Project Summary/Abstract
A central challenge in neuroscience biomedical research is to define the neural circuits that underlie behavior.
Investigations of spinal cord circuits are ideally suited to answer these questions: the direct link between sensory
input and motor output affords an exquisite experimental tractability that has been leveraged since Sherrington’s
pioneering work on the proprioceptive reflex pathway1. Indeed, great progress has been made since then in
understanding how proprioceptors (i.e., muscle sensory neurons) shape motor activity. Touch receptors in skin
(i.e., cutaneous sensory neurons) encoding sensory modalities like vibration, indentation, and slip, are also
critical for adapting the way we walk in response to changes in our environment. However, spinal cord integration
of touch pathways that sculpt motor activity remains profoundly poorly understood. To address key conceptual
and technical challenges in this field, we have built an extensive mouse genetic toolbox to visualize, quantify and
manipulate touch-specific spinal cord circuits. In addition, we merge these powerful genetic tools with motor
assays involving high-speed cameras, computer vision, and machine learning to quantify somatosensory
behavior with unprecedented sensitivity. Combining these technologies, we identified a novel touch-specific
premotor network important for sensorimotor function. Our overall hypothesis is that this network represents a
critical node for integrating touch information to influence specific patterns of muscle groups that facilitate both
corrective movements during locomotion and motor ‘switching’ during naturalistic behaviors. We interrogate this
novel network to address fundamental questions whose answers will enable a deeper understanding of how
touch pathways converge in the spinal cord to shape movement. In Aims 1 and 2 we combine genetic approaches,
high-resolution synaptic analysis, slice electrophysiology and in-vivo muscle recordings to test the hypothesis
that this network integrates multimodal sensory information to coordinate specific muscles in response to
cutaneous input. Aim 3 combines joint and muscle activity recordings to test the hypothesis that this network
shapes cutaneous responses to facilitate corrective movements during locomotion. We extend these behavioral
studies by implementing computer vision and machine learning to parse out naturalistic behaviors into sub-
second movements to test the hypothesis that touch-specific premotor networks sculpt how micro-movements
are pieced together into complex motor behaviors
. By understanding the final path for movement organization
(i.e., the spinal cord) our research will lead to new therapies aimed at improving the quality of life of people
suffering from a variety of neurological disorders. Thus, this research lays the critical foundation for novel ways
to modulate spinal circuits for improving motor function.
