Astrocytes Contribute to Remote Memory Formation by Modulating Hippocampal-Cortical Communication During Learning
Bibliographic record
Abstract
ABSTRACT The consolidation and retrieval of remote memories depend on the coordinated activity of the hippocampus and frontal cortices. However, the exact time at which these regions are recruited to support memory and the interactions between them are still debated. Astrocytes can sense and modify neuronal activity with great precision, but their role in cognitive function has not been extensively explored. To investigate the role of astrocytes in remote memory we expressed the Gi-coupled receptor hM4Di in CA1 astrocytes, allowing their manipulation by a designer drug. We discovered that astrocytic modulation during learning resulted in a specific impairment in remote, but not recent, memory recall, accompanied by decreased neuronal activity in the anterior cingulate cortex (ACC) during retrieval. We revealed a massive recruitment of ACC-projecting neurons in CA1 during memory acquisition, accompanied by activation of ACC neurons. Astrocytic Gi activation disrupted CA3 to CA1 communication in-vivo, and reduced the downstream response in the ACC. This same manipulation in behaving mice induced a projection-specific inhibition of ACC-projecting CA1 neurons during learning, consequently preventing the recruitment of the ACC. Our findings suggest that the foundation of remote memory is established in the ACC during acquisition, engaging a distinct process from the one supporting consolidation of recent memory. Furthermore, the mechanism underlying remote memory involves projection-specific functions of astrocytes in regulating neuronal activity.
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".