covSTATIS: A multi-table technique for network neuroscience
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 <i>a priori</i> 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.
Authors
- Vincent Guillemot (ORCID: https://orcid.org/0000-0002-7421-0655)
- Derek Beaton (ORCID: https://orcid.org/0000-0001-6118-4366)
- Jenny Rieck (ORCID: https://orcid.org/0000-0002-3242-6407)
- Cheryl Grady (ORCID: https://orcid.org/0000-0001-9533-8802)
- Ju‐Chi Yu
- Hervé Abdi (ORCID: https://orcid.org/0000-0002-9522-1978)
- R. Nathan Spreng (ORCID: https://orcid.org/0000-0003-1530-8916)
- Giulia Baracchini (ORCID: https://orcid.org/0000-0001-9198-2780)
Institutions
- Montreal Neurological Institute and Hospital (CA)
- Centre for Addiction and Mental Health (CA)
- Institut Pasteur (FR)
- The University of Texas at Dallas (US)
- University of Toronto (CA)
- Université Paris Cité (FR)
- Baycrest Hospital (CA)
- Unity Health Toronto
- McGill University (CA)
Publication Details
- Journal
- Aperture Neuro
- Published
- 2024-11-05
- DOI
- https://doi.org/10.52294/001c.124518
- Citations
- 2
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.69