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Record W4281289348 · doi:10.1101/2022.05.19.492685

Parieto-frontal Oscillations Show Hand Specific Interactions with Top-Down Movement Plans

2022· preprint· en· W4281289348 on OpenAlexafffund
Gunnar Blohm, Douglas Cheyne, J. Douglas Crawford

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsSickKids FoundationHospital for Sick ChildrenQueen's UniversityYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPremotor cortexNeurosciencePrimary motor cortexSupplementary motor areaMotor cortexPosterior parietal cortexSupramarginal gyrusPsychologyCentral sulcusCortex (anatomy)MagnetoencephalographyIntraparietal sulcusSomatosensory systemDorsumFunctional magnetic resonance imagingElectroencephalographyBiologyAnatomy

Abstract

fetched live from OpenAlex

Abstract To generate a hand-specific reach plan, the brain must integrate hand-specific signals with the desired movement strategy. Although various neurophysiology / imaging studies have investigated hand-target interactions in simple reach-to-target tasks, the whole-brain timing and distribution of this process remain unclear, especially for more complex, instruction-dependent motor strategies. Previously, we showed that a pro/anti-pointing instruction influences magnetoencephalographic (MEG) signals in frontal cortex that then propagate recurrently through parietal cortex (Blohm et al., 2019). Here, we contrasted left versus right hand pointing in the same task to investigate 1) which cortical regions of interest show hand specificity, and 2) which of those areas interact with the instructed motor plan. Eight bilateral areas – the parietooccipital junction (POJ), superior parietooccipital cortex (SPOC), supramarginal gyrus (SMG), middle / anterior interparietal sulcus (mIPS/aIPS), primary somatosensory / motor cortex (S1/M1), and dorsal premotor cortex (PMd) – showed hand-specific changes in beta band power, with four of these (M1, S1, SMG, aIPS) showing robust activation before movement onset. M1, SMG, SPOC, and aIPS showed significant interactions between contralateral hand specificity and the instructed motor plan, but not with bottom-up target signals. Separate hand / motor signals emerged relatively early and lasted through execution, whereas hand-motor interactions only occurred close to movement onset. Taken together with our previous results, these findings show that instruction-dependent motor plans emerge in frontal cortex and interact recurrently with hand-specific parietofrontal signals before movement onset to produce hand-specific motor behaviors. Impact Statement The brain must generate different motor signals, depending which hand is used. The distribution and timing of hand use / instructed motor plan integration is not understood at the whole-brain level. Using whole-brain MEG recordings we show that different sub-networks involved in action planning code for hand usage (alpha and beta frequencies) and integrating hand use information into a hand-specific motor plan (beta band). The timing of these signals indicates that frontal cortex first creates a general motor plan and then integrates hand-specific frontoparietal information to produce a hand-specific motor plan.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.024
GPT teacher head0.235
Teacher spread0.211 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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