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Record W3132741286 · doi:10.1152/jn.00642.2020

Interhemispheric interactions between the right angular gyrus and the left motor cortex: a transcranial magnetic stimulation study

2021· article· en· W3132741286 on OpenAlexafffund
Julianne Baarbé, Michael Vesia, Matt J.N. Brown, Karlo J. Lizárraga, Carolyn Gunraj, Gaayathiri Jegatheeswaran, Neil M. Drummond, Cricia Rinchon, Anne Weißbach, James Saravanamuttu, Robert Chen

Bibliographic record

VenueJournal of Neurophysiology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchParkinson CanadaElse Kröner-Fresenius-StiftungDeutsche ForschungsgemeinschaftGovernment of CanadaDystonia Medical Research Foundation
KeywordsTranscranial magnetic stimulationNeuroscienceStimulationMotor cortexAngular gyrusCortex (anatomy)PsychologyFunctional magnetic resonance imaging

Abstract

fetched live from OpenAlex

Goal-directed hand movements rely on the right and left angular gyri (RAG and LAG) and motor cortex (M1), yet how these brain areas functionally interact is unclear. Here, we show that RAG stimulation facilitated right hand motor output from the left M1 but inhibited indirect (I)-waves in M1. Combined RAG and LAG stimulation increased GABAB, but not GABAA, receptor-mediated inhibition in left M1. These findings highlight unique brain interactions between the RAG and left M1.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.280
Teacher spread0.258 · 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 designBench or experimental
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

Citations9
Published2021
Admission routes2
Has abstractyes

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