Unbiased Average Age-Appropriate Atlases for Pediatric Studies

By Vladimir S Fonov, Alan C. Evans, K. Botteron, C. Almli, R. McKinstry, D. Louis Collins. NeuroImage, 2010

2,295 citations recorded by the source corpus.

Spatial normalization, registration, and segmentation techniques for Magnetic Resonance Imaging (MRI) often use a target or template volume to facilitate processing, take advantage of prior information, and define a common coordinate system for analysis. In the neuroimaging literature, the MNI305 Talairach-like coordinate system is often used as a standard template. However, when studying pediatric populations, variation from the adult brain makes the MNI305 suboptimal for processing brain images of children. Morphological changes occurring during development render the use of age-appropriate templates desirable to reduce potential errors and minimize bias during processing of pediatric data. This paper presents the methods used to create unbiased, age-appropriate MRI atlas templates for pediatric studies that represent the average anatomy for the age range of 4.5–18.5 years, while maintaining a high level of anatomical detail and contrast. The creation of anatomical T1-weighted, T2-weighted, and proton density-weighted templates for specific developmentally important age-ranges, used data derived from the largest epidemiological, representative (healthy and normal) sample of the U.S. population, where each subject was carefully screened for medical and psychiatric factors and characterized using established neuropsychological and behavioral assessments. . Use of these age-specific templates was evaluated by computing average tissue maps for gray matter, white matter, and cerebrospinal fluid for each specific age range, and by conducting an exemplar voxel-wise deformation-based morphometry study using 66 young (4.5–6.9 years) participants to demonstrate the benefits of using the age-appropriate templates. The public availability of these atlases/templates will facilitate analysis of pediatric MRI data and enable comparison of results between studies in a common standardized space specific to pediatric research.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Surgery

View Paper: https://www.semanticscholar.org/paper/21e57e73a9045c822fdcffa2682e937044fd2881

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (2012, positive): “More recently, Fonov et al. (2011) proposed an atlas generation technique which is based on the work of Guimond et al. (2000).”
On the interpretation of weight vectors of linear models in multivariate neuroimaging (2014, methodology): “The dipole ﬁ ts were carried out in a realistically shaped three-shell head model based on nonlinearly averaged MRI scans of 152 subjects (Fonov et al., 2011).”
BEaST: Brain extraction based on nonlocal segmentation technique (2012, methodology): “, 1994) to the publicly available ICBM152 average (Fonov et al., 2011) that defines the MNI Talairach-like stereotaxic space, and resampled on a 193×229×193 voxel grid with isotropic 1 mm spacing.”
neuromaps: structural and functional interpretation of brain maps (2022, background): “, the MNI ICBM 152 template; [22, 49]); however, standardized triangular (i.”
Lead-DBS: A toolbox for deep brain stimulation electrode localizations and visualizations (2015, positive): “Normalization ofMR-images was performed following the approach of Schönecker et al., using FSL 5 (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/) and the ICBM 152 Nonlinear 2009b template (Fonov et al., 2011).”
Robust artifactual independent component classification for BCI practitioners (2014, methodology): “We considered 2142 locations arranged in a 1 cm spaced 3D-grid, formulated the forward problem according to [41–43] and sought the source distribution with minimal l2-norm (i.”
Multi-Contrast Human Neonatal Brain Atlas: Application to Normal Neonate Development Analysis (2011, positive): “For pediatric brain analysis, attempts have been made to create age-specific brain templates (Fonov et al., 2010; Wilke et al., 2008; Wilke et al., 2003; Yoon et al., 2009).”
The UNC/UMN Baby Connectome Project (BCP): An Overview of the Study Design and Protocol Development (2018, positive): “Past studies have established multiple forms of criterion validity for the MEFS (Doom et al., 2014; Fuglestad et al., 2015; Hassinger-Das et al., 2014; Prager, Sera, & Carlson, 2016).”
Brain templates and atlases (2012, methodology): “40th GenerationMNI152 Recently, an unbiased non-linear average of the MNI152 database has been generated that combines the attractions of both high-spatial resolution and signal-to-noise while not being subject to the vagaries of any single brain (Fonov et al., 2011).”
Investigating White Matter Development in Infancy and Early Childhood using Myelin Water Faction and Relaxation Time Mapping (2012, background): “Similarly, pediatric derived anatomical atlases are also now becoming more readily available (Fonov et al., 2011), which may increase anatomical specificity.”
The Neuro Bureau ADHD-200 Preprocessed Repository (2016, methodology): “5y age-specific assymmetric template (Fonov et al., 2011) using a two step procedure that calculates a linear transform (Jenkinson et al.”