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Delayed brain development of Rolandic epilepsy profiled by deep learning–based neuroanatomic imaging

  • Imaging Informatics and Artificial Intelligence
  • Published:
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Abstract

Objectives

Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether RE has brain developmental delay. This study employed deep learning–based neuroanatomic biomarker to measure the changed feature of “brain age” in RE.

Methods

The study constructed a 3D-CNN brain age prediction model through 1155 cases of typically developing children’s morphometric brain MRI from open-source datasets and further applied to a local dataset of 167 RE patients and 107 typically developing children. The brain-predicted age difference was measured to quantitatively estimate brain age changes in RE and further investigated the relevancies with cognitive and clinical variables.

Results

The brain age estimation network model presented a good performance for brain age prediction in typically developing children. The children with RE showed a 0.45-year delay of brain age by contrast with typically developing children. Delayed brain age was associated with neuroanatomic changes in the Rolandic regions and also associated with cognitive dysfunction of attention.

Conclusion

This study provided neuroimaging evidence to support the notion that RE has delayed brain development.

Key Points

• The children with Rolandic epilepsy showed imaging phenotypes of delayed brain development with increased GM volume and decreased WM volume in the Rolandic regions.

• The children with Rolandic epilepsy had a 0.45-year delay of brain-predicted age by comparing with typically developing children, using 3D-CNN-based brain age prediction model.

• The delayed brain age was associated with morphometric changes in the Rolandic regions and attentional deficit in Rolandic epilepsy.

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Abbreviations

ADHD:

Attention-deficit/hyperactivity disorder

BAENET:

Brain age estimation network

brain-PAD:

Brain-predicted age difference

CNN:

Convolution neural network

GM:

Gray matter

GRF:

Gauss random field

ILAE:

International League Against Epilepsy

IVA-CPT:

Integrated visual and auditory continuous performance test

MAE:

Mean absolute error

RE:

Rolandic epilepsy

RSPM:

Raven’s standard progressive matrices

TDC:

Typically developing children

WM:

White matter

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Funding

This study has received funding by grants of the National Natural Scientific Foundation of China (Grant Nos. 81701680, 81871345, 81790653, and 81790650), National Key Research and Development Program of the Ministry of Science and Technology of PR. China (Grant No. 2018YFA0701703), grants of the key talent project in Jiangsu province (Grant No. ZDRCA2016093), and Natural scientific foundation-social development (Grant No. BE2016751).

Author information

Author notes

  1. Qirui Zhang and Yan He contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Imaging, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, China

    Qirui Zhang, Guangming Lu & Zhiqiang Zhang

  2. Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China

    Qirui Zhang, Qiang Xu, Guangming Lu & Zhiqiang Zhang

  3. Department of Neurology, Children’s Hospital of Nanjing Medical University, Nanjing, China

    Yan He & Zheng Hu

  4. Deepwise AI Lab, Deepwise Inc., Beijing, China

    Taiping Qu & Xiuli Li

  5. Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China

    Fang Yang & Ying Lin

  6. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Qiang Xu

  7. Department of Radiology, Third Affiliated Hospital of Soochow University & Changzhou First People’s Hospital, Changzhou, China

    Wei Xing

  8. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Valentina Gumenyuk, Steven M. Stufflebeam & Hesheng Liu

Authors
  1. Qirui Zhang
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  2. Yan He
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Corresponding authors

Correspondence to Guangming Lu or Zhiqiang Zhang.

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Guarantor

The scientific guarantor of this publication is Guang Ming Lu.

Conflict of interest

Taiping Qu and Xiuli Li are employees of Deepwise Inc. The remaining authors have no conflicts of interest.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

This study conforms with the World Medical Association Declaration of Helsinki. This study was approved by the medical ethics committee in Jinling Hospital, Nanjing University School of Medicine (Rec no: 2018NZKY-020-02), and written informed consent was obtained from the guardian of each local participated children.

Methodology

• retrospective

• cross-sectional study

• performed at one institution

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Zhang, Q., He, Y., Qu, T. et al. Delayed brain development of Rolandic epilepsy profiled by deep learning–based neuroanatomic imaging. Eur Radiol 31, 9628–9637 (2021). https://doi.org/10.1007/s00330-021-08048-9

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  • DOI: https://doi.org/10.1007/s00330-021-08048-9

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