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Thursday, November 29, 2018

Computational neuroanatomy of baby brains: A review

Publication date: 15 January 2019

Source: NeuroImage, Volume 185

Author(s): Gang Li, Li Wang, Pew-Thian Yap, Fan Wang, Zhengwang Wu, Yu Meng, Pei Dong, Jaeil Kim, Feng Shi, Islem Rekik, Weili Lin, Dinggang Shen

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.

Graphical abstract

T1w, T2w, FA images, tissue segmentation results 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.Image 1



from Imaging via a.sfakia on Inoreader https://ift.tt/2BHGPFr

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