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Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images

Author

Listed:
  • Yuchao Jiang

    (Fudan University)

  • Wei Li

    (Sichuan University
    China National Clinical Research Center for Geriatric Medicine)

  • Jinmei Li

    (Sichuan University)

  • Xiuli Li

    (Sichuan University)

  • Heng Zhang

    (Sichuan University)

  • Xiutian Sima

    (Sichuan University)

  • Luying Li

    (Sichuan University)

  • Kang Wang

    (Zhejiang University)

  • Qifu Li

    (Hainan Medical University and the Key Laboratory of Brain Science Research and Transformation in Tropical Environment of Hainan Province)

  • Jiajia Fang

    (Zhejiang University)

  • Lu Jin

    (The First Affiliated Hospital of Xinjiang Medical University)

  • Qiyong Gong

    (Sichuan University)

  • Dezhong Yao

    (University of Electronic Science and Technology of China
    University of Electronic Science and Technology of China
    Chinese Academy of Medical Sciences)

  • Dong Zhou

    (Sichuan University)

  • Cheng Luo

    (University of Electronic Science and Technology of China
    University of Electronic Science and Technology of China
    Chinese Academy of Medical Sciences)

  • Dongmei An

    (Sichuan University)

Abstract

Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication – a necessary step for precise medicine.

Suggested Citation

  • Yuchao Jiang & Wei Li & Jinmei Li & Xiuli Li & Heng Zhang & Xiutian Sima & Luying Li & Kang Wang & Qifu Li & Jiajia Fang & Lu Jin & Qiyong Gong & Dezhong Yao & Dong Zhou & Cheng Luo & Dongmei An, 2024. "Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46629-6
    DOI: 10.1038/s41467-024-46629-6
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    References listed on IDEAS

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    1. Michael J. Hawrylycz & Ed S. Lein & Angela L. Guillozet-Bongaarts & Elaine H. Shen & Lydia Ng & Jeremy A. Miller & Louie N. van de Lagemaat & Kimberly A. Smith & Amanda Ebbert & Zackery L. Riley & Chr, 2012. "An anatomically comprehensive atlas of the adult human brain transcriptome," Nature, Nature, vol. 489(7416), pages 391-399, September.
    2. Alexandra L Young & Razvan V Marinescu & Neil P Oxtoby & Martina Bocchetta & Keir Yong & Nicholas C Firth & David M Cash & David L Thomas & Katrina M Dick & Jorge Cardoso & John Swieten & Barbara Borr, 2018. "Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference," Nature Communications, Nature, vol. 9(1), pages 1-16, December.
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