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.
Highlights
Many neurological disorders, such as attention deficit/hyperactivity (ADHD) and autism spectrum disorder (ASD), have an early life onset
To address the problem of early diagnosis, we introduce a deep neural network that automatically classifies spontaneous behavior and extracts, in a data-driven way, movements that distinguish control and experimental groups of animals
We applied our network to study the rat pups born to maternal preconception nicotine exposed (MPNE) mothers
Summary
Many neurological disorders, such as attention deficit/hyperactivity (ADHD) and autism spectrum disorder (ASD), have an early life onset. To address the problem of early diagnosis, we introduce a deep neural network that automatically classifies spontaneous behavior and extracts, in a data-driven way, movements that distinguish control and experimental groups of animals. We applied our network to study the rat pups born to maternal preconception nicotine exposed (MPNE) mothers. There is limited research into the effects of MPNE on behavior (Holloway et al, 2007; Vassoler et al, 2014; Zhu et al, 2014; Yohn et al, 2015; Renaud and Fountain, 2016) and currently no studies consider its impact on early postnatal development. We present how to extract knowledge from the deep neural network in order to identify novel behavioral components that distinguished the nicotine exposed group from the control group
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