Researchers combined a laboratory protocol that tags active cells with mathematical and computational analysis to follow neurons and networks over time in mouse models. The study appears in PLOS Biology and involved collaborators in three countries. University of Michigan researchers created the mathematical and computational workflows, while teams in Japan and Switzerland developed experimental tools.
The Japanese group, led by Hiroki Ueda at RIKEN, used light-sheet microscopy to make three-dimensional images of mouse brains. A genetic tagging method made active neurons glow, so the team could see when and where cells were active. The main finding was a consistent shift in activity across the brain during the daily cycle: activity tended to start in inner layers when mice woke and then hubs of activity moved toward the cortex.
The work was partly motivated by a desire to understand fatigue. Daniel Forger, senior author and professor at the University of Michigan, said the team hopes to develop signatures that indicate when people are fatigued. The authors note the imaging cannot be used directly in humans, but the computational approaches can be adapted to coarser human data.
Difficult words
- protocol — set of steps for a scientific procedurelaboratory protocol
- computational — relating to computers or computer calculations
- workflow — planned series of tasks or processesworkflows
- light-sheet microscopy — imaging method that makes three-dimensional pictures
- genetic — related to genes and inherited material in cells
- fatigue — feeling of tiredness or low energy
- cortex — outer layer of the brain in mammals
Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.
Discussion questions
- How could signatures that indicate fatigue be useful in everyday life?
- What challenges might appear when adapting computational approaches to coarser human data?
- Do you think combining a laboratory protocol and mathematical analysis is useful for neuroscience research? Why or why not?
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