Robust circular cluster-based statistics for respiration-brain coupling
4 affiliations
- 1Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany
- 2Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
- 3Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
- 4Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
Abstract
The rapidly developing research field of brain-body neuroscience faces methodological challenges, as analysts continue to develop new analysis strategies in the absence of established best practices. This quest for valid methods is further complicated by the (naturally) circular data involved in the study of phase-locked effects, e.g., in respiration-brain coupling. Various available approaches for phase extraction, constructing adequate surrogate data for statistical comparison, and accounting for the circularity of respiratory data lead to poor cross-study generalisability of results. Interpretation of effects is particularly affected by the problem of multiple comparisons in phase-related inferential statistics. In this tutorial, we propose a robust pipeline for respiration phase-related analyses based on a novel circular extension of cluster-based permutation testing. We highlight and offer guidance on critical parameters in the analysis, systematically compare various approaches being used in the field today, and provide open-access software code for flexible use and future development of our proposed pipeline. Author summary: Conventional best-practice statistical approaches have so far been difficult to implement in cases where the underlying data are circular, for example in the analysis of phase data. Such analyses are common in the up-and-coming research field of brain-body neuroscience, which poses significant challenges. In this tutorial, we use the case of respiration to propose a robust pipeline for phase-related analyses based on a novel circular extension of cluster-based permutation testing. We highlight and offer guidance on critical parameters in the analysis, systematically compare various approaches being used in the field today, and provide open-access software code for flexible use and future development of our proposed pipeline.
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BibTeX
@article{berther2026robust,
title = {{Robust circular cluster-based statistics for respiration-brain coupling}},
author = {Teresa Berther and Elio Balestrieri and Martina Saltafossi and Laura Bock Paulsen and Lau M. Andersen and Daniel S. Kluger},
journal = {PLOS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014672},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014672},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Robust circular cluster-based statistics for respiration-brain coupling
AU - Teresa Berther
AU - Elio Balestrieri
AU - Martina Saltafossi
AU - Laura Bock Paulsen
AU - Lau M. Andersen
AU - Daniel S. Kluger
PY - 2026
DA - 2026/09/03
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014672
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014672
AB - The rapidly developing research field of brain-body neuroscience faces methodological challenges, as analysts continue to develop new analysis strategies in the absence of established best practices. This quest for valid methods is further complicated by the (naturally) circular data involved in the study of phase-locked effects, e.g., in respiration-brain coupling. Various available approaches for phase extraction, constructing adequate surrogate data for statistical comparison, and accounting for the circularity of respiratory data lead to poor cross-study generalisability of results. Interpretation of effects is particularly affected by the problem of multiple comparisons in phase-related inferential statistics. In this tutorial, we propose a robust pipeline for respiration phase-related analyses based on a novel circular extension of cluster-based permutation testing. We highlight and offer guidance on critical parameters in the analysis, systematically compare various approaches being used in the field today, and provide open-access software code for flexible use and future development of our proposed pipeline. Author summary: Conventional best-practice statistical approaches have so far been difficult to implement in cases where the underlying data are circular, for example in the analysis of phase data. Such analyses are common in the up-and-coming research field of brain-body neuroscience, which poses significant challenges. In this tutorial, we use the case of respiration to propose a robust pipeline for phase-related analyses based on a novel circular extension of cluster-based permutation testing. We highlight and offer guidance on critical parameters in the analysis, systematically compare various approaches being used in the field today, and provide open-access software code for flexible use and future development of our proposed pipeline.
SN - 1553-7358
ER -