Researchers at Dartmouth College developed a new cortical surface template called 'OpenNeuro Average' (onavg) based on data from 1,031 brains.

The onavg template offers uniform sampling of the cerebral cortex, improving multivariate pattern classification accuracy and inter-participant correlation of representational geometry.

It requires fewer participants to achieve similar results compared to previous templates.

The onavg template reduces computational time for algorithms using searchlight analysis.

A comparative study showed that onavg significantly improves cortical sampling uniformity, enhancing results in multivariate pattern analysis tasks.

The template outperformed traditional brain surface templates in MVPA algorithms, demonstrating consistent improvements in accuracy and regional similarity analysis.

The onavg template has wide-ranging applications in cognitive and clinical neuroscience, including research on vision, language, and neurodegenerative diseases.

Overall, the onavg template promises to improve the efficiency and accuracy of neuroimaging data analysis, enhancing replicability and reproducibility in neuroscience research.