Multimodal Proprioceptive Integration in Sensorimotor Networks of an Insect Leg

An animal’s nervous system monitors the actions of the body using its sense of proprioception. This information is used for precise motor control and to enable coordinated interaction with the animal’s surroundings. Proprioception is a multimodal sense that includes feedback about limb movement and loading from various peripheral sense organs. The sensory information from distinct sense organs must be integrated by the network to form a coherent representation of the current proprioceptive state and to elicit appropriate motor behavior. By combining intra- and extracellular electrophysiological recording techniques with precise mechanical sensory stimulation paradigms, I studied multimodal proprioceptive integration in the sensorimotor network of the stick insect leg. The findings demonstrate where, when, and how sensory feedback from load-sensing campaniform sensilla (CS) is integrated with movement information from the femoral chordotonal organ (fCO) in the sensorimotor network controlling movement of the femur-tibia (FTi) joint. Proprioceptive information about distinct sensory modalities (load / movement) and from distinct sense organs of the same sensory modality (trochanterofemoral CS (tr/fCS) / tibial CS (tiCS)) was distributed into one network of local premotor nonspiking interneurons (NSIs). The NSIs’ processing of fCO, tr/fCS, and tiCS was antagonistic with respect to a given NSI’s effect on the motor output of extensor tibiae motor neurons (ExtTi MNs). Spatial summation of load and movement feedback occurred in the network of premotor NSIs, whereas temporal summation was shifted between sensory modalities. Load feedback (tr/fCS / tiCS) was consistently delayed relative to movement signals (fCO) throughout the sensorimotor pathways of sensory afferents, premotor NSIs, and ExtTi MNs. The connectivity between these neuron types was inferred using transmission times and followed distinct patterns for individual sense organs. At the motor output level of the system, the temporal shift of simultaneously elicited load and movement feedback caused load responses to be superimposed onto ongoing movement responses. These results raised the hypothesis that load could alter movement signal processing. Load (tiCS) affected movement (fCO) signal gain by presynaptic afferent inhibition. In postsynaptic premotor NSIs, this led to altered movement parameter dependence and nonlinear summation of load and movement signals. Specifically, the amplitude dependence of NSIs opposing ExtTi MN output was increased, and, consistently, the movement response gain of the slow ExtTi MN was decreased. Movement signal processing in the premotor network was altered depending on the proprioceptive context, i.e. the presence or absence of load feedback. Lateral presynaptic interactions between load (tiCS) and movement (fCO) afferents were reciprocal, i.e. existed from fCO to tiCS afferents and vice versa, and also occurred between sensory afferents of the same sense organ. Additionally, a new type of presynaptic interaction was identified. Load signals increased the gain of directional movement information by releasing unidirectionally velocity- or acceleration-sensitive fCO afferents from tonic presynaptic inhibition. Paired double recordings showed lateral connectivity also at the level of the premotor NSI network. NSIs interacted via reciprocal excitatory connections. Additionally, the activity of individual NSIs was correlated in the absence of external stimuli, and specific types of NSIs showed rhythmic 30 Hz oscillations of the resting membrane potential, indicating an underlying mechanism of network synchronization. Taken together, the results of this dissertation provide an understanding of the integration of multimodal proprioceptive feedback in the sensorimotor network by identifying neuronal pathways and mechanism underlying spatial and temporal signal summation. The local network uses multimodal signal integration for context-dependent sensory processing, thereby providing insights into the mechanism by which a local network can adapt sensory processing to the behavioral context. Initial results clearly highlight the necessity to consider lateral connections along sensorimotor pathways to unravel the complex computations underlying proprioceptive processing and motor control. The findings on the integration of proprioceptive signals, obtained in the resting animal, broaden our understanding of sensorimotor processing and motor control not only in the stationary, but also in the walking animal

Temporal differences between load and movement signal integration in the sensorimotor network of an insect leg

Nervous systems face a torrent of sensory inputs, including proprioceptive feedback. Signal integration depends on spatially and temporally coinciding signals. It is unclear how relative time delays affect multimodal signal integration from spatially distant sense organs. We measured transmission times and latencies along all processing stages of sensorimotor pathways in the stick insect leg muscle control system, using intra- and extracellular recordings. Transmission times of signals from load-sensing tibial and trochanterofemoral campaniform sensilla (tiCS, tr/fCS) to the premotor network were longer than from the movement-sensing femoral chordotonal organ (fCO). We characterized connectivity patterns from tiCS, tr/fCS, and fCO afferents to identified premotor nonspiking interneurons (NSIs) and motor neurons (MNs) by distinguishing short- and long-latency responses to sensory stimuli. Functional NSI connectivity depended on sensory context. The timeline of multisensory integration in the NSI network showed an early phase of movement signal processing and a delayed phase of load signal integration. The temporal delay of load signals relative to movement feedback persisted into MN activity and muscle force development. We demonstrate differential delays in the processing of two distinct sensory modalities generated by the sensorimotor network and affecting motor output. The reported temporal differences in sensory processing and signal integration improve our understanding of sensory network computation and function in motor control. NEW & NOTEWORTHY Networks integrating multisensory input face the challenge of not only spatial but also temporal integration. In the local network controlling insect leg movements, proprioceptive signal delays differ between sensory modalities. Specifically, signal transmission times to and neuronal connectivity within the sensorimotor network lead to delayed information about leg loading relative to movement signals. Temporal delays persist up to the level of the motor output, demonstrating its relevance for motor control

Distributed processing of load and movement feedback in the premotor network controlling an insect leg joint

In legged animals, integration of information from various proprioceptors in and on the appendages by local premotor networks in the central nervous system is crucial for controlling motor output. To ensure posture maintenance and precise active movements, information about limb loading and movement is required. In insects, various groups of campaniform sensilla (CS) measure forces and loads acting in different directions on the leg, and the femoral chordotonal organ (fCO) provides information about movement of the femur-tibia (FTi) joint. In this study, we used extra- and intracellular recordings of extensor tibiae (ExtTi) and retractor coxae (RetCx) motor neurons (MNs) and identified local premotor nonspiking interneurons (NSIs) and mechanical stimulation of the fCO and tibial or trochanterofemoral CS (tiCS, tr/fCS), to investigate the premotor network architecture underlying multimodal proprioceptive integration. We found that load feedback from tiCS altered the strength of movement-elicited resistance reflexes and determined the specificity of ExtTi and RetCx MN responses to various load and movement stimuli. These responses were mediated by a common population of identified NSIs into which synaptic inputs from the fCO, tiCS, and tr/fCS are distributed, and whose effects onto ExtTi MNs can be antagonistic for both stimulus modalities. Multimodal sensory signal interaction was found at the level of single NSIs and MNs. The results provide evidence that load and movement feedback are integrated in a multimodal, distributed local premotor network consisting of antagonistic elements controlling movements of the FTi joint, thus substantially extending current knowledge on how legged motor systems achieve fine-tuned motor control. NEW & NOTEWORTHY Proprioception is crucial for motor control in legged animals. We show the extent to which processing of movement (fCO) and load (CS) signals overlaps in the local premotor network of an insect leg. Multimodal signals converge onto the same set of interneurons, and our knowledge about distributed, antagonistic processing is extended to incorporate multiple modalities within one perceptual neuronal framework

Non-linear multimodal integration in a distributed premotor network controls proprioceptive reflex gain in the insect leg

Producing context-appropriate motor acts requires integrating multiple sensory modalities. Presynaptic inhibition of proprioceptive afferent neurons(1-4) and afferents of different modalities targeting the same motor neurons (MNs)(5-7) underlies some of this integration. However, in most systems, an interneuronal network is interposed between sensory afferents and MNs. How these networks contribute to this integration, particularly at single-neuron resolution, is little understood. Context-specific integration of load and movement sensory inputs occurs in the stick insect locomotory system,(6,8-12) and both inputs feed into a network of premotor nonspiking interneurons (NSIs).(8) We analyzed how load altered movement signal processing in the stick insect femur-tibia (FTi) joint control system by tracing the interaction of FTi movement(13-15) (femoral chordotonal organ [fCO]) and load(13,15,16) (tibial campaniform sensilla [CS]) signals through the NSI network to the slow extensor tibiae (SETi) MN, the extensor MN primarily active in non-walking animals.(17-19) On the afferent level, load reduced movement signal gain by presynaptic inhibition. In the NSI network, graded responses to movement and load inputs summed nonlinearly, increasing the gain of NSIs opposing movement-induced reflexes and thus decreasing the SETi and extensor tibiae muscle movement reflex responses. Gain modulation was movement-parameter specific and required presynaptic inhibition. These data suggest that gain changes in distributed premotor networks, specifically the relative weighting of antagonistic pathways, could be a general mechanism by which multiple sensory modalities are integrated to generate context-appropriate motor activity

Gradients in mechanotransduction of force and body weight in insects

Posture and walking require support of the body weight, which is thought to be detected by sensory receptors in the legs. Specificity in sensory encoding occurs through the numerical distribution, size and response range of sense organs. We have studied campaniform sensilla, receptors that detect forces as strains in the insect exoskeleton. The sites of mechanotransduction (cuticular caps) were imaged by light and confocal microscopy in four species (stick insects, cockroaches, blow flies and Drosophila). The numbers of receptors and cap diameters were determined in projection images. Similar groups of receptors are present in the legs of each species (flies lack Group 2 on the anterior trochanter). The number of receptors is generally related to the body weight but similar numbers are found in blow flies and Drosophila, despite a 30 fold difference in their weight. Imaging data indicate that the gradient (range) of cap sizes may more closely correlate with the body weight: the range of cap sizes is larger in blow flies than in Drosophila but similar to that found in juvenile cockroaches. These studies support the idea that morphological properties of force-detecting sensory receptors in the legs may be tuned to reflect the body weight. (C) 2020 The Authors. Published by Elsevier Ltd