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Correlations Between Primary Motor Cortex Activity with Recent Past and Future Limb Motion During Unperturbed Reaching

2018· article· en· W2884246448 on OpenAlexafffund
Tomohiko Takei, Frédéric Crevecoeur, Troy M. Herter, Kevin P. Cross, Stephen H. Scott

Bibliographic record

VenueJournal of Neuroscience · 2018
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchUehara Memorial FoundationGlaxoSmithKline
KeywordsPrimary motor cortexNeuroscienceMotor cortexCorrelationPsychologyMotor controlMotor systemPremotor cortexMotion (physics)Physical medicine and rehabilitationBiologyComputer scienceMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Many studies highlight that human movements are highly successful yet display a surprising amount of variability from trial to trial. There is a consistent pattern of variability throughout movement: initial motor errors are corrected by the end of movement, suggesting the presence of a powerful online control process. Here, we analyze the trial-by-trial variability of goal-directed reaching in nonhuman primates (five male Rhesus monkeys) and demonstrate that they display a similar pattern of variability during reaching, including a strong negative correlation between initial and late hand motion. We then demonstrate that trial-to-trial neural variability of primary motor cortex (M1) is positively correlated with variability of future hand motion (τ = ∼160 ms) during reaching. Furthermore, the variability of M1 activity is also correlated with variability of past hand motion (τ = ∼90 ms), but in the opposite polarity (i.e., negative correlation). Partial correlation analysis demonstrated that M1 activity independently reflects the variability of both past and future hand motions. These findings provide support for the hypothesis that M1 activity is involved in online feedback control of motor actions. SIGNIFICANCE STATEMENT Previous studies highlight that primary motor cortex (M1) rapidly responds to either visual or mechanical disturbances, suggesting its involvement in online feedback control. However, these studies required external disturbances to the motor system and it is not clear whether a similar feedback process addresses internal noise/errors generated by the motor system itself. Here, we introduce a novel analysis that evaluates how variations in the activity of M1 neurons covary with variations in hand motion on a trial-to-trial basis. The analyses demonstrate that M1 activity is correlated with hand motion in both the near future and the recent past, but with opposite polarity. These results suggest that M1 is involved in online feedback motor control to address errors/noise within the motor system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 score0.001
Threshold uncertainty score0.003

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.248
Teacher spread0.219 · 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".

Quick stats

Citations14
Published2018
Admission routes2
Has abstractyes

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