Computational Neuroanatomy of Baby Brains: A Review

Gang Li, Li Wang, Pew-Thian Yap, Fan Wang, Zhengwang Wu, Yu Meng, Pei Dong, Jaeil Kim, Shide Feng, Islem Rekik, Weili Lin, Dinggang Shen (Lead / Corresponding author)

Research output: Contribution to journalReview articlepeer-review

117 Citations (Scopus)
410 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)906-925
Number of pages20
Early online date21 Mar 2018
Publication statusPublished - 15 Jan 2019


  • Brain atlas
  • Cortical surface
  • Infant brain
  • Parcellation
  • Registration
  • Segmentation

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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