Serotonin, norepinephrine, and acetylcholine differentially affect astrocytic potassium clearance to modulate somatosensory signaling in male mice
Bibliographic record
Abstract
Abstract Changes in extracellular potassium ([K + ] e ) modulate neuronal networks via changes in membrane potential, voltage‐gated channel activity, and alteration to transmission at the synapse. Given the limited extracellular space in the central nervous system, potassium clearance is crucial. As activity‐induced potassium transients are rapidly managed by astrocytic Kir4.1 and astrocyte‐specific Na + /K + ‐ATPase, any neurotransmitter/neuromodulator that can regulate their function may have indirect influence on network activity. Neuromodulators differentially affect cortical/thalamic networks to align sensory processing with differing behavioral states. Given serotonin (5HT), norepinephrine (NE), and acetylcholine (ACh) differentially affect spike frequency adaptation and signal fidelity (“signal‐to‐noise”) in somatosensory cortex, we hypothesize that [K + ] e may be differentially regulated by the different neuromodulators to exert their individual effects on network function. This study aimed to compare effects of individually applied 5HT, NE, and ACh on regulating [K + ] e in connection to effects on cortical‐evoked response amplitude and adaptation in male mice. Using extracellular field and K + ion‐selective recordings of somatosensory stimulation, we found that differential effects of 5HT, NE, and ACh on [K + ] e regulation mirrored differential effects on amplitude and adaptation. 5HT effects on transient K + recovery, adaptation, and field post‐synaptic potential amplitude were disrupted by barium (200 µM), whereas NE and ACh effects were disrupted by ouabain (1 µM) or iodoacetate (100 µM). Considering the impact [K + ] e can have on many network functions; it seems highly efficient that neuromodulators regulate [K + ] e to exert their many effects. This study provides functional significance for astrocyte‐mediated buffering of [K + ] e in neuromodulator‐mediated shaping of cortical network activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".