Behavioral dataset for Long-Evans and its schizophrenia-like substrain through several generations

Area of Science:

• Neuroscience
• Behavioral Science
• Genetics

Background:

• High-throughput behavioral analysis is crucial for understanding complex neurological conditions.
• Rodent models offer valuable insights into human brain disorders.
• Limited availability of comprehensive, standardized behavioral datasets hinders research.

Purpose of the Study:

• To present a high-throughput behavioral dataset for rodent models.
• To enable longitudinal and multivariate studies of behavioral phenotypes.
• To provide a benchmark for machine learning-based marker discovery.

Main Methods:

• Utilized Ambitus, an automated reward-based corridor system, for data acquisition.
• Recorded locomotor, exploratory activities, and cognitive functions in Long-Evans rats.
• Collected 91 raw and derived variables across four consecutive trials for 1,342 rats.

Main Results:

• Acquired a comprehensive behavioral dataset from a large cohort of rats, including a schizophrenia-like substrain.
• Ensured open availability of all data files, metadata, and analysis scripts on Zenodo.
• Established a valuable resource for comparative behavioral studies and computational neuroscience.

Conclusions:

• The Ambitus system provides a robust method for high-throughput behavioral phenotyping.
• The open dataset facilitates research into trans-generational effects and strain differences in behavior.
• This resource serves as a benchmark for developing and validating machine learning approaches in behavioral neuroscience.