Transcranial Magnetic Stimulation of the Prefrontal Cortex in Awake Nonhuman Primates Evokes a Polysynaptic Neck Muscle Response That Reflects Oculomotor Activity at the Time of Stimulation
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) has emerged as an important technique in cognitive neuroscience, permitting causal inferences about the contribution of a given brain area to behavior. Despite widespread use, exactly how TMS influences neural activity throughout an interconnected network, and how such influences ultimately change behavior, remain unclear. The oculomotor system of nonhuman primates (NHPs) offers a potential animal model to bridge this gap. Here, based on results suggesting that neck muscle activity provides a sensitive indicator of oculomotor activation, we show that single pulses of TMS over the frontal eye fields (FEFs) in awake NHPs evoked rapid (within ∼25 ms) and fairly consistent (∼50-75% of all trials) expression of a contralateral head-turning synergy. This neck muscle response resembled that evoked by subsaccadic electrical microstimulation of the FEF. Systematic variation in TMS location revealed that this response could also be evoked from the dorsolateral prefrontal cortex (dlPFC). Combining TMS with an oculomotor task revealed state dependency, with TMS evoking larger neck muscle responses when the stimulated area was actively engaged. Together, these results advance the suitability of the NHP oculomotor system as an animal model for TMS. The polysynaptic neck muscle response evoked by TMS of the prefrontal cortex is a quantifiable trial-by-trial reflection of oculomotor activation, comparable to the monosynaptic motor-evoked potential evoked by TMS of primary motor cortex. Our results also speak to a role for both the FEF and dlPFC in head orienting, presumably via subcortical connections with the superior colliculus.
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 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".