Studying how animals react to predation or food stimuli helps us understand how their brain works, and in turn can help us better understand the human brain. Researchers from Harvard Medical School (HMS) have developed a new method of organizing the body movements of mice into notions of syllables and grammar, which can then be interpreted to better understand how individual genes or neural circuits influence body 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."
Advances in genomic sequencing shed light on gene mutations that are linked to various disorders, researchers say. Therefore, studying how these genetic alterations change patterns of behavior is particularly important. Harvard's new technique is based on machine-learning technology, which they hope will help them better understand how the brain builds patterns of action.
Researchers were interested in studying mice based on previous research involving how sensory cues, such as smell, triggered various reactions. For example, lead author Alexander Wiltschko observed videos of mice reacting to the scent of a fox, from which he deciphered specific movements such as sniffing, freezing, and curling into a ball. It follows then, animals use repeated motifs and researchers wanted to know how many different ones there are, what they look like and how long they last.
"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.
But how do you do this?
Using their new method, researchers created a 3D model of a mouse's body as it was moving, which revealed how various poses and transitions were interrelated. From there, researchers organized changes in the mouse's posture into short, distinct sequences that they found reoccurred across many contexts.
Next, researchers tested their model in mice genetically engineered with two copies of a mutation that made them waddle – a striking, but subtle abnormal behavior. While the differences between these genetically modified mice and normal ones could not be seen by scientists directly, the new model picked up on the behavioral syllables. This means that researchers can pinpoint the behavioral effects of genetic mutations.
"The old way of characterizing and classifying behavior depends on what humans think 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."
Their findings were recently published in the journal Neuron.
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