Behavioral, neuroanatomical, and molecular correlates of resilience and susceptibility to maternal immune activation
Bibliographic record
Abstract
Infectious or noninfectious maternal immune activation (MIA) is an environmental risk factor for psychiatric and neurological disorders with neurodevelopmental etiologies. Whilst there is increasing evidence for significant health consequences, the effects of MIA on the offspring appear to be variable. Here, we aimed to identify and characterize subgroups of isogenic mouse offspring exposed to identical MIA, which was induced in C57BL6/N mice by administration of the viral mimetic, poly(I:C), on gestation day 12. Cluster analysis of behavioral data obtained from a first cohort containing >150 MIA and control offspring revealed that MIA offspring could be stratified into distinct subgroups that were characterized by the presence or absence of multiple behavioral dysfunctions. The two subgroups also differed in terms of their transcriptional profiles in cortical and subcortical brain regions and brain networks of structural covariance, as measured by ex vivo structural magnetic resonance imaging (MRI). In a second, independent cohort containing 50 MIA and control offspring, we identified a subgroup of MIA offspring that displayed elevated peripheral production of innate inflammatory cytokines, including IL-1β, IL-6, and TNF-α, in adulthood. This subgroup also showed significant impairments in social approach behavior and sensorimotor gating, whereas MIA offspring with a low inflammatory cytokine status did not. Taken together, our results highlight the existence of subgroups of MIA-exposed offspring that show dissociable behavioral, transcriptional, brain network, and immunological profiles even under conditions of genetic homogeneity. These data have relevance for advancing our understanding of the variable neurodevelopmental effects induced by MIA and for biomarker-guided approaches in preclinical psychiatric research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".