Data mining in a behavioral test detects early symptoms in a model of amyotrophic lateral sclerosis

Abstract

"What's wrong with my genetically engineered animal?" is a common yet often difficult to answer question in behavioral phenotyping. We present here a method termed Pattern Array for mining movement patterns and isolating those that best capture an effect of a genetic manipulation. We demonstrate the method by searching for early motor symptoms in the open-field behavior of SOD1 mutant rats, an animal model of amyotrophic lateral sclerosis. Pattern Array was able to identify a unique motor pattern that differentiated the SOD1 mutants from the wild-type controls 2 months before disease onset. This pattern included heavy braking while moving near the arena wall but turning away from it. SOD1 mutants performed this pattern significantly less than wild-type controls in 2 independent data sets. At such early age the SOD1 mutants could not be differentiated from the controls by standard behavioral measures or by subjective observation. The early discovered symptom may enable investigators to test therapies aimed for intervention rather than remediation. Our results demonstrate the feasibility and potential of detecting subtle behavioral effects using data mining strategies.