Dartmouth researchers introduce “onavg,” a novel cortical surface template that enhances the precision and efficiency of neuroimaging studies. This template, based on over 1,000 brain scans, aims to revolutionize cognitive and clinical neuroscience research.
In a significant stride for neuroscience, Dartmouth researchers have unveiled “OpenNeuro Average” (onavg), a new cortical surface template for analyzing neuroimaging data. This advancement promises to enhance both the accuracy and efficiency of brain studies, addressing longstanding challenges in the field.
The human brain is a marvel of complexity, orchestrating functions such as perception, memory, language, thinking and emotions. To decode its mysteries, scientists rely on neuroimaging, which records brain activity during various states. These imaging studies often employ a “cortical surface model” to examine the functional organization across the brain’s outer layer, the cerebral cortex.
However, since brain structures vary from person to person, researchers need a consistent template to compare data across individuals. These templates aim to pinpoint equivalent anatomical locations, known as “vertices,” despite the differing shapes of individual brains.
Traditional cortical surface templates, based on data from just 40 brains, have influenced neuroimaging for 25 years. The onavg template shifts this paradigm, drawing upon the anatomy of 1,031 brains from 30 datasets within OpenNeuro, an open-source platform for neuroimaging data.
“Our cortical surface template, onavg, is the first to sample different parts of the brain uniformly,” Feilong Ma, lead author and postdoctoral fellow at Dartmouth’s Haxby Lab, said in a news release. “It’s a less biased map that is more computationally efficient.”
Onavg surpasses previous models by providing an even representation of the cortex and utilizing a geometric approach to define cortical vertices. This contrasts with earlier sphere-based methods that introduced biases, thus limiting the accuracy of neuroimaging analysis.
A notable advantage of onavg is its ability to yield precise results with fewer data. Given the high cost and logistical challenges of acquiring neuroimaging data — especially from clinical populations with rare diseases — this efficiency is a game-changer.
“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,” Ma added.
The template’s development could significantly bolster the replicability and reproducibility of neuroscientific studies, addressing a critical issue in modern research.
“With more efficient data usage, our template can potentially increase the replicability and reproducibility of results in academic studies,” added Ma.
James Haxby, co-author and professor in Dartmouth’s Department of Psychological and Brain Sciences, emphasizes the broad applications of this innovation.
“I think that onavg represents a methodological advancement that has broad applications across all aspects of cognitive and clinical neuroscience,” he said in the news release.
Haxby anticipates the template will be instrumental in myriad research areas, from studying sensory processes like vision and hearing to investigating individual differences and neurological disorders such as autism and Alzheimer’s.
“We think it’s going to have a broad and deep impact in the field,” Haxby added.
With contributions from Jiahui Guo of the University of Texas at Dallas and Maria Ida Gobbini from the University of Bologna, the onavg template embodies a collaborative effort to advance the frontiers of neuroscience.
For more information on this groundbreaking research, the full study is available in the journal Nature Methods.