Hyeon Bobae, Shin Jaehyun, Lee Jae-Hun, Kim Woori, Kwon Jea, Lee Heeyoung, Kim Dae-Gun, Kim Choong Yeon, Choi Sian, Jeong Jae-Woong, Kim Kwang-Soo, Lee C Justin, Kim Daesoo, Heo Won Do
Department of Life Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Molecular Neurobiology Laboratory, McLean Hospital and Department of Psychiatry, Harvard Medical School, Belmont, MA, USA.
Nat Commun. 2025 Aug 21;16(1):7797. doi: 10.1038/s41467-025-63025-w.
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.
帕金森病（PD）是一种进行性神经退行性疾病，表现出复杂的运动症状，且缺乏有效的疾病修饰治疗方法。在此，我们表明，将人工智能（AI）与光遗传学干预相结合，即optoRET，调节c-RET（转染过程中重排）信号传导，能够在自由活动的雄性AAV-hA53T小鼠中实现与任务无关的行为评估和治疗益处。利用3D姿态估计技术，我们开发了基于树的AI模型，该模型比传统方法能更早且更准确地检测出PD严重程度队列。采用可解释的AI技术，我们识别出了一系列全面的PD行为标志物，包括步态和频谱时间特征。此外，我们的AI驱动分析突出表明，optoRET通过改善肢体协调性和运动能力以及减少胸部震颤，有效缓解了PD的进展。我们的研究证明了整合AI和光遗传学技术的协同作用，以提供一种具有广泛行为评估的高效诊断方法，并为PD的创新治疗策略奠定了基础。
