covSTATIS: a multi-table technique for network neuroscience

TL;DR

covSTATIS is a new multi-table analysis method that enables the integrated, unsupervised exploration of complex similarity data in network neuroscience, facilitating the identification of structured patterns at individual and group levels.

Contribution

It introduces covSTATIS, a versatile linear unsupervised method for analyzing multiple similarity tables without prior data simplification or supervision.

Findings

Enables integration of multiple similarity tables

Identifies structured patterns in multi-table data

Supports interpretation at individual and group levels

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.