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By understanding how animals react to "predation or food stimuli", we can study how the brain works too. Researchers from Harvard Medical School (HMS) have evolved a new method of organizing body movements of mice into "notions of syllables and grammar", which enables experts to figure out how genes or neural circuits influence the body's function.

"If you look into the brain and ask how any individual brain cell fires as an animal generates any given behavior, what you find is the brain is a very noisy place, and so understanding how brain activity leads to action is hard," Sandeep Datta, HMS assistant professor of neurobiology and senior author of the paper, explained in a news release. "We think that by developing this method we can get new insight into how the brain creates behavior, and how that process goes wrong in models of disease. That's going to be a great way to build better and more targeted therapeutics."

Through genomic sequencing, researchers get an insight into gene mutations linked to a number of disorders. Hence, studying the manner in which such genetic alterations can impact behavior is important.

Harvard's new method is based on machine-learning technology. Scientists hope that it can help them to understand the manner in which the brain builds up patterns of action.

"When you have a mouse behaving in a particular way, you want to turn that behavior into some numbers that you can analyze," Wiltschko said.

Scientists built a 3D model of a mouse's body in motion, showing how its "poses and transitions were interrelated". They then organized changes in their postures into "short, distinct sequences" that repeatedly recurred in different situations.

Next, researchers tested their model in genetically engineered mice, using a couple of copies of a mutation making them "waddle", which was a striking yet "subtle abnormal behaviour". Researchers could point out the behavioral effects of genetic mutations, although the difference between the normal and engineered mice was not apparent.

"The old way of characterizing and classifying behavior depends on what humans think the behavior is," Datta said in the university's release. "Our new way of characterizing and classifying behavior depends on the underlying structure hidden in the behavioral data itself. We've shown it's possible to objectively count the behaviors, to understand how they flow over each other over time, and to use that kind of framework to really get a deep understanding without any human bias for the underlying structure of actions."

The study was published in the journal Neuron.