Using DeepLabCut-Live to probe state dependent neural circuits of behavior with closed-loop optogenetic stimulation

Closed-loop behavior paradigms allow for real-time investigation of state-dependent neural circuits underlying behavior. However, studying context-dependent locomotor perturbations is challenging due to limitations in molecular tools and techniques for real-time manipulation of spinal circuits.

We developed a novel closed-loop optogenetic stimulation paradigm that leverages 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 system enabled LED triggered photo-stimulation of sensory neurons expressing channelrhodopsin based on user-defined pose-based criteria, such as stance or swing phase.

Optogenetic activation of nociceptive TRPV1⁺ sensory neurons during treadmill locomotion reliably evoked paw withdrawal responses. Stimulation during the stance phase generated a brief withdrawal and impacted the duration of the following swing phase. Stimulation during the swing phase increased the height of paw withdrawal during swing and reduced the duration of the following stance phase.

This method allows for high spatiotemporal precision in manipulating spinal circuits based on locomotor phase. Unlike previous approaches, this closed-loop system accounts for state-dependent nature of sensorimotor responses, enabling controlled, real-time modulation of locomotion.

Integrating DeepLabCut-Live with optogenetics provides a powerful tool for dissecting the context-dependent role of sensory feedback and spinal interneurons in 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.