Physiopy: a Python suite for handling physiological data recorded in neuroimaging settings
Presented During:
Monday, June 24, 2024: 5:45 PM - 7:00 PM
COEX
Room:
Grand Ballroom 103
Poster No:
2275
Submission Type:
Abstract Submission
Authors:
Roza Bayrak1, Catie Chang1, Katherine Bottenhorn2, Molly Bright3, Cesar Caballero-Gaudes4, Ines Chavarria5, Niall Duncan6, Inês Esteves7, Vicente Ferrer4, Raphael Fournier8, Daniel Glen9, Sarah Goodale1, Tomas Lenc10, François Lespinasse11, Neville Magielse12, Mary Miedema13, Stefano Moia14, Robert Oostenveld15, Marie-Eve Picard16, Joana Pinto17, Céline Provins18, Taylor Salo19, Simon Steinkamp20, Rachael Stickland21, Mi Xue Tan22, Hao-Ting Wang23, Kristina Zvolanek3, Marcel Zwiers24, Physiopy Community25
Institutions:
1Vanderbilt University, Nashville, TN, 2University of Southern California, Los Angeles, CA, 3Northwestern University, Chicago, IL, 4Basque Center of Cognition, Brain and Language, San Sebastián, Spain, 5Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, 6Taipei Medical University, Taiwan, Taipei, 7ISR-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Lisboa, 8Swiss Federal Institute of Technology Zurich (ETHZ), Zurich, Zurich, 9NIMH, Bethesda, MD, 10Université Catholique de Louvain, Louvain, Louvain, 11Université de Montreal, Montreal, Quebec, 12INM-7, Research Center Jülich, Jülich, NRW, 13McGill University, Montreal, Quebec, 14Maastricht University, Maastricht, Linburg, 15Donders Centre For Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands, 16Université de Montréal, Centre de Recherche de l’Institut Universitaire de Montréal, Montreal, Quebec, 17University of Oxford, Oxford, Oxfordshire, 18Lausanne University Hospital and University of Lausanne, Lausanne, Vaud, 19University of Pennsylvania, Philadelphia, PA, 20Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark, Hvidovre, Region Hovedstaden, 21The Alan Turing Institute, London, London, 22University of Geneva, Geneva, Geneva, 23CRIUGM, Montreal, Quebec, 24Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Gelderland, 25Physiopy, Maastricht, Linburg
First Author:
Co-Author(s):
Inês Esteves
ISR-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa
Lisboa, Lisboa
ISR-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa
Lisboa, Lisboa
Robert Oostenveld
Donders Centre For Cognitive Neuroimaging, Radboud University
Nijmegen, Netherlands
Donders Centre For Cognitive Neuroimaging, Radboud University
Nijmegen, Netherlands
Marie-Eve Picard
Université de Montréal, Centre de Recherche de l’Institut Universitaire de Montréal
Montreal, Quebec
Université de Montréal, Centre de Recherche de l’Institut Universitaire de Montréal
Montreal, Quebec
Simon Steinkamp
Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
Hvidovre, Region Hovedstaden
Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
Hvidovre, Region Hovedstaden
Marcel Zwiers
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Introduction:
Functional Magnetic Resonance Imaging (fMRI), a pivotal tool for neuroscientific research, leverages blood oxygenation levels to infer neural activity. However, its reliance on hemodynamic responses also renders it sensitive to various physiological processes affecting blood oxygenation. This dual nature presents both challenges and opportunities: while these physiological factors can introduce confounds in interpreting neural signals [1], they simultaneously offer valuable insights into essential human functions encompassing cognition, emotion, and health [2-4]. To this end, we underscore the necessity of acquiring concurrent physiological data such as cardiac and respiratory activity, gas exchange metrics (O2/CO2 levels), and skin conductance. Adoption of concurrent physiological signals is growing within the neuroimaging community, reflecting a broader appreciation of physiological dynamics in brain imaging studies. Emphasizing the critical role of physiological monitoring in fMRI data quality, physiopy is a dynamic, collaborative initiative designed to streamline the integration of physiological data with fMRI research. The foundation of physiopy rests on four key pillars: (1) Accessible Software Suite: Offering a range of user-friendly software tools specifically tailored for efficient physiological data processing, (2) Comprehensive Documentation: Ensuring clarity and ease of use through detailed guides and instructional materials, (3) Community-Driven Practices: Fostering a culture of shared knowledge and collaborative development of best practices, and (4) Engaged Community: Cultivating an active network of users, developers, and researchers, all united by a shared interest in the integration of physiology within neuroimaging research.
Methods:
Physiopy is currently composed of four Python packages released under Apache-2.0 licenses, all at different stages of development: phys2bids [5] provides a command line tool for conversion of physiological data into the standardized BIDS format [6]; 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 has comprehensive documentation that outlines community recommendations and comprehensive guidelines for collecting, processing, and using physiological data.
Results:
Current development efforts are focused on (1) a new release of documentation enriched with information from the regular Community Practices meetings based on discussions on the acquisition and use of cardiac, respiratory, and blood gas data, (2) restructuring semi-automated workflows for more flexible and reproducible usage, and to better interface with non-physiopy workflows, (3) creating a quality control workflow for physiological data, implementing a set of useful metrics and visualizations to generate an HTML report, similar to MRIQC [7]. Altogether, physiopy is evolving to become a comprehensive toolkit and guide, addressing all aspects of physiological data preparation in neuroimaging research.
Conclusions:
Physiopy (https://github.com/physiopy) is a community-driven open-source development effort to facilitate the adoption of physiological data in MRI settings. The use of physiopy can simplify the construction of reproducible pipelines for physiological data management. As an open and inclusive project, new contributions of any level of expertise are always welcome. We adopt the all-contributors acknowledgement. To join the monthly meetings, reach out with questions or suggestions, email to physiopy.community@gmail.com.
Neuroinformatics and Data Sharing:
Databasing and Data Sharing
Workflows 1
Physiology, Metabolism and Neurotransmission :
Cerebral Metabolism and Hemodynamics
Neurophysiology of Imaging Signals
Physiology, Metabolism and Neurotransmission Other 2
Keywords:
Data analysis
Data Organization
Data Registration
Design and Analysis
Informatics
Open Data
Open-Source Code
Open-Source Software
Workflows
1|2Indicates the priority used for review
Provide references using author date format
2. Barrett, L. F. (2015) ‘Interoceptive predictions in the brain’, Nature reviews neuroscience, vol. 16, no. 7, pp. 419-429.
3. Shokri-Kojori, E. (2018) ‘An autonomic network: synchrony between slow rhythms of pulse and brain resting state is associated with personality and emotions’, Cerebral Cortex, vol. 28, no. 9, pp.3356-3371.
4. Azzalini, D. (2019) ‘Visceral signals shape brain dynamics and cognition’, Trends in cognitive sciences, vol. 23, no. 6, pp. 488-509.
5. Alcalá, D. (2020) ‘Physiopy/Phys2bids: BIDS Formatting of Physiological Recordings’, Zenodo, https://doi.org/10.5281/zenodo.3470091
6. Gorgolewski, K.J. (2016) ‘The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments’, Scientific Data, vol. 3, no. 1, pp. 1-9.
7. Esteban, Oscar, Daniel Birman, Marie Schaer, Oluwasanmi O. Koyejo, Russell A. Poldrack, and Krzysztof J. Gorgolewski. (2017) ‘MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites.’ PLOS ONE 12, no. 9: e0184661–e0184661. https://doi.org/10.1371/journal.pone.0184661.