Skip to main content
SpringerLink
Log in

Fusing Concurrent EEG and fMRI Intrinsic Networks

  • Chapter
  • First Online:
Magnetoencephalography

Abstract

Different imaging modalities are sensitive to different aspects of brain activity, and integrating information from multiple modalities can provide an improved picture of brain dynamics. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are often integrated since they make up for each other’s limitations. FMRI can reveal localized intrinsic networks whose BOLD signals have periods from 100 s to about 10 s. EEG recordings, in contrast, reflect cortical electrical fluctuations with periods up to 20 ms or higher. The following chapter surveys the physiological differences between EEG and fMRI recordings and the implications and results of their integration. EEG-fMRI findings are reviewed in cases where individuals do not participate in an explicit task (e.g. during “rest”). The results are discussed in the context of different methodological approaches to EEG-fMRI integration, including correlation and GLM-based analysis, and ICA decomposition of group EEG-fMRI datasets. The resulting EEG-fMRI networks capture a broader range of brain dynamics compared to EEG or fMRI alone, and can serve as a reference for studies integrating MEG and fMRI.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Note that the voltage at a single electrode reflects the difference in potential between that electrode and a reference electrode. The electrode is commonly re-referenced to the average of all electrodes.

  2. 2.

    The term “BOLD” is not technically accurate since the response depends upon deoxygenated hemoglobin.

  3. 3.

    “Sources” here refers to the independent sources estimated through ICA. These sources are different from the cortical “equivalent dipole sources” thought to generate EEG.

References

  • Aguirre GK, Zarahn E, D’Esposito M (1998) The variability of human, BOLD hemodynamic responses. NeuroImage 8:360–369

    Article  Google Scholar 

  • Ahlfors SP, Simpson GV (2004) Geometrical interpretation of fMRI-guided MEG/EEG inverse estimates. NeuroImage 22(1):323–332

    Article  Google Scholar 

  • Attwell D, Laughlin SB (2001) An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab 21(10):1133–1145

    Article  Google Scholar 

  • Beckmann CF, Smith SM (2005) Tensorial extensions of independent component analysis for multisubject FMRI analysis. NeuroImage 25(1):294–311

    Article  Google Scholar 

  • Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159

    Article  Google Scholar 

  • Berger H (1929) Uber das elektrenkephalogramm des menschen. Eur Arch Psychiatry Clin Neurosci 87:527–570

    Google Scholar 

  • Bollimunta A, Mo J, Schroeder CE, Ding M (2011) Neuronal mechanisms and attentional modulation of corticothalamic alpha oscillations. J Neurosci 31(13):4935–4943. doi:10.1523/JNEUROSCI.5580-10.2011

    Article  Google Scholar 

  • Bridwell DA, Wu L, Eichele T, Calhoun VD (2013) The spatiospectral characterization of brain networks: fusing concurrent EEG spectra and fMRI maps. NeuroImage 1(69):101–111

    Article  Google Scholar 

  • Buxton RB (2010) Interpreting oxygenation-based neuroimaging signals: the importance and the challenge of understanding brain oxygen metabolism. Front Neuroenergetics 2:1–15

    Google Scholar 

  • Buxton RB, Uludag K, Dubowitz DJ, Liu TT (2004) Modeling the hemodynamic response to brain activation. NeuroImage 23(Suppl 1):S220–S233

    Article  Google Scholar 

  • Calhoun V, Adali T (2012) Multi-subject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev Biomed Eng 5:60–72

    Article  Google Scholar 

  • Calhoun VD, Adali T, Pearlson GD, Kiehl KA (2006) Neuronal chronometry of target detection: fusion of hemodynamic and event-related potential data. NeuroImage 30(2):544–553

    Article  Google Scholar 

  • Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14(3):140–151

    Article  Google Scholar 

  • Calhoun VD, Liu J, Adalı T (2009) A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage 45(1 Suppl):S163

    Article  Google Scholar 

  • Calhoun VD, Pekar JJ, McGinty VB, Adali T, Watson TD, Pearlson GD (2002) Different activation dynamics in multiple neural systems during simulated driving. Hum Brain Mapp 16(3):158–167

    Article  Google Scholar 

  • Carter AR, Astafiev SV, Lang CE, Connor LT, Rengachary J, Strube MJ, Corbetta M (2010) Resting inter-hemispheric fMRI connectivity predicts performance after stroke. Ann Neurol 67(3):365–375

    Google Scholar 

  • Chauveau N, Franceries X, Doyon B, Rigaud B, Morucci JP, Celsis P (2004) Effects of skull thickness, anisotropy, and inhomogeneity on forward EEG/ERP computations using a spherical three-dimensional resistor mesh model. Hum Brain Mapp 21(2):86–97

    Article  Google Scholar 

  • Cohen D, Cuffin BN (1983) Demonstration of useful differences between magnetoencephalogram and electroencephalogram. Electroencephalogr Clin Neurophysiol 56(1):38–51

    Article  Google Scholar 

  • Corbetta M, Patel G, Shulman GL (2008) The reorienting system of the human brain: from environment to theory of mind. Neuron 58(3):306–324. doi:10.1016/j.neuron.2008.04.017

    Article  Google Scholar 

  • Cuffin BN (1993) Effects of local variations in skull and scalp thickness on EEG’s and MEG’s. IEEE Trans Biomed Eng 40(1):42–48

    Article  Google Scholar 

  • de Munck JC, Goncalves SI, Huijboom L, Kuijer JPA, Pouwels PJW, Heethaar RM, da Lopes Silva FH (2007) The hemodynamic response of the alpha rhythm: an EEG/fMRI study. NeuroImage 35(3):1142–1151

    Article  Google Scholar 

  • de Munck JC, Goncalves SI, Mammoliti R, Heethaar RM, da Lopes Silva FH (2009) Interactions between different EEG frequency bands and their effect on alpha-fMRI correlations. NeuroImage 47(1):69–76

    Article  Google Scholar 

  • Debener S, Ullsperger M, Siegel M, Engel AK (2006) Single-trial EEG-fMRI reveals the dynamics of cognitive function. Trends Cogn Sci 10(12):558–563

    Article  Google Scholar 

  • Deco G, Jirsa VK, McIntosh AR (2011) Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 12(1):43–56. doi:10.1038/nrn2961

    Article  Google Scholar 

  • Edelman GM, Tononi G (2000) A universe of consciousness. Basic Books, New York

    Google Scholar 

  • Eichele T, Calhoun VD, Debener S (2009) Mining EEG–fMRI using independent component analysis. Int J Psychophysiol 73(1):53–61. doi:10.1016/j.ijpsycho.2008.12.018

    Article  Google Scholar 

  • Eichele T, Rachakonda S, Brakedal B, Eikeland R, Calhoun VD (2011) EEGIFT: group independent component analysis for event-related EEG data. Comput Intell Neurosci 2011:9

    Article  Google Scholar 

  • Ekstrom A (2010) How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Res Rev 62(2):233–244

    Article  Google Scholar 

  • Erhardt EB, Rachakonda S, Bedrick EJ, Allen EA, Adali T, Calhoun VD (2011) Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp 32(12):2075–2095. doi:10.1002/hbm.21170

    Article  Google Scholar 

  • Esposito F, Scarabino T, Hyvarinen A, Himberg J, Formisano E, Comani S, Di Salle F (2005) Independent component analysis of fMRI group studies by self-organizing clustering. NeuroImage 25(1):193–205

    Article  Google Scholar 

  • Goense JBM, Logothetis NK (2008) Neurophysiology of the BOLD fMRI signal in awake monkeys. Curr Biol 18(9):631–640

    Article  Google Scholar 

  • Goldman RI, Stern JM, Engel J, Cohen MS (2002) Simultaneous EEG and fMRI of the alpha rhythm. NeuroReport 13(18):2487–2492. doi:10.1097/00001756-200212200-00022

    Article  Google Scholar 

  • Guo Y, Pagnoni G (2008) A unified framework for group independent component analysis for multi-subject fMRI data. NeuroImage 42(3):1078–1093

    Article  Google Scholar 

  • Handwerker DA, Ollinger JM, D’Esposito M (2004) Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. NeuroImage 21(4):1639–1651

    Article  Google Scholar 

  • Heeger DJ, Ress D (2002) What does fMRI tell us about neuronal activity? Nat Rev Neurosci 3:142–150

    Article  Google Scholar 

  • Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New York

    Book  Google Scholar 

  • Klimesch W, Sauseng P, Hanslmayr S (2007) EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res Rev 53(1):63–88. doi:10.1016/j.brainresrev.2006.06.003

    Article  Google Scholar 

  • Lamme VAF, Roelfsema PR (2000) The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci 23(11):571–579

    Article  Google Scholar 

  • Laufs H, Krakow K, Sterzer P, Eger E, Beyerle A, Salek-Haddadi A, Kleinschmidt A (2003) Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. Proc Natl Acad Sci USA 100(19):11053

    Article  Google Scholar 

  • Lin FH, Belliveau JW, Dale AM, Hämäläinen MS (2005) Distributed current estimates using cortical orientation constraints. Hum Brain Mapp 27(1):1–13

    Article  Google Scholar 

  • Logothetis NK (2008) What we can do and what we cannot do with fMRI. Nature 453(7197):869–878

    Article  Google Scholar 

  • Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843):150–157

    Article  Google Scholar 

  • Luo Q, Glover GH (2012) Influence of dense-array EEG cap on FMRI signal. Magn Reson Med 68(3):807–815

    Article  Google Scholar 

  • Makeig S, Debener S, Onton J, Delorme A (2004) Mining event-related brain dynamics. Trends Cogn Sci 8(5):204–210

    Article  Google Scholar 

  • Malonek D, Grinvald A (1996) Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science 272(5261):551–554

    Article  Google Scholar 

  • Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci 104(32):13170

    Article  Google Scholar 

  • McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, Sejnowski TJ (1998) Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp 6:160–629

    Article  Google Scholar 

  • Mizuhara H, Wang L-Q, Kobayashi K, Yamaguchi Y (2004) A long-range cortical network emerging with theta oscillation in a mental task. NeuroReport 15(8):1233–1238. doi:10.1097/01.wnr.0000126755.09715.b3

    Article  Google Scholar 

  • Mo J, Schroeder CE, Ding M (2011) Attentional modulation of alpha oscillations in macaque inferotemporal cortex. J Neurosci 31(3):878–882. doi:10.1523/JNEUROSCI.5295-10.2011

    Article  Google Scholar 

  • Moosmann M, Ritter P, Krastel I, Brink A, Thees S, Blankenburg F, Villringer A (2003) Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. NeuroImage 20(1):145–158. doi:10.1016/S1053-8119(03)00344-6

    Article  Google Scholar 

  • Niedermeyer E (1997) Alpha rhythms as physiological and abnormal phenomena. Int J Psychophysiol 26(1–3):31–49

    Article  Google Scholar 

  • Nunez PL (2000) Toward a quantitative description of large-scale neocortical dynamic function and EEG. Behav Brain Sci 23(3):371–398

    Article  Google Scholar 

  • Nunez P, Srinivasan R (2006) Electric fields of the brain: the neurophysics of EEG, 2nd edn. Oxford University Press, New York

    Book  Google Scholar 

  • Nunez P, Wingeier BM, Silbertein RB (2001) Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks. Hum Brain Mapp 13(3):125–164

    Article  Google Scholar 

  • O’Reilly JX, Woolrich MW, Behrens TEJ, Smith SM, Johansen-Berg H (2012) Tools of the trade: psychophysiological interactions and functional connectivity. Soc Cogn Affect Neurosci 7(5):604–609

    Article  Google Scholar 

  • Plis SM, Calhoun VD, Weisend MP, Eichele T, Lane T (2010) MEG and fMRI Fusion for non-linear estimation of neural and BOLD signal changes. Front Neuroinform 4:1–17. doi:10.3389/fninf.2010.00114

    Article  Google Scholar 

  • Porcaro C, Ostwald D, Bagshaw AP (2010) Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent EEG-fMRI. NeuroImage 1:112–123

    Article  Google Scholar 

  • Porcaro C, Ostwald D, Hadjipapas A, Barnes GR, Bagshaw AP (2011) The relationship between the visual evoked potential and the gamma band investigated by blind and semi-blind methods. NeuroImage 56(3):1059–1071

    Article  Google Scholar 

  • Ritter P, Villringer A (2006) Simultaneous EEG-fMRI. Neurosci Biobehav Rev 30:823–838

    Article  Google Scholar 

  • Sadaghiani S, Scheeringa R, Lehongre K, Morillon B, Giraud A-L, Kleinschmidt A (2010) Intrinsic connectivity networks, alpha oscillations, and tonic alertness: a simultaneous electroencephalography/functional magnetic resonance imaging study. J Neurosci 30(30):10243–10250. doi:10.1523/JNEUROSCI.1004-10.2010

    Article  Google Scholar 

  • Sammer G, Blecker C, Gebhardt H, Bischoff M, Stark R, Morgen K, Vaitl D (2007) Relationship between regional hemodynamic activity and simultaneously recorded EEG-theta associated with mental arithmetic-induced workload. Hum Brain Mapp 28(8):793–803. doi:10.1002/hbm.20309

    Article  Google Scholar 

  • Scheeringa R, Bastiaansen M, Petersson KM, Oostenveld R, Norris DG, Hagoort P (2008) Frontal theta EEG activity correlates negatively with the default mode network in resting state. Int J Psychophysiol 67(3):242–251

    Article  Google Scholar 

  • Scheeringa R, Petersson KM, Kleinschmidt A, Jensen O, Bastiaansen MCM (2012) EEG alpha power modulation of fMRI resting state connectivity. Brain Connectivity 2:254–264

    Article  Google Scholar 

  • Scheeringa R, Fries P, Petersson K-M, Oostenveld R, Grothe I, Norris DG, Bastiaansen MCM (2011) Neuronal dynamics underlying high- and low-frequency EEG oscillations contribute independently to the human BOLD signal. Neuron 69(3):572–583. doi:10.1016/j.neuron.2010.11.044

    Article  Google Scholar 

  • Schmithorst VJ, Holland SK (2004) Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data. J Magn Reson Imaging 19(3):365–368

    Google Scholar 

  • Siegel M, Donner TH, Engel AK (2012) Spectral fingerprints of large-scale neuronal interactions. Nat Rev Neurosci. doi:10.1038/nrn3137

    Google Scholar 

  • Srinivasan R (2005) High-resolution EEG: theory and practice. In: Handy T (ed) Event-related potentials: a methods handbook. The MIT Press, Cambridge

    Google Scholar 

  • Srinivasan R, Nunez PL, Tucker DM, Silberstein RB, Cadusch PJ (1996) Spatial sampling and filtering of EEG with spline Laplacians to estimate cortical potentials. Brain Topogr 8(4):355–366

    Article  Google Scholar 

  • Steffener J, Tabert M, Reuben A, Stern Y (2010) Investigating hemodynamic response variability at the group level using basis functions. NeuroImage 49(3):2113–2122. doi:10.1016/j.neuroimage.2009.11.014

    Article  Google Scholar 

  • Stone JV (2004) Independent component analysis: a tutorial introduction. MIT press, Cambridge

    Google Scholar 

  • Varela F, Lachaux JP, Rodriguez E, Martinerie J (2001) The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2(4):229–239

    Article  Google Scholar 

  • Wu L, Eichele T, Calhoun VD (2010) Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: a concurrent EEG-fMRI study. NeuroImage 52(4):1252–1260

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mind Research Network, Albuquerque, NM, USA

    David Bridwell

  2. Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA

    Vince Calhoun

Authors
  1. David Bridwell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vince Calhoun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Bridwell .

Editor information

Editors and Affiliations

  1. Faculty of Science Dep. of Physics, University of Zagreb, Zagreb, Croatia

    Selma Supek

  2. School of Medicine Department of Radiology, University of New Mexico, Albuquerque, USA

    Cheryl J. Aine

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bridwell, D., Calhoun, V. (2014). Fusing Concurrent EEG and fMRI Intrinsic Networks. In: Supek, S., Aine, C. (eds) Magnetoencephalography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33045-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33045-2_9

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33044-5

  • Online ISBN: 978-3-642-33045-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation