Feilong et al. (2018, NeuroImage) doi.org/10.1016/j.ne...
Feilong et al. (2021, @elife.bsky.social ) elifesciences.org/articles/64058
Busch et al. (2024, J Neurosci @sfnjournals.bsky.social) www.jneurosci.org/content/44/6...
Feilong et al. (2023, @imagingneurosci.bsky.social) doi.org/10.1162/imag...

Check out the Dartmouth Center for Cognitive Neuroscience website for more videos from an amazing lineup of speakers! www.dartmouth.edu/ccn/workshop...

The code and data of onavg are openly available. See additional information (including code) at our website feilong.github.io/tpl-onavg/ and template files at TemplateFlow github.com/templateflow...

Surface registration methods are also rapidly developing, such as MSM by @metricsemma.bsky.social , SUGAR by
Jianxun Ren et al. Onavg can potentially be used in combination with these methods to further improve the workflow of surface-based analysis. www.sciencedirect.com/science/arti...

Our functional connectivity was inspired by Ciantar et al. (2022) doi.org/10.1007/s004...
which was recently replicated by Jeganathan et al. www.biorxiv.org/content/10.1...
See also related work on volumetric data: www.um.edu.mt/library/oar/...

The creation of onavg has greatly benefited from open science. We used 1,031 high-quality brains openly available from OpenNeuro, and we deeply appreciate the participants and researchers who generously shared their data. We also thank DataLad, StudyForrest, and Center for Open Neuroscience.

In densely sampled regions, there are more vertices in each searchlight and more searchlight centers in the region, leading to prolonged computations. By uniformly sampling the cortex, onavg reduces the computational time of various commonly used algorithms.

These benefits, in theory, generalizes to any analyses that rely on sampling density, uniformity, or spatial patterns. In Extended Data Figs 7–9, we showcased how onavg benefits resting-state functional connectivity, localizer contrast maps, and modeling individual differences.

As a result of uniform sampling, the quality of spatial patterns is greatly improved, leading to increased performance of various MVPA algorithms.

We optimized the location of cortical vertices by penalizing a pair of vertices if they were too close on the anatomical surface. After the optimization, we obtained the onavg template, where vertices are evenly distributed on the anatomical surface.

Traditional templates choose their vertices based on the inflated spherical surface. Vertices are mostly uniform on the spherical surface. However, on the actual anatomical surface, vertices are often sparsely distributed on gyri and densely distributed on sulci.