To understand how the brain works, scientists often use neuroimaging to record participants’ brain activity when the brain is performing a task or at rest. Researchers often use what is called a “cortical surface model” to analyze neuroimaging data and study the functional organization of the human brain. Over the past 25 years, there have been several iterations of such templates, and the most commonly used cortical surface templates today are based on data collected from 40 brains. Now, researchers at Dartmouth say they have created a new cortical surface template called “OpenNeuro Average,” or “onavg” for short, which provides greater accuracy and efficiency in analyzing neuroimaging data.
The findings are published in Nature Methods in an article titled, “A cortical surface template for human neuroscience.”
“Neuroimaging data analysis relies on normalization to standard anatomical templates to resolve macroanatomical differences across brains,” the researchers wrote. “Existing human cortical surface templates sample locations unevenly because of distortions introduced by inflation of the folded cortex into a standard shape. Here we present the onavg template, which affords uniform sampling of the cortex. We created the onavg template based on openly available high-quality structural scans of 1,031 brains—25 times more than existing cortical templates.”
“Our cortical surface template, onavg, is the first to sample different parts of the brain uniformly,” said lead author Feilong Ma, PhD, a postdoctoral fellow and member of the Haxby Lab in the department of psychological and brain sciences at Dartmouth. “It’s a less biased map that is more computationally efficient.”
The team built the template based on the cortical anatomy of 1,031 brains from 30 datasets in OpenNeuro, a free and open-source platform for sharing neuroimaging data. According to the co-authors, it is also the first cortical surface template based on the geometric shape of the brain.
“It’s very expensive to obtain data through neuroimaging and for some clinical populations—such as if you’re 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,” said Feilong. “With more efficient data usage, our template can potentially increase the replicability and reproducibility of results in academic studies.”
“I think that onavg represents a methodological advancement that has broad applications across all aspects of cognitive and clinical neuroscience,” said co-author James Haxby, a professor in the department of psychological and brain sciences and former director of the Center for Cognitive Neuroscience at Dartmouth.
He says their cortical surface template could be used for studies on vision, hearing, language, and individual differences, as well as on disorders such as autism and neurodegenerative diseases like Alzheimer’s and Parkinson’s.
“We think it’s going to have a broad and deep impact in the field,” concluded Haxby.
