Berezhnoi Daniil, Chehade Hiba Douja, Simms Gabriel, Chen Liqiang, Narasimhan Kishore Kumar S, Dravid Shashank M, Chu Hong-Yuan
Department of Pharmacology and Physiology, Georgetown University of Medical Center, Washington, DC 20007.
Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20852.
eNeuro. 2025 Aug 5;12(8). doi: 10.1523/ENEURO.0014-25.2025. Print 2025 Aug.
The degeneration of midbrain dopamine (DA) neurons disrupts the neural control of natural behavior, such as walking, posture, and gait in Parkinson's disease. While some aspects of motor symptoms can be managed by DA replacement therapies, others respond poorly. Recent advancements in machine learning-based technologies offer opportunities to better understand the organizing principles of behavior modules at fine timescales and its dependence on dopaminergic modulation. In the present study, we applied the motion sequencing (MoSeq) platform to study the spontaneous locomotor activities of neurotoxin and genetic mouse models of parkinsonism as the midbrain DA neurons progressively degenerate. We also evaluated the treatment efficacy of levodopa (l-DOPA) on behavioral modules at fine timescales. We revealed robust changes in the kinematics and usage of the behavioral modules that encode spontaneous locomotor activity. Further analysis demonstrates that fast behavioral modules with higher velocities were more vulnerable to loss of DA and preferentially affected at early stages of Parkinsonism. Last, l-DOPA effectively improved the velocity, but not the usage and transition probability, of behavioral modules in parkinsonian animals. In conclusion, the hypokinetic phenotypes in parkinsonism involve the decreased velocities of behavioral modules and their disrupted temporal organization during movement. Moreover, we showed that the therapeutic effect of l-DOPA is mainly mediated by its effect on the velocities of behavior modules at fine timescales. This work documents robust changes in the velocity, usage, and temporal organization of behavioral modules and their responsiveness to dopaminergic treatment under the parkinsonian state.
中脑多巴胺（DA）神经元的退化会破坏对自然行为的神经控制，如帕金森病中的行走、姿势和步态。虽然运动症状的某些方面可以通过多巴胺替代疗法来控制，但其他方面的反应却很差。基于机器学习的技术的最新进展为在精细时间尺度上更好地理解行为模块的组织原则及其对多巴胺能调节的依赖性提供了机会。在本研究中，我们应用运动序列（MoSeq）平台来研究帕金森病神经毒素和基因小鼠模型在中脑DA神经元逐渐退化时的自发运动活动。我们还在精细时间尺度上评估了左旋多巴（l-DOPA）对行为模块的治疗效果。我们揭示了编码自发运动活动的行为模块在运动学和使用方面的显著变化。进一步分析表明，速度较高的快速行为模块更容易受到多巴胺缺失的影响，并且在帕金森病早期优先受到影响。最后，左旋多巴有效地提高了帕金森病动物行为模块的速度，但没有改善其使用和转换概率。总之，帕金森病中的运动减少表型涉及行为模块速度的降低及其在运动过程中时间组织的破坏。此外，我们表明左旋多巴的治疗效果主要是通过其在精细时间尺度上对行为模块速度的影响来介导的。这项工作记录了帕金森病状态下行为模块在速度、使用和时间组织方面的显著变化及其对多巴胺能治疗的反应。
