Classification of Fiber Tracts Changed by Nerve Injury and Electrical Brain Stimulation Using Machine Learning Algorithm in the Rat Brain

신경 손상과 전기 뇌 자극에 의한 흰쥐의 뇌 섬유 경로 변화에 대한 기계학습 판별

  • Sohn, Jin-Hun (Department of Physiology, Yonsei University College of Medicine) ;
  • Eum, Young-Ji (Center for Research Equipment, Korea Basic Science Institute) ;
  • Cheong, Chaejoon (Center for Research Equipment, Korea Basic Science Institute) ;
  • Cha, Myeounghoon (Department of Physiology, Yonsei University College of Medicine) ;
  • Lee, Bae Hwan (Department of Physiology, Yonsei University College of Medicine)
  • 손진훈 (연세대학교 의과대학, 생리학 교실) ;
  • 음영지 (한국기초과학지원연구원, 연구장비운영부) ;
  • 정재준 (한국기초과학지원연구원, 연구장비운영부) ;
  • 차명훈 (연세대학교 의과대학, 생리학 교실) ;
  • 이배환 (연세대학교 의과대학, 생리학 교실)
  • Published : 2021.07.14

Abstract

The purpose of the study was to identify fiber changes induced by electrical stimulation of a certain neural substrate in the rat brain. In the stimulation group, the peripheral nerve was injured and the brain area associated to inhibit sensory information was electrically stimulated. There were sham and sham stimulation groups as controls. Then high-field diffusion tensor imaging (DTI) was acquired. 35 features were taken from the DTI measures from 7 different brain pathways. To compare the efficacy of the classification for 3 animal groups, the linear regression analysis (LDA) and the machine learning technique (MLP) were applied. It was found that the testing accuracy by MLP was about 77%, but that of accuracy by LDA was much higher than MLP. In conclusion, machine learning algorithm could be used to identify and predict the changes of the brain white matter in some situations. The limits of this study will be discussed.

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Acknowledgement

This study was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (2020R1A2C3008481).