Diseases that disrupt the central nervous system (CNS) are major public health problems with devastating consequences for afflicted individuals, their families, and society. Understanding the mechanisms underlying CNS diseases and discovering efficacious new treatments for these disorders relies critically on preclinical in vivo behavioral research. Despite tremendous advances in genetics and molecular biology, the success rate for CNS drugs in human clinical trials is only 8%. For many human CNS disorders, disease pathology is poorly understood and impacts multiple behavioral domains resulting in complex phenotypes. This creates a need for comprehensive behavioral assessment in animal models to capture the breadth of behavioral disruption so frequently observed in human CNS diseases. However, current approaches for automated behavioral monitoring do not provide the analytic tools required to categorize and integrate broad and continuous behavioral data streams into an interpretable context. A novel academic prototype system has been developed to collect long-duration, high-resolution data for multiple behaviors exhibited by mice in home cages (e.g. patterns of feeding/drinking and rest/activity, circadian entrainment). Similar behaviors are frequently disrupted by human disease process. This academic prototype uses sophisticated algorithms to identify fundamental behavioral building blocks and characterize their regulation and coordination over a wide range of time scales. This innovation in behavioral analysis makes this prototype a unique platform for the development of a new preclinical behavioral assessment technology. The short-term goal of this proposal is to convert the basic functionality of the academic prototype into a scalable commercial system for automated mouse home cage data collection and analysis. This will (1) greatly decrease the time and cost required for behavioral investigations, (2) increase the sensitivity with which experimental effects on behavior may be detected, (3) increase reproducibility of data, (4) provide novel insights into integrated behavioral analysis, and (5) complement or replace standard individual and separate focal behavioral tests. Our long-term goal is to develop a technology that significantly improves early predictability of drug therapies for CNS diseases such as obesity, addiction, insomnia, neurodegenerative, anxiety, and affective disorders.
Public Health Relevance: Diseases that disrupt the central nervous system (CNS) are major public health problems with devastating consequences for afflicted individuals, their families, and society. The success rate for CNS drugs in human clinical trials is low (~8%) and could be improved by developing better tools for preclinical behavioral assessment in animal models. This proposal aims to develop a unique and powerful new technology for robust, automated, and comprehensive behavioral analysis of preclinical CNS disease models to facilitate discovery of new treatments for CNS diseases.
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