MétaCan
Menu
Back to cohort
Record W2334852487 · doi:10.1152/jn.00626.2013

Effects of unilateral deactivations of dorsolateral prefrontal cortex and anterior cingulate cortex on saccadic eye movements

2013· article· en· W2334852487 on OpenAlexafffund
Michael J. Koval, R. Matthew Hutchison, Stephen G. Lomber, Stefan Everling

Bibliographic record

VenueJournal of Neurophysiology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsWestern University
FundersH2020 European Research CouncilCanadian Institutes of Health ResearchMultidisciplinary University Research Initiative
KeywordsDorsolateral prefrontal cortexNeuroscienceMacaqueSaccadic maskingAnterior cingulate cortexPsychologyPrefrontal cortexPrimateEye movementSaccadeCortex (anatomy)Cognition

Abstract

fetched live from OpenAlex

The dorsolateral prefrontal cortex (dlPFC) and anterior cingulate cortex (ACC) have both been implicated in the cognitive control of saccadic eye movements by single neuron recording studies in nonhuman primates and functional imaging studies in humans, but their relative roles remain unclear. Here, we reversibly deactivated either dlPFC or ACC subregions in macaque monkeys while the animals performed randomly interleaved pro- and antisaccades. In addition, we explored the whole-brain functional connectivity of these two regions by applying a seed-based resting-state functional MRI analysis in a separate cohort of monkeys. We found that unilateral dlPFC deactivation had stronger behavioral effects on saccades than unilateral ACC deactivation, and that the dlPFC displayed stronger functional connectivity with frontoparietal areas than the ACC. We suggest that the dlPFC plays a more prominent role in the preparation of pro- and antisaccades than the ACC.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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

Citations19
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of NeurophysiologySame topicFunctional Brain Connectivity StudiesFrench-language works237,207