Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.12.16.628548

Changes in Muscle Synergy Structure and Activation Patterns Underlie Force Field Adaptation, Retention, and Generalization

Abstract

1Humans can adapt their motor commands in response to errors when they perform reaching movements in new dynamic conditions, a process called motor adaptation. They acquire knowledge about the new dynamics, which they can use when they are re-exposed and, limitedly, generalize to untrained reaching directions. While force field adaptation, retention, and generalization have been thoroughly investigated at a kinematic and kinetic task level, the underlying coordination at a muscular level remains unclear. Many studies propose that the central nervous system uses low- dimensional control, i.e., coordinates muscles in functional groups: so-called muscle synergies. Accordingly, we hypothesized that changes in muscle synergy structure and activation patterns represent the acquired knowledge underlying force field adaptation, retention, and generalization. To test this, 36 male humans practiced reaching to a single target in a viscous force field and were tested for retention and generalization to new directions, while we simultaneously measured muscle activity from 13 upper-body muscles. We found that muscle synergies used for unperturbed reaching cannot explain the muscle patterns when adapted. Instead, muscle synergies specific to this adapted state were necessary, alongside a novel four-phasic pattern of muscle synergy activation. Furthermore, these structural changes and patterns were also evident during retention and generalization. Our results suggest that reaching in an environment with altered dynamics requires structural changes to muscle synergies compared to unperturbed reaching, and that these changes facilitate retention and generalization. These findings provide new insights into how the central nervous system coordinates the muscles underlying motor adaptation, retention, and generalization. 2 Significance StatementHumans can adapt their reaching movements to new dynamic conditions. They acquire knowledge about the new dynamics and can use it not only when re-exposed to these conditions but also, in part, to generalize unpracticed reaching directions. While adaptation, retention, and generalization in a force field with new dynamics have been thoroughly investigated at a kinematic and kinetic task level, coordination of the underlying muscles remains elusive. Our results show how muscle synergies - functional groupings of co-activated muscles - underlie adaptation, retention, and generalization. In particular, we observed structural changes in the muscle synergies after adaptation compared to unperturbed reaching. These changes facilitate retention and spatial generalization. Thus, muscle synergies provide new insights into human motor adaptation.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Herzog, M., Berger, D. J., Russo, M., d'Avella, A., Stein, T.. 2024-12-17. Changes in Muscle Synergy Structure and Activation Patterns Underlie Force Field Adaptation, Retention, and Generalization. https://doi.org/10.1101/2024.12.16.628548

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↗