Resume: Researchers have developed “OpenNeuro Average” (onavg), a novel cortical surface template that improves the accuracy and efficiency of neuroimaging data analysis.
This template is based on 1,031 brains, which provides a more uniform and less biased map compared to previous models. It allows for better data utilization, which is crucial for studies with limited datasets.
The ONAVG template is expected to have broad applications in cognitive and clinical neuroscience.
Key Facts:
- Uniform sampling: onavg samples brain regions uniformly, reducing biases.
- Data efficiency: Less data is needed for accurate analysis, supporting studies with limited datasets.
- Wide applications: Useful for research into vision, language and neurodegenerative diseases.
Source: Dartmouth College
The human brain is responsible for crucial functions including perception, memory, language, thinking, consciousness and emotions.
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 in the cerebral cortex, the outermost 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 has a different shape. To analyze neuroimaging data from multiple people, researchers must register the data to the same brain template, which makes it possible to identify the same anatomical location on different brains, even though brains have different shapes. These locations are known as “vertices.”
Over the past 25 years, several versions of such templates have been created. The most widely used cortical surface templates are based on data collected from 40 brains.
Dartmouth researchers have now created a new cortical surface template, called “OpenNeuro Average” or “onavg” for short, that allows for greater accuracy and efficiency when analyzing neuroimaging data.
The findings were published in Natural methods.
“Our cortical surface template, onavg, is the first to uniformly sample different parts of the brain,” said lead author Feilong Ma, a postdoctoral researcher and member of the Haxby Lab in the Department of Psychological and Brain Sciences at Dartmouth. “It’s a less biased map that’s computationally more 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.
In contrast, previous templates sampled different parts of the cortex unevenly and determined the location of cortical vertices using a spherical shape. This resulted in biases in the distribution of vertices.
The ONAVG template reduces the amount of data required for analysis.
“It is very expensive to get data from neuroimaging, and for some clinical populations, like when you are studying a rare disease, it can be difficult or impossible to get a large amount of data. So the ability to get better results with less data is an advantage,” Feilong said.
“With more efficient data use, our template has the potential to increase the reproducibility and replicability of results in academic studies.”
“I think ONAVG represents a methodological advance that has broad applications in 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.
According to him, their cortical surface template can be used to study vision, hearing, language and individual differences, as well as conditions such as autism and neurodegenerative diseases such as Alzheimer’s and Parkinson’s.
“We think it will have a broad and deep impact on the field,” Haxby said. Jiahui Guo, a former postdoctoral researcher in psychology 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.
About this news about brain mapping research
Author: Amy Olson
Source: Dartmouth College
Contact: Amy Olson – Dartmouth College
Image: The image is attributed to Neuroscience News
Original research: Open access.
“A cortical surface template for human neuroscience” by Feilong Ma et al. Natural methods
Abstract
A cortical surface template for human neuroscience
Neuroimaging data analysis relies on normalization to standard anatomical templates to resolve macroanatomical differences between brains. Existing human cortical surface templates sample locations unevenly due to distortions introduced by inflation of the folded cortex into a standard shape.
Here we present the onavg template, which allows for uniform sampling of the cortex.
We created the onavg template from publicly available, high-quality structural scans of 1,031 brains, 25 times more than existing cortical templates. We optimized vertex locations based on cortical anatomy, achieving a uniform distribution.
We observed consistently higher accuracies of multivariate pattern classification and representational geometry interparticipant correlations based on onavg than on other templates. Furthermore, onavg requires only three-quarters of the amount of data to achieve the same performance compared to other templates.
The optimized sampling also reduces CPU time across algorithms by 1.3–22.4%, because there is less variation in the number of vertices in each searchlight.