Over the past few decades, neuroscientists have discovered numerous cell types in the brain. The main focal point has been to construct an atlas of all cell types in one area: This is the primary motor cortex.
Behind this activity is the BRAIN initiative consortium (officially titled the U.S. National Institutes of Health’s Brain Research Through Advancing Innovative Neurotechnologies). The research collaboration has conducted a census of motor cortex cells in mice, marmoset and humans.
The generated map now stands as the first comprehensive list and a starting point for tracing cellular networks to understand how they control the human body and mind. This understanding should lead to the unpicking of how these networks are disrupted in mental and physical disorders.
Describing the process and value of the atlas, one of the lead sexists, Helen Bateu, says: “If you think of the brain as an extremely complex machine, how could we understand it without first breaking it down and knowing the parts?”
She explains further, noting: “The first page of any manual of how the brain works should read: Here are all the cellular components, this is how many of them there are, here is where they are located and who they connect to.”
The problem statement that the science team have been grappling with is to address the current limitation in developing effective therapies for human brain disorders. This is hampered because the medical world do not know enough about which cells and connections are being affected by a particular disease and therefore, they cannot currently pinpoint with precision what and where they need to target. The new atlas is the first steppingstone to address this issue.
The research paper appears in the journal Nature, titled “A multimodal cell census and atlas of the mammalian primary motor cortex.”
In related neuroscience news, British researchers have found that variability between brain cells might speed up learning and improve the performance of the brain and future artificial intelligence.
This research has discovered that by tweaking the electrical properties of individual cells in simulations of brain networks, the networks move on to learn faster than simulations with identical cells. The networks also needed fewer of the tweaked cells to get the same results, and that the method is less energy intensive than models with identical cells.
This study appears in Nature Communications, with the paper headed: “Neural heterogeneity promotes robust learning.”
