Supplementary Data for [Environmental enrichment partially rescues neurodevelopmental milestone delays in the prenatal VPA rat model of ASD.]
Description
This dataset contains supplementary files related to the manuscript entitled 'Environmental enrichment partially rescues neurodevelopmental milestone delays in the prenatal VPA rat model of ASD.' It includes raw body weights, neurodevelopmental milestone acquisition data, statistical model outputs, and standardized composite scores.
Files
Steps to reproduce
The datasets and analyses included in this repository were generated as part of our experimental study investigating the effects of early environmental enrichment (EE) on neurodevelopmental milestones and body weight in a VPA-induced rat model of autism. Below, we outline the procedures and analytical workflows to allow replication of the study. 1. Experimental Design and Data Collection Animal Model: Wistar rats were used, and autism-like phenotypes were induced by prenatal exposure to valproic acid (VPA, 500 mg/kg) administered intraperitoneally at gestational day 12.5. Environmental Conditions: Dams and their offspring were assigned to standard housing (SH) or environmental for eight weeks, beginning two weeks before conception and continuing through lactation Measured Variables: Body weights were recorded for each pup at PND1, PND7, and PND21. Neurodevelopmental milestones (e.g., righting reflex, eye opening, negative geotaxis, grasp reflex) were assessed from PND1 to PND17. All statistical analyses, including data preprocessing, composite-score calculations, and visualization, were performed using Python 3.12. The following libraries were utilized: pandas (v2.0) for data handling, NumPy (v1.25) for numerical computations, scikit-learn (v1.3) for machine learning-based analyses, SciPy (v1.11) for statistical testing, Pingouin (v0.5) for advanced statistical metrics, and statsmodels (v0.14) for linear models and multiple-testing corrections. Multiple-testing corrections were implemented using the statsmodels.stats.multitest module to control false discovery rates. All datasets were analyzed separately for males and females when appropriate. Data are expressed as mean ± standard error of the mean (SEM). All figures and visualizations were generated using GraphPad Prism 8 to ensure clear and standardized representation of results. For full reproducibility, we provide three independent Jupyter notebooks: body_weight_linear.ipynb – body weight analysis across postnatal days. NRDV_COR_F.ipynb – analysis of neurodevelopmental milestones in female offspring, including the computation of the composite neurodevelopmental score. NRDV_M_COR.ipynb – analysis of neurodevelopmental milestones in male offspring, including the computation of the composite neurodevelopmental score.
Institutions
- Universite Ibn Tofail Kenitra Faculte des Sciences