Unifying equivalences across unsupervised learning, network science, and imaging/network neuroscience

TL;DR

This paper unifies diverse analytical methods across unsupervised learning, network science, and neuroscience, providing a framework that simplifies interpretation and integration of complex datasets and models.

Contribution

It introduces equivalences that connect different analyses and models across these fields, facilitating scientific integration and reducing redundancy.

Findings

Unified objectives across clustering and dimensionality reduction methods.

Equated connectional measures with communication and control metrics.

Provided a toolbox for applying these unified analyses to brain imaging data.

Abstract

Modern scientific fields face the challenge of integrating a wealth of data, analyses, and results. We recently showed that a neglect of this integration can lead to circular analyses and redundant explanations. Here, we help advance scientific integration by describing equivalences that unify diverse analyses of datasets and networks. We describe equivalences across analyses of clustering and dimensionality reduction, network centrality and dynamics, and popular models in imaging and network neuroscience. First, we equate foundational objectives across unsupervised learning and network science (from k means to modularity to UMAP), fuse classic algorithms for optimizing these objectives, and extend these objectives to simplify interpretations of popular dimensionality reduction methods. Second, we equate basic measures of connectional magnitude and dispersion with six measures of communication, control, and diversity in network science and network neuroscience. Third, we describe three semi-analytical vignettes that clarify and simplify the interpretation of structural and dynamical analyses in imaging and network neuroscience. We illustrate our results on example brain-imaging data and provide abct, an open multi-language toolbox that implements our analyses. Together, our study unifies diverse analyses across unsupervised learning, network science, imaging neuroscience, and network neuroscience.