m-miedema
commented
11 months ago 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
Authors
Mary Miedema [mary.miedema@mail.mcgill.ca](mailto:mary.miedema@mail.mcgill.ca) Simon R. Steinkamp [simons@drcmr.dk](mailto:simons@drcmr.dk) Céline Provins [celine.provins@unil.ch](mailto:celine.provins@unil.ch) Sarah Goodale [sarah.e.goodale@vanderbilt.edu](mailto:sarah.e.goodale@vanderbilt.edu) Marie-Eve Picard [marie-eve.picard.2@umontreal.ca](mailto:marie-eve.picard.2@umontreal.ca) François Lespinasse [francois.lespinasse96@gmail.com](mailto:francois.lespinasse96@gmail.com) Stefano Moia [s.moia.research@gmail.com](mailto:s.moia.research@gmail.com) The physiopy community [physiopy.community@gmail.com](mailto: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} }