Sensory filtering disruption caused by poly I:C - Timing of exposure and other experimental considerations
Bibliographic record
Abstract
Maternal immune activation (MIA) in response to infection during pregnancy has been linked through various epidemiological and preclinical studies to an increased risk of neurodevelopmental disorders such as autism spectrum disorder (ASD) and schizophrenia in exposed offspring. Sensory filtering disruptions occur in both of these disorders and are typically measured using the acoustic startle response in both humans and rodents. Our study focuses on characterizing the baseline reactivity, habituation and prepulse inhibition (PPI) of the acoustic startle response following exposure to MIA. We induced MIA using polyinosinic: polycytidylic acid (poly I:C) at gestational day (GD) 9.5 or 14.5, and we tested sensory filtering phenotypes in adolescent and adult offspring. Our results show that startle reactivity was robustly increased in adult GD9.5 but not GD14.5 poly I:C offspring. In contrast to some previous studies, we found no consistent changes in short-term habituation, long-term habituation or prepulse inhibition of startle. Our study highlights the importance of MIA exposure timing and discusses sensory filtering phenotypes as they relate to ASD, schizophrenia and the poly I:C MIA model. Moreover, we analyze and discuss the potential impact of between- and within-litter variability on behavioural findings in poly I:C studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".