The Consequences of Early‐Life Adversity: Neurobiological, Behavioural and Epigenetic Adaptations
Bibliographic record
Abstract
During the perinatal period, the brain is particularly sensitive to remodelling by environmental factors. Adverse early-life experiences, such as stress exposure or suboptimal maternal care, can have long-lasting detrimental consequences for an individual. This phenomenon is often referred to as 'early-life programming' and is associated with an increased risk of disease. Typically, rodents exposed to prenatal stress or postnatal maternal deprivation display enhanced neuroendocrine responses to stress, increased levels of anxiety and depressive-like behaviours, and cognitive impairments. Some of the phenotypes observed in these models of early-life adversity are likely to share common neurobiological mechanisms. For example, there is evidence for impaired glucocorticoid negative-feedback control of the hypothalamic-pituitary-adrenal axis, altered glutamate neurotransmission and reduced hippocampal neurogenesis in both prenatally stressed rats and rats that experienced deficient maternal care. The possible mechanisms through which maternal stress during pregnancy may be transmitted to the offspring are reviewed, with special consideration given to altered maternal behaviour postpartum. We also discuss what is known about the neurobiological and epigenetic mechanisms that underpin early-life programming of the neonatal brain in the first generation and subsequent generations, with a view to abrogating programming effects and potentially identifying new therapeutic targets for the treatment of stress-related disorders and cognitive impairment.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".