Investigating gene-environment interaction on attention in a double-hit model for Autism Spectrum Disorder
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental behavioral disorder characterized by social, communicative, and motor deficits. There is no single etiological cause for ASD, rather, there are various genetic and environmental factors that increase the risk for ASD. It is thought that some of these factors influence the same underlying neural mechanisms, and that an interplay of both genetic and environmental factors would better explain the pathogenesis of ASD. To better appreciate the influence of genetic-environment interaction on ASD-related behaviours, rats lacking a functional copy of the ASD-linked gene Cntnap2 were exposed to maternal immune activation (MIA) during pregnancy and assessed in adolescence and adulthood. We hypothesized that Cntnap2 deficiency interacts with poly I:C MIA to aggravate ASD-like symptoms in the offspring. In this double-hit model, we assessed attention, a core deficit in ASD due to prefrontal cortical dysfunction. We employed a well-established attentional paradigm known as the 5-choice serial reaction time task (5CSRTT). Cntnap2-/- rats exhibited greater perseverative responses which is indicative of repetitive behaviors. Additionally, rats exposed to poly I:C MIA exhibited premature responses, a marker of impulsivity. The rats exposed to both the genetic and environmental challenge displayed an increase in impulsive activity; however, this response was only elicited in the presence of an auditory distractor. This implies that exacerbated symptomatology in the double-hit model may situation-dependent and not generally expressed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".