Motor patterns of the small intestine explained by phase-amplitude coupling of two pacemaker activities: the critical importance of propagation velocity
Bibliographic record
Abstract
Phase-amplitude coupling of two pacemaker activities of the small intestine, the omnipresent slow wave activity generated by interstitial cells of Cajal of the myenteric plexus (ICC-MP) and the stimulus-dependent rhythmic transient depolarizations generated by ICC of the deep muscular plexus (ICC-DMP), was recently hypothesized to underlie the orchestration of the segmentation motor pattern. The aim of the present study was to increase our understanding of phase-amplitude coupling through modeling. In particular the importance of propagation velocity of the ICC-DMP component was investigated. The outcome of the modeling was compared with motor patterns recorded from the rat or mouse intestine from which propagation velocities within the different patterns were measured. The results show that the classical segmentation motor pattern occurs when the ICC-DMP component has a low propagation velocity (<0.05 cm/s). When the ICC-DMP component has a propagation velocity in the same order of magnitude as that of the slow wave activity (∼1 cm/s), cluster type propulsive activity occurs which is in fact the dominant propulsive activity of the intestine. Hence, the only difference between the generation of propagating cluster contractions and the Cannon-type segmentation motor pattern is the propagation velocity of the low-frequency component, the rhythmic transient depolarizations originating from the ICC-DMP. Importantly, the proposed mechanism explains why both motor patterns have distinct rhythmic waxing and waning of the amplitude of contractions. The hypothesis is brought forward that the velocity is modulated by neural regulation of gap junction conductance within the ICC-DMP network.
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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".