covSTATIS: a multi-table technique for network neuroscience
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929313099546624 |
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| author | Baracchini, Giulia Yu, Ju-Chi Rieck, Jenny Beaton, Derek Guillemot, Vincent Grady, Cheryl Abdi, Herve Spreng, R. Nathan |
| author_facet | Baracchini, Giulia Yu, Ju-Chi Rieck, Jenny Beaton, Derek Guillemot, Vincent Grady, Cheryl Abdi, Herve Spreng, R. Nathan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14481 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | covSTATIS: a multi-table technique for network neuroscience Baracchini, Giulia Yu, Ju-Chi Rieck, Jenny Beaton, Derek Guillemot, Vincent Grady, Cheryl Abdi, Herve Spreng, R. Nathan Quantitative Methods 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. |
| title | covSTATIS: a multi-table technique for network neuroscience |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2403.14481 |