New human brain model developed by researchers

The human brain is responsible for essential functions including perception, memory, language, thought, consciousness, and emotion.

To understand how the brain works, scientists often use neuroimaging to record participants’ brain activity while the brain is performing a task or at rest. Brain functions are systematically organized on the cerebral cortex, the outer layer of the human brain. Researchers often use what is called a “cortical surface model” to analyze neuroimaging data and study the functional organization of the human brain.

Every brain is shaped differently. To analyze neuroimaging data from multiple individuals, researchers must register the data on the same brain model, which allows them to identify the same anatomical location on different brains, even if they have different shapes. These locations are called “vertices.”

Over the past 25 years, several iterations of these models have been made, and the cortical surface models most commonly used today are based on data collected from 40 brains.

Now, Dartmouth researchers have created a new cortical surface model called “OpenNeuro Average,” or “onavg” for short, that offers greater accuracy and efficiency in analyzing neuroimaging data.

The results are published in Methods of nature.

“Our cortical surface model, onavg, is the first to uniformly sample different parts of the brain,” says lead author Ma Feilongpostdoctoral researcher and member of the Haxby Laboratory in the Department of Psychological and Brain Sciences at Dartmouth. “It’s a less biased and more computationally efficient map.”

The team built the model from the cortical anatomy of 1,031 brains from 30 datasets in OpenNeuro, a free and open-source platform for sharing neuroimaging data. It is also the first cortical surface model based on the geometric shape of the brain, according to the co-authors.

In contrast, previous models sampled different parts of the cortex unequally and relied on a spherical shape to define the location of cortical vertices, leading to biases in the distribution of vertices.

With the onavg model, less data is needed for analysis.

“Obtaining neuroimaging data is very expensive, and for some clinical populations, such as those studying a rare disease, it can be difficult or impossible to acquire a large amount of data. So the ability to access better results with less data is an asset,” Feilong says. “By using data more efficiently, our model has the potential to increase the reproducibility and reproducibility of results in academic studies.”

“I believe the ONAVG represents a methodological advance that has broad applications in all aspects of cognitive and clinical neuroscience,” says co-author James Haxbyprofessor at the Department of Psychological and Brain Sciences and former director of the Center for Cognitive Neuroscience at Dartmouth.

He says their cortical surface model could be used for studies of vision, hearing, language and individual differences, as well as disorders such as autism and neurodegenerative diseases like Alzheimer’s and Parkinson’s.

“We think this will have a broad and profound impact on the field,” Haxby says.

Jiahui Guo, a former postdoctoral researcher in psychological and brain sciences and assistant professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas, and Maria Ida Gobbini, an associate professor in the Department of Medical and Surgical Sciences at the University of Bologna, also contributed to the study.