eLife Assessment: Early-life stress induces persistent astrocyte dysfunction resulting in fear generalisation
Bibliographic record
Abstract
Early-life stress can have lifelong consequences, enhancing stress susceptibility and resulting in behavioral and cognitive deficits. While the effects of early-life stress on neuronal function have been well-described, we still know very little about the contribution of non-neuronal brain cells. Investigating the complex interactions between distinct brain cell types is critical to fully understand how cellular changes manifest as behavioral deficits following early-life stress. Here, using male and female mice we report that early-life stress induces anxiety-like behavior and fear generalisation in an amygdala-dependent learning and memory task. These behavioral changes were associated with impaired synaptic plasticity, increased neural excitability, and astrocyte dysfunction. Genetic perturbation of amygdala astrocyte function by either silencing these cells or reducing astrocyte network function was sufficient to replicate cellular, synaptic, and fear memory changes associated with early-life stress. These data provide mechanistic links between early-life stress and astrocyte dysfunction. Our data reveal a role of astrocytes in tuning emotionally salient memory with astrocyte dysfunction resulting in fear generalisation. Further understanding of how astrocytes are affected by stress might offer new insights into the long-term impact of early-life stress on affective states.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".