Primary sensorimotor cortex exhibits complex dependencies of spike-field coherence on neuronal firing rates, field power, and behavior
Bibliographic record
Abstract
Spike-field coherence (SFC) is widely used to assess cortico-cortical interactions during sensorimotor behavioral tasks by measuring the consistency of the relative phases between the spike train of a neuron and the concurrent local field potentials (LFPs). Interpretations of SFC as a measure of functional connectivity are complicated by theoretical work suggesting that estimates of SFC depend on overall neuronal activity. We evaluated the dependence of SFC on neuronal firing rates, LFP power, and behavior in the primary motor (MIo) and primary somatosensory (SIo) areas of the orofacial sensorimotor cortex of monkeys ( Macaca mulatta) during performance of a tongue-protrusion task. Although we occasionally observed monotonically increasing linear relationships between coherence and firing rate, we most often found highly complex, nonmonotonic relationships in both SIo and MIo and sometimes even found that coherence decreased with increasing firing rate. The lack of linear relationships was also true for both LFP power and tongue-protrusive force. Moreover, the ratio between maximal firing rate and the firing rate at peak coherence deviated significantly from unity, indicating that MIo and SIo neurons achieved maximal SFC at a submaximal level of spiking. Overall, these results point to complex relationships of SFC to firing rates, LFP power, and behavior during sensorimotor cortico-cortical interactions: coherence is a measure of functional connectivity whose magnitude is not a mere monotonic reflection of changes in firing rate, LFP power, or the relevantly controlled behavioral parameter. NEW & NOTEWORTHY The concern that estimates of spike-field coherence depend on the firing rates of single neurons has influenced analytical methods employed by experimental studies investigating the functional interactions between cortical areas. Our study shows that the overwhelming majority of the estimated spike-field coherence exhibited complex relations with firing rates of neurons in the orofacial sensorimotor cortex. The lack of monotonic relations was also evident after testing the influence of local field potential power and force on spike-field coherence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".