I have no financial interests or relationship to disclose with regard to the subject matter of this presentation.

Disclaimers

1. I am an "open" scientist. I have a bias toward the
     core tenets of Open Science as better practices.

2. I am a co-founder and current member of the
     physiopy community.

This is a new chapter

This is a new chapter

Take home #0

This is a take home message

Brains in vats?

No, brains in bodies

Neurovascular Coupling

Image courtesy of Martin Havlicheck and Dimo Ivanov

Changes in
Haemodynamics

Neurovascular
Coupling

Changes in Oxygen Metabolism

Changes in
BOLD signal

(Venous) Vasculature

Arrows indicate causal influence

Impact of physiology on data variance

1. Bianciardi et al., 2009 (Magn. Reson. Imaging.);    2. Triantafyllou et al., 2005 (NeuroImage);

3. jorge et al., 2013 (Magn. Reson. Imaging.), Reynaud et al., 2017 (Magn. Reson. Med.)

Physiology-related variance varies:

  • By voxel size¹ ² and position¹
  • By field strength²
  • By sequence type and TR³

Impact of physiology on data variance

1. Krentz et al., 2023 (bioRxiv), Carlton et al., 2024 (bioRxiv), Moia et al., 2024 (bioRxiv);

2. Birn et al., 2009 (NeuroImage), image courtesy of Jingyuan Chen;    3. Lee et al., 2023 (HBM)

Physiology-related variance varies:

  • By individual & session¹
  • By task (task-locked)²
  • By Resting State Network³

Ventilation

Task (convolved)

RETROICOR variability

Denoising is strongly linked to interpretation

Are you interested in individual or group level effects?

Are physiological responses part of your interpretation?
(e.g. affective neuroscience)

Should your interpretation include autonomic nervous system changes?

[...]

Are your comparisons robust to physiological responses?

Physiological data processing

Raw data

BIDSification

phys. data preprocessing
(peak detect.)

phys. denoising

phys. imaging

Data acquisition

QA/QC

Why physiopy

In neuroimaging, integration of physiological measures to data collection and analyses are still a niche topic. By raising awareness, we can inspire researchers and clinicians become interested in the topic.

*Open Source Software Development is the idea of developing a software publicly, sharing it from the beginning of the development, fostering a democratic community of contributors in support of the project, using version control and software testing.

physiopy adopts a Community driven, BIDS-based, Open Development* approach, aimed at bringing governance back to the users.

Sharing physiological data, toolboxes, and documentation following the concepts of Open Science could improve the exposition to this topic.

Community practices meetings, community consensus, and community guidelines.

Spark interest!

The more we share, the better it becomes

This is (not)
the way!

Of the People, by the People, for the People

Core components

A set of easily adoptable toolboxes

Community of users, developers, and researchers interested in physiology

Clear and approachable documentation

Community practices based on consensus

Core components

physiopy's status

  • Highly inspired by the tedana community and The Turing Way
  • Founded in 2019 by 9 people in 3 labs (during the DC Code Convergence 2019!)
  • It now counts over 35 contributors from over 25 institutions (not all currently active)
  • All contributors are recognised via all-contributors schema and authorship, alphabetically
  • All volunteers are... volunteering (so far)  -  and development spikes during Brainhacks
  • We got a new governance based on the Minimal Viable Governance model
  • We are also restructuring contributors' guidelines, to make them more friendly!

Raw data

BIDSification

phys. data preprocessing
(peak detect.)

phys. denoising

phys. imaging

Data acquisition

QA/QC

physiopy's projects

physiopy's projects

Raw data

BIDSification

phys. data preprocessing
(peak detect.)

phys. denoising

phys. imaging

Data acquisition

Process description

QA/QC

physiopy's projects

Raw data

phys2bids


peakdet
 

phys2denoise

phys. imaging

Data acquisition

physiopy's documentation
&

Coordinated testing suite

BIDS Extension Proposal

physioQC

Physiopy's Community Practices

physiopy's projects

Raw data

phys2bids


peakdet
 

phys2denoise

phys. imaging

Data acquisition

physiopy's documentation
&

Coordinated testing suite

BIDS Extension Proposal

physioQC

Physiopy's Community Practices


prep4phys

 

How to record physiological data

Ventilation

Respiration (O2/CO2)

Pulse

Electrodermal Activity

Community practices

Community practices

More Community Practices!

Response functions and convolutions

Physiology and vigilance

Physiological confounds in resting state

ECG, PPG, and Heart Rate Variability

Blood Pressure and Cerebral Autoregulation

Brain-Body Interaction
Physiological workflows

Open meetings
every 3rd Thursday of the month
at 17h00 UTC

New cycle starting soon!

Data standardisation (BIDSification)

Alcalà et al., 2023 (Zenodo)

BIDS Extension Proposal(s)

  1. Physio as independent modality & multimodal concurrency
     
  2. Identification of jey metadata
     
  3. Provenance of derivatives
     
  4. Guidance for formatting output files
     
  5. Flexibility and specificity for different processing stages

https://github.com/physiopy/bids-specification-physio/discussions/1

Open meetings
every 2 weeks
at 14h00 UTC

Quality Control

images courtesy of Kristina Zvolanek, Elenor Morgenroth, and César Caballero-Gaudes

Quality Assessment/Control

Goodale et al., 2024 (Zenodo)

"Denoise" the "noise"

Bottenhorn et al., 2023 (Aperture Neuro)

ECG before scanner

ECG during fMRI

ECG after Bottenhorn filter

Scanner off

Scanner on

PPG

Make sure you're removing what you should

data = np.genfromtxt('sub-007_ses-05_task-rest_run-01_physio.tsv.gz', usecols=[0, 1, 3])
ph = peakdet.Physio(data[:, 1], fs=10000, suppdata=data[:, 2])
ph = peakdet.operations.peakfind_physio(ph, thresh=thr, dist=dist)
ph = peakdet.operations.edit_physio(ph)

DuPre et al., 2024 (Zenodo)

PPG

Ventilation

CO2

Phase changes over physiological cycles

image courtesy of Marta Bianciardi

Denoising with physiological data: RETROICOR¹

1. Glover et al., 2000 (Magn. Reson. Med.);    2. Kasper et al, 2017 (J. Neurosci. Methods);    3. Krentz et al., 2023 (bioRxiv)

Whole
Brain

GM

Brainstem

LC

RETROICOR variability across subjects³

²

Physiological harmonics

Image courtesy of Blaise Frederick

Cardiac

Respiratory

Denoising with physiological data: slow fluctuations

HRV: Shmueli et al., 2007 (NeuroImage);    HBI: Chen et al., 2020 (NeuroImage), img from Lee et al., 2023 (HBM)

phys2denoise: Bottenhorn et al., 2024 (Zenodo);    phys2cvr: Moia et al., 2024 (Zenodo);     PhysIO Toolbox: Kasper et al., 2017 (J. Neurosci. Methods)

RVT: Birn et al., 2006 (NeuroImage), img from Lee et al., 2023 (HBM);    RV: Chang & Glover, 2009 (NeuroImage), img from Chen et al., 2010 (NeuroImage)

Respiratory Variance (RV)

Pressure of End Tidal CO2 (PetCO2)

Heart Rate Variability (HRV)

PhysIO toolbox

Physiological denoising in ICC

HRV: Shmueli et al., 2007 (NeuroImage);    HBI: Chen et al., 2020 (NeuroImage), img from Lee et al., 2023 (HBM)

phys2denoise: Bottenhorn et al., 2024 (Zenodo);    phys2cvr: Moia et al., 2024 (Zenodo);     PhysIO Toolbox: Kasper et al., 2017 (J. Neurosci. Methods)

Denoising through data decomposition: CompCorr¹

1. Behzadi et al., 2007 (NeuroImage);    image courtesy of César Caballero-Gaudes

Impact of physiological denoising on data reliability

Belli, 2025 (UM)

Motion regressors

Motion + Physio regressors

Motion + tissue-based regressors
& BandPass

Motion regressors & BandPass

Motion + Physio regressors
& BandPass

Reconstructing physiological data from fMRI

1. Aslan et al., 2019 (Neuroimage);    2. Wang, Xu, et al., 2024 (arXiv)

Cardiac waveform from multislice data¹

RV and HRV from parcellated BOLD fMRI²

1. Bright et al. 2020 (NeuroImage), Chen et al., 2020 (NeuroImage)

BOLD-based fMRI, in conjunction with physiological signals, reveals the existence of physiological and vascular brain networks¹.

Denoising is strongly linked to interpretation

More voices & more diverse expertise improve our community

(and we're not here to tell you what to do - we want to figure it out with you)

Join us!

Send an email to:
physiopy.community@gmail.com

(Even if you are not a physiology/python guru!)

Any question [/opinions/objections/...]?

Take home messages

  1. Denoising is linked to interpretation
  2. Physiological signals should be considered when comparing sequences, subjects, tasks, networks
  3. Physiological must be quality controlled to avoid bad denoising
  4. RETROICOR explains part of the data variance that other models do not explain
  5. When lacking (good) physiological data, data-driven approaches can be used
  6. Physiological data can be partially retrieved from BOLD MRI
  7. Brain vessels may bias your functional data

Physiological BIDS
extension proposal(s)

Physiopy Community Guidelines

Stefano Moia, 2025

Find the presentation at:

slides.com/smoia/hanover2025/scroll

Enhancements!

  • ECG vs PPG
  • EDA
  • Eye Tracking

Add guidelines on

  • Siemens
  • Bitalino
  • non-MRI modalities

Extend support to

  • NeuroKit
  • NiPreps
  • AFNI & FSL

Integrations

  • (Semi-)Automation
  • BIDS Apps
  • Integrated workflows

More functionalities

Contributions and communities

  • Development is a straight(er) line
  • Time effective (on the short run)
  • Low(er) engagement
  • Less mentors
  • Small(er) user base
  • Less tests
  • Consensus not guaranteed
  • Clear attribution
  • Development takes its own path
  • (More) Time consuming
  • High(er) engagement
  • More mentors
  • Big(ger) user base
  • More tests
  • Consensus based
  • Fuzzier attribution