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A regional brain volume–based age prediction model for neonates and the derived brain maturation index

  • Paediatric
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A Commentary to this article was published on 22 December 2023

Abstract

Objective

To develop a postmenstrual age (PMA) prediction model based on segmentation volume and to evaluate the brain maturation index using the proposed model.

Methods

Neonatal brain MRIs without clinical illness or structural abnormalities were collected from four datasets from the Developing Human Connectome Project, the Catholic University of Korea, Hammersmith Hospital (HS), and Dankook University Hospital (DU). T1- and T2-weighted images were used to train a brain segmentation model. Another model to predict the PMA of neonates based on segmentation data was developed. Accuracy was assessed using mean absolute error (MAE), root mean square error (RMSE), and mean error (ME). The brain maturation index was calculated as the difference between the PMA predicted by the model and the true PMA, and its correlation with postnatal age was analyzed.

Results

A total of 247 neonates (mean gestation age 37 ± 4 weeks; range 24–42 weeks) were included. Thirty-one features were extracted from each neonate and the three most contributing features for PMA prediction were the right lateral ventricle, left caudate, and corpus callosum. The predicted and true PMA were positively correlated (coefficient = 0.88, p < .001). MAE, RMSE, and ME of the external dataset of HS and DU were 1.57 and 1.33, 1.79 and 1.37, and 0.37 and 0.06 weeks, respectively. The brain maturation index negatively correlated with postnatal age (coefficient =  − 0.24, p < .001).

Conclusion

A model that calculates the regional brain volume can predict the PMA of neonates, which can then be utilized to show the brain maturation degree.

Clinical relevance statement

A brain maturity index based on regional volume of neonate’s brain can be used to measure brain maturation degree, which can help identify the status of early brain development.

Key Points

• Neonatal brain MRI segmentation model could be used to assess neonatal brain maturation status.

• A postmenstrual age (PMA) prediction model was developed based on a neonatal brain MRI segmentation model.

• The brain maturation index, derived from the PMA prediction model, enabled the estimation of the neonatal brain maturation status.

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Abbreviations

CU:

The Catholic University of Korea, Eunpyeong St. Mary’s Hospital

dHCP:

Developing Human Connectome Project

DU:

Dankook University Hospital

HS:

Hammersmith Hospital

MAE:

Mean absolute error

ME:

Mean error

PMA:

Postmenstrual age

RMSE:

Root mean square error

ROIs:

Regions of interest

XGBoost:

Extreme gradient boosting

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Funding

This study has received funding by the National Research Foundation of Korea (NRF-2021R1A2C1007831 and RS-2023-00233871).

Author information

Authors and Affiliations

  1. Research Institute, NEUROPHET Inc., Seoul, 06234, Republic of Korea

    Sunghwan Park, Hyeonsik Yang, Minho Lee, Regina E. Y. Kim & Donghyeon Kim

  2. Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, 03312, Republic of Korea

    Hyun Gi Kim & JeeYoung Kim

  3. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Sun Hyung Kim & Martin A. Styner

  4. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Martin A. Styner

  5. Department of Radiology, Dankook University Hospital, Dankook University College of Medicine, Cheonan-Si, Chungcheongnam-Do, Republic of Korea

    Jeong Rye Kim

Authors
  1. Sunghwan Park
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Corresponding authors

Correspondence to Hyun Gi Kim or Donghyeon Kim.

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Guarantor

The scientific guarantor of this publication is Hyun Gi Kim.

Conflict of interest

Author Donghyeon Kim has equity in Neurophet, Inc. Authors Sunghwan Park, Hyeonsik Yang, Minho Lee, and Regina E. Y. Kim are employed by Neurophet, Inc. No other author has reported a potential conflict of interest relevant to this article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

This retrospective study was approved by the Institutional Review Board, and the requirement for informed consent was waived.

Ethical approval

Approval from the Institutional Review Board of The Catholic University of Korea, Eunpyeong St. Mary’s Hospital, was obtained.

Study subjects or cohorts overlap

This study has used public datasets.

Some cases of the Catholic University of Korea dataset have been previously reported in “Three-dimensional magnetic resonance fingerprinting in neonates: quantifying regional difference and maturation in the brain”. Investigative Radiology. 2022 Jan 1;57(1):44–51.

However, the previous study used magnetic resonance fingerprinting of the neonates whereas this study used T1WI and T2WI.

Methodology

• retrospective

• experimental

• multicenter study

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Sunghwan Park and Hyun Gi Kim are co-first authors.

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Park, S., Kim, H.G., Yang, H. et al. A regional brain volume–based age prediction model for neonates and the derived brain maturation index. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10408-6

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  • DOI: https://doi.org/10.1007/s00330-023-10408-6

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