bioRxiv Science⌕ Search

bioRxiv · 10.1101/2022.05.21.492933

Inhibitory interneurons within the deep dorsal horn integrate convergent sensory input to regulate motor performance

Abstract

To achieve smooth motor performance in a changing sensory environment, motor outputs must be constantly updated in response to sensory feedback. Inhibitory interneurons in the spinal cord play an essential role in shaping motor activity by gating the transmission of sensory information and setting the pattern and rhythm of motor neurons. Here, we identify the medial deep dorsal horn of the spinal cord as a "hot zone" of convergent proprioceptive and cutaneous information from the hindlimb, where inhibitory neurons show increased responsiveness to sensory input and are preferentially recruited during locomotion in comparison to excitatory neurons. We identify a novel population of glycinergic inhibitory neurons within the deep dorsal horn that express parvalbumin (dPV) and receive convergent proprioceptive and cutaneous input from the paw. We show that dPVs possess intrinsic properties that support spontaneous discharge, even in the absence of synaptic input. However, a drug cocktail mimicking descending input (5-HT, dopamine, NMDA) amplifies dPV output, while cutaneous and proprioceptive inputs shape the temporal dynamics of dPV activity. These findings suggest dPV-mediated inhibition is modulated by behavioral state and can be fine-tuned by sensory input. Using intersectional genetic strategies, we selectively target spinal cord dPVs and demonstrate their capacity to provide divergent ipsilateral inhibition to both pre-motor and motor networks of the ventral horn, thereby controlling the timing and magnitude of cutaneous-evoked muscle activity. Manipulating the activity of dPVs during treadmill locomotion results in altered limb kinematics at the transition of stance to swing and altered step cycle timing at increased speeds. To investigate the effects of manipulating dPV activity on broader sets of motor behaviors, we used depth vision and machine learning to quantify and scale naturalistic behavior. We find that although sub-movements remain stable, the transitions between sub-movements are reduced, suggesting a role in movement switching. In sum, our study reveals a new model by which sensory convergence and inhibitory divergence produce a surprisingly flexible influence on motor networks to increase the diversity of mechanisms by which sensory input facilitates smooth movement and context-appropriate transitions. HighlightsO_LIInhibitory deep dorsal horn interneurons integrate convergent proprioceptive and cutaneous sensory inputs from the paw and are preferentially recruited during locomotion. C_LIO_LIDeep dorsal horn parvalbumin+ interneurons (dPVs) represent a population of glycinergic interneurons that can provide sustained inhibitory control. C_LIO_LISensory input engages dPVs to facilitate inhibition with high temporal precision and reduced variability. C_LIO_LIdPVs contribute to the ipsilateral inhibitory control of motor and premotor networks of the ventral horn, thereby gating the magnitude and timing of cutaneous-evoked flexor and extensor muscle activity. C_LIO_LIIn vivo, dPVs modulate gait dynamics in a state- and phase-dependent manner, to ensure smooth movement transitions between step-cycle phases and naturalistic sub-movements. C_LI

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ozeri-Engelhard, N., Gradwell, M. A., Laflamme, O. D., Upadhyay, A., Aoki, A., Shrier, T., Gandhi, M., Gonzalez, M., Eisdorfer, J. T., Abbas- Zadeh, G., Yusuf, N., Katz, J., Haas, M., Akay, T., Abraira, V. E.. 2022-05-21. Inhibitory interneurons within the deep dorsal horn integrate convergent sensory input to regulate motor performance. https://doi.org/10.1101/2022.05.21.492933

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Same Movements, Different Skills: How Context Shapes Learning in Humans and Neural Networks

What determines whether experiences are integrated into shared representations or split? Motor skill learning offers a revealing test: when two tasks require an identical sequence of movements, both intuition and theory suggest that practice in one should generalize to the other. Yet we find that changing context can make identical movements different skills. In simple recurrent networks trained on the same sequence across contexts, splitting was greatest when strong contextual inputs coincided with a high initial weight scale; neither alone sufficed. Greater representational separation coincided with separate context-specific learning curves. Humans practicing the same finger sequence while alternating between two performance-irrelevant contexts likewise exhibited context-specific curves, rather than the single continuous trajectory observed when practicing in a single context. State-space modeling favored split learning states in humans and very weakly coupled states in the most context-dependent networks. These findings have broader implications for understanding how learning generalizes across contexts.

neuroscience↗

Postpartum resource scarcity reshapes maternal care, offspring prioritization, and the steroid-sensitive medial preoptic area

Parental care requires balancing offspring needs with environmental constraints, yet how resource scarcity alters the organization of maternal responses remains poorly understood. Here, we examined maternal behavior, offspring prioritization, and steroid receptor-associated extracellular matrix organization in postpartum rats exposed to limited bedding and nesting (LBN) or control conditions with ample nesting materials. LBN increased pup-directed and reduced self-directed behaviors but also increased behavioral entropy, a measure of behavioral fragmentation, indicating less predictable caregiving sequences. LBN dams preferentially retrieved male pups first, whereas control dams preferentially retrieved females first. LBN also increased the proportion of pup-investigation time directed toward males compared with controls. In offspring on postnatal day 10, corticosterone was elevated in female but not male LBN pups, a pattern that paralleled the maternal male bias. To identify associated neurobiological changes, we assessed steroid receptors and extracellular matrix organization in the medial preoptic area (mPOA), a key region for maternal behavior. LBN increased numbers of estrogen receptor (ER)- and androgen receptor (AR)-immunoreactive cells, reduced perineuronal net (PNN) abundance, and decreased receptor-positive cell enwrapment. Because PNNs constrain structural plasticity, these findings suggest that the postpartum environment alters the organization of steroid receptor-associated extracellular matrix, potentially modulating experience-dependent plasticity in neurosteroid-sensitive circuits. In sum, LBN increases the levels of maternal engagement while reshaping its temporal organization and allocation, accompanied by selective remodeling of steroid-sensitive preoptic architecture. Therefore, maternal adaptation to adversity may be better understood as changes in caregiving allocation strategies rather than a simple gain or loss of maternal care.

neuroscience↗

The Computational Grammar of Human Behaviour in a Biological Environment

Human behaviour unfolds as an extended stream of spontaneous actions shaped by environmental demands and the agent's goals. The organisation of these actions, situated between setting abstract goals and concrete movement execution, remains poorly characterised. Here, we introduce DynaClade, an unsupervised temporal action segmentation algorithm for human full-body motion capture data, and apply it to freely moving participants navigating ecologically realistic risky foraging environments in virtual reality. DynaClade identifies discrete actions whose kinematic characteristics and spatial distributions correspond to environmental affordances, supporting the validity of the approach. Among these actions, we discovered a hitherto undescribed movement pattern defined by increased body sway without locomotion. Action sequences were highly non-random and showed clear history dependence beyond first-order Markov structure, most of which was explained by a second-order prediction. This higher-order structure was well captured in a hidden Markov model, in which some of the observed actions were associated with more than one latent state. Each of these latent states corresponded to the same action in a distinct action context. These findings suggest that spontaneous human actions are predominantly selected based on the current action and context, rather than following pre-planned or habitually assembled sequences.

neuroscience↗