bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.12.22.572982

Role of spinal sensorimotor circuits in triphasic command: a simulation approach using Goal Exploration Process

Abstract

During rapid voluntary limb movement about a single joint, a stereotyped triphasic pattern is typically observed in the electromyograms (EMGs) of antagonistic muscles acting at this joint. To explain the origin of such triphasic commands, two types of theories have been proposed. Peripheral theories consider that triphasic commands result from sensorimotor spinal networks, either through a combination of reflexes or through a spinal central pattern generator. Central theories consider that the triphasic command is elaborated in the brain. Although both theories were partially supported by physiological data, there is still no consensus about how exactly triphasic commands are elaborated. Moreover, capacities of simple spinal sensorimotor circuits to elaborate triphasic commands on their own have not been tested yet. In order to test this, we modelled arm musculoskeletal system, muscle activation dynamics, proprioceptive spindle and Golgi afferent activities and spinal sensorimotor circuits. Step commands were designed to modify the activity of spinal neurons and the strength of their synapses, either to prepare (SET) the network before movement onset, or to launch the movement (GO). Since these step commands do not contain any dynamics, changes in muscle activities responsible for arm movement rest entirely upon interactions between the spinal network and the musculo-skeletal system. Critically, we selected step commands using a Goal Exploration Process inspired from baby babbling during development. In this task, the Goal Exploration Process proved very efficient at discovering step commands that enabled spinal circuits to handle a broad spectrum of functional behaviors, displayed in a behavioral space characterized by movement amplitude and maximal speed. All over the behavioral space, specific SET and GO commands elicited natural triphasic commands, thereby substantiating the inherent capacity of the spinal network in generating them. Key points SummaryO_LISpinal sensorimotor circuits, despite extensive physiological analyses, remain poorly understood in the context of voluntary movement. C_LIO_LIThis study models the capabilities of these spinal networks, alongside musculoskeletal dynamics, to achieve single-joint arm flexion following minimum jerk principles. C_LIO_LIPreparatory (SET) and launch (GO) step commands modulate spinal network states, including neuron activity and synaptic strength. C_LIO_LINotably, arm movement results from sensorimotor interactions within spinal circuits, even with basic step commands that have no dynamics. C_LIO_LIA Goal Exploration Process (GEP), inspired by developmental baby babbling through trial and error, was used to refine step commands. C_LIO_LIThe spinal networks, when in appropriate states, were shown to spontaneously generate triphasic commands across a broad range of valid movement amplitudes and speeds. C_LIO_LIThese findings highlight the intrinsic capacity of spinal sensorimotor circuits to manage complex movement dynamics, offering novel testable insights as to their possible functional role in voluntary motor control. C_LI

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cattaert, D., Guemann, M., Paclet, F., Chung, B., Oudeyer, P.-Y., de Rugy, A.. 2023-12-23. Role of spinal sensorimotor circuits in triphasic command: a simulation approach using Goal Exploration Process. https://doi.org/10.1101/2023.12.22.572982

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

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗