Cortical Surface Template Enhances Neuroimaging Research

Dartmouth College researchers have created a new cortical surface template called "OpenNeuro Average" (onavg), which they say will provide greater accuracy and efficiency in neuroimaging data analysis. This innovative template is described in Nature Methods.

Unlike previous templates based on just 40 brains, onavg utilizes data from 1,031 brains across 30 datasets from OpenNeuro, a free neuroimaging data-sharing platform. The template's unique feature is its uniform sampling of different brain parts, creating a less biased map that is more computationally efficient.

According to lead author Feilong Ma, "Our cortical surface template, onavg, is the first to sample different parts of the brain uniformly. It's a less biased map that is more computationally efficient."

Onavg stands out as the first cortical surface template based on the brain's geometric shape, departing from previous models that relied on sphere-like shapes to define cortical vertices. This approach eliminates biases in vertex distribution and allows for more accurate analysis with less data.

The template's efficiency is particularly valuable in studying rare diseases or conditions where large data sets are challenging to obtain. Ma notes, "With more efficient data usage, our template can potentially increase the replicability and reproducibility of results in academic studies."

Co-author James Haxby emphasizes the broad applicability of onavg across cognitive and clinical neuroscience. He states, "We think it's going to have a broad and deep impact in the field," highlighting its potential use in studies on vision, hearing, language, individual differences, and disorders such as autism, Alzheimer's, and Parkinson's.