Researchers have more than doubled the known number of brain regions using a mix of magnetic resonance imaging (MRI) and data-derived algorithms. The study, which is part of NIH’s $40 million Human Connectome Project (HCP), marks the first significant advance in knowledge of brain regions since 1907.
A team of researchers from academic institutions on both side of the Atlantic uncovered the 97 new areas of the cortex by analyzing the architecture, activity, connectivity and topography of the brains of 210 healthy participants in HCP. Using a mix of human interpretation and algorithms, the team turned the MRI data into a more detailed map of the brain. Prior to the study, 83 regions had been identified. Now, the total is up to 180.
This sharp increase in understanding of how the brain is divided up has the potential to support the advance of research into the underlying biology of neurological disorders.
"Being able to discriminate differences, say, in location and size as well as connectivity means investigators will know if we’re talking about the same thing, as opposed to a neighboring area,” Matthew Glasser, Washington University School of Medicine neuroscientist and lead author of study, told The Washington Post.
The team is keen to share these newfound capabilities. Having generated the brain map, researchers created a machine-learning algorithm to spot the same areas in a different cohort of 210 people. And, although the location of the cortex areas was atypical in some participants, the software was still capable of correctly identifying the regions. This “areal classifier” and other tools have been shared by the researchers.
Sharing the tools could lead to further improvements to the map. As it stands, the map does a good job of showing how the brain is parceled up into regions, but, being based on MRI data, says little about how the synapses are organized and connected. The team acknowledge this makes the map a work in progress.
“This map you should think of as version 1.0,” Glasser told The New York Times. “There may be a version 2.0 as the data get better and more eyes look at the data. We hope the map can evolve as the science progresses.”