Scientists found that this machine learning-assisted 3D video analysis outperformed the traditional approach, in which analyses rely on human observation to label the behavioral signs of epilepsy in animal models during seizures. The labor-intensive process requires constant video monitoring of the mice over many days or weeks while recording their brain wave activity with electroencephalography (EEG). The team led by Stanford researchers studied mice with acquired and genetic epilepsies. They found that machine analysis was better able to distinguish epileptic vs non-epileptic mice than trained human observers. The AI program also identified distinct behavioral phenotypes at different points in the development of epilepsy.
Notably, researchers were able to use the AI program to distinguish different patterns of behavior in mice after they were given one of three anti-epileptic drugs. This demonstrates that the tool could be used for rapid, automated anti-epileptic drug testing. Ultimately, the use of automated phenotyping for animal studies of the epilepsies could revolutionize how research is done, speeding discovery and reducing costs.
The machine-learning technology used in the study, called MoSeq for Motion Sequencing, locates, tracks, and quantifies the behavior of freely moving mice in each frame of the video. The information is used to train the unsupervised machine learning model to identify repeated motifs of behavior (called “syllables” – e.g., a right turn or headbob to the left). MoSeq predicts the order (or “grammar”) in which syllables occur, allowing fast and objective characterization of mouse behavior.
About the National Institutes of Health (NIH): NIH, the US's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit
www.nih.gov.