Scientists have revealed the first high-resolution map of the mouse brain after three years of intensive data gathering. The mouse brain is very small at about half an inch long and weighs less than a jellybean, but was incredibly complicated to map. The mapmakers from the Allen Institute call their creation the Allen Mouse Brain Common Coordinate Framework.
The framework is designed to be a reference point for neuroscience, according to the creators. Lab mice are widely used for biomedical research, and their brains contain approximately 100 million cells across hundreds of different regions. Scientists say that as neuroscience datasets grow larger and more complicated, a common spatial map of the brain is more critical.
The creators of the map say it’s similar to GPS on the phone. Rather than manually searching for a location on a paper map, the GPS tells people exactly where they are. The mouse brain map the team created will do the same thing for neuroscientists. It will allow scientists with datasets with thousands or millions of different pieces of information use a common set of coordinates to pinpoint the corresponding brain landmarks for those coordinates.
The newly updated framework improves on a partial version that was released in 2016 that mapped the entire mouse cortex. The cortex is the outermost shell of the brain. Those previous versions were lower resolution with the new map offering enough resolution to pinpoint an individual cells’ location.
A researcher from the University of Washington recently used the Atlas study that looked at neuron activity as mice chose between different images in laboratory tests. The study used Neuropixels, which are tiny electrical probes that can record the activity of hundreds of neurons across different brain regions at once. When analyzing the data, it was clear that more parts of the brain were involved in the visual choice than previously realized. The new framework enabled that discovery. The team believes that future iterations of the Atlas will likely rely on machine learning or other forms of automation rather than the manual creation that went into the current version.