Correction of Physiological Artifacts in Multi-Echo fMRI Data-Evaluation of Possible RETROICOR Implementations
Jazyk angličtina Země Spojené státy americké Médium print
Typ dokumentu časopisecké články
Grantová podpora
LM2023050
Ministerstvo Školství, Mládeže a Tělovýchovy
23-06957S
Grantová Agentura České Republiky
PubMed
40548466
PubMed Central
PMC12183604
DOI
10.1002/hbm.70264
Knihovny.cz E-zdroje
- Klíčová slova
- RETROICOR, denoising, fMRI, multi‐echo,
- MeSH
- artefakty * MeSH
- dospělí MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- mapování mozku * metody MeSH
- mladý dospělý MeSH
- mozek * diagnostické zobrazování fyziologie MeSH
- počítačové zpracování obrazu * metody MeSH
- poměr signál - šum MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
The study evaluates the efficacy of RETROICOR (Retrospective Image Correction) in mitigating physiological artifacts within multi-echo (ME) fMRI data. Two RETROICOR implementations were compared: applying corrections to individual echoes (RTC_ind) versus composite multi-echo data (RTC_comp). Data from 50 healthy participants were collected using diverse acquisition parameters, including multiband acceleration factors and varying flip angles, on a Siemens Prisma 3T scanner. Key metrics such as temporal signal-to-noise ratio (tSNR), signal fluctuation sensitivity (SFS), and variance of residuals demonstrated improved data quality in both RETROICOR models, particularly in moderately accelerated runs (multiband factors 4 and 6) with lower flip angles (45°). Differences between RTC_ind and RTC_comp were minimal, suggesting both methods are viable for practical applications. While the highest acceleration (multiband factor 8) degraded data quality, RETROICOR's compatibility with faster acquisition sequences was confirmed. These findings underscore the importance of optimizing acquisition parameters and noise correction techniques for reliable fMRI investigations.
1st Department of Neurology Faculty of Medicine Masaryk University Brno Czech Republic
CEITEC Central European Institute of Technology Masaryk University Brno Czech Republic
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Barth, M. , Breuer F., Koopmans P. J., Norris D. G., and Poser B. A.. 2016. “Simultaneous Multislice (SMS) Imaging Techniques.” Magnetic Resonance in Medicine 75, no. 1: 63–81. 10.1002/mrm.25897. PubMed DOI PMC
Barton, M. , Marecek R., Rektor I., Filip P., Janousova E., and Mikl M.. 2015. “Sensitivity of PPI Analysis to Differences in Noise Reduction Strategies.” Journal of Neuroscience Methods 253: 218–232. 10.1016/j.jneumeth.2015.06.021. PubMed DOI
Bartoň, M. , Mareček R., Krajčovičová L., et al. 2019. “Evaluation of Different Cerebrospinal Fluid and White Matter FMRI Filtering Strategies—Quantifying Noise Removal and Neural Signal Preservation.” Human Brain Mapping 40, no. 4: 1114–1138. 10.1002/hbm.24433. PubMed DOI PMC
Birn, R. M. 2012. “The Role of Physiological Noise in Resting‐State Functional Connectivity.” NeuroImage 62, no. 2: 864–870. 10.1016/j.neuroimage.2012.01.016. PubMed DOI
Birn, R. M. , Diamond J. B., Smith M. A., and Bandettini P. A.. 2006. “Separating Respiratory‐Variation‐Related Fluctuations From Neuronal‐Activity‐Related Fluctuations in FMRI.” NeuroImage 31, no. 4: 1536–1548. 10.1016/j.neuroimage.2006.02.048. PubMed DOI
Biswal, B. , Zerrin Yetkin F., Haughton V. M., and Hyde J. S.. 1995. “Functional Connectivity in the Motor Cortex of Resting Human Brain Using Echo‐Planar Mri.” Magnetic Resonance in Medicine 34, no. 4: 537–541. 10.1002/mrm.1910340409. PubMed DOI
Bright, M. G. , and Murphy K.. 2015. “Is FMRI ‘Noise’ Really Noise? Resting State Nuisance Regressors Remove Variance With Network Structure.” NeuroImage 114: 158–169. 10.1016/j.neuroimage.2015.03.070. PubMed DOI PMC
DeDora, D. J. , Nedic S., Katti P., et al. 2016. “Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting‐State FMRI Networks.” Frontiers in Neuroscience 10, no. MAY: 1–15. 10.3389/fnins.2016.00180. PubMed DOI PMC
Gajdoš, M. , Mikl M., and Mareček R.. 2016. “Mask_explorer: A Tool for Exploring Brain Masks in FMRI Group Analysis.” Computer Methods and Programs in Biomedicine 134, no. October: 155–163. 10.1016/J.CMPB.2016.07.015. PubMed DOI
Glover, G. H. 2011. “Overview of Functional Magnetic Resonance Imaging.” Neurosurgery Clinics of North America 22, no. 2: 133–139. 10.1016/j.nec.2010.11.001. PubMed DOI PMC
Glover, G. H. , Li T.‐Q., and Ress D.. 2000. “Image‐Based Method for Retrospective Correction of Physiological Motion Effects in FMRI: RETROICOR.” Magnetic Resonance in Medicine 44: 162–167. 10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E. PubMed DOI
Gonzalez‐Castillo, J. , Roopchansingh V., Bandettini P. A., and Bodurka J.. 2011. “Physiological Noise Effects on the Flip Angle Selection in BOLD FMRI.” NeuroImage 54, no. 4: 2764–2778. 10.1016/j.neuroimage.2010.11.020. PubMed DOI PMC
Gonzalez‐Castillo, J. , Panwar P., Buchanan L. C., et al. 2016. “Evaluation of Multi‐Echo ICA Denoising for Task Based FMRI Studies: Block Designs, Rapid Event‐Related Designs, and Cardiac‐Gated FMRI.” NeuroImage 141: 452–468. 10.1016/j.neuroimage.2016.07.049. PubMed DOI PMC
Jezzard, P. , and Clare S.. 1999. “Sources of Distortion in Functional MRI Data.” Human Brain Mapping 8, no. 2–3: 80–85. 10.1002/(SICI)1097-0193(1999)8:2/3<80::AID-HBM2>3.0.CO;2-C. PubMed DOI PMC
Kovářová, A. , Gajdoš M., Rektor I., and Mikl M.. 2022. “Contribution of the Multi‐Echo Approach in Accelerated Functional Magnetic Resonance Imaging Multiband Acquisition.” Human Brain Mapping 43, no. 3: 955–973. 10.1002/hbm.25698. PubMed DOI PMC
Krüger, G. , and Glover G. H.. 2001. “Physiological Noise in Oxygenation‐Sensitive Magnetic Resonance Imaging.” Magnetic Resonance in Medicine 46, no. 4: 631–637. 10.1002/mrm.1240. PubMed DOI
Kruggel, F. , Pélégrini‐Issac M., and Benali H.. 2002. “Estimating the Effective Degrees of Freedom in Univariate Multiple Regression Analysis.” Medical Image Analysis 6, no. 1: 63–75. 10.1016/S1361-8415(01)00052-4. PubMed DOI
Kundu, P. , Inati S. J., Evans J. W., Luh W. M., and Bandettini P. A.. 2012. “Differentiating BOLD and Non‐BOLD Signals in FMRI Time Series Using Multi‐Echo EPI.” NeuroImage 60, no. 3: 1759–1770. 10.1016/j.neuroimage.2011.12.028. PubMed DOI PMC
Kundu, P. , Voon V., Balchandani P., Lombardo M. V., Poser B. A., and Bandettini P. A.. 2017. “Multi‐Echo FMRI: A Review of Applications in FMRI Denoising and Analysis of BOLD Signals.” NeuroImage 154: 59–80. 10.1016/j.neuroimage.2017.03.033. PubMed DOI
Logothetis, N. K. 2008. “What we Can Do and What we Cannot Do With FMRI.” Nature 453, no. 7197: 869–878. 10.1038/nature06976. PubMed DOI
Luo, W. L. , and Nichols T. E.. 2003. “Diagnosis and Exploration of Massively Univariate Neuroimaging Models.” NeuroImage 19, no. 3: 1014–1032. 10.1016/S1053-8119(03)00149-6. PubMed DOI
Marcus, D. S. , Harms M. P., Snyder A. Z., et al. 2013. “Human Connectome Project Informatics: Quality Control, Database Services, and Data Visualization.” NeuroImage 80: 202–219. 10.1016/j.neuroimage.2013.05.077. PubMed DOI PMC
McDowell, A. R. , and Carmichael D. W.. 2019. “Optimal Repetition Time Reduction for Single Subject Event‐Related Functional Magnetic Resonance Imaging.” Magnetic Resonance in Medicine 81, no. 3: 1890–1897. 10.1002/mrm.27498. PubMed DOI PMC
Poser, B. A. , Versluis M. J., Hoogduin J. M., and Norris D. G.. 2006. “BOLD Contrast Sensitivity Enhancement and Artifact Reduction With Multiecho EPI: Parallel‐Acquired Inhomogeneity‐Desensitized FMRI.” Magnetic Resonance in Medicine 55, no. 6: 1227–1235. 10.1002/mrm.20900. PubMed DOI
Posse, S. , Wiese S., Gembris D., et al. 1999. “Enhancement of BOLD‐Contrast Sensitivity by Single‐Shot Multi‐Echo Functional MR Imaging.” Magnetic Resonance in Medicine 42, no. 1: 87–97. 10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O. PubMed DOI
Power, J. D. , Barnes K. A., Snyder A. Z., Schlaggar B. L., and Petersen S. E.. 2012. “Spurious but Systematic Correlations in Functional Connectivity MRI Networks Arise From Subject Motion.” NeuroImage 59, no. 3: 2142–2154. 10.1016/J.NEUROIMAGE.2011.10.018. PubMed DOI PMC
Power, J. D. , Plitt M., Gotts S. J., et al. 2018. “Ridding FMRI Data of Motion‐Related Influences: Removal of Signals With Distinct Spatial and Physical Bases in Multiecho Data.” Proceedings of the National Academy of Sciences of the United States of America 115, no. 9: E2105–E2114. 10.1073/pnas.1720985115. PubMed DOI PMC
Shirer, W. R. , Jiang H., Price C. M., Ng B., and Greicius M. D.. 2015. “Optimization of Rs‐FMRI Pre‐Processing for Enhanced Signal‐Noise Separation, Test‐Retest Reliability, and Group Discrimination.” NeuroImage 117: 67–79. 10.1016/j.neuroimage.2015.05.015. PubMed DOI
Smith, S. M. , Beckmann C. F., Andersson J., et al. 2013. “Resting‐State FMRI in the Human Connectome Project.” NeuroImage 80: 144–168. 10.1016/j.neuroimage.2013.05.039. PubMed DOI PMC
Triantafyllou, C. , Wald L. L., and Hoge R. D.. 2011. “Echo‐Time and Field Strength Dependence of BOLD Reactivity in Veins and Parenchyma Using Flow‐Normalized Hypercapnic Manipulation.” PLoS One 6, no. 9: e24519. 10.1371/journal.pone.0024519. PubMed DOI PMC
Tzourio‐Mazoyer, N. , Landeau B., Papathanassiou D., et al. 2002. “Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single‐Subject Brain.” NeuroImage 15, no. 1: 273–289. 10.1006/nimg.2001.0978. PubMed DOI