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

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Comparison of the Relationships Between Body Size and Cardiorespiratory Fitness With High Frequency Head-Motion Contamination in fMRI.
Συγγραφείς: Pipoly M; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.; Cognitive Control Collaborative, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Interdisciplinary Graduate Program in Neuroscience, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.; Center for Vital Longevity, Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas, USA.; Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Texas, USA., Chappell H; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA., DuBose LE; Department of Health, Sport, and Human Physiology, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Department of Health and Exercise Science, College of Health and Human Sciences, Colorado State University, Fort Collins, Colorado, USA., Madero B; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Rivera-Dompenciel A; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.; Interdisciplinary Graduate Program in Neuroscience, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Sodoma M; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Oehler C; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Daniels W; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Department of Health, Sport, and Human Physiology, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA., Baller K; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Department of Psychological & Brain Sciences, College of Arts & Sciences, Washington University, St. Louis, Missouri, USA., Springer J; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Department of Health, Sport, and Human Physiology, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA., Armstrong M; Department of Health, Sport, and Human Physiology, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Department of Exercise Science, Falk College of Sport, Syracuse University, Syracuse, New York, USA., Nuckols V; Department of Health, Sport, and Human Physiology, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Department of Kinesiology & Applied Physiology, College of Health Sciences, University of Delaware, Newark, Delaware, USA., Magnotta V; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.; Interdisciplinary Graduate Program in Neuroscience, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Long J; Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.; Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, USA., Pierce GL; Department of Health, Sport, and Human Physiology, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA., Voss MW; Department of Psychological and Brain Sciences, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.; Cognitive Control Collaborative, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA.; Interdisciplinary Graduate Program in Neuroscience, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.
Πηγή: Human brain mapping [Hum Brain Mapp] 2026 Aug; Vol. 47 (11), pp. e70586.
Τύπος έκδοσης: Journal Article; Comparative Study
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley Country of Publication: United States NLM ID: 9419065 Publication Model: Print Cited Medium: Internet ISSN: 1097-0193 (Electronic) Linking ISSN: 10659471 NLM ISO Abbreviation: Hum Brain Mapp Subsets: MEDLINE
Imprint Name(s): Publication: New York : Wiley
Original Publication: New York : Wiley-Liss, c1993-
Ιατρικοί όροι (MeSH): Magnetic Resonance Imaging*/standards , Magnetic Resonance Imaging*/methods , Cardiorespiratory Fitness*/physiology , Body Size*/physiology , Brain*/diagnostic imaging , Brain*/physiology , Head Movements*/physiology , Functional Neuroimaging*/standards , Functional Neuroimaging*/methods , Artifacts*, Humans ; Male ; Female ; Aged ; Middle Aged ; Aged, 80 and over
Περίληψη: 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.
(© 2026 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)
References: Baniqued, P. L., C. L. Gallen, M. W. Voss, et al. 2018. “Brain Network Modularity Predicts Exercise‐Related Executive Function Gains in Older Adults.” Frontiers in Aging Neuroscience 9: 426. https://doi.org/10.3389/fnagi.2017.00426.
Borg, G. A. V. 1982. “Psychophysical Bases of Perceived Exertion.” Medicine & Science in Sports & Exercise 14, no. 5: 377–381.
Brier, M. R., J. B. Thomas, A. Z. Snyder, et al. 2012. “Loss of Intranetwork and Internetwork Resting State Functional Connections With Alzheimer's Disease Progression.” Journal of Neuroscience 32, no. 26: 8890–8899. https://doi.org/10.1523/JNEUROSCI.5698‐11.2012.
Ciric, R., A. F. G. Rosen, G. Erus, et al. 2018. “Mitigating Head Motion Artifact in Functional Connectivity MRI.” Nature Protocols 13, no. 12: 2801–2826. https://doi.org/10.1038/s41596‐018‐0065‐y.
Ciric, R., D. H. Wolf, J. D. Power, et al. 2017. “Benchmarking of Participant‐Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity.” NeuroImage 154: 174–187. https://doi.org/10.1016/j.neuroimage.2017.03.020.
Dixon, A. E., and U. Peters. 2018. “The Effect of Obesity on Lung Function.” Expert Review of Respiratory Medicine 12, no. 9: 755–767. https://doi.org/10.1080/17476348.2018.1506331.
Dosenbach, N. U. F., J. M. Koller, E. A. Earl, et al. 2017. “Real‐Time Motion Analytics During Brain MRI Improve Data Quality and Reduce Costs.” NeuroImage 161: 80–93. https://doi.org/10.1016/j.neuroimage.2017.08.025.
DuBose, L. E., T. B. Weng, G. L. Pierce, et al. 2022. “Association Between Cardiorespiratory Fitness and Cerebrovascular Reactivity to a Breath‐Hold Stimulus in Older Adults: Influence of Aerobic Exercise Training.” Journal of Applied Physiology 132, no. 6: 1468–1479. https://doi.org/10.1152/japplphysiol.00599.2021.
Fair, D. A., O. Miranda‐Dominguez, A. Z. Snyder, et al. 2020. “Correction of Respiratory Artifacts in MRI Head Motion Estimates.” NeuroImage 208: 116400. https://doi.org/10.1016/j.neuroimage.2019.116400.
Friston, K. J., S. Williams, R. Howard, R. S. J. Frackowiak, and R. Turner. 1996. “Movement‐Related Effects in fMRI Time‐Series.” Magnetic Resonance in Medicine 35, no. 3: 346–355. https://doi.org/10.1002/mrm.1910350312.
Glasser, M. F., T. S. Coalson, J. D. Bijsterbosch, et al. 2018. “Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data.” NeuroImage 181: 692–717. https://doi.org/10.1016/j.neuroimage.2018.04.076.
Gratton, C., A. Dworetsky, R. S. Coalson, et al. 2020. “Removal of High Frequency Contamination From Motion Estimates in Single‐Band fMRI Saves Data Without Biasing Functional Connectivity.” NeuroImage 217: 116866. https://doi.org/10.1016/j.neuroimage.2020.116866.
Hodgson, K., R. A. Poldrack, J. E. Curran, et al. 2016. “Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index.” Cerebral Cortex 27: 5539–5546. https://doi.org/10.1093/cercor/bhw321.
Intzandt, B., D. Sabra, C. Foster, et al. 2020. “Higher Cardiovascular Fitness Level Is Associated With Lower Cerebrovascular Reactivity and Perfusion in Healthy Older Adults.” Journal of Cerebral Blood Flow & Metabolism 40, no. 7: 1468–1481. https://doi.org/10.1177/0271678X19862873.
Jacobs, E. J., C. C. Newton, Y. Wang, et al. 2010. “Waist Circumference and All‐Cause Mortality in a Large US Cohort.” Archives of Internal Medicine 170, no. 15: 1293. https://doi.org/10.1001/archinternmed.2010.201.
Jayedi, A., S. Soltani, A. Emadi, M.‐S. Zargar, and A. Najafi. 2024. “Aerobic Exercise and Weight Loss in Adults: A Systematic Review and Dose–Response Meta‐Analysis.” JAMA Network Open 7, no. 12: e2452185. https://doi.org/10.1001/jamanetworkopen.2024.52185.
Jenkinson, M., P. Bannister, M. Brady, and S. Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” NeuroImage 17, no. 2: 825–841. https://doi.org/10.1006/nimg.2002.1132.
Jones, M. S., Z. Zhu, A. Bajracharya, A. Luor, and J. E. Peelle. 2022. “A Multi‐Dataset Evaluation of Frame Censoring for Motion Correction in Task‐Based fMRI.” Aperture Neuro 2: 1–25. https://doi.org/10.52294/ApertureNeuro.2022.2.NXOR2026.
Littleton, S. W. 2012. “Impact of Obesity on Respiratory Function.” Respirology 17, no. 1: 43–49. https://doi.org/10.1111/j.1440‐1843.2011.02096.x.
Luce, J. M. 1980. “Respiratory Complications of Obesity.” Chest 78, no. 4: 626–631. https://doi.org/10.1378/chest.78.4.626.
Milani, S. A., M. Marsiske, L. B. Cottler, X. Chen, and C. W. Striley. 2018. “Optimal Cutoffs for the Montreal Cognitive Assessment Vary by Race and Ethnicity.” Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 10, no. 1: 773–781. https://doi.org/10.1016/j.dadm.2018.09.003.
Moore, D., M. Jung, C. H. Hillman, M. Kang, and P. D. Loprinzi. 2022. “Interrelationships Between Exercise, Functional Connectivity, and Cognition Among Healthy Adults: A Systematic Review.” Psychophysiology 59, no. 6: e14014. https://doi.org/10.1111/psyp.14014.
Nasreddine, Z. S., N. A. Phillips, V. Bédirian, et al. 2005. “The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool for Mild Cognitive Impairment.” Journal of the American Geriatrics Society 53, no. 4: 695–699. https://doi.org/10.1111/j.1532‐5415.2005.53221.x.
Pipoly, M., H. K. Lee, E. Hazeltine, and M. W. Voss. 2024. “Educational Attainment Moderates Task‐State Control Network Connectivity Relations to Response Conflict Among Healthy Older Adults.” Journals of Gerontology, Series B: Psychological Sciences and Social Sciences 79: gbae077. https://doi.org/10.1093/geronb/gbae077.
Power, J. D., K. A. Barnes, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen. 2012. “Spurious but Systematic Correlations in Functional Connectivity MRI Networks Arise From Subject Motion.” NeuroImage 59, no. 3: 2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018.
Power, J. D., C. J. Lynch, M. J. Dubin, B. M. Silver, A. Martin, and R. M. Jones. 2020. “Characteristics of Respiratory Measures in Young Adults Scanned at Rest, Including Systematic Changes and “Missed” Deep Breaths.” NeuroImage 204: 116234. https://doi.org/10.1016/j.neuroimage.2019.116234.
Power, J. D., C. J. Lynch, B. M. Silver, M. J. Dubin, A. Martin, and R. M. Jones. 2019. “Distinctions Among Real and Apparent Respiratory Motions in Human fMRI Data.” NeuroImage 201: 116041. https://doi.org/10.1016/j.neuroimage.2019.116041.
Power, J. D., A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar, and S. E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” NeuroImage 84: 320–341. https://doi.org/10.1016/j.neuroimage.2013.08.048.
Power, J. D., M. Plitt, P. Kundu, P. A. Bandettini, and A. Martin. 2017. “Temporal Interpolation Alters Motion in fMRI Scans: Magnitudes and Consequences for Artifact Detection.” PLoS One 12, no. 9: e0182939. https://doi.org/10.1371/journal.pone.0182939.
Rapp, D., J. Scharhag, S. Wagenpfeil, and J. Scholl. 2018. “Reference Values for Peak Oxygen Uptake: Cross‐Sectional Analysis of Cycle Ergometry‐Based Cardiopulmonary Exercise Tests of 10,090 Adult German Volunteers From the Prevention First Registry.” BMJ Open 8, no. 3: e018697. https://doi.org/10.1136/bmjopen‐2017‐018697.
Ross, R., S. N. Blair, R. Arena, et al. 2016. “Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association.” Circulation 134, no. 24: e653–e699. https://doi.org/10.1161/CIR.0000000000000461.
Satterthwaite, T. D., D. H. Wolf, J. Loughead, et al. 2012. “Impact of In‐Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth.” NeuroImage 60, no. 1: 623–632. https://doi.org/10.1016/j.neuroimage.2011.12.063.
Siegel, J. S., J. D. Power, J. W. Dubis, et al. 2014. “Statistical Improvements in Functional Magnetic Resonance Imaging Analyses Produced by Censoring High‐Motion Data Points.” Human Brain Mapping 35, no. 5: 1981–1996. https://doi.org/10.1002/hbm.22307.
Stillman, C. M., A. M. Weinstein, A. L. Marsland, P. J. Gianaros, and K. I. Erickson. 2017. “Body–Brain Connections: The Effects of Obesity and Behavioral Interventions on Neurocognitive Aging.” Frontiers in Aging Neuroscience 9: 115. https://doi.org/10.3389/fnagi.2017.00115.
Tari, A. R., T. L. Walker, A. M. Huuha, S. B. Sando, and U. Wisloff. 2025. “Neuroprotective Mechanisms of Exercise and the Importance of Fitness for Healthy Brain Ageing.” Lancet 405, no. 10484: 1093–1118. https://doi.org/10.1016/S0140‐6736(25)00184‐9.
U.S. Department of Health & Human Services. 2023. “About Adult BMI. U.S. Centers for Disease Control and Prevention.” https://www.cdc.gov/bmi/adult‐calculator/bmi‐categories.html.
Voss, M. W., and S. Jain. 2022. “Getting Fit to Counteract Cognitive Aging: Evidence and Future Directions.” Physiology 37, no. 4: 197–206. https://doi.org/10.1152/physiol.00038.2021.
Voss, M. W., L. S. Nagamatsu, T. Liu‐Ambrose, and A. F. Kramer. 2011. “Exercise, Brain, and Cognition Across the Life Span.” Journal of Applied Physiology 111, no. 5: 1505–1513. https://doi.org/10.1152/japplphysiol.00210.2011.
Voss, M. W., C. Oehler, W. Daniels, et al. 2024. “Exercise Effects on Brain Health and Learning From Minutes to Months: The Brain EXTEND Trial.” Contemporary Clinical Trials 145: 1–9. https://doi.org/10.1016/j.cct.2024.107647.
Voss, M. W., C. Soto, S. Yoo, M. Sodoma, C. Vivar, and H. Van Praag. 2019. “Exercise and Hippocampal Memory Systems.” Trends in Cognitive Sciences 23, no. 4: 318–333. https://doi.org/10.1016/j.tics.2019.01.006.
Wier, L. T., A. S. Jackson, G. W. Ayers, and B. Arenare. 2006. “Nonexercise Models for Estimating V̇O2max With Waist Girth, Percent Fat, or BMI.” Medicine & Science in Sports & Exercise 38, no. 3: 555–561. https://doi.org/10.1249/01.mss.0000193561.64152.
Won, J., K. A. Nielson, and J. C. Smith. 2023. “Large‐Scale Network Connectivity and Cognitive Function Changes After Exercise Training in Older Adults With Intact Cognition and Mild Cognitive Impairment.” Journal of Alzheimer's Disease Reports 7, no. 1: 399–413. https://doi.org/133/ADR‐220062.
Grant Information: S10 OD025025 United States OD NIH HHS; S10OD025025 Office of Research Infrastructure Program Division of Construction and Instruments; R01AG055500 United States AG NIA NIH HHS; R01 AG055500 United States AG NIA NIH HHS; T32 GM108540 United States GM NIGMS NIH HHS; T32GM108540 United States GM NIGMS NIH HHS
Contributed Indexing: Keywords: exercise; health behavior; intervention; neuroimaging; older adults
Entry Date(s): Date Created: 20260811 Date Completed: 20260811 Latest Revision: 20260815
Update Code: 20260815
PubMed Central ID: PMC13459195
DOI: 10.1002/hbm.70586
PMID: 42578518
Βάση Δεδομένων: MEDLINE