Physiopy - Practices and Tools for Physiological Signals

[m-miedema]

Authors

Mary Miedema <mary.miedema@mail.mcgill.ca>
Simon R. Steinkamp <simons@drcmr.dk>
Céline Provins <celine.provins@unil.ch>
Sarah Goodale <sarah.e.goodale@vanderbilt.edu>
Marie-Eve Picard <marie-eve.picard.2@umontreal.ca>
François Lespinasse <francois.lespinasse96@gmail.com>
Stefano Moia <s.moia.research@gmail.com>
The physiopy community <physiopy.community@gmail.com>

Summary

The acquisition and analysis of peripheral signals such as cardiac and respiratory measures alongside neuroimaging data provides crucial insight into physiological sources of variance in fMRI data. The physiopy community aims to develop and support a comprehensive suite of resources for researchers to integrate physiological data collection and analysis with their studies. This is achieved through regular discussion and documentation of community practices alongside the active development of open-source toolboxes for reproducibly working with physiological signals. At the OHBM 2023 Brainhack, we advanced physiopy’s goals through three parallel projects:

Documentation of Physiological Signal Acquisition Community Practices

We have been working to build “best community practices” documentation from experts in the physiological monitoring realm of neuroimaging. The aim of this project was to draft a new version of our documentation adding information from six meetings throughout the year discussing good practices in acquisition and use of cardiac, respiratory, and blood gas data. The documentation is finished and ready for editorial review from the community before we release this version publicly.

Semi-Automated Workflows for Physiological Signals

The aim of this project was to upgrade the existing code base for the peakdet and phys2denoise packages to achieve a unified workflow encompassing all steps in physiological data processing and model estimation. We mapped out and began implementing a restructured workflow for both toolboxes incorporating configuration files for more flexible and reproducible usage. To better interface with non-physiopy workflows, we added support for NeuroKit2 functionalities. As well, we added visualization support to the phys2denoise toolbox.

PhysioQC: A Physiological Data Quality Control Toolbox

This project was about creating a quality control pipeline for physiological data, similar to MRIQC, and leveraging NiReports. At the hackathon, we implemented a set of useful metrics and visualizations, as well as a proof-of-concept workflow for gathering the latter in an HTML report.

Going forward, development of these toolboxes and revision of our community practices continues. We welcome further contributions and contributors, at any skill level or with any background experience with physiological data.

References (Bibtex)

@software{phys2bids,
author = {Daniel Alcalá and
Apoorva Ayyagari and
Katie Bottenhorn and
Molly Bright and
César Caballero-Gaudes and
Inés Chavarría and
Vicente Ferrer and
Soichi Hayashi and
Vittorio Iacovella and
François Lespinasse and
Ross Markello and
Stefano Moia and
Robert Oostenveld and
Taylor Salo and
Rachael Stickland and
Eneko Uruñuela and
Merel van der Thiel and
Kristina Zvolanek},
title = {{physiopy/phys2bids: BIDS formatting of
physiological recordings}},
month = jun,
year = 2021,
publisher = {Zenodo},
version = {},
doi = {10.5281/zenodo.3470091},
url = {https://doi.org/10.5281/zenodo.3470091}
}

@Article{Makowski2021neurokit,
author = {Dominique Makowski and Tam Pham and Zen J. Lau and Jan C. Brammer and Fran{\c{c}}ois Lespinasse and Hung Pham and Christopher Schölzel and S. H. Annabel Chen},
title = {{NeuroKit}2: A Python toolbox for neurophysiological signal processing},
journal = {Behavior Research Methods},
volume = {53},
number = {4},
pages = {1689--1696},
publisher = {Springer Science and Business Media {LLC}},
doi = {10.3758/s13428-020-01516-y},
url = {https://doi.org/10.3758%2Fs13428-020-01516-y},
year = 2021,
month = {feb}
}

[m-miedema]

Figure caption: The physiopy libraries are based in python and currently consist of four toolboxes released under Apache-2.0 licenses, all at different stages of development: phys2bids provides a command line tool for conversion of physiological data into the standardized BIDS format; peakdet preprocesses physiological signals and performs automatic and manual peak detection; phys2denoise models physiological signals and their derivatives for the purpose of denoising in fMRI data; physioQC facilitates quality assessment of physiological data through descriptive metrics and visual reports. Alongside these toolboxes, physiopy’s documentation transparently outlines community recommendations and comprehensive tutorials for collecting, processing, and using physiological data.

[anibalsolon]

hello all, could you please provide the authors information in the following link by May 15th?
https://docs.google.com/forms/d/e/1FAIpQLSckbC4F6KOtge1KOyzwj5yIbWR7tB8HrnqQ4KPZB7Mr3UcvMw/viewform