OSCR

TRACC-PHYSIO: Time-Domain Resolution-Aligned Cross-Correlation to Estimate PHYSIOlogical Coupling and Time Delays in Dynamic MRI.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Simulation‐Based Validation of TRACC‐PHYSIO › Physiological Signal Generation ↔ TRACC_simulation_code/simulated_signal_examples_wFigure.m, the whole file · a weak match · score 0.79 · simulated respiratory signal, simulated PPG, simulated MR signal, respiratory ratio, PPG signal, SNR
  2. [2] § Methods › Simulation‐Based Validation of TRACC‐PHYSIO › Physiological Signal Generation ↔ TRACC_simulation_code/respiration_generation.m, lines 5–37 · score 0.77 · respiratory belt signals, simulated respiratory belt, respiratory rate, sinusoidal, asymmetry, breathing
  3. [3] § Methods › Simulation‐Based Validation of TRACC‐PHYSIO › Simulation Experiments ↔ TRACC_simulation_code/simulated_signal_examples_wFigure.m, the whole file · a weak match · score 0.69 · cardiac respiratory, respiratory ratios, simulated signals, MR signal, physiological signals, RR
  4. [4] § Methods › TRACC‐PHYSIO Method ↔ TRACC_realData_code/get_TRACC_Cardiac_bySlice.m, lines 20–64 · score 0.68 · Pearson correlation coefficient, cross correlation, TRACC Cardiac, shifts, temporal, PHYSIO
  5. [5] § Methods › Simulation‐Based Validation of TRACC‐PHYSIO › Physiological Signal Generation ↔ TRACC_simulation_code/respiration_generation.m, lines 5–37 · score 0.67 · respiration belt signal, simulated respiratory signal, respiration rate, breaths, minute, amplitude
  6. [6] § Methods › Simulation‐Based Validation of TRACC‐PHYSIO › Synthetic MR Signal Generation ↔ TRACC_simulation_code/Example_TRACC_Cardiac_simulation.m, lines 17–99 · score 0.56 · imaging speeds, downsampled, MR signal, physiological signal, fast, noise
  7. [7] § Methods › Simulation‐Based Validation of TRACC‐PHYSIO › Simulation Experiments ↔ TRACC_simulation_code/Example_TRACC_Cardiac_simulation.m, lines 17–99 · score 0.56 · simulated signals, composition, MR signal, physiological signals, shift, TimeDelay
  8. [8] § Methods › TRACC‐PHYSIO Method ↔ TRACC_realData_code/get_TRACC_Cardiac_bySlice.m, lines 20–64 · score 0.55 · Correlation Coefficient, cross correlation, temporal, TRACC, PHYSIO, signal
  9. [9] § Methods › Simulation‐Based Validation of TRACC‐PHYSIO › Physiological Signal Generation ↔ TRACC_simulation_code/pulse_generation.m, lines 27–72 · score 0.51 · heart rate variability, beat, HRV, simulations, physiologically, cardiac

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 133 lines · 3.7 KB · MIT · 2 matches

  2. % Written by: Adam Wright
  3. % Email: [email hidden]
  5. % Example generation and plots of physiological signals -- PPG and Resp.
  7. %Generate PPG data
  8. fs = 400; %Hz
  9. HR = 60; %bpm
  10. HRV = 50; %ms -- this will be slightly variable for the SDNN HRV measure
  11. T = 600; %s -- ten minutes of data
  12. N = T/(1/fs);
  13. time = linspace(0,T-1/fs,N);
  15. %Get simulated PPG signal
  16. pulse_sim = pulse_generation(HR,HRV,T,fs);
  18. %Generate Resp data
  19. RR = 12; %bpm
  20. RRV = 500; %ms -- -- this will be slightly variable for the SDNN RRV measure
  22. %Get simulated Resp signal
  23. resp_sim = respiration_generation(RR,RRV,T,fs);
  25. %Simulated MR signal with 2:1 cardiac-respiratory ratio, with a TR of 1
  26. %sec and acquisition time of 360 s.
  27. cardiac_amp = 2;
  28. resp_amp = 1;
  30. %Start with mixed physiological signal with longer acquisition time and no
  31. %noise and fs = 400 Hz
  32. [mixed_physio, ~, ~] = generatePhysiologicalSignal(HR, HRV, RR, RRV, cardiac_amp, resp_amp, T, fs);
  34. TR = 1; %1 sec
  35. T_mr = 360;
  36. fs_mr = 1/TR;
  37. N_mr = fs_mr*T_mr;
  39. %Sample the mr signal 5 seconds after the start of the 10 minute simulated mixed
  40. %physiology signal (this avoids issues when longer lags are applied to have
  41. %a non defined physiological signal). In real data the physiological signal
  42. %recordings are recorded before and aftet the MR acquition, if this isn't
  43. %the case the edge effects need to be accounted for properly.
  44. t_start = ceil(5/TR);
  45. t_mr = linspace(t_start,T_mr+t_start-TR,N_mr);
  46. t_mr_plot = t_mr-t_start;
  48. [~,idx_in_mixed] = ismembertol(t_mr, time, 0.000001);
  50. %Getting the mr signal with proper TR acquisitions
  51. mr_simulated_noNoise = mixed_physio(idx_in_mixed);
  53. % Add noise with a certain SNR.
  54. SNR_dB = 20;
  55. mr_signal_power = mean(mr_simulated_noNoise.^2);
  56. noise_power = mr_signal_power / (10^(SNR_dB / 10)); %Noise power needed for a certain SNR
  57. noise = sqrt(noise_power)* randn(size(mr_simulated_noNoise)); %Generate noise
  59. %mr signal with noise
  60. mr_simulated_wNoise = mr_simulated_noNoise + noise;
  63. %Get freqeuncy spectrums for physiological and MR signals
  64. f_physio = (0:N-1)*(fs/N);
  65. f_mr =(0:N_mr-1)*(fs_mr/N_mr);
  67. %Calculate the pulse frequency spectrum
  68. fft_pulse = abs(fft(pulse_sim) / N);
  69. fft_pulse(2:ceil(N/2)) = 2*fft_pulse(2:ceil(N/2));
  71. %Calculate the resp frequency spectrum
  72. fft_resp = abs(fft(resp_sim) / N);
  73. fft_resp(2:ceil(N/2)) = 2*fft_resp(2:ceil(N/2));
  75. %Calculate the mr frequency spectrum
  76. fft_mr = abs(fft(mr_simulated_wNoise) / N_mr);
  77. fft_mr(2:ceil(N_mr/2)) = 2*fft_mr(2:ceil(N_mr/2));
  80. fontSize = 12;
  82. %Figure
  84. h = figure('Color','w', 'Visible', 'on');
  85. set(h, 'Position', [173 147 1150 657]);
  86. t = tiledlayout(3, 5, 'TileSpacing', 'loose');
  87. nexttile([1 3])
  88. plot(time,pulse_sim,'LineWidth',2);
  89. xlim([0 10])
  90. ylim([-1.5 1.5])
  91. xlabel('Time [s]')
  92. ylabel('PPG Signal')
  93. title('Simulated PPG Signal')
  94. set(gca, 'FontSize', fontSize)
  96. nexttile([1 2])
  97. plot(f_physio(1:ceil(N/2)),fft_pulse(1:ceil(N/2)),'LineWidth',1.5)
  98. xlim([0 5])
  99. xlabel('Frequency [Hz]')
  100. ylabel('PPG Amplitude')
  101. set(gca, 'FontSize', fontSize)
  103. nexttile([1 3])
  104. plot(time,resp_sim,'LineWidth',2);
  105. xlim([0 30])
  106. ylim([-1.5 1.5])
  107. xlabel('Time [s]')
  108. ylabel('Resp Signal')
  109. title('Simulated Respiration Signal')
  110. set(gca, 'FontSize', fontSize)
  112. nexttile([1 2])
  113. plot(f_physio(1:ceil(N/2)),fft_resp(1:ceil(N/2)),'LineWidth',1.5)
  114. xlim([0 1])
  115. xlabel('Frequency [Hz]')
  116. ylabel('Resp Amplitude')
  117. set(gca, 'FontSize', fontSize)
  119. nexttile([1 3])
  120. plot(t_mr_plot, mr_simulated_wNoise, 'LineWidth',2)
  121. xlim([0 60])
  122. ylim([-4 4])
  123. xlabel('Time [s]')
  124. ylabel('MR Signal')
  125. title('Simulated MR Signal w/Noise (TR = 1s, Acq duration = 360 s)')
  126. set(gca, 'FontSize', fontSize)
  128. nexttile([1 2])
  129. plot(f_mr(1:ceil(N_mr/2)),fft_mr(1:ceil(N_mr/2)),'LineWidth',1.5)
  130. xlim([0 1])
  131. xlabel('Frequency [Hz]')
  132. ylabel('MR Amplitude')
  133. set(gca, 'FontSize', fontSize)

simulated_signal_examples_wFigure.m at commit 01fa2f3, under MIT · at the source

Overview

Authors: Adam M Wright1,2, Jianing Zhang1, Yunjie Tong2, Qiuting Wen1,2
ORCID iDs: Adam M Wright, Qiuting Wen
  1. Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
  2. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
Institutions: Purdue University West Lafayette (United States); Indiana University Indianapolis (United States); Indiana University School of Medicine
Journal: Magnetic resonance in medicine, volume 96, issue 4, pages 1605-1616
Dates: received 15 March 2026; accepted 15 May 2026; published online 24 May 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/mrm.70446 · PMID 42178506 · PMCID PMC13419326 · OpenAlex W7162300670
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), fMRI (modality), human (organism), computational (subfield)
Methods: Preprocessing, Connectivity, Statistics, Machine learning, Physiology & signal measures
Keywords: cardiac and respiratory pulsations, coupling strength, dynamic diffusion‐weighted imaging (dynDWI), functional MRI (fMRI), neurofluid dynamics, pulse time delays
MeSH: Brain*, Heart*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Algorithms, Computer Simulation, Humans, Reproducibility of Results, Respiration (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: NIA NIH HHS (F30AG084336, RF1 AG083762, RF1AG083762, F30 AG084336); National Institute on Aging (F30AG084336, RF1AG083762); NIGMS NIH HHS (T32 GM148382, T32GM148382); National Institute of General Medical Sciences (T32GM148382)
Citations: not cited yet (Europe PMC); 27 references in the paper

Abstract

Purpose: To validate a method to assess cardiac and respiratory brain pulsations in slowly sampled dynamic MR scans. Cardiac and respiratory pulsations are key drivers of neurofluid circulation. However, resolving their temporal dynamics requires fast imaging, which is not achievable in many dynamic MR acquisitions.

Methods: We systematically validated TRACC‐PHYSIO, a Time‐domain Resolution‐Aligned Cross‐Correlation framework for estimating physiological coupling and time delays in dynamic MRI. This method uses simultaneously recorded cardiac and respiratory waveforms as external references and applies time‐shifted cross‐correlation to estimate physiological coupling strength and relative pulse delay in dynamic MRI data. Two metrics are derived: Peak Coupling Coefficient (Peak CorrCoeff), which quantifies coupling strength, and TimeDelay, which estimates the relative arrival time of the physiological impulse in the brain. TRACC‐PHYSIO was first evaluated with in vivo fMRI (TR = 363 ms) by comparing the Peak CorrCoeff with physiological bandpower derived from spectral analysis. Performance was further assessed using simulations of realistically modeled dynamic MR signals across repetition time (TR = 50–3000 ms) and acquisition durations (60–360 s).

Results: In fMRI, TRACC‐derived cardiac and respiratory Peak CorrCoeff were strongly associated with their respective spectrum‐derived physiological bandpower (all tests Pearson r > 0.90). In simulations, both Peak CorrCoeff and TimeDelay were estimated with no mean bias and low temporal errors across TRs and acquisition durations.

Conclusion: TRACC‐PHYSIO is a validated time‐domain framework for quantifying cardiac and respiratory coupling strength and estimating millisecond‐scale relative pulse delays in standard dynamic MR acquisitions.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

Wen-Imaging-Lab/TRACC_PHYSIO

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 01fa2f3e269c00858e5e1a35ba2511486a1ea868, 19 March 2026
Languages: MATLAB (12)
Size: 17 files, 12 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (2 files), Signal Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
14 files
At the source: github.com/Wen-Imaging-Lab/TRACC_PHYSIO

Zenodo 18762411

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 13 files
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.18762411

wright-adam/TRACC_PHYSIO

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 01fa2f3e269c00858e5e1a35ba2511486a1ea868, 19 March 2026
Languages: MATLAB (12)
Size: 17 files, 12 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (2 files), Signal Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
14 files
At the source: github.com/wright-adam/TRACC_PHYSIO

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 24 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

Code for the TRACC‐PHYSIO method and the simulated signal generation are available on GitHub (https://github.com/Wen‐Imaging‐Lab/TRACC_PHYSIO (https://github.com/Wen-Imaging-Lab/TRACC_PHYSIO)). Code for implementation with real data is also provided, along with an example TRACC‐Cardiac dataset available on Zenodo (10.5281/zenodo.18762411">https://doi.org/10.5281/zenodo.18762411">10.5281/zenodo.18762411).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 9 MeSH terms, 4 funders, 26 references.

Cite

This paper

Wright, A. M., Zhang, J., Tong, Y., & Wen, Q. (2026). TRACC-PHYSIO: Time-Domain Resolution-Aligned Cross-Correlation to Estimate PHYSIOlogical Coupling and Time Delays in Dynamic MRI. Magnetic resonance in medicine, 96(4), 1605-1616. https://doi.org/10.1002/mrm.70446

BibTeX

@article{wright2026tracc,
author = {Wright, Adam M and Zhang, Jianing and Tong, Yunjie and Wen, Qiuting},
title = {{TRACC-PHYSIO: Time-Domain Resolution-Aligned Cross-Correlation to Estimate PHYSIOlogical Coupling and Time Delays in Dynamic MRI}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = may,
volume = {96},
number = {4},
pages = {1605--1616},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70446},
url = {https://doi.org/10.1002/mrm.70446},
pmid = {42178506},
pmcid = {PMC13419326}
}

RIS

TY - JOUR
AU - Wright, Adam M
AU - Zhang, Jianing
AU - Tong, Yunjie
AU - Wen, Qiuting
TI - TRACC-PHYSIO: Time-Domain Resolution-Aligned Cross-Correlation to Estimate PHYSIOlogical Coupling and Time Delays in Dynamic MRI
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/05/24
VL - 96
IS - 4
SP - 1605
EP - 1616
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70446
UR - https://doi.org/10.1002/mrm.70446
LA - en
ER -

CSL-JSON

{
"id": "10.1002/mrm.70446",
"type": "article-journal",
"title": "TRACC-PHYSIO: Time-Domain Resolution-Aligned Cross-Correlation to Estimate PHYSIOlogical Coupling and Time Delays in Dynamic MRI",
"container-title": "Magnetic resonance in medicine",
"author": [
{
"family": "Wright",
"given": "Adam M"
},
{
"family": "Zhang",
"given": "Jianing"
},
{
"family": "Tong",
"given": "Yunjie"
},
{
"family": "Wen",
"given": "Qiuting"
}
],
"container-title-short": "Magn Reson Med",
"volume": "96",
"issue": "4",
"page": "1605-1616",
"DOI": "10.1002/mrm.70446",
"PMID": "42178506",
"PMCID": "PMC13419326",
"ISSN": "0740-3194",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/mrm.70446",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
24
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1002/mrm.70438 [code]
Quantifying Cardiac, Respiratory, and Low Frequency Components of CSF Motion From fMRI Inflow Effects.
Journal: Magnetic resonance in medicine
In common: fMRI, 7 references
[2] doi:10.1038/s41467-026-76306-9 [code]
Non-invasive characterization of perivascular subarachnoid spaces.
Journal: Nature communications
In common: Image Processing Toolbox, Statistics and Machine Learning Toolbox, structural MRI / diffusion, 2 references
[3] doi:10.1002/mrm.70429
Ultrafast fMRI Detects Age-Related Changes in Harmonics of Cardiac Pulsations in the Brain at 7 T.
Journal: Magnetic resonance in medicine
In common: fMRI, 3 references
[4] doi:10.1038/s41467-026-74215-5 [code]
Multi-metric evaluations of acute psychedelic effects on fMRI brain entropy.
Journal: Nature communications
In common: Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, computational, fMRI
[5] doi:10.1186/s12888-026-08304-6
Transcranial direct current stimulation improves reduced global BOLD-CSF coupling in patients with insomnia disorder and comorbid anxiety: a resting-state functional MRI study.
Journal: BMC psychiatry
In common: fMRI, 3 references
[6] doi:10.1016/j.nicl.2026.104012 [code]
Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder.
Journal: NeuroImage. Clinical
In common: Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, fMRI, structural MRI / diffusion
[7] doi:10.1007/s10548-026-01247-x [code]
Salience Network Dynamics Across Spontaneous Attacks, Interictal Rest and Interictal Pain Imagery in Menstrually Related Migraine.
Journal: Brain topography
In common: Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, fMRI
[8] doi:10.7554/elife.108408 [code]
Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI.
Journal: eLife
In common: Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, fMRI
[9] doi:10.1016/j.isci.2026.116900 [code]
Dog brain representations of human facial expressions: Encoding happiness and differentiating specific negative expressions.
Journal: iScience
In common: Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, fMRI
[10] doi:10.1002/hbm.70624 [code]
Exploring the Sensitivity Limits of Neuronal Current Imaging With MRI and MEG in the Human Brain.
Journal: Human brain mapping
In common: Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, fMRI

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.