Maternal separation affects fronto-cortical activity in rat pups during dam-pup interactions and behavioral transitions
Bibliographic record
Abstract
Early-life stress is known to impair neurodevelopment. Prior work from our group showed that prolonged physical and emotional separation necessitated by the medical needs of preterm infants (born <37 weeks) is associated with lower electroencephalogram (EEG) power in frontal areas, and that trend can be reversed by an intervention that enhances the physical and emotional contact between preterm infant and mother. Here we sought to model the changes in preterm infant frontal EEG power in a rodent model. We examined effects of daily maternal separation (MS) on frontal cortex electrophysiological (electrocorticography [EcoG]) activity in neonatal rats. We also explored the effects of dam-pup behavioral interactions on EcoG activity. From postnatal days (P) 2-10, rat pups were separated daily from their dam and isolated from their littermates for 3 hours. Control rats were normally reared. On P10, pups were implanted with telemetry devices and an electrode placed on the left frontal dura. EcoG activity was recorded during daily sessions over the next four days while pups remained in the home cage, as well as in response to a pup-dam isolation-reunion paradigm at P12. EcoG power was computed in 1 Hz frequency bins between 1-100 Hz. Dam and pup behavioral interactions during recording sessions were coded and synchronized to EcoG data. MS pups showed lower EcoG power during dam-pup interactions. These data parallel human and provide evidence of lower fronto-cortical activity as an early marker of early-life stress and possible mechanism for long-term effects of maternal separation on neurobehavioral development.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".