The Comet Toolbox: Improving robustness in network neuroscience through multiverse analysis

In network neuroscience, a broad range of methods for estimating dynamic functional connectivity from fMRI data and subsequent analyses using network-based approaches have been introduced in recent years. However, in the absence of ground truths about the validity of analytical steps in capturing true brain dynamics, researchers are often faced with a multitude of arbitrary yet defensible choices, raising concerns about the robustness of results. Here, we aim to address this issue by implementing a comprehensive suite of dynamic functional connectivity methods in a unified Python software package, allowing for a diverse exploration of brain dynamics. Anchored in the framework of multiverse analysis, the present work introduces a workflow for systematically exploring different methodological choices. The developed toolbox includes a graphical user interface to enhance ease of use and accessibility for those who prefer to work outside a script-based pipeline. Comprehensive documentation and demo scripts are included to support adoption and usability. By promoting transparency and robustness, Comet aims to advance best practices in the study of brain dynamics.