A cortical surface template for human neuroscience

Abstract

Neuroimaging data analysis relies on normalization to standard anatomical templates to resolve macroanatomical differences across brains. Existing human cortical surface templates sample locations unevenly because of distortions introduced by inflation of the folded cortex into a standard shape. Here we present the onavg template, which affords uniform sampling of the cortex. We created the onavg template based on openly-available high-quality structural scans of 1,031 brains—25 times more than existing cortical templates. We optimized the vertex locations based on cortical anatomy, achieving an even distribution. We observed consistently higher multivariate pattern classification accuracies and representational geometry inter-subject correlations based on onavg than on other templates, and onavg only needs 3⁄4 as much data to achieve the same performance compared to other templates. The optimized sampling also reduces CPU time across algorithms by 1.3%–22.4% due to less variation in the number of vertices in each searchlight.

Spurious correlations in surface-based functional brain imaging

“…A recent promising approach has been the introduction of surface templates with more homogenous inter-vertex distances. The onavg template, developed through an iterative procedure of adjusting vertex locations, reduces inter-vertex spacing by about 90% compared to standard meshes (Feilong et al, 2023). This template samples gyri and sulci more uniformly and improves predictive accuracy in multi-voxel pattern analysis.…”

“…A "perfect" group-level template is impossible because vertex locations with uniform inter-vertex distances in one participant would not have the same uniform spacings in another. For withinparticipant analyses like multi-voxel pattern analysis, generating participant-specific meshes using the same iterative method (Feilong et al, 2023) may be the best option.…”

“…The bias occurs due to up-sampling from volume data to high-resolution surface meshes. Here we expand on the precise connection between uneven inter-vertex distances (Feilong et al, 2023) and inflated local correlations (Ciantar et al, 2022). We show that inflated local correlations arise not only from volume to surface mapping, but also from current implementations of subsequent surface smoothing.…”

“…Recent work has found that commonly used template surfaces possess uneven inter-vertex distances, with large variations across different brain regions (Feilong et al, 2023). This problem is not widely appreciated and potentially impacts most surface-based analyses of fMRI data.…”