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A Survey of the Sources of Noise in fMRI

Published online by Cambridge University Press:  01 January 2025

Douglas N. Greve ,
Gregory G. Brown ,
Bryon A. Mueller ,
Gary Glover ,
Thomas T. Liu  and
Function Biomedical Research Network
Douglas N. Greve*
Affiliation:
Department of Radiology, Massachusetts General Hospital
Gregory G. Brown
Affiliation:
VA San Diego Healthcare System and Department of Psychiatry, University of California San Diego
Bryon A. Mueller
Affiliation:
Department of Psychiatry, University of Minnesota Twin Cities
Gary Glover
Affiliation:
Department of Radiology, Stanford University
Thomas T. Liu
Affiliation:
Center for Functional MRI, University of California San Diego
*
Requests for reprints should be sent to Douglas N. Greve, Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA. E-mail: greve@nmr.mgh.harvard.edu
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Abstract

Functional magnetic resonance imaging (fMRI) is a noninvasive method for measuring brain function by correlating temporal changes in local cerebral blood oxygenation with behavioral measures. fMRI is used to study individuals at single time points, across multiple time points (with or without intervention), as well as to examine the variation of brain function across normal and ill populations. fMRI may be collected at multiple sites and then pooled into a single analysis. This paper describes how fMRI data is analyzed at each of these levels and describes the noise sources introduced at each level.

Keywords

functional MRIblood oxygen level dependentfirst-level analysishigher level analysissources of noise
Type
Original Paper
Information
Psychometrika , Volume 78 , Issue 3 , July 2013 , pp. 396 - 416
Copyright
Copyright © 2012 The Psychometric Society

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References

Aguirre, G.K., Zarahn, E., D’Esposito, M. (1998). The variability of human, BOLD hemodynamic responses. NeuroImage, 8(4), 360369CrossRefGoogle ScholarPubMed
Alsop, D.C., Detre, J.A. (1996). Reduced transit-time sensitivity in noninvasive magnetic resonance imaging of human cerebral blood flow. Journal of Cerebral Blood Flow and Metabolism, 16(6), 12361249CrossRefGoogle ScholarPubMed
Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95113CrossRefGoogle ScholarPubMed
Beckmann, C., Jenkinson, M., Smith, S. (2003). General multi-level linear modelling for group analysis in FMRI. NeuroImage, 20, 1052–1063CrossRefGoogle Scholar
Beckmann, C.F., Smith, S.M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Transactions on Medical Imaging, 23(2), 137152CrossRefGoogle ScholarPubMed
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(4), 15361548CrossRefGoogle ScholarPubMed
Biswal, B., Yetkin, F., Haughton, V., Hyde, J. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar imaging. Magnetic Resonance in Medicine, 34, 537–541CrossRefGoogle Scholar
Brosch, J.R., Talavage, T.M., Ulmer, J.L., Nyenhuis, J.A. (2002). Simulation of human respiration in fMRI with a mechanical model. IEEE Transactions on Biomedical Engineering, 49(7), 700707CrossRefGoogle ScholarPubMed
Brown, G.G., Mathalon, D.H., Stern, H., Ford, J., Mueller, B., Greve, D.N., McCarthy, G., Voyvodic, J., Glover, G., Diaz, M., Yetter, E., Ozyurt, I.B., Jorgensen, K.W., Wible, C.G., Turner, J.A., Thompson, W.K., Potkin, S.G. (2011). Multisite reliability of cognitive BOLD data. NeuroImage, 54(3), 21632175CrossRefGoogle ScholarPubMed
Brown, G.G., Perthen, J.E., Liu, T.T., Buxton, R.B. (2007). A primer on functional magnetic resonance imaging. Neuropsychology Review, 17(2), 107125CrossRefGoogle ScholarPubMed
Bullmore, E., Long, C., Suckling, J., Fadili, J., Calvert, G., Zelaya, F., Carpenter, T.A., Brammer, M. (2001). Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains. Human Brain Mapping, 12(2), 61783.0.CO;2-W>CrossRefGoogle ScholarPubMed
Burock, M.A., Dale, A.M. (2000). Estimation and detection of event-related fMRI signals with temporally correlated noise: a statistically efficient and unbiased approach. Human Brain Mapping, 11, 249–2603.0.CO;2-5>CrossRefGoogle ScholarPubMed
Buxton, R.B. (2009). Introduction to functional magnetic resonance imaging: principles and techniques, (2nd ed.). New York: Cambridge University PressCrossRefGoogle Scholar
Buxton, R.B., Uludag, K., Dubowitz, D.J., Liu, T.T. (2004). Modeling the hemodynamic response to brain activation. NeuroImage, 23(Suppl 1), S220S233CrossRefGoogle ScholarPubMed
Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140151CrossRefGoogle ScholarPubMed
Calhoun, V.D., Stevens, M.C., Pearlson, G.D., Kiehl, K.A. (2004). fMRI analysis with the general linear model: removal of latency-induced amplitude bias by incorporation of hemodynamic derivative terms. NeuroImage, 22(1), 252257CrossRefGoogle ScholarPubMed
Chang, C., Cunningham, J.P., Glover, G.H. (2009). Influence of heart rate on the BOLD signal: the cardiac response function. NeuroImage, 44(3), 857869CrossRefGoogle ScholarPubMed
Chang, C., Glover, G.H. (2009). Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. NeuroImage, 47(4), 14481459CrossRefGoogle ScholarPubMed
Cohen, M.S. (1997). Parametric analysis of fMRI data using linear systems methods. NeuroImage, 6(2), 93103CrossRefGoogle ScholarPubMed
Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18(2), 192205CrossRefGoogle ScholarPubMed
Cox, R., Jesmanowicz, A. (1999). Real-time 3D image registration for functional MRI. Magnetic Resonance Imaging, 42, 1014–1018Google ScholarPubMed
D’Esposito, M., Zarahn, E., Aguirre, G.K., Rypma, B. (1999). The effect of normal aging on the coupling of neural activity to the bold hemodynamic response. NeuroImage, 10(1), 614CrossRefGoogle Scholar
Dale, A.M. (1999). Optimal experimental design for event-related fMRI. Human Brain Mapping, 8, 109–1143.0.CO;2-W>CrossRefGoogle ScholarPubMed
Fischl, B., Dale, A.M., Sereno, M.I., Tootell, R.B.H., Rosen, B.R. (1998). A coordinate system for the cortical surface. NeuroImage, 7(4),CrossRefGoogle Scholar
Fleisher, A.S., Podraza, K.M., Bangen, K.J., Taylor, C., Sherzai, A., Sidhar, K., Liu, T.T., Dale, A.M., Buxton, R.B. (2009). Cerebral perfusion and oxygenation differences in Alzheimer’s disease risk. Neurobiology of Aging, 30(11), 17371748CrossRefGoogle ScholarPubMed
Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 96739678CrossRefGoogle ScholarPubMed
Friedman, L., Glover, G.H. (2006). Report on a multicenter fMRI quality assurance protocol. Journal of Magnetic Resonance Imaging, 23(6), 827839CrossRefGoogle ScholarPubMed
Friedman, L., Stern, H., Brown, G.G., Mathalon, D.H., Turner, J., Glover, G.H., Gollub, R.L., Lauriello, J., Lim, K.O., Cannon, T., Greve, D.N., Bockholt, H.J., Belger, A., Mueller, B., Doty, M.J., He, J., Wells, W., Smyth, P., Pieper, S., Kim, S., Kubicki, M., Vangel, M., Potkin, S.G. (2008). Test-retest and between-site reliability in a multicenter fMRI study. Human Brain Mapping, 29(8), 958972CrossRefGoogle Scholar
Friston, K.J., Fletcher, P., Josephs, O., Holmes, A., Rugg, M.D., Turner, R. (1998). Event-related fMRI: characterizing differential responses. NeuroImage, 7(1), 3040CrossRefGoogle ScholarPubMed
Friston, K.J., Frith, C.D., Turner, R., Frackowiak, R.S. (1995). Characterizing evoked hemodynamics with fMRI. NeuroImage, 2(2), 157165CrossRefGoogle ScholarPubMed
Friston, K.J., Holmes, A.P., Worsley, K.J. (1999). How many subjects constitute a study?. NeuroImage, 10(1), 15CrossRefGoogle ScholarPubMed
Friston, K.J., Jezzard, P., Turner, R. (1994). Analysis of functional MRI time-series. Human Brain Mapping, 1, 153–171CrossRefGoogle Scholar
Friston, K.J., Williams, S., Howard, R., Frackowiak, R.S., Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35(3), 346355CrossRefGoogle ScholarPubMed
Genovese, C.R., Lazar, N.A., Nichols, T.E. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15, 870–878CrossRefGoogle ScholarPubMed
Glover, G.H. (1999). Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage, 9(4), 416429CrossRefGoogle ScholarPubMed
Glover, G.H., Li, T.Q., Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44(1), 1621673.0.CO;2-E>CrossRefGoogle ScholarPubMed
Glover, G.H., Mueller, B.A., Turner, J.A., van Erp, T.G., Liu, T.T., Greve, D.N., Voyvodic, J.T., Rasmussen, J., Brown, G.G., Keator, D.B., Calhoun, V.D., Lee, H.J., Ford, J.M., Mathalon, D.H., Diaz, M., O’Leary, D.S., Gadde, S., Preda, A., Lim, K.O., Wible, C.G., Stern, H.S., Belger, A., McCarthy, G., Ozyurt, B., Potkin, S.G. (2012). Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. Journal of Magnetic Resonance Imaging, 36(1), 3954CrossRefGoogle Scholar
Greve, D.N., Mueller, B.A., Liu, T., Turner, J.A., Voyvodic, J., Yetter, E., Diaz, M., McCarthy, G., Wallace, S., Roach, B.J., Ford, J.M., Mathalon, D.H., Calhoun, V.D., Wible, C.G., Brown, G.G., Potkin, S.G., Glover, G. (2010). A novel method for quantifying scanner instability in fMRI. Magnetic Resonance in Medicine, 65(4), 10531061CrossRefGoogle ScholarPubMed
Handwerker, D.A., Ollinger, J.M., D’Esposito, M. (2004). Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. NeuroImage, 21(4), 16391651CrossRefGoogle ScholarPubMed
Hanson, L. (2008). Is quantum mechanics necessary for understanding magnetic resonance?. Concepts in Magnetic Resonance. Part A, 32A(5), 329340CrossRefGoogle Scholar
Hayasaka, S., Nichols, T.E. (2003). Validating cluster size inference: random field and permutation methods. NeuroImage, 20(4), 23432356CrossRefGoogle ScholarPubMed
Henson, R.N., Price, C.J., Rugg, M.D., Turner, R., Friston, K.J. (2002). Detecting latency differences in event-related BOLD responses: application to words versus nonwords and initial versus repeated face presentations. NeuroImage, 15(1), 8397CrossRefGoogle ScholarPubMed
Hopfinger, J.B., Buchel, C., Holmes, A.P., Friston, K.J. (2000). A study of analysis parameters that influence the sensitivity of event-related fMRI analyses. NeuroImage, 11(4), 326333CrossRefGoogle ScholarPubMed
Huettel, S.A., Song, A.W., McCarthy, G. (2009). Functional magnetic resonance imaging, (2nd ed.). Sunderland: Sinauer AssociatesGoogle Scholar
Huettel, S.A., McCarthy, G. (2000). Evidence for a refractory period in the hemodynamic response to visual stimuli as measured by MRI. NeuroImage, 11(5), 547553CrossRefGoogle ScholarPubMed
Jezzard, P., Balaban, R.S. (1995). Correction for geometric distortion in echo planar images from Bo field variations. Magnetic Resonance in Medicine, 34, 65–73CrossRefGoogle Scholar
Jezzard, P., Matthews, P., Smith, S.M. (2001). Functional MRI: an introduction to methods, Oxford: Oxford University PressGoogle Scholar
Kelly, A.M., Garavan, H. (2005). Human functional neuroimaging of brain changes associated with practice. Cerebral Cortex, 15(8), 10891102CrossRefGoogle ScholarPubMed
Kruger, G., Glover, G.H. (2001). Physiological noise in oxygenation-sensitive magnetic resonance imaging. Magnetic Resonance in Medicine, 46(4), 631637CrossRefGoogle ScholarPubMed
Kumari, V., Gray, J.A., ffytche, D.H., Mitterschiffthaler, M.T., Das, M., Zachariah, E., Vythelingum, G.N., Williams, S.C., Simmons, A., Sharma, T. (2003). Cognitive effects of nicotine in humans: an fMRI study. NeuroImage, 19(3), 10021013CrossRefGoogle ScholarPubMed
Levin, J.M., Ross, M.H., Mendelson, J.H., Kaufman, M.J., Lange, N., Maas, L.C., Mello, N.K., Cohen, B.M., Renshaw, P.F. (1998). Reduction in BOLD fMRI response to primary visual stimulation following alcohol ingestion. Psychiatry Research, 82(3), 135146CrossRefGoogle ScholarPubMed
Liang, Z., Lauterbur, P.C. (2000). Principles of magnetic resonance imaging: a signal processing perspective, Piscataway: IEEE PressGoogle Scholar
Liu, T.T., Glover, G.H., Mueller, B.A., Greve, D.N., & Brown, G.G. (2012). An introduction to normalization and calibration methods in functional MRI. Psychometrika. Google Scholar
Liu, T.T., Behzadi, Y., Restom, K., Uludag, K., Lu, K., Buracas, G.T., Dubowitz, D.J., Buxton, R.B. (2004). Caffeine alters the temporal dynamics of the visual BOLD response. NeuroImage, 23(4), 14021413CrossRefGoogle ScholarPubMed
Liu, T.T., Frank, L.R. (2004). Efficiency, power, and entropy in event-related FMRI with multiple trial types. Part I: theory. NeuroImage, 21(1), 387400CrossRefGoogle ScholarPubMed
Logothetis, N.K., Wandell, B.A. (2004). Interpreting the BOLD signal. Annual Review of Physiology, 66, 735–769CrossRefGoogle ScholarPubMed
Maus, B., Van Breukelen, G.J., Goebel, R., Berger, M.P. (2010). Optimization of blocked designs in fMRI studies. Psychometrika, 56, 1338–1352Google Scholar
Maxim, V., Sendur, L., Fadili, J., Suckling, J., Gould, R., Howard, R., Bullmore, E. (2005). Fractional Gaussian noise, functional MRI and Alzheimer’s disease. NeuroImage, 25(1), 141158CrossRefGoogle ScholarPubMed
McGonigle, D.J., Howseman, A.M., Athwal, B.S., Friston, K.J., Frackowiak, R.S., Holmes, A.P. (2000). Variability in fMRI: an examination of intersession differences. NeuroImage, 11(6 Pt 1), 708–734CrossRefGoogle Scholar
Miezin, F.M., Maccotta, L., Ollinger, J.M., Petersen, S.E., Buckner, R.L. (2000). Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. NeuroImage, 11(6 Pt 1), 735759CrossRefGoogle ScholarPubMed
Monti, M.M. (2011). Statistical analysis of fMRI time-series: a critical review of the GLM approach. Frontiers in Human Neuroscience, 5, 28CrossRefGoogle Scholar
Nishimura, D.G. (1996). Principles of magnetic resonance imaging, Stanford: Standford UniversityGoogle Scholar
Payne, S.J. (2006). A model of the interaction between autoregulation and neural activation in the brain. Mathematical Biosciences, 204(2), 260–281CrossRefGoogle Scholar
Postelnicu, G., Zollei, L., Fischl, B. (2009). Combined volumetric and surface registration. IEEE Transactions on Medical Imaging, 28(4), 508522CrossRefGoogle ScholarPubMed
Seber, G.A.F., Lee, A.J. (2003). Linear regression analysis, (2nd ed.). Hoboken: WileyCrossRefGoogle Scholar
Shen, S., Sterr, A., Szameitat, A. (2005). A template effect study on voxel-based morphometry in statistic parametric mapping. Conference Proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), 3, 3051–3054Google Scholar
Smith, S.M., Beckmann, C.F., Ramnani, N., Woolrich, M.W., Bannister, P.R., Jenkinson, M., Matthews, P.M., McGonigle, D.J. (2005). Variability in fMRI: a re-examination of inter-session differences. Human Brain Mapping, 24(3), 248257CrossRefGoogle ScholarPubMed
Strother, S., La Conte, S., Hansen, L.K., Anderson, J., Zhang, J., Pulapura, S., Rottenberg, D.A. (2004). Optimizing the fMRI data-processing pipeline using prediction and reproducibility performance metrics: I. A preliminary group analysis. NeuroImage, 23, S196S207CrossRefGoogle Scholar
Suckling, J., Ohlssen, D., Andrew, C., Johnson, G., Williams, S.C., Graves, M., Chen, C.H., Spiegelhalter, D., Bullmore, E. (2008). Components of variance in a multicentre functional MRI study and implications for calculation of statistical power. Human Brain Mapping, 29(10), 11111122CrossRefGoogle Scholar
Thesen, S., Heid, O., Mueller, E., Schad, L.R. (2000). Prospective acquisition correction for head motion with image-based tracking for real-time fMRI. Magnetic Resonance in Medicine, 44(3), 4574653.0.CO;2-R>CrossRefGoogle ScholarPubMed
Thomas, R.J., Kwong, K. (2006). Modafinil activates cortical and subcortical sites in the sleep-deprived state. Sleep, 29(11), 14711481CrossRefGoogle ScholarPubMed
Tisdall, M., Hess, A., & van der Kouwe, A. (2010). Prospective motion correction of anatomical brain sequences using EPI navigators. Paper presented at the ISMRM Workshop on Current Concepts of Motion Correction for MRI & MRS. Google Scholar
Triantafyllou, C., Hoge, R.D., Krueger, G., Wiggins, C.J., Potthast, A., Wiggins, G.C., Wald, L.L. (2005). Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. NeuroImage, 26(1), 243250CrossRefGoogle ScholarPubMed
Turner, G.H., Twieg, D.B. (2005). Study of temporal stationarity and spatial consistency of fMRI noise using independent component analysis. IEEE Transactions on Medical Imaging, 24(6), 712718CrossRefGoogle ScholarPubMed
van der Kouwe, A.J., Benner, T., Fischl, B., Schmitt, F., Salat, D.H., Harder, M., Sorensen, A.G., Dale, A.M. (2005). On-line automatic slice positioning for brain MR imaging. NeuroImage, 27(1), 222230CrossRefGoogle ScholarPubMed
Vincent, J.L., Snyder, A.Z., Fox, M.D., Shannon, B.J., Andrews, J.R., Raichle, M.E., Buckner, R.L. (2006). Coherent spontaneous activity identifies a hippocampal-parietal memory network. Journal of Neurophysiology, 96(6), 35173531CrossRefGoogle ScholarPubMed
Ward, H.A., Riederer, S.J., Grimm, R.C., Ehman, R.L., Felmlee, J.P., Jack, C.R. Jr.. (2000). Prospective multiaxial motion correction for fMRI. Magnetic Resonance in Medicine, 43(3), 4594693.0.CO;2-1>CrossRefGoogle ScholarPubMed
Wise, R.G., Ide, K., Poulin, M.J., Tracey, I. (2004). Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. NeuroImage, 21(4), 16521664CrossRefGoogle ScholarPubMed
Woolrich, M., Ripley, B., Brady, J., Smith, S. (2001). Temporal autocorrelation in univariate linear modelling of FMRI data. NeuroImage, 14(6), 13701386CrossRefGoogle ScholarPubMed
Woolrich, M.W., Behrens, T.E., Smith, S.M. (2004). Constrained linear basis sets for HRF modelling using variational Bayes. NeuroImage, 21(4), 17481761CrossRefGoogle ScholarPubMed
Woolrich, M.W., Jenkinson, M., Brady, J.M., Smith, S.M. (2004). Fully Bayesian spatio-temporal modeling of FMRI data. IEEE Transactions on Medical Imaging, 23(2), 213231CrossRefGoogle ScholarPubMed
Worsley, K.J., Liao, C.H., Aston, J., Pere, V., Dunackn, G.H., Morales, F., Evans, A.C. (2002). A general statistical analysis for fMRI data. NeuroImage, 15, 1–15CrossRefGoogle Scholar
Worsley, K.J., Marrett, S., Neelin, P., Vandal, A.C., Friston, K.J., Evans, A.C. (1996). A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping, 4(1), 58733.0.CO;2-O>CrossRefGoogle ScholarPubMed
Zhou, B., Konstorum, A., Duong, T., Tiue, K.H., Wells, W.M., Grown, G.G., Stern, H.S., & Shanbaba, B. (2012). A hierarchical modeling approach to data analysis and study desgin in a multi-site experimental FMRI study. Psychometrika. Google Scholar