AUTHOR=Blesa Manuel , Serag Ahmed , Wilkinson Alastair G. , Anblagan Devasuda , Telford Emma J. , Pataky Rozalia , Sparrow Sarah A. , Macnaught Gillian , Semple Scott I. , Bastin Mark E. , Boardman James P. 

TITLE=Parcellation of the Healthy Neonatal Brain into 107 Regions Using Atlas Propagation through Intermediate Time Points in Childhood

JOURNAL=Frontiers in Neuroscience

VOLUME=10

YEAR=2016

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00220

DOI=10.3389/fnins.2016.00220

ISSN=1662-453X

ABSTRACT=<p>Neuroimage analysis pipelines rely on parcellated atlases generated from healthy individuals to provide anatomic context to structural and diffusion MRI data. Atlases constructed using adult data introduce bias into studies of early brain development. We aimed to create a neonatal brain atlas of healthy subjects that can be applied to multi-modal MRI data. Structural and diffusion 3T MRI scans were acquired soon after birth from 33 typically developing neonates born at term (mean postmenstrual age at birth 39<sup>+5</sup> weeks, range 37<sup>+2</sup>–41<sup>+6</sup>). An adult brain atlas (SRI24/TZO) was propagated to the neonatal data using temporal registration via childhood templates with dense temporal samples (NIH Pediatric Database), with the final atlas (Edinburgh Neonatal Atlas, ENA33) constructed using the Symmetric Group Normalization (SyGN) method. After this step, the computed final transformations were applied to T2-weighted data, and fractional anisotropy, mean diffusivity, and tissue segmentations to provide a multi-modal atlas with 107 anatomical regions; a symmetric version was also created to facilitate studies of laterality. Volumes of each region of interest were measured to provide reference data from normal subjects. Because this atlas is generated from step-wise propagation of adult labels through intermediate time points in childhood, it may serve as a useful starting point for modeling brain growth during development.</p>