Asymmetric effective connectivity between primate anterior cingulate and lateral prefrontal cortex revealed by electrical microstimulation
Bibliographic record
Abstract
Abstract The anterior cingulate cortex (ACC) and lateral prefrontal cortex (IPFC) of the non-human primate show neural firing correlations and synchronize at theta and beta frequencies during the monitoring and shifting of attention. These functional interactions might be based on synaptic connectivity that is equally efficacious in both directions, but it might be that there are systematic asymmetries in connectivity consistent with reports of more effective inhibition within the ACC than IPFC, or with a preponderance of ACC projections synapsing onto inhibitory neurons in the IPFC. Here, we tested effective ACC-IPFC connectivity in awake monkeys and report systematic asymmetries in the temporal patterning and latencies of effective connectivity as measured using electrical microstimulation. We found that ACC stimulation triggered evoked fields (EFPs) were more likely to be multiphasic in the IPFC than in the reverse direction, with a large proportion of connections showing 2-4 inflection points resembling resonance in the 20-30 Hz beta frequency range. Stimulation of ACC → IPFC resulted, on average, in shorter-latency EFPs than IPFC → ACC. Overall, latencies and connectivity strength varied more than two-fold depending on the precise anterior-to-posterior location of the connections. These findings reveal systematic asymmetries in effective connectivity between ACC and IPFC in the awake non-human primate and document the spatial and temporal patchiness of effective synaptic connections. We speculate that measuring effective connectivity profiles will be essential for understanding how local synaptic efficacy and synaptic connectivity translates into functional neuronal interactions to support adaptive behaviors.
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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) | 0.000 | 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".