ArticleImaging neuroscience (Cambridge, Mass.)2024
VertexWiseR: A package for simplified vertex-wise analyses of whole-brain and hippocampal surfaces in R.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Individualized Network Topography in Pre-adolescent Children and Adults Using Naturalistic Precision fMRI.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Social mind at rest: resting-state connectivity associated with social thought relates to friendship quality.Social cognitive and affective neuroscience · 2026Article
- ChatGPT-estimated occupational complexity predicts cognitive outcomes and cortical thickness above and beyond socioeconomic status among older adults.GeroScience · 2025Article
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Authors and funding
2 authors.
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Abstract
Currently, whole-brain vertex-wise analyses on brain surfaces commonly require specially configured operating systems/environments to run and are largely inaccessible to R users. As such, these analyses are inconvenient to execute and inaccessible to many aspiring researchers. To address these limitations, we present VertexWiseR, a user-friendly R package, to run cortical and hippocampal surface vertex-wise analyses, in just about any computer, requiring minimal technical expertise and computational resources. The package allows cohort-wise anatomical surface data to be highly compressed into a single, compact, easy-to-share file. Users can then run a range of vertex-wise statistical analyses with that single file without requiring a special operating system/environment and direct access to the preprocessed file directories. This enables the user to easily take the analyses "offline", which would be highly appropriate and conducive in classroom settings. This R package includes a conventional suite of tools for extracting, manipulating, analyzing, and visualizing vertex-wise data, and is designed to be easy for beginners to use. Furthermore, it also contains novel or advanced functionalities such as hippocampal surface analyses, meta-analytic decoding, threshold-free cluster enhancement, and mixed-effects models that would appeal to experienced researchers as well. In the current report, we showcase these functionalities in the analyses of two publicly accessible datasets. Overall, our R package opens up new frontiers for the R's user base/community and makes such neuroimaging analyses accessible to the masses.
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