The best way to understand the brain is to watch how organisms interact with the world. "Behavior drives everything we do," the senior author said.
A new unsupervised machine learning algorithm developed by the authors makes studying behavior much easier and more accurate. The researchers published a paper on the new tool, B-SOiD (Behavioral segmentation of open field in DeepLabCut), in "Nature Communications."
Previously, the standard method to capture animal behavior was to track very simple actions, like whether a trained mouse pressed a lever or whether an animal was eating food or not. Alternatively, the experimenter could spend hours and hours manually identifying behavior, usually frame by frame on a video, a process prone to human error and bias.
B-SOiD discovers behaviors by identifying patterns in the position of an animal's body. The algorithm works with computer vision software and can tell researchers what behavior is happening at every frame in a video.
"It uses an equation to consistently determine when a behavior starts," the author explained. "Once you reach that threshold, the behavior is identified, every time. A human experimenter might toggle between two frames or several categories, try to decide where behavior begins and become fatigued over time."
The senior author said B-SOiD provides a huge improvement and opens up several avenues for new research.
"It removes user bias and, more importantly, removes the time cost and arduous work," the author said. "We can accurately process hours of data in a matter of minutes."
Additionally, B-SOiD is very user friendly and openly available to any researcher. The lab and their collaborators have used the new algorithm in research on many important areas, including research to better understand chronic pain, obsessive compulsive disorder and more.
Collaborators have even begun to use B-SOiD to study human movement in Parkinson's disease.
"We are beginning to see if this can be used as part of an objective test by a doctor to show how far a patient's disease has progressed. The hope is that a patient anywhere in the world would be diagnosed with one standardized metric," the author said.
This is a breakthrough in how scientists can study natural behavior and how it changes rather than the overly simplistic or subjective measures that predominate neuroscience and ethology.
Machine learning algorithm to study unsupervised behavior
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