A Neural Network Reveals Motoric Effects of Maternal Preconception Exposure to Nicotine on Rat Pup Behavior: A New Approach for Movement Disorders Diagnosis
Bibliographic record
Abstract
Neurodevelopmental disorders can stem from pharmacological, genetic, or environmental causes and early diagnosis is often a key to successful treatment. To improve early detection of neurological motor impairments, we developed a deep neural network for data-driven analyses. The network was applied to study the effect of maternal nicotine exposure prior to conception on 10-day-old rat pup motor behavior in an open field task. Female Long-Evans rats were administered nicotine (15 mg/L) in sweetened drinking water (1% sucralose) for seven consecutive weeks immediately prior to mating. The neural network outperformed human expert designed animal locomotion measures in distinguishing rat pups born to nicotine exposed dams vs. control dams (87 vs. 64% classification accuracy). Notably, the network discovered novel movement alterations in posture, movement initiation and a stereotypy in "warm-up" behavior (repeated movements along specific body dimensions) that were predictive of nicotine exposure. The results suggest novel findings that maternal preconception nicotine exposure delays and alters offspring motor development. Similar behavioral symptoms are associated with drug-related causes of disorders such as autism spectrum disorder and attention-deficit/hyperactivity disorder in human children. Thus, the identification of motor impairments in at-risk offspring here shows how neuronal networks can guide the development of more accurate behavioral tests to earlier diagnose symptoms of neurodevelopmental disorders in infants and children.
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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 | 0.000 | 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".