Researchers have developed a new template for cortical surface, known as ‘OpenNeuro Average,’ or simply ‘onavg,’ which aims to enhance both the precision and efficiency of neuroimaging data analysis.
The human brain governs essential functions such as perception, memory, language, thought, consciousness, and emotions.
To investigate brain functionality, scientists frequently employ neuroimaging techniques to capture brain activity during specific tasks or while at rest. The cerebral cortex, which is the brain’s outer layer, organizes brain functions in a systematic manner. For the analysis of neuroimaging data and to comprehend the functional layout of the human brain, researchers typically utilize a “cortical surface model.”
Every individual’s brain has a distinct shape. To analyze neuroimaging data collected from various subjects, researchers must align this data to a common brain template. This allows for the identification of the same anatomical points, called “vertices,” across different brains, even though their shapes vary.
Over the last 25 years, numerous templates have emerged. Currently, the most widely used cortical surface templates are derived from information obtained from 40 different brains.
Recently, researchers at Dartmouth introduced a new cortical surface template named “OpenNeuro Average,” often abbreviated as “onavg,” which significantly boosts the accuracy and efficiency of neuroimaging analysis.
The results of their study have been published in Nature Methods.
“Our cortical surface template, onavg, uniquely samples various areas of the brain uniformly,” states lead author Feilong Ma, a postdoctoral fellow at the Haxby Lab in the Department of Psychological and Brain Sciences at Dartmouth. “This results in a less biased map that enhances computational efficiency.”
The team constructed the template using cortical anatomy data from 1,031 brains derived from 30 datasets hosted on OpenNeuro, a free platform that facilitates the sharing of neuroimaging data. The co-authors emphasize that it is the first template built upon the geometric structure of the brain.
In contrast, earlier templates sampled various regions of the cortex in an uneven manner and relied on a spherical shape to position cortical vertices, which led to biases in vertex distribution.
With the onavg template, less data is necessary for thorough analysis.
“Neuroimaging data collection is expensive, and in some clinical scenarios—like studying a rare disease—gathering a substantial amount of data can present challenges. Therefore, achieving reliable results with less data is a significant advantage,” states Feilong. “By allowing for more efficient data utilization, our template could enhance the replicability and reproducibility of academic research outcomes.”
“I believe that onavg signifies a methodological advancement with wide-ranging applications in cognitive and clinical neuroscience,” mentions 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 adds that their cortical surface template could benefit research in areas such as vision, hearing, language, individual differences, and conditions like autism and neurodegenerative diseases, including Alzheimer’s and Parkinson’s.
“We anticipate it will create a substantial impact in this field,” says Haxby. Jiahui Guo, a former postdoctoral fellow in psychological and brain sciences now serving as an assistant professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas, and Maria Ida Gobbini, an associate professor at the Department of Medical and Surgical Sciences at the University of Bologna, also played a role in this study.
