Resting-State Neural Firing Rate Is Linked to Cardiac-Cycle Duration in the Human Cingulate and Parahippocampal Cortices
Bibliographic record
Abstract
Stimulation and functional imaging studies have revealed the existence of a large network of cortical regions involved in the regulation of heart rate. However, very little is known about the link between cortical neural firing and cardiac-cycle duration (CCD). Here, we analyze single-unit and multiunit data obtained in humans at rest, and show that firing rate covaries with CCD in 16.7% of the sample (25 of 150). The link between firing rate and CCD was most prevalent in the anterior medial temporal lobe (entorhinal and perirhinal cortices, anterior hippocampus, and amygdala), where 36% (18 of 50) of the units show the effect, and to a lesser extent in the mid-to-anterior cingulate cortex (11.1%, 5 of 45). The variance in firing rate explained by CCD ranged from 0.5 to 11%. Several lines of analysis indicate that neural firing influences CCD, rather than the other way around, and that neural firing affects CCD through vagally mediated mechanisms in most cases. These results show that part of the spontaneous fluctuations in firing rate can be attributed to the cortical control of the cardiac cycle. The fine tuning of the regulation of CCD represents a novel physiological factor accounting for spontaneous variance in firing rate. It remains to be determined whether the “noise” introduced in firing rate by the regulation of CCD is detrimental or beneficial to the cognitive information processing carried out in the parahippocampal and cingulate regions. SIGNIFICANCE STATEMENTFluctuations in heart rate are known to be under the control of cortical structures, but spontaneous fluctuations in cortical firing rate, or “noise,” have seldom been related to heart rate. Here, we analyze unit activity in humans at rest and show that spontaneous fluctuations in neural firing in the medial temporal lobe, as well as in the mid-to-anterior cingulate cortex, influence heart rate. This phenomenon was particularly pronounced in the entorhinal and perirhinal cortices, where it could be observed in one of three neurons. Our results show that part of spontaneous firing rate variability in regions best known for their cognitive role in spatial navigation and memory corresponds to precise physiological regulations.
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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 | 0.000 | 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".