Unbiased Behavioral Discovery Platforms

Objective: The DoD seeks to develop unguided behavioral discovery technologies to accelerate detection and medical countermeasure development against current and future threats. These platforms will enable: Quantification of novel behaviors in pre-clinical animal models that are more sensitive than state-of-the-art. High-throughput evaluation of intervention efficacy in animal models with high translational value. Description: It is impossible to ethically conduct human studies on emerging CBRN threats for the development of effective medical countermeasures (MCM); therefore, the DoD relies on pre-clinical animal models for these efforts [1]. However, animal model behavioral evaluations remain un-modernized, insensitive, and prone to significant variability between operators and protocols. Furthermore, the threat agent doses required to elicit an observable symptomatic response in animal models are much higher than doses that are effective threats to the warfighter. These limitations in reliability and sensitivity hinder rapid emerging threat detection and MCM development. It is therefore paramount to develop more sensitive and unbiased platforms for the discovery and quantification of animal behavior more quickly and at lower levels of effect. Recent innovations in machine learning have enabled sensitive detection of animal behaviors in indications including pain response, psychiatric conditions and social interactions [2,3,4]. Novel movement patterns and relationships between physical features and movements of animals that are indicative of a response to stimuli can now be quantitatively captured beyond what historical “gold standard” assays can accomplish. The development of technologies that can capture these nuanced novel behaviors and that are agnostic to the stimuli in interest are therefore highly desirable [5]. The end goal of this effort is to provide automated and high-throughput behavioral profiling technologies to accelerate the discovery of both therapeutics and countermeasures that can serve to protect the warfighter. The evaluation of chemical and biological threat agents poses additional challenges in the sphere of animal behavior: The behavioral feature space sampled must be sufficiently broad to capture unknown phenotypes with unknown kinetics following exposure. The desired speed of response to a given threat agent necessitates high-throughput processing and rapid identification/ detection of features. Use of lethality as an endpoint poses ethical considerations. Platforms must overcome restricted handling and be compatible with high levels of containment. The DoD therefore seeks scalable solutions for the evaluation of toxic endpoints at sublethal doses across animal models spanning insects to mammalian species. Ultimately, an unbiased behavioral detection system is desired for sensitive evaluation of animal behaviors to support both defense health capabilities as well as chemical and biological defense initiatives throughout the DoD. Physiologies of interest include but are not limited to: Toxidromes Neuropsychiatric disorders Seizure characterization Traumatic brain injury Neurodegenerative disorders Sleep State The proposed final solution should: Use non-invasive measurements. Surgery is disallowed. Not require human annotation of behaviors. Identify behavioral responses not previously quantified through human observation for assessing the indication area(s) or interest. Demonstrate that incorporating novel behavior detection outperforms relevant state-of-the-art behavioral assays in accurately identifying indication area(s) of interest. State-of-the-art behavioral assays vary by field and should be appropriately included by the proposer. Demonstrate increased sensitivity towards identifying exposed animal to subclinical drug dose or identifying acutely diseased animal in a presymptomatic disease state. (Diseases with acute (<2 week) progressions are of interest. Genetic models of disease are not allowed.) Integrate multiple behavioral feature spaces: (e.g. movement tracking, orofacial recognition, auditory cues). Minimize animal handling and training by the operator. Keywords: Animal Behavior, Biotechnology, Preclinical studies, Animal Models, Medical Countermeasures, Deep Learning, Machine Vision