthony N. Hughes, Emer Edwards, A. David Hajnal, Joseph V. Counsell, Serena J. Tournier, J-Donald Neuroimage Article We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation. Academic Press 2019-02-01 /pmc/articles/PMC6347572/ /pubmed/30391562 http://dx.doi.org/10.1016/j.neuroimage.2018.10.060 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pietsch, Maximilian
Christiaens, Daan
Hutter, Jana
Cordero-Grande, Lucilio
Price, Anthony N.
Hughes, Emer
Edwards, A. David
Hajnal, Joseph V.
Counsell, Serena J.
Tournier, J-Donald
A framework for multi-component analysis of diffusion MRI data over the neonatal period
title A framework for multi-component analysis of diffusion MRI data over the neonatal period
title_full A framework for multi-component analysis of diffusion MRI data over the neonatal period
title_fullStr A framework for multi-component analysis of diffusion MRI data over the neonatal period
title_full_unstemmed A framework for multi-component analysis of diffusion MRI data over the neonatal period
title_short A framework for multi-component analysis of diffusion MRI data over the neonatal period
title_sort framework for multi-component analysis of diffusion mri data over the neonatal period
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347572/
https://www.ncbi.nlm.nih.gov/pubmed/30391562
http://dx.doi.org/10.1016/j.neuroimage.2018.10.060
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