Prenatal and postnatal animal models of immune activation: Relevance to a range of neurodevelopmental disorders
Bibliographic record
Abstract
Epidemiological evidence has established links between immune activation during the prenatal or early postnatal period and increased risk of developing a range of neurodevelopment disorders in later life. Animal models have been used to great effect to explore the ramifications of immune activation during gestation and neonatal life. A range of behavioral, neurochemical, molecular, and structural outcome measures associated with schizophrenia, autism, cerebral palsy, and epilepsy have been assessed in models of prenatal and postnatal immune activation. However, the epidemiology-driven disease-first approach taken by some studies can be limiting and, despite the wealth of data, there is a lack of consensus in the literature as to the specific dose, timing, and nature of the immunogen that results in replicable and reproducible changes related to a single disease phenotype. In this review, we highlight a number of similarities and differences in models of prenatal and postnatal immune activation currently being used to investigate the origins of schizophrenia, autism, cerebral palsy, epilepsy, and Parkinson's disease. However, we describe a lack of synthesis not only between but also within disease-specific models. Our inability to compare the equivalency dose of immunogen used is identified as a significant yet easily remedied problem. We ask whether early life exposure to infection should be described as a disease-specific or general vulnerability factor for neurodevelopmental disorders and discuss the implications that either classification has on the design, strengths and limitations of future experiments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".