Gonzalez Melissa, Gradwell Mark A, Thackray Joshua K, Patel Komal R, Temkar Kanaksha K, Abraira Victoria E
Cell Biology and Neuroscience Department, Rutgers University, The State University of New Jersey, New Brunswick, NJ, United States of America.
W.M. Keck Center for Collaborative Neuroscience, Rutgers University, The State University of New Jersey, New Brunswick, NJ, United States of America.
bioRxiv. 2024 Aug 19:2024.07.28.605489. doi: 10.1101/2024.07.28.605489.
Closed-loop behavior paradigms enable us to dissect the state-dependent neural circuits underlying behavior in real-time. However, studying context-dependent locomotor perturbations has been challenging due to limitations in molecular tools and techniques for real-time manipulation of spinal cord circuits.
We developed a novel closed-loop optogenetic stimulation paradigm that utilizes DeepLabCut-Live pose estimation to manipulate primary sensory afferent activity at specific phases of the locomotor cycle in mice. A compact DeepLabCut model was trained to track hindlimb kinematics in real-time and integrated into the Bonsai visual programming framework. This allowed an LED to be triggered to photo-stimulate sensory neurons expressing channelrhodopsin at user-defined pose-based criteria, such as during the stance or swing phase.
Optogenetic activation of nociceptive TRPV1 sensory neurons during treadmill locomotion reliably evoked paw withdrawal responses. Photoactivation during stance generated a brief withdrawal, while stimulation during swing elicited a prolonged response likely engaging stumbling corrective reflexes.
This new method allows for high spatiotemporal precision in manipulating spinal circuits based on the phase of the locomotor cycle. Unlike previous approaches, this closed-loop system can control for the state-dependent nature of sensorimotor responses during locomotion.
Integrating DeepLabCut-Live with optogenetics provides a powerful new approach to dissect the context-dependent role of sensory feedback and spinal interneurons in modulating locomotion. This technique opens new avenues for uncovering the neural substrates of state-dependent behaviors and has broad applicability for studies of real-time closed-loop manipulation based on pose estimation.
闭环行为范式使我们能够实时剖析行为背后的状态依赖性神经回路。然而，由于用于实时操纵脊髓回路的分子工具和技术存在局限性，研究上下文依赖性运动扰动一直具有挑战性。
我们开发了一种新型闭环光遗传学刺激范式，该范式利用DeepLabCut-Live姿态估计来操纵小鼠运动周期特定阶段的初级感觉传入活动。训练了一个紧凑的DeepLabCut模型以实时跟踪后肢运动学，并将其集成到盆景视觉编程框架中。这使得能够根据用户定义的基于姿态的标准（例如在站立或摆动阶段）触发发光二极管对表达通道视紫红质的感觉神经元进行光刺激。
在跑步机运动期间对伤害性TRPV1感觉神经元进行光遗传学激活可靠地诱发了爪子缩回反应。站立期间的光激活产生短暂的缩回，而摆动期间的刺激引发可能涉及绊倒纠正反射的延长反应。
这种新方法允许基于运动周期的阶段对脊髓回路进行高时空精度的操纵。与以前的方法不同，这个闭环系统可以控制运动期间感觉运动反应的状态依赖性。
将DeepLabCut-Live与光遗传学相结合，为剖析感觉反馈和脊髓中间神经元在调节运动中的上下文依赖性作用提供了一种强大的新方法。该技术为揭示状态依赖性行为的神经基础开辟了新途径，并且对于基于姿态估计的实时闭环操纵研究具有广泛的适用性。
