covSTATIS: a multi-table technique for network neuroscience
Abstract
Similarity analyses between multiple correlation or covariance tables constitute the cornerstone of network neuroscience. Here, we introduce covSTATIS, a versatile, linear, unsupervised multi-table method designed to identify structured patterns in multi-table data, and allow for the simultaneous extraction and interpretation of both individual and group-level features. With covSTATIS, multiple similarity tables can now be easily integrated, without requiring a priori data simplification, complex black-box implementations, user-dependent specifications, or supervised frameworks. Applications of covSTATIS, a tutorial with Open Data and source code are provided. CovSTATIS offers a promising avenue for advancing the theoretical and analytic landscape of network neuroscience.
Cite
@article{arxiv.2403.14481,
title = {covSTATIS: a multi-table technique for network neuroscience},
author = {Giulia Baracchini and Ju-Chi Yu and Jenny Rieck and Derek Beaton and Vincent Guillemot and Cheryl Grady and Herve Abdi and R. Nathan Spreng},
journal= {arXiv preprint arXiv:2403.14481},
year = {2024}
}
Comments
The first two authors contributed equally to this work