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Record W2396228609 · doi:10.1113/jp271854

Context‐dependent use of muscle spindles for human position sense

2016· letter· en· W2396228609 on OpenAlexaff
Brandon G. Rasman, Jean‐Sébastien Blouin

Bibliographic record

VenueThe Journal of Physiology · 2016
Typeletter
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)Position (finance)Orientation (vector space)Muscle spindlePerceptionGRASPSensory systemProprioceptionTask (project management)PsychologyCommunicationComputer scienceComputer visionNeuroscienceBiologyEngineering

Abstract

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To stand, walk, reach and grasp, we use internal representations of our body position. Among the contributors to our position sense are the muscle spindles. In 1972, Goodwin and colleagues established that these mechanoreceptors have a profound role for indicating body orientation. Muscle spindles are now regarded as the dominant source of body positional information encoding static and dynamic cues of muscle length (Proske & Gandevia, 2012), thus providing information referenced to the body. Position sense, however, can also originate from externally referenced signals, such as vision, that depict where our body is located with respect to the environment. Our brain is consistently challenged to integrate and consolidate body- and externally-referenced sensory information for spatial representations used in perception and the control of movement (Paillard 1991). Are all sensory signals used in a universal manner for position sense? In particular, muscle spindle cues are crucial for perceiving inter-limb orientation, but are such inputs relied upon to determine a limb's position in external space? In this issue of The Journal of Physiology, Tsay and colleagues (2016) address this question by biasing the muscle spindle signals during an elbow joint localization task. The authors demonstrate that the use of muscle spindle cues for limb localization is context dependent. The localization task utilized by Tsay et al. (2016) involved obstructing one arm from view and passively moving it to a position of elbow flexion or extension (the reference arm). Participants were required to spatially localize and indicate its perceived position in two ways: (1) by aligning its perceived position with the contralateral arm (limb matching task) and (2) by adjusting an external device to the perceived arm position (pointing task). The important difference between these two tasks is the available sensory cues which can be used for joint localization. During limb matching, the brain can compare sensory signals from both arms, whereas the pointing task involves using signals from the reference arm to indicate its position in external space. To evaluate muscle spindle contributions to these position sense tasks, the authors conditioned elbow muscles with isometric contractions to 'set' muscle spindles to a desired state and vibrated elbow muscles to increase spindle afferent discharge. Through these experimental manipulations, Tsay et al. (2016) reveal that muscle spindle signals influence the performance of elbow joint localization during limb matching, but not in pointing. These results may come as a surprise but, as the authors propose, they are a likely reflection of the context in which limb position is perceived. The authors suggest that during limb matching, the perceived limb position relies on the orientation of body segments with respect to each other. A comparison of body-referenced muscle spindle signals between arms is not only relevant, but useful for the limb matching performance. This fits with previous findings of inter-limb matching (Proske & Gandevia, 2012) and supports the hypothesis that the brain relies on the difference of sensory signals between limbs during such localization tasks (Tsay et al. 2014). Conversely, pointing involves identifying the spatial coordinates of a limb in external space. The external pointer device is visually guided for the localization task and a difference of limb-referenced cues does not aid in representing this orientation. The possible use of sensory signals is therefore determined by whether the context of position sense concerns the body alone or the body in external space, fitting with previous models of sensorimotor control (Paillard 1991). The findings of Tsay and colleagues represent a significant addition to our understanding of human position sense. Muscle spindles are confirmed to be a primary source of positional information, but mainly to estimate body-referenced orientation of the limbs. Consequently, their findings establish that, although human positional sense is based on the integration of cues from multiple sources, the relative contribution of sensory signals to positional sense is task dependent. These findings lay the foundation for future studies but also computational models to reveal the neural principles underlying human positional sense. Such a context-dependent use of spindle information for position sense could stem from learning: under normal circumstances, estimating the state of one limb with respect to another can be derived from a simple (approximate) solution comparing spindle information from both limbs. On the other hand, such approximation is not possible when the state of a limb is estimated with respect to the environment. In this latter scenario, state estimation of the target limb must be performed through fusion of available sensory signals, probably ignoring the bias in spindle information induced by Tsay et al. (2016). Further, we may speculate regarding the potential implications of the findings by Tsay and colleagues to sensorimotor processes outside of perception. Many sensory transformations require the use and integration of limb or whole-body positional information to function. Whether the context of the sensory transformation requires a body-referenced or an externally referenced representation of limb, trunk or head position may influence the relative importance of muscle spindle information in the transformation process. Similarly, sensory contributions to motoneuronal drive during movement may be affected by the context in which movements are performed. Based on the findings reported in the focus paper, it is tempting to propose that motoneuronal drive to the elbow flexors and extensors during matching bilateral elbow movements may involve greater relative contribution from muscle spindles than similar movements performed under visual guidance. These as well as other potential implications highlight the novelty and importance of the findings by Tsay et al. (2016). No competing interests declared.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.244
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2016
Admission routes1
Has abstractyes

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