Structural and functional brain templates

Brain templates are the cornerstone of neuroimaging studies, providing a common reference space for aligning and comparing brain data across individuals. To this end, we have developed the onavg (OpenNeuro Average) cortical surface template (Feilong et al. 2024), which is derived from high-quality MRI data of 1,031 individuals from the OpenNeuro platform.

The onavg cortical surface template

Distribution of cortical vertices on different surface templates

Fig 1. Distribution of cortical vertices on different surface templates. Traditional templates, such as fsaverage and fsLR (fsavg and fslr in the figure, respectively), sample cortical vertices based on the spherical surface (bottom row), resulting in non-uniform distributions on the anatomical surface (top row). The onavg template (Feilong et al. 2024), in contrast, uniformly samples cortical vertices on the anatomical surface.

Compared to traditional templates, the onavg template provides better performance for a wide range of neuroimaging analyses—including multivariate pattern classification (MVPC), representational similarity analysis (RSA), localizer contrast maps, and functional connectivity—as well as faster computations.

See the paper and project page for more information, including usage instructions and example code.

Functional brain templates and the HyperBrain project

We are working with the Haxby Lab at Dartmouth College on the HyperBrain project, which aims to create a high-quality functional brain template for hyperalignment and other functional alignment methods.

Feilong presented preliminary results at the Society for Neuroscience (SfN) 2025 conference in San Diego, and introduced the HyperBrain-Sparta functional template at CCN 2026 in New York (abstract). Stay tuned for more updates!

Yuqi Zhang, a Ph.D. student in the Haxby Lab, is leading a project on optimizing functional brain templates for different age groups (e.g., children, adolescents, older adults) in order to improve hyperalignment performance. See our paper (Zhang et al. 2026) for details.

References

Feilong, Ma, Guo Jiahui, Maria Ida Gobbini, and James V Haxby. 2024. “A Cortical Surface Template for Human Neuroscience.” Nature Methods 21 (9): 1736–42. https://doi.org/https://doi.org/10.1038/s41592-024-02346-y.
Zhang, Yuqi, Maria Ida Gobbini, James V Haxby, and Ma Feilong. 2026. “Boosting Hyperalignment Performance with Age-Specific Templates.” eLife 15: RP110566. https://doi.org/https://doi.org/10.7554/eLife.110566.