Anterior cingulate neurons display subregion-specific interaction with frontal eye fields as revealed by combined antidromic stimulation and resting state imaging
Bibliographic record
Abstract
Abstract The anterior cingulate cortex (ACC) is thought to exert cognitive control over saccade generation in the frontal eye fields (FEF), but the nature of this interaction remains unclear. Although prior imaging studies have suggested ACC interacts with FEF, few studies have confirmed this by electrophysiological recordings. This study aimed to characterize the functional connectivity between ACC and medial and lateral FEF during cognitive saccade tasks. We combined resting-state functional MRI (rs-fMRI) with single-unit electrophysiology in two macaque monkeys performing memory-guided saccade and pro-/anti-saccade tasks. Anti- and ortho-dromic stimulation was used to electrophysiologically identify ACC neurons mono- and polysynaptically connected to FEF. We analyzed ACC neuronal selectivity for different task aspects and correlated these properties with both positive and negative rs-fMRI functional connectivity between ACC and FEF subregions. Anti- and ortho-dromically identified ACC neurons were predominantly connected to medial FEF, which showed stronger positive functional connectivity with ACC compared to lateral FEF. Sites with higher proportions of task-selective neurons yielded stronger functional connectivity with FEF. This stronger functional connectivity was particularly related to the delay and saccadic periods of different cognitive saccade tasks. Using combined imaging and electrophysiology, our findings provide converging evidence for functional interactions between ACC and FEF, predominantly medial FEF regions which encode large amplitude saccades. The correlation between functional connectivity and task-related neuronal selectivity supports ACC’s interaction with FEF in the modulation of saccade generation and cognitive control. Additionally, we report suggestive evidence that mono- and poly-synaptic connections may be related to positive functional connectivity, but we found no such relationship for negative functional connectivity (anticorrelations). These results advance our understanding of prefrontal cortical interactions in oculomotor control and the electrophysiological mechanisms of positive and negative resting-state functional connectivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".