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Learning How the Brain Recovers from Disruptions

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04/14/16 | Learning How the Brain Recovers from Disruptions

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Svoboda and Druckmann
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Group Leaders Karel Svoboda (left) and Shaul Druckmann (right) discuss their collaborative research at HHMI's Janelia Research Campus.

New research from scientists at the Howard Hughes Medical Institute's Janelia Research Campus suggests that the brain is organized into modules that work together to maintain critical functions, even in the face of disturbances.

This structural organization may explain how neurons that store short-term memories can recover from significant disruptions—for example, enabling a quarterback to remember a planned play despite the distractions he encounters before he throws the football. According to the Janelia group leaders who led the study, experimental neuroscientist Karel Svoboda and theoretical neuroscientist Shaul Druckmann, this motif is likely to underlie other essential circuitry within the brain as well.

“This is how an engineer would build a mission-critical system,” says Svoboda. “You distribute the critical systems over multiple modules, and then the modules talk to each other so they can sense when one of them isn't doing well and can correct for each other.”

The study, reported April 13, 2016 in an advance online publication in Nature, began with a surprising observation by postdoctoral fellow Nuo Li in Svoboda's lab. The team had been studying neurons in mice that are involved in planning motor activity. After an animal is instructed by researchers to move in a particular way, groups of these neurons become active, signaling for several seconds until the animal completes the movement—a form of memory that outlasts the milliseconds that any single neuron can signal on its own. Scientists knew that an animal's motor plan can persist even if this signaling is temporarily disrupted. Svoboda and his colleagues wanted to find out just how robust the underlying neural activity was.

To find out, they used a laser to switch off motor-planning neurons briefly before the animals in their experiments were allowed to complete a task. Monitoring the subsequent activity, they found that once the neurons were allowed to resume normal signaling, they quickly adjusted their activity to make up for the lost time. The mice carried on as if undisturbed, remembering which way they had been instructed to move and successfully completed their task. “We quenched [neural] activity to zero and saw that it came back to exactly the levels where it should have been,” Svoboda says. “It was a perfect—almost eerily perfect—example of robustness.”

Theoretical neuroscientists have modeled several ways in which neural circuits can establish robustness, so Svoboda shared his data with Druckmann and postdoctoral fellow Kayvon Daie, seeking an explanation for how the motor-planning neurons were able to recover so completely. “There is a rich history of models that have been suggested for such systems,” Druckmann says “But when we tried to compare experimental results to the models, we found that none of them show such strong robustness.”

“The fact that it recovers to exactly where it would have been had you not shut down the system is where all the models go wrong,” Druckmann says. Existing models showed neural activity picking up where it had left off after a disruption, so that the pause introduces a persistent delay in the normal activity pattern and activity remains slightly displaced from where it should be. “But what you see in the experiment is the exact opposite,” Druckmann says. “Somehow the activity catches up to where it needs to be.”

Something was missing. So, Svoboda says, “we went back to the biology to give us hints about how to construct the next level of models.”  

Knowing that no group of neurons works in isolation, the researchers began to wonder if another brain area might have compensated for the disruption Svoboda's team had introduced in their experiments. “The simplest thing that could be is that maybe this brain area is just looking at what another brain area is doing and copying it,” Druckmann says.

His modeling suggested the situation was slightly more complex, and that neuronal activity might return to where it should be after a disruption if the cells were in communication with another brain area carrying out the same function. “It's like two roller coasters running in parallel and connected with a big rubber band,” Druckmann explains: If one of the coasters falls off the track, the other keeps going, and the rubber band eventually snaps the wayward coaster back where it should be.

Such an organization would explain the striking robustness observed in the experiments. “Once we had the right architectural principles, all of the preexisting models could be rescued,” Druckmann says. “We realized that we need two modules, they need to be redundant in the sense that each of them can independently generate the right dynamics, and they need to be connected. Once we rewrote them according to these principles, all of the models worked.”

The scientists devised a series of experiments in which they tested their model by disrupting motor-planning circuits on opposite sides of the brain, both separately and together. As expected, when they interrupted signaling in one region at a time, neurons recovered well. But when they disrupted both motor-planning regions at the same time, recovery was impaired and animals performed their task poorly. It looked as if maintaining the motor plan did indeed depend on at least one of the modules operating undisturbed.

In a final round of experiments, the researchers disconnected the two motor-planning regions from one another, then blocked signaling on one side. Although one module remained undisturbed, the disrupted module was unable to recover, supporting the idea that two modules must be in communication to correct for lapses in activity. 

The scientists suspect their new model may explain the robustness of neural circuits beyond those they tested in the current study. “We think that this modularity probably happens in many incarnations,” Svoboda says. “From circuit analysis, we know that the right kind of circuit elements are there.”