Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011 Jan 1;54(1):313-27.
doi: 10.1016/j.neuroimage.2010.07.033. Epub 2010 Jul 23.

Unbiased average age-appropriate atlases for pediatric studies

Vladimir Fonov  1 , Alan C Evans, Kelly Botteron, C Robert Almli, Robert C McKinstry, D Louis Collins; Brain Development Cooperative Group
Collaborators, Affiliations

Unbiased average age-appropriate atlases for pediatric studies

Vladimir Fonov et al. Neuroimage. .

Abstract

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Schematic representation of the model building algorithm: dotted lines represent mapping of a voxel in the initial model (Model 0) to each subject, solid lines represent mapping of individual subjects into the next model (Model 1), dashed lines represent the voxel-wise residual error of the models at each iteration.
Figure 2
Figure 2
NIHPD 4.5–18.5 age distribution (left) of the 324 subjects that passed QC and were included in template generation; ICBM 152 age distribution (right).
Figure 3
Figure 3
Average asymmetric template (4.5–18.5 y.o.) generated at each level of fitting. The grey scale images show the intensity average anatomy, while the rainbow colour scale shows the intensity standard deviation for selected iterations in the hierarchical fitting process. One can see that as fitting progresses, anatomical features become less blurred and the intensity variability is reduced. The intensity range of the average data sets runs from 0 to 100.
Figure 4
Figure 4
RMS magnitude of the residual error vector field for each iteration (i.e., the bias in the average deformation for the current template), x axis shows the step-size in mm. On the top image, the symmetric (red circles) and asymmetric (black squares) NIHPD 4.5–18.5 models are compared. On the bottom, the different NIHPD age sub-ranges are plotted for the asymmetric atlas creation. One can see that at each iteration for each step size, the average RMS residual error magnitude is reduced, indicating that the optimization procedure is reaching a minima.
Figure 5
Figure 5
RMS of intensity standard deviation (SD) between individual scans at each iteration for the creation of the NIHPD-4.5–18.5 yo atlas, x axis shows the step-size in mm. As the procedure advances, the RMS intensity SD between iterations decreases progressively for creation of both symmetric (red circles) and asymmetric (black squares) models.
Figure 6
Figure 6
NIHPD asymmetric templates (first six columns) + ICBM asymmetric template (rightmost column) for the T1w modality.
Figure 7
Figure 7
Close up of the T1w, T2w and PDw (from top to bottom) atlas data to show cortical detail.
Figure 8
Figure 8
NIHPD 4.5–18.5 template (left) and ICBM 18.5–43.0 template (right), showing the T1w, T2w and PDw average templates for each group.
Figure 9
Figure 9
Comparison of probabilistic atlas of the brain tissue types (GM, WM, CSF) for the NIHPD 4.5–18.5 atlas (leftmost 3 columns) and the ICBM 18.5–43.5 atlas (rightmost 3 columns). The brightest voxels indicate high probability of that tissue class. Note that the skin and skull outlines are overlaid on each subimage to facilitate comparisons.
Figure 10
Figure 10
NIHPD templates (leftmost 6 columns) + ICBM template (rightmost column) of the combined tissue class atlas with red representing gray matter; green, white matter and blue color, CSF.
Figure 11
Figure 11
Comparison between NIHPD 4.5–8.5 template (red) and ICBM 18.5–43.5 template (green), overlapping regions in yellow. The following anatomical differences are highlighted: 1) thicker insular cortex in pediatric atlas, 2) more posterior occipital pole in pediatric atlas, 3) different shape and GM/WM ratio in cerebellum, 4) more anterior temporal pole in pediatric atlas, 5) slightly different hippocampal shape, 6) flatter, thinner, longer corpus callosum in adult atlas, 7) thicker GM in pediatric atlas.
Figure 12
Figure 12
Comparison between NIHPD 4.5–8.5 and ICBM 18.5–43.5 templates. When compared to the the ICBM atlas, the NIHPD 4.5–8.5 atlas has thinner skull and scalp, narrower cortical suci (A = Post Central Sulcus, B = Parieto-Occipital Sulcus, C = Calcarine Fissure), decreased separation of the cerebellar folia (D), thinner corpus callosum (E), smaller lateral ventricles (F), and thicker cortex overall, Internal architecture of the thalamus has a slightly different shape (G), Different shape of the pituitary gland (H), and the presence of the spheno-occipital synchondrosis (I), smaller pons (J).
Figure 13
Figure 13
Regions of potential bias when using different atlases. Map of statistically significant differences in log Jacobians when mapping the NIHPD 4.5–6.9 age group to the NIHPD 7.0–11.0 (baseline for comparison) and the NIHPD 10.0–14.0 (top row), NIHPD13.0–18.5 (middle row) and ICBM18.5–45.0 (bottom row) templates, all presented in the space of the ICBM 18.5–45.0 template. Red color indicates regions where the selected templates produces significantly (5% False Discovery Rate (Genovese, Lazar et al. 2002)) bigger log Jacobian determinant (i.e., a significant difference in local volume) compared to the NIHPD 7.0–11.0 template, and blue color indicates where the selected template yields a statistically significant smaller Jacobian determinant. One can see that the red regions are much larger than the blue regions, indicating potential bias non-age appropriate template for analysis of pediatric data in the 4.5–6.9y range
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Almli CR, Rivkin MJ, et al. The NIH MRI study of normal brain development (Objective-2): Newborns, infants, toddlers, and preschoolers. Neuroimage. 2007;35(1):308–325. - PubMed
    1. Amit Y, Grenander U, et al. Structural Image Restoration Through Deformable Templates. Journal of the American Statistical Association. 1991;86(414):376–387.
    1. Ardekani BA, Guckemus S, et al. Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans. Journal of Neuroscience Methods. 2005;142(1):67–76. - PubMed
    1. Arsigny V, Commowick O, et al. A Log-Euclidean Framework for Statistics on Diffeomorphisms. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. 2006:924–931. - PubMed
    1. Ashburner J, Friston K. Multimodal Image Coregistration and Partitioning--A Unified Framework. NeuroImage. 1997;6(3):209–217. - PubMed

Publication types