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

Abstract

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+5 weeks, range 37+2–41+6). 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.

Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis

“…Our algorithm, in particular, showed robust performance due to 1) the use of a robust state estimation technique (Agamennoni et al, 2012) for inter-slice motion correction through dynamic motion tracking, and 2) detection and rejection of intra-slice motion and robust reconstruction through weighted linear least squares estimation. The processing pipeline developed in this study for fetal brain DWI analysis, technical advances in fetal brain MRI reconstruction, new advances in fetal brain functional MRI analysis (Seshamani et al, 2016), along with spatiotemporal atlases of the fetal brain (Gholipour et al, 2014a, 2017; Serag et al, 2012) and neonates (Blesa et al, 2016; Makropoulos et al, 2016) can significantly improve the use of in-vivo MRI and DWI to study the development of human brain connectome in-utero . This in-turn facilitates the use of MRI as a powerful imaging modality for the analysis of neurodevelopmental disorders caused by preterm birth, growth restriction, or congenital anomalies.…”

“…Atlas-based parcellation was performed based on 90 anatomical regions mapped to the spatiotemporal fetal brain MRI atlas (Gholipour et al, 2017) from the neonatal brain MRI atlas developed by Blesa et al (2016). We studied structural connectivity based on streamlines connecting all pairs of anatomical regions.…”

“…The corrected masked image is registered to the atlas space using the algorithm discussed by Gholipour et al (2012), and tissue segmentation and anatomical parcellation is obtained from multi-atlas segmentation using label propagation through ANTS deformable registration (Avants et al, 2008) and probabilistic label fusion (Akhondi-Asl and Warfield, 2013). A spatiotemporal fetal brain MRI atlas (Gholipour et al, 2014b, 2017) is used in this process with anatomical parcellations adopted from the neonatal brain atlases by Blesa et al (2016). The DWI stacks of slices are processed with the algorithm developed and discussed in section 2.1.…”

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group

“…congenital heart disease or preterm neonates), using bespoke processing pipelines or pipelines developed using adult data but optimised for neonates (Boardman et al, 2006; Miller et al, 2007; Ball et al, 2010; Serag et al, 2016). There are some fetal and neonatal structural MRI atlases (for example: Gousias et al, 2012; Shi et al, 2011; Blesa et al, 2016; Makropoulos et al, 2016; Kabdebon et al, 2014; Oishi et al, 2011), but to our knowledge there are no perinatal image banks hosting normal data acquired from multiple studies and sites. A perinatal subsection of the BRAINS database is under development (www.brainsimagebank.ac.uk), and the developing Human Connectome Project (http://wp.doc.ic.ac.uk/dhcp/) aims to make data available from 1500 fetuses and newborns between 20–44 weeks’ post-menstrual age.…”

Hub distribution of the brain functional networks of newborns prenatally exposed to maternal depression and SSRI antidepressants

“…Following preprocessing, we defined 90 cortical and subcortical regions according to the Automated Anatomical Labeling (Tzourio-Mazoyer et al, 2002) algorithm of the Edinburgh Neonatal Atlas ENA33 segmentation tool (Cabez et al, 2016). According to Cabez et al, the ENA33 tool was transformed from an adult atlas, so it is consistent with adult label protocols, and the size of each brain region in the atlas corresponds to its actual size in the neonatal brain.…”