Journal article
Giulia Baracchini, Ju-Chi Yu, Jenny Rieck, Derek Beaton, Vincent Guillemot, Cheryl L. Grady, Hervé Abdi, R. N. Spreng
Aperture Neuro, 2024
Aperture Neuro, 2024
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APA
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Baracchini, G., Yu, J.-C., Rieck, J., Beaton, D., Guillemot, V., Grady, C. L., … Spreng, R. N. (2024). covSTATIS: A multi-table technique for network neuroscience. Aperture Neuro.
Chicago/Turabian
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Baracchini, Giulia, Ju-Chi Yu, Jenny Rieck, Derek Beaton, Vincent Guillemot, Cheryl L. Grady, Hervé Abdi, and R. N. Spreng. “CovSTATIS: A Multi-Table Technique for Network Neuroscience.” Aperture Neuro (2024).
MLA
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Baracchini, Giulia, et al. “CovSTATIS: A Multi-Table Technique for Network Neuroscience.” Aperture Neuro, 2024.
BibTeX Click to copy
@article{giulia2024a,
title = {covSTATIS: A multi-table technique for network neuroscience},
year = {2024},
journal = {Aperture Neuro},
author = {Baracchini, Giulia and Yu, Ju-Chi and Rieck, Jenny and Beaton, Derek and Guillemot, Vincent and Grady, Cheryl L. and Abdi, Hervé and Spreng, R. N.}
}
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.