Integrating artificial intelligence and optogenetics for Parkinson's disease diagnosis and therapeutics in male mice.

Journal: Nature communications
Published Date:

Abstract

Parkinson's disease (PD), a progressive neurodegenerative disorder, presents complex motor symptoms and lacks effective disease-modifying treatments. Here we show that integrating artificial intelligence (AI) with optogenetic intervention, termed optoRET, modulating c-RET (REarranged during Transfection) signalling, enables task-independent behavioural assessments and therapeutic benefits in freely moving male AAV-hA53T mice. Utilising a 3D pose estimation technique, we developed tree-based AI models that detect PD severity cohorts earlier and with higher accuracy than conventional methods. Employing an explainable AI technique, we identified a comprehensive array of PD behavioural markers, encompassing gait and spectro-temporal features. Moreover, our AI-driven analysis highlights that optoRET effectively alleviates PD progression by improving limb coordination and locomotion and reducing chest tremor. Our study demonstrates the synergy of integrating AI and optogenetic techniques to provide an efficient diagnostic method with extensive behavioural evaluations and sets the stage for an innovative treatment strategy for PD.

Authors

  • Bobae Hyeon
    Department of Life Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Jaehyun Shin
    a School of Engineering , RMIT University , Bundoora , Australia.
  • Jae-Hun Lee
    Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
  • Woori Kim
    Molecular Neurobiology Laboratory, McLean Hospital and Department of Psychiatry, Harvard Medical School, Belmont, MA, USA.
  • Jea Kwon
    Max Planck Institute for Security and Privacy (MPI-SP), Bochum, Germany.
  • Heeyoung Lee
    Department of Life Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Dae-Gun Kim
    Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Choong Yeon Kim
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
  • Sian Choi
    Department of Life Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Jae-Woong Jeong
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
  • Kwang-Soo Kim
    Molecular Neurobiology Laboratory, McLean Hospital and Department of Psychiatry, Harvard Medical School, Belmont, MA, USA.
  • C Justin Lee
    Center for Cognition and Sociality, Institute for Basic Science (IBS), Daejeon, Republic of Korea. cjl@ibs.re.kr.
  • Daesoo Kim
    Department of Brain and Cognitive Sciences, KAIST, Daejeon, Republic of Korea. daesoo@kaist.ac.kr.
  • Won Do Heo
    Department of Biological Sciences, KAIST, Daejeon, Republic of Korea. wdheo@kaist.ac.kr.