Updates to AFNI's physio_calc.py: new respiratory response regressors and more
Poster No:
1568
Submission Type:
Abstract Submission
Authors:
Peter Lauren1, Richard Reynolds2, Daniel Glen3, Paul Taylor1
Institutions:
1National Institute of Mental Health, Bethesda, MD, 2NIMH, Bethesda, MD, 3National Institute of Mental Health (NIMH), NIH, Bethesda, MD
First Author:
Co-Author(s):
Introduction:
FMRI's BOLD signal includes both neuronal and non-neuronal contributions. Breathing and heart rate both have strong influences on blood oxygenation levels, and these sources are unlikely to be turned off during any in vivo scan. Therefore, it can be helpful to include measures of these biological phenomena within an FMRI model to more fully account for underlying signal contributions.
The AFNI software package [1] has recently developed the physio_calc.py program to take physiological time series (such as breathing and heart rate measures) acquired during a scan session and create regressors for single subject analysis, such as within afni_proc.py [2] or other pipeline tools. The program estimates slicewise RETROICOR regressors [3], as well as respiration volume per time (RVT) [4]. Birn et al [5] discussed how convolving RVT with the respiration response function (RRF), a model of the average BOLD response to a single deep breath, produced a new vector (RVTRRF) to more accurately model breath-related changes in the BOLD signal. In practice, RVTRRF should have an enhanced cross-correlation with spontaneous fluctuations in end-tidal CO2 (PETCO2) [6] and explain more non-neuronal variability than just RVT. Chang et al [6] discussed convolving the heart rate (HR) time series with the cardiac response function (CRF) [6] to produce a new vector (HRCRF) to more accurately model cardiac-related changes in the BOLD signal. In this work, we present an implementation of RVTRRF and HRCRF within physio_calc.py, and compare them to using each separately and with RVT, or cardiac regressors, for real FMRI data.
The AFNI software package [1] has recently developed the physio_calc.py program to take physiological time series (such as breathing and heart rate measures) acquired during a scan session and create regressors for single subject analysis, such as within afni_proc.py [2] or other pipeline tools. The program estimates slicewise RETROICOR regressors [3], as well as respiration volume per time (RVT) [4]. Birn et al [5] discussed how convolving RVT with the respiration response function (RRF), a model of the average BOLD response to a single deep breath, produced a new vector (RVTRRF) to more accurately model breath-related changes in the BOLD signal. In practice, RVTRRF should have an enhanced cross-correlation with spontaneous fluctuations in end-tidal CO2 (PETCO2) [6] and explain more non-neuronal variability than just RVT. Chang et al [6] discussed convolving the heart rate (HR) time series with the cardiac response function (CRF) [6] to produce a new vector (HRCRF) to more accurately model cardiac-related changes in the BOLD signal. In this work, we present an implementation of RVTRRF and HRCRF within physio_calc.py, and compare them to using each separately and with RVT, or cardiac regressors, for real FMRI data.
Methods:
Single-echo FMRI data was used to test the value of RVTRRF and of HRCRF. The physio_calc.py program was updated to optionally compute the RVTRRF and/or HRVRF regressor. These followed the kernel-based convolution formulation of Birn et al. [5] and Chang et al [6] respectively; like RVT, RVTRRF and HRCRF are typically applied as a set of 5 time-shifted regressors. To avoid boundary effects, the mean RVT/HR value was subtracted from the RVT/HR vector (mean centering) before convolution with RRFHR to obtain RVTRRF/HRCRF. For comparison, three types of physio regressor files were created: one containing the 5, shifted RVT regressors, one containing the 5 shifted RVTRRF regressors and one containing the 5, shifted HRCRF regressors. These were separately applied to FMRI processing using AFNI's afni_proc.py [2] pipeline tool. The effect of each set of regressors is directly compared in terms of variance reduction (i.e., filtering physiological features out). In the comparison, we subtracted the RVT/sinusoidal cardiac regressor variance reduction from that of using RVTRRF/HRCRF, so that positive values reflect where RVTRRF or HRCRF explains more variance than RVT/sinusoidal cardiac regressors.
Results:
Figure 1 shows the original RVT time series (A) and original HR time series (D) estimated by physio_calc.py the new, convolved RVTRRF time series (B&C) and the new, convolved RVTRRF time series (E&F). Boundary effects result (B&E) if the unmodified RVT/HR is convolved with RRF/CRF. These are avoided (C&F) by mean centering RVT/HR before convolution with RRF/CRF. Figure 2 shows where RVTRRF explains more physiological variance (hot colors) than RVT. This is most pronounced in the cortical and posterior regions. Figure 2 also shows where HRCRF explains more or less variance than the cardiac sinusoid regressors [3]. Again, the hot colors show where it explains more variance and the cold colors where it explains less. There is good complementarity.
Conclusions:
FMRI regressor outputs, RVTRRF and HRVRF, have been added to physio_calc.py. RVTRRF appears to complement or improve upon using RVT by explaining more physiological variance in certain parts of the brain, particularly in the cortical and posterior regions. HRVRF has good complementarity with the cardiac sinusoid regressors.
Modeling and Analysis Methods:
Methods Development 1
Other Methods 2
Keywords:
Computational Neuroscience
Computed Tomography (CT)
Data analysis
Data Organization
Modeling
Open-Source Code
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
[2] Reynolds RC, Glen DR, Chen G, Saad ZS, Cox RW, Taylor PA (2024). Processing, evaluating and understanding FMRI data with afni_proc.py. Imaging Neuroscience 2:1-52.
[3] Glover GH, Li TQ, Ress D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med. 2000 Jul;44(1):162-7
[4] Birn, R.M., Diamond, J.B., Smith, M.A., Bandettini, P.A., 2006. Separating respiratory- variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage 31, 1536–1548
[5] Birn RM, Smith MA, Jones TB, & Bandettini PA (2008) The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration”. Neuroimage 2008;40:644–654
[6] Chang, C & Glover, GH (2009). “Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI”, Neuroimage 47(4) (2009), pp. 1381–1393