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Record W2966216336 · doi:10.3390/brainsci9080177

Using Dual-Site Transcranial Magnetic Stimulation to Probe Connectivity between the Dorsolateral Prefrontal Cortex and Ipsilateral Primary Motor Cortex in Humans

2019· article· en· W2966216336 on OpenAlexafffund
Matt J.N. Brown, Elana R. Goldenkoff, Robert Chen, Carolyn Gunraj, Michael Vesia

Bibliographic record

VenueBrain Sciences · 2019
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsOntario Brain InstituteUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsTranscranial magnetic stimulationNeuroscienceDorsolateral prefrontal cortexStimulus (psychology)Motor cortexPrimary motor cortexIsometric exerciseStimulationPsychologyPrefrontal cortexMedicineCognitionInternal medicineCognitive psychology

Abstract

fetched live from OpenAlex

Dual-site transcranial magnetic stimulation to the primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC) can be used to probe functional connectivity between these regions. The purpose of this study was to characterize the effect of DLPFC stimulation on ipsilateral M1 excitability while participants were at rest and contracting the left- and right-hand first dorsal interosseous muscle. Twelve participants were tested in two separate sessions at varying inter-stimulus intervals (ISI: 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, and 20 ms) at two different conditioning stimulus intensities (80% and 120% of resting motor threshold). No significant effect on ipsilateral M1 excitability was found when applying a conditioning stimulus over DLPFC at any specific inter-stimulus interval or intensity in either the left or right hemisphere. Our findings suggest neither causal inhibitory nor faciliatory influences of DLPFC on ipsilateral M1 activity while participants were at rest or when performing an isometric contraction in the target hand muscle.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.757
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.301
Teacher spread0.253 · 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 teacher head, 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

Citations17
Published2019
Admission routes2
Has abstractyes

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