Baby brain atlases

Abstract

The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.

Keywords: Brain atlas; Early development; Infant; Neonate; Prenatal exposure; Preterm birth.

Fig. 1

Brain parcellation map overlaid on the JHU-neonate brain template.

Fig. 2

Atlas-based quantification of baby brains. The mean diffusivity of the left inferior longitudinal fasciculus is plotted against postmenstrual age in weeks. Nineteen full-term and 30 preterm-born babies were each scanned longitudinally at three time points, and line-plots from the same subject are connected by solid lines (Akazawa et al., 2016). Cyan: term-born babies. Red: preterm-born babies. * The thick red lines denote the outliers (the trajectories outside 95% of the term-born trajectories). Note that the outliers can be identified based on the normal developmental trajectories (cyan lines). (Figure from (Akazawa et al., 2016) with permission)

Fig. 3

Developmental changes in functional connectivity between the left precentral gyrus and other brain areas in 40 term and preterm born infants. The first row indicates the brain regions (orange areas) with significant age effects. The second, third, and fourth rows demonstrate the evolving connectivity seen in three different age groups based on their ages at the scans: group 1, 31.3 – 35.3 postmenstrual weeks; group 2, 35.6 – 38.4 postmenstrual weeks; and group 3, 38.7 – 41.7 postmenstrual weeks. Group 1, group 2, and group 3 included both preterm and term-born neonates scanned at the ages described above. Functional connectivity of the term group is shown in the fifth row as a reference. The term group included only term-born neonates (>38.0 postmenstrual weeks at birth) scanned at the age of 38.4–41.7 postmenstrual weeks. A corrected-P < 0.01 was applied as the threshold (Adapted with permission from Cao et al., 2017).

Fig. 4

Comparison between neonatal brain (A) and adult brain (B). The T1- and T2-weighted images and the DTI are co-registered. DTI provides greater contrasts in the white matter areas, compared to T1- or T2-weighted images. This feature of DTI is particularly advantageous in identifying white matter bundles in the neonatal brain. The anterior limb of the internal capsule (alic, yellow contour) and the posterior limb of the internal capsule (plic, cyan contour) are almost invisible on conventional T1- and T2-weighted images of the neonatal brain, but these tracts are well visualized in the adult brain.

Fig. 5

Example of neonates with and without the cavum septum pellucidum (indicated by red arrows)

Fig. 6

Comparison between T2-based image transformation and DTI-based image transformation. The original FA map from a 40-postmenstrual-weeks neonate is shown in the upper row, left. T2-LDDMM: The original b0 image was transformed to the JHU-neonate-b0 template, and the resultant transformation matrix was applied to the original tensor field, from which the transformed FA map was calculated (upper row, middle, with the magnified view in the lower row, left). DTI-LDDMM: The original FA map was transformed to the JHU-neonate FA template, and the resultant transformation matrix was applied to the original tensor field, from which the transformed FA map was calculated (upper row, right, with the magnified view in the lower row, right). Both images are overlaid by the parcellation map that qualitatively demonstrated the registration accuracy. Note that the anterior limb of internal capsule (ALIC, high FA string indicated by red arrowheads) was not well co-registered to the atlas space when the T2-LDDMM was applied, but was well co-registered when the DTI-LDDMM was applied.

Fig. 7

Concept of the disease atlas library. Each MRI within the library has non-image information, such as demographics (e.g., sex and age), clinical (e.g., diagnosis, prognosis, and responsiveness to treatments), or genetic (e.g., gene mutation or SNP) information attached. Suppose there are N atlases within the library. New images with the non-image information attached are mixed into the library (now N +1) and clustered based on the image and non-image features. The new images would iteratively be directed to the atlases (stored in the library) that has similar image and non-image features. From the atlases within the same cluster, probabilities and statistics about possible diagnosis or prognosis can be calculated (red arrows). This algorithm is similar to the multi-atlas label fusion methods.