Computational neuroanatomy of baby brains: A review

Abstract

The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.

Keywords: Brain atlas; Cortical surface; Infant brain; Parcellation; Registration; Segmentation.

Fig. 1

T1w, T2w, FA images, tissue segmentation results (by LINKS, (Wang et al., 2015)) as well as the reconstructed inner and outer surfaces of a typically-developing infant, scanned longitudinally at 2 weeks, 3, 6, 9 and 12 months of age. Inner surfaces are color-coded with the maximum principal curvature, and outer surfaces are color-coded with cortical thickness.

Fig. 2

Flowchart of a typical pipeline for analyzing T1w and T2w structural MR images. Note that, in infant studies, many steps need to be infant-specific.

Fig. 3

An example of age-specific atlases for neonate, 1 year and 2 years with multiple components from (Shi et al., 201 1b).

Fig. 4

A typical computational pipeline for cortical surface-based analysis of infant brains.

Fig. 5

UNC 4D infant cortical surface atlases and parcellations, created by the method in (Li et al., 2015d). (a) and (c) are the average convexity maps on the spherical space and mean cortical shape, respectively. (b) and (d) are the mean curvature maps on the spherical space and mean cortical shape, respectively. (e) and (f) are the FreeSurfer parcellation and HCP parcellation, respectively. The left-hand column indicates the age in months.