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 article

4 Citations (Scopus)
6 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)906-925
Number of pages20
JournalNeuroImage
Volume185
Early online date21 Mar 2018
DOIs
Publication statusPublished - 15 Jan 2019

Fingerprint

Neuroanatomy
Brain
Atlases
Human Development

Keywords

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

Cite this

Li, G., Wang, L., Yap, P-T., Wang, F., Wu, Z., Meng, Y., ... Shen, D. (2019). Computational Neuroanatomy of Baby Brains: A Review. NeuroImage, 185, 906-925. https://doi.org/10.1016/j.neuroimage.2018.03.042
Li, Gang ; Wang, Li ; Yap, Pew-Thian ; Wang, Fan ; Wu, Zhengwang ; Meng, Yu ; Dong, Pei ; Kim, Jaeil ; Feng, Shide ; Rekik, Islem ; Lin, Weili ; Shen, Dinggang. / Computational Neuroanatomy of Baby Brains : A Review. In: NeuroImage. 2019 ; Vol. 185. pp. 906-925.
@article{ab125499013a4e0a8763b21b19e184f0,
title = "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",
author = "Gang Li and Li Wang and Pew-Thian Yap and Fan Wang and Zhengwang Wu and Yu Meng and Pei Dong and Jaeil Kim and Shide Feng and Islem Rekik and Weili Lin and Dinggang Shen",
note = "This work was partially supported by NIH grants (MH100217, MH107815, MH108914, MH109773, MH110274 and NS093842).",
year = "2019",
month = "1",
day = "15",
doi = "10.1016/j.neuroimage.2018.03.042",
language = "English",
volume = "185",
pages = "906--925",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Elsevier",

}

Li, G, Wang, L, Yap, P-T, Wang, F, Wu, Z, Meng, Y, Dong, P, Kim, J, Feng, S, Rekik, I, Lin, W & Shen, D 2019, 'Computational Neuroanatomy of Baby Brains: A Review' NeuroImage, vol. 185, pp. 906-925. https://doi.org/10.1016/j.neuroimage.2018.03.042

Computational Neuroanatomy of Baby Brains : A Review. / Li, Gang; Wang, Li ; Yap, Pew-Thian; Wang, Fan ; Wu, Zhengwang ; Meng, Yu ; Dong, Pei ; Kim, Jaeil ; Feng, Shide; Rekik, Islem; Lin, Weili; Shen, Dinggang (Lead / Corresponding author).

In: NeuroImage, Vol. 185, 15.01.2019, p. 906-925.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Computational Neuroanatomy of Baby Brains

T2 - A Review

AU - Li, Gang

AU - Wang, Li

AU - Yap, Pew-Thian

AU - Wang, Fan

AU - Wu, Zhengwang

AU - Meng, Yu

AU - Dong, Pei

AU - Kim, Jaeil

AU - Feng, Shide

AU - Rekik, Islem

AU - Lin, Weili

AU - Shen, Dinggang

N1 - This work was partially supported by NIH grants (MH100217, MH107815, MH108914, MH109773, MH110274 and NS093842).

PY - 2019/1/15

Y1 - 2019/1/15

N2 - 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.

AB - 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.

KW - Brain atlas

KW - Cortical surface

KW - Infant brain

KW - Parcellation

KW - Registration

KW - Segmentation

UR - http://www.scopus.com/inward/record.url?scp=85057290031&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2018.03.042

DO - 10.1016/j.neuroimage.2018.03.042

M3 - Review article

VL - 185

SP - 906

EP - 925

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -

Li G, Wang L, Yap P-T, Wang F, Wu Z, Meng Y et al. Computational Neuroanatomy of Baby Brains: A Review. NeuroImage. 2019 Jan 15;185:906-925. https://doi.org/10.1016/j.neuroimage.2018.03.042