A framework for multi-component analysis of diffusion MRI data over the neonatal period
- Submitting institution
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King's College London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 136871393
- Type
- D - Journal article
- DOI
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10.1016/j.neuroimage.2018.10.060
- Title of journal
- NeuroImage
- Article number
- -
- First page
- 321
- Volume
- 186
- Issue
- -
- ISSN
- 1053-8119
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2018
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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9
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The neonatal brain is undergoing rapid development, making diffusion MRI analysis difficult since most microstructure models have been developed for adults. This work takes advantage of the data-driven nature of our multi-tissue constrained spherical deconvolution approach (doi.org/10.1016/j.neuroimage.2014.07.061) and the exceptional data quality of the developing Human Connectome Project (https://doi.org/10.1002/mrm.26765) to build a time-resolved group average template of the developing brain, showing different rates and stages of maturation across different white matter pathways, even where they co-exist with other pathways. This work was featured on Nature Outlook (https://www.nature.com/articles/d41586-019-02208-0) and is freely available for download (https://gin.g-node.org/maxpietsch/dHCP_neonatal_HARDI_atlas).
- Author contribution statement
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- Non-English
- No
- English abstract
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