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Comparison of the Relationships Between Body Size and Cardiorespiratory Fitness With High Frequency Head-Motion Contamination in fMRI.

Code ↔ Paper

1 match 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.

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  1. [1] § Materials and Methods › MRI Acquisition and Head‐Motion Processing ↔ mot_FFT.m, the whole file · a weak match · score 0.71 · 0–100, power spectra, relative power, PMTM, motion parameter, Matlab

Paper

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The authors' code

MATLAB · 90 lines · 2.5 KB · GPL-3.0 · 1 match

  1. function [pwr_high, pwr_high_rel, pwr_high_rel_max, pd, pd_prop, pd_relative, freq] = mot_FFT(mot_data,TR,varargin)
  2. % mot_FFT(mot_data,TR,varargin)
  3. %
  4. % mot_data = time X motionparam (assumes 6)
  5. % TR = TR in seconds
  6. % varargin = info about whether or not to make extra plots (if 1 = plot,
  7. % default is not to plot)
  8. %
  9. % look at FD_analysis.m for original version of this function with further
  10. % testing of different options
  11. %
  12. % C Gratton
  14. if ~isempty(varargin)
  15. plot_results = varargin{1};
  16. else
  17. plot_results = 0;
  18. end
  20. Fs = 1/TR; % Sampling frequency
  22. % PMTM approach (as used in D. Fair 2019 NI paper)
  23. for x = 1:6
  24. [pd(:,x) freq] = pmtm(mot_data(:,x),8,[],Fs);
  26. % store pd as just a percentage
  27. pd_prop(:,x) = pd(:,x)./sum(pd(:,x));
  29. pd_scaled(:,x) = 10.*log10(pd(:,x));
  30. pd_normal(:,x) = zscore(pd_scaled(:,x));
  32. % convert to percentiles
  33. thisDir = pd_normal(:,x);
  34. pd_relative(:,x) = (thisDir - min(thisDir))./(max(thisDir) - min(thisDir))*100;
  35. end
  38. % Calculate HF motion
  39. inds = freq > 0.1; % indices of freq > 0.1 Hz.
  40. pwr_high = sum(pd(inds,:),1)./sum(pd,1); %as a proportion of total pwr
  41. pwr_high_rel = sum(pd_relative(inds,:),1)./sum(pd_relative,1); %as a proportion of total pwr
  42. pwr_high_rel_max = max(pd_relative(inds,:));
  44. %from colorblind_colormap function on Matlab exchange
  45. motion_colors = [ 1.0000 0 0; %red
  46. 0 0 1.0000; % blue
  47. 0.6602 0.6602 0.6602; % gray
  48. 0.85 0.85 0; %originally yellow
  49. 0 0 0; % black
  50. 1.0000 0.6445 0]; % orange
  53. if plot_results
  55. % plot results
  56. figure('Position',[0 0 1000 800]);
  57. subplot(2,2,1:2); hold on;
  58. for m = 1:6
  59. plot(mot_data(:,m),'color',motion_colors(m,:));
  60. end
  61. xlabel('TR');
  62. ylabel('mm');
  63. title(['motion data']);
  65. % Frequency per motion parameter
  66. subplot(2,2,3); hold on;
  67. for m = 1:6
  68. plot(freq,pd(:,m),'color',motion_colors(m,:),'LineWidth',1.5);
  69. end
  70. ylabel('Power');
  71. ylim([0,0.5]);
  72. box off;
  73. xlabel('Freq (Hz)');
  74. title(sprintf('Power Spectrum, y HF: %.02f, Sum HF: %.02f',pwr_high(2),sum(pwr_high)));
  76. % Relative frequency
  77. subplot(2,2,4); hold on;
  78. for m = 1:6
  79. plot(freq,pd_relative(:,m),'color',motion_colors(m,:),'LineWidth',1.5);
  80. end
  81. ylabel('Relative Power');
  82. ylim([0 100]);
  83. box off;
  84. xlabel('Freq (Hz)');
  85. title(sprintf('Relative Power, y HF: %.02f, All HF: %.02f, y HFmax: %.02f',pwr_high_rel(2),mean(pwr_high_rel),pwr_high_rel_max(2)));
  86. end
  90. end

mot_FFT.m at commit 4b32645, under GPL-3.0 · at the source

Overview

Authors: Marco Pipoly1,2,3,4,5,6, Hayley Chappell1, Lyndsey E DuBose7,8, Bryan Madero1,2, Adriana Rivera‐Dompenciel1,2,4, Matthew Sodoma1,2, Chris Oehler1,2, Will Daniels1,7, Kelsey Baller1,9, Jenna Springer1,7, Matthew Armstrong7,10, Virginia Nuckols7,11, Vincent Magnotta1,2,4, Jeffrey Long12,13, Gary L Pierce7, Michelle W Voss1,2,3,4
ORCID iDs: Marco Pipoly, Matthew Sodoma, Virginia Nuckols
13 affiliations
  1. Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA
  2. Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
  3. Cognitive Control Collaborative, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA
  4. Interdisciplinary Graduate Program in Neuroscience, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
  5. Center for Vital Longevity, Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas, USA
  6. Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Texas, USA
  7. Department of Health, Sport, and Human Physiology, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA
  8. Department of Health and Exercise Science, College of Health and Human Sciences, Colorado State University, Fort Collins, Colorado, USA
  9. Department of Psychological & Brain Sciences, College of Arts & Sciences, Washington University, St. Louis, Missouri, USA
  10. Department of Exercise Science, Falk College of Sport, Syracuse University, Syracuse, New York, USA
  11. Department of Kinesiology & Applied Physiology, College of Health Sciences, University of Delaware, Newark, Delaware, USA
  12. Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
  13. Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, USA
Institutions: University of Iowa (United States); The University of Texas at Dallas (United States); Colorado State University (United States); Washington University in St. Louis (United States); Syracuse University (United States); University of Delaware (United States)
Journal: Human brain mapping, volume 47, issue 11, article e70586
Dates: received 10 November 2025; accepted 18 May 2026; published online 11 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/hbm.70586 · PMID 42578518 · PMCID PMC13459195 · OpenAlex W7202166506
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), clinical / translational (subfield)
Methods: Statistics, Spectral & time-frequency, fMRI & imaging, Physiology & signal measures
Keywords: exercise, health behavior, intervention, neuroimaging, older adults
MeSH: Artifacts*, Body Size*, Brain*, Cardiorespiratory Fitness*, Functional Neuroimaging*, Head Movements*, Magnetic Resonance Imaging*, Aged, Aged, 80 and over, Female, Humans, Male, Middle Aged (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: NIGMS NIH HHS (T32GM108540, T32 GM108540); NIH HHS (S10 OD025025); NIA NIH HHS (R01 AG055500, R01AG055500); Office of Research Infrastructure Program Division of Construction and Instruments (S10OD025025)
Citations: not cited yet (Europe PMC); 42 references in the paper

Abstract

Functional MRI (fMRI) is widely used to assess brain function, but neural‐derived fMRI signals are susceptible to contamination from in‐scanner head motion. Preprocessing pipelines often regress out and censor head motion artifacts using six rigid‐body motion parameters. However, recent research suggests these head‐motion estimates are susceptible to contamination from other sources of noise, such as respiratory rate, body size, and estimated cardiorespiratory fitness (eCRF), that correspond to apparent head motion at higher frequencies (HF‐motion; > 0.1 Hz). Thus, these health variables are thought to be linked to the artifactual inflation of head motion estimates, introducing additional challenges to appropriate modeling and preprocessing procedures that reduce noise. Whether this artifactual relationship extends to changes in body size, midline abdominal fat (i.e., body composition), CRF measured with gold‐standard methods, and respiratory rate measured during scanning remains unclear. The Brain EXTEND Trial acquired multiple health variables before and after an exercise intervention that changed CRF, offering a means to test the relationship between these health variables and HF‐motion in a longitudinal design. Subjects (55–80 years of age) completed a 6‐month chronic exercise intervention at two intensities. We analyzed baseline and longitudinal (EXTEND, baseline n = 122; longitudinal n = 84) relationships between HF‐motion and health variables that included body mass index (BMI), waist circumference (WC), CRF measured with a maximal exercise test (VO2max), and resting respiratory rate. HF‐motion was examined in each head‐motion parameter as the proportion of HF‐motion above > 0.1 Hz—the typical upper bound of fMRI signals at rest. At baseline, HF‐motion for all translational axes was positively associated with body size (BMI, WC) while CRF was negatively associated with only the roll y‐rotation. Additionally, longitudinally, decreased BMI related to reduced HF‐motion in the z‐translation. Results indicate health changes related to body size associate with the presentation of high‐frequency MRI head‐motion artifacts. We caution researchers against ignoring the presence of HF‐motion in their analyses as their use of contaminated motion traces will negatively affect their modeling of fMRI measures. Results have implications for a wide range of investigations in health neuroscience involving body size, respiratory function, and cardiorespiratory fitness.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

grattonlab/gratton2020_ni_hfmotion

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 4b326453a4aaadcbb6943633a714d88cf23be43d, 24 April 2020
Languages: MATLAB (10)
Size: 12 files, 10 scripts
Software Heritage: not archived
Found in: the text, “MRI Acquisition and Head‐Motion Processing”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (5 files), Signal Processing Toolbox (2 files), export_fig (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
12 files
At the source: github.com/GrattonLab/Gratton2020_NI_HFmotion/blob/master/mot_FFT.m

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • 1 match 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

Datasets cited

Data Availability Statement

The data that support the findings of this study are openly available in Open Science Framework at https://osf.io/zvpc3/overview.

Reproduced under the paper's license (CC BY-NC), 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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 5 keywords, 13 MeSH terms, 4 funders, 41 references.

Cite

This paper

Pipoly, M., Chappell, H., DuBose, L. E., Madero, B., Rivera‐Dompenciel, A., Sodoma, M., Oehler, C., Daniels, W., Baller, K., Springer, J., Armstrong, M., Nuckols, V., Magnotta, V., Long, J., Pierce, G. L., & Voss, M. W. (2026). Comparison of the Relationships Between Body Size and Cardiorespiratory Fitness With High Frequency Head-Motion Contamination in fMRI. Human brain mapping, 47(11), e70586. https://doi.org/10.1002/hbm.70586

BibTeX

@article{pipoly2026comparison,
author = {Pipoly, Marco and Chappell, Hayley and DuBose, Lyndsey E and Madero, Bryan and Rivera‐Dompenciel, Adriana and Sodoma, Matthew and Oehler, Chris and Daniels, Will and Baller, Kelsey and Springer, Jenna and Armstrong, Matthew and Nuckols, Virginia and Magnotta, Vincent and Long, Jeffrey and Pierce, Gary L and Voss, Michelle W},
title = {{Comparison of the Relationships Between Body Size and Cardiorespiratory Fitness With High Frequency Head-Motion Contamination in fMRI}},
journal = {Human brain mapping},
year = {2026},
month = aug,
volume = {47},
number = {11},
pages = {e70586},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70586},
url = {https://doi.org/10.1002/hbm.70586},
pmid = {42578518},
pmcid = {PMC13459195}
}

RIS

TY - JOUR
AU - Pipoly, Marco
AU - Chappell, Hayley
AU - DuBose, Lyndsey E
AU - Madero, Bryan
AU - Rivera‐Dompenciel, Adriana
AU - Sodoma, Matthew
AU - Oehler, Chris
AU - Daniels, Will
AU - Baller, Kelsey
AU - Springer, Jenna
AU - Armstrong, Matthew
AU - Nuckols, Virginia
AU - Magnotta, Vincent
AU - Long, Jeffrey
AU - Pierce, Gary L
AU - Voss, Michelle W
TI - Comparison of the Relationships Between Body Size and Cardiorespiratory Fitness With High Frequency Head-Motion Contamination in fMRI
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/08/01
VL - 47
IS - 11
SP - e70586
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70586
UR - https://doi.org/10.1002/hbm.70586
LA - en
ER -

CSL-JSON

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