Computational Neuroanatomy of Baby Brains: A Review

Overview

Journal Neuroimage

Specialty Radiology

Date 2018 Mar 26

PMID 29574033

Citations 87

Authors

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

Affiliations

Soon will be listed here.

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.

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