Community-recommended practices for collecting physiological data in functional MRI: The “Physiopy” approach

The practice of combining neuroimaging data with concurrent physiological data is crucial for interpreting neuroimaging results, yet it is often overlooked. The barriers to collecting and using physiological data often stem from the cost and complexity of the extra equipment needed and, more importantly, from the lack of expertise and knowledge required to collect, assess, analyze, and integrate such data. Consequently, the use of physiological data is still uncommon outside of a few specialized groups or centers. To improve the uptake of concurrent physiological and neuroimaging data collection practice, as well as the quality and consistency of physiological recordings collected within neuroimaging settings, the Physiopy community is compiling and maintaining a live, open-access, collaborative set of community practices describing physiological data acquisition, its processing, and its usage in neuroimaging. We present here the methods used to compile this resource, as well as its aims and its future development.

Authors

Institutions

Publication Details

Journal
Aperture Neuro
Published
2026-02-13
DOI
https://doi.org/10.52294/001c.155157
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Community-recommended practices for collecting physiological data in functional MRI: The “Physiopy” approach

Patrícia Figueiredo, Ian D. Driver, Kevin P. Murphy, Roza G. Bayrak et al.
Aperture Neuro
Functional Brain Connectivity Studies
article

Community-recommended practices for collecting physiological data in functional MRI: The “Physiopy” approach

Patrícia Figueiredo, Ian D. Driver, Kevin P. Murphy, Roza G. Bayrak, Rebecca G. Clements, Sarah E. Goodale, Kristina M. Zvolanek, Joana Medeiros Pinto, Rachael Stickland, Marie-Ève Picard, Stefano Moia, Inês Esteves, Molly Bright, Mary Jo Miedema, Daniel R. Glen, Johanna M.M. Bayer, The Physiopy Community
article en

Abstract

The practice of combining neuroimaging data with concurrent physiological data is crucial for interpreting neuroimaging results, yet it is often overlooked. The barriers to collecting and using physiological data often stem from the cost and complexity of the extra equipment needed and, more importantly, from the lack of expertise and knowledge required to collect, assess, analyze, and integrate such data. Consequently, the use of physiological data is still uncommon outside of a few specialized groups or centers. To improve the uptake of concurrent physiological and neuroimaging data collection practice, as well as the quality and consistency of physiological recordings collected within neuroimaging settings, the Physiopy community is compiling and maintaining a live, open-access, collaborative set of community practices describing physiological data acquisition, its processing, and its usage in neuroimaging. We present here the methods used to compile this resource, as well as its aims and its future development.

Aperture NeuroVol. 6(SI 1)
University of Geneva (CH), University of Lisbon (PT), Radboud University Nijmegen (NL), Vanderbilt University (US), Maastricht University (NL), University of Oxford (GB), Basque Center on Cognition, Brain and Language (ES), Institut Universitaire de Gériatrie de Montréal (CA), McGill University (CA), École Polytechnique Fédérale de Lausanne (CH), Université de Montréal (CA), Cardiff University (GB), University of Birmingham (GB)
Openalex Percentile: Top 58%
Functional Brain Connectivity Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.