Latest revision as of 21:51, 3 February 2021

Abstract

The infant brain is unlike the adult brain, with considerable differences in morphological, neurodynamic, and haemodynamic features. As the majority of current MRI analysis tools were designed for use in adults, a primary objective of the Developing Human Connectome Project (dHCP) is to develop optimised methodological pipelines for the analysis of neonatal structural, resting state, and diffusion MRI data. Here, in an independent neonatal dataset we have extended and optimised the dHCP fMRI preprocessing pipeline for the analysis of stimulus-response fMRI data. We describe and validate this extended dHCP fMRI preprocessing pipeline to analyse changes in brain activity evoked following an acute noxious stimulus applied to the infant's foot. We compare the results obtained from this extended dHCP pipeline to results obtained from a typical FSL FEAT-based analysis pipeline, evaluating the pipelines' outputs using a wide range of tests. We demonstrate that a substantial increase in spatial specificity and sensitivity to signal can be attained with a bespoke neonatal preprocessing pipeline through optimised motion and distortion correction, ICA-based denoising, and haemodynamic modelling. The improved sensitivity and specificity, made possible with this extended dHCP pipeline, will be paramount in making further progress in our understanding of the development of sensory processing in the infant brain.

Highlights • The dHCP fMRI preprocessing pipeline is generalizable to stimulus-evoked datasets. • Customised FIX denoising in infant fMRI data substantially improves data quality. • The dHCP pipeline greatly improves spatial specificity and sensitivity to signal. • Bespoke fMRI data analysis outperforms typical analytical methods for neonatal data.

Original document

The different versions of the original document can be found in:

• http://europepmc.org/articles/PMC6347570

• http://ora.ox.ac.uk/objects/uuid:

• https://doi.org/10.1016/j.neuroimage.2018.11.006 under the license cc-by

• https://dx.doi.org/10.1016/j.neuroimage.2018.11.006

• https://api.elsevier.com/content/article/PII:S1053811918320767?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S1053811918320767?httpAccept=text/plain,

http://dx.doi.org/10.1016/j.neuroimage.2018.11.006 under the license https://www.elsevier.com/tdm/userlicense/1.0/

• https://dblp.uni-trier.de/db/journals/neuroimage/neuroimage186.html#BaxterFMGJSADS19,

https://www.sciencedirect.com/science/article/pii/S1053811918320767,

https://www.paediatrics.ox.ac.uk/publications/943724,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347570,

https://www.ndcn.ox.ac.uk/publications/943724,

https://neuroimaging.paediatrics.ox.ac.uk/publications/943724,

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https://www.bdi.ox.ac.uk/publications/943724,

https://ora.ouls.ox.ac.uk/objects/uuid:b86b65bc-3e01-4075-b4ce-873991b1a0da,

https://academic.microsoft.com/#/detail/2899981653

• https://discovery.ucl.ac.uk/id/eprint/10063907,

https://discovery.ucl.ac.uk/id/eprint/10063907/1/1-s2.0-S1053811918320767-main.pdf

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