Prefrontal gamma oscillations and fear extinction learning require early postnatal interneuron-oligodendroglia communication
Bibliographic record
Abstract
Emerging evidence links oligodendrocyte (OL) lineage cells and myelin to cognitive processes, yet the role of myelination in shaping neuronal networks critical for cognitive tasks remains unknown. We demonstrate in mice that early postnatal GABAergic signaling between interneurons and oligodendrocyte precursor cells (OPCs) is crucial for myelination of parvalbumin (PV) interneurons, which facilitates in vivo low-gamma oscillations in the medial prefrontal cortex (mPFC) and supports fear extinction learning. Disruption of this signaling results in PV interneuron dysmyelination, decreases low-gamma power, and impairs tone fear extinction. These deficits are specific to PV interneuron dysmyelination, as mPFC myelination, high-gamma oscillations and contextual fear extinction are not significantly altered. Increasing PV interneuron activity or enhancing myelination do not reverse the deficits, indicating the long-term consequences of these early myelination impairments. Our findings reveal the role of OPC GABAergic signaling in PV interneuron myelination and mPFC circuit maturation, with lasting impacts on gamma rhythms and cognition.
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 | 0.001 |
| 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".