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covSTATIS: a multi-table technique for network neuroscience
Giulia Baracchini1, Ju-Chi Yu2, Jenny Rieck3
1Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Canada.
Aperture Neuro
|March 23, 2026
Summary
We developed covSTATIS, a new method for analyzing multiple similarity tables in network neuroscience. This tool helps uncover group and individual patterns without complex setups, advancing data analysis in the field.
Area of Science:
- Neuroscience
- Data Science
- Network Science
Background:
- Similarity analyses of correlation or covariance tables are fundamental in network neuroscience.
- Existing methods may require data simplification or complex implementations.
Purpose of the Study:
- Introduce covSTATIS, a novel unsupervised method for analyzing multi-table data.
- Enable simultaneous extraction and interpretation of individual and group-level features from multiple similarity tables.
Main Methods:
- Developed covSTATIS, a versatile, linear, unsupervised multi-table analysis method.
- Designed for direct integration of multiple similarity tables without prior simplification.
- Avoids complex black-box implementations and supervised frameworks.
Main Results:
- covSTATIS effectively identifies structured patterns in multi-table data.
- Facilitates the simultaneous extraction of individual and group-level features.
- Demonstrated through applications, tutorial, and provided open-source code.
Conclusions:
- covSTATIS offers a powerful and accessible approach for network neuroscience research.
- Advances the theoretical and analytical capabilities for multi-table data integration.
- Promotes broader adoption of advanced analytical techniques in the field.
