Cortical GABAergic inhibition dynamics around hippocampal sharp-wave ripples
Bibliographic record
Abstract
Abstract Hippocampal sharp-wave ripples (SWRs) coordinate hippocampal–neocortical interactions for memory consolidation, yet how cortical GABA signaling is organized around SWRs remains unclear. Here we approach this problem by combining wide-field mesoscale imaging of extracellular GABA using iGABASnFR2 with simultaneous dorsal CA1 electrophysiology and sleep-state monitoring in mice, enabling GABA dynamics to be mapped across 17 cortical regions during natural sleep and wakefulness. Using ripple-triggered activity mapping and singular value decomposition, we identified a cortex-wide GABA response consisting of a dominant global component and regionally structured components that were reconfigured across brain states. Across cortical subnetworks, SWRs were associated with a reduction in GABA signaling followed by widespread activation, with both components enhanced during NREM sleep. During NREM sleep, GABA responses emerged earliest and most strongly in the retrosplenial and other medial cortical regions before progressing laterally. During wakefulness, responses were faster, preferentially recruited lateral sensory regions and progressed towards medial cortex. Transitions between NREM sleep, REM sleep and wakefulness were also accompanied by distinct changes in GABA signaling and interregional network organization. Our findings suggest a model in which hippocampal SWRs recruit a shared cortex-wide GABA response whose regional expression and direction of propagation are reconfigured by brain state, providing a dynamic inhibitory framework for regulating hippocampal–neocortical communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".