Functional rhythmogenic domains defined by astrocytic networks in the trigeminal main sensory nucleus
Bibliographic record
Abstract
Abstract Stimuli that induce rhythmic firing in trigeminal neurons also increase astrocytic coupling and reveal networks that define the boundaries of this particular population. Rhythmic firing depends on astrocytic coupling which in turn depends on S100β. In many nervous functions that rely on the ability of neuronal networks to generate a rhythmic pattern of activity, coordination of firing is an essential feature. Astrocytes play an important role in some of these networks, but the contribution of astrocytic coupling remains poorly defined. Here we investigate the modulation and organization of astrocytic networks in the dorsal part of the trigeminal main sensory nucleus (NVsnpr), which forms part of the network generating chewing movements. Using whole‐cell recordings and the dye coupling approach by filling a single astrocyte with biocytin to reveal astrocytic networks, we showed that coupling is limited under resting conditions, but increases importantly under conditions that induce rhythmic firing in NVsnpr neurons. These are: repetitive electrical stimulation of the sensory inputs to the nucleus, local application of NMDA and decrease of extracellular Ca2+. We have previously shown that rhythmic firing induced in NVsnpr neurons by these stimuli depends on astrocytes and their Ca2+‐binding protein S100β. Here we show that extracellular blockade of S100β also prevents the increase in astrocytic coupling induced by local application of NMDA. Most of the networks were small and remained confined to the functionally distinct area of dorsal NVsnpr. Disrupting coupling by perfusion with the nonspecific gap junction blocker, carbenoxolone or with GAP26, a selective inhibitor of connexin 43, mostly expressed in astrocytes, abolished NMDA‐induced rhythmic firing in NVsnpr neurons. These results suggest that astrocytic coupling is regulated by sensory inputs, necessary for neuronal bursting, and organized in a region specific manner.
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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".