Fusing Concurrent EEG and fMRI Intrinsic Networks

  • David BridwellEmail author
  • Vince Calhoun
Reference work entry


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.


BOLD fMRI EEG ERP Networks Oscillations Intrinsic connectivity Spatiotemporal dynamics Data fusion Source separation 


  1. Aguirre GK, Zarahn E, D’Esposito M (1998) The variability of human, BOLD hemodynamic responses. NeuroImage 8:360–369PubMedCrossRefPubMedCentralGoogle Scholar
  2. Ahlfors SP, Simpson GV (2004) Geometrical interpretation of fMRI-guided MEG/EEG inverse estimates. NeuroImage 22(1):323–332PubMedPubMedCentralCrossRefGoogle Scholar
  3. Attwell D, Laughlin SB (2001) An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab 21(10):1133–1145PubMedCrossRefPubMedCentralGoogle Scholar
  4. Beckmann CF, Smith SM (2005) Tensorial extensions of independent component analysis for multisubject FMRI analysis. NeuroImage 25(1):294–311PubMedCrossRefPubMedCentralGoogle Scholar
  5. Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159PubMedCrossRefPubMedCentralGoogle Scholar
  6. Berger H (1929) Uber das elektrenkephalogramm des menschen. Eur Arch Psychiatry Clin Neurosci 87:527–570Google Scholar
  7. Bola M, Sabel B (2015) Dynamic reorganization of brain functional networks during cognition. NeuroImage 114:398–413. Scholar
  8. Bollimunta A, Mo J, Schroeder CE, Ding M (2011) Neuronal mechanisms and attentional modulation of corticothalamic alpha oscillations. J Neurosci 31(13):4935–4943. Scholar
  9. 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):101–111PubMedCrossRefPubMedCentralGoogle Scholar
  10. Bridwell DA, Rachakonda S, Silva RF, Pearlson GD, Calhoun VD (2018) Spatiospectral decomposition of multi-subject EEG: evaluating blind source separation algorithms on real and realistic simulated data. Brain Topogr 31(1):47–61. Scholar
  11. Buxton RB (2010) Interpreting oxygenation-based neuroimaging signals: the importance and the challenge of understanding brain oxygen metabolism. Front Neuroenerg 2:1–15Google Scholar
  12. Buxton RB, Uludag K, Dubowitz DJ, Liu TT (2004) Modeling the hemodynamic response to brain activation. NeuroImage 23(Suppl 1):S220–S233PubMedCrossRefPubMedCentralGoogle Scholar
  13. 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 5:60–72PubMedPubMedCentralCrossRefGoogle Scholar
  14. 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):140–151CrossRefGoogle Scholar
  15. 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):158–167PubMedCrossRefPubMedCentralGoogle Scholar
  16. 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):544–553PubMedCrossRefPubMedCentralGoogle Scholar
  17. 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):S163PubMedCrossRefPubMedCentralGoogle Scholar
  18. 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):365–375PubMedPubMedCentralGoogle Scholar
  19. 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):86–97PubMedCrossRefPubMedCentralGoogle Scholar
  20. Cohen D, Cuffin BN (1983) Demonstration of useful differences between magnetoencephalogram and electroencephalogram. Electroencephalogr Clin Neurophysiol 56(1):38–51CrossRefPubMedPubMedCentralGoogle Scholar
  21. 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. Scholar
  22. Cuffin BN (1993) Effects of local variations in skull and scalp thickness on EEG’s and MEG’s. IEEE Trans Biomed Eng 40(1):42–48PubMedCrossRefPubMedCentralGoogle Scholar
  23. 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):1142–1151PubMedCrossRefPubMedCentralGoogle Scholar
  24. 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):69–76PubMedCrossRefPubMedCentralGoogle Scholar
  25. Debener S, Ullsperger M, Siegel M, Engel AK (2006) Single-trial EEG-fMRI reveals the dynamics of cognitive function. Trends Cogn Sci 10(12):558–563PubMedCrossRefPubMedCentralGoogle Scholar
  26. 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. Scholar
  27. Edelman GM, Tononi G (2000) A universe of consciousness. Basic Books, New YorkGoogle Scholar
  28. Eichele T, Calhoun VD, Debener S (2009) Mining EEG–fMRI using independent component analysis. Int J Psychophysiol 73(1):53–61. Scholar
  29. Eichele T, Rachakonda S, Brakedal B, Eikeland R, Calhoun VD (2011) EEGIFT: group independent component analysis for event-related EEG data. Comput Intell Neurosci 2011:9CrossRefGoogle Scholar
  30. Ekstrom A (2010) How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Res Rev 62(2):233–244PubMedCrossRefPubMedCentralGoogle Scholar
  31. 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. Scholar
  32. 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):193–205PubMedCrossRefPubMedCentralGoogle Scholar
  33. Gauthier CJ, Fan AP (2018) BOLD signal physiology: models and applications. NeuroImage. Scholar
  34. Goense JBM, Logothetis NK (2008) Neurophysiology of the BOLD fMRI signal in awake monkeys. Curr Biol 18(9):631–640CrossRefGoogle Scholar
  35. Goldman RI, Stern JM, Engel J, Cohen MS (2002) Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport 13(18):2487–2492. Scholar
  36. Guo Y, Pagnoni G (2008) A unified framework for group independent component analysis for multi-subject fMRI data. NeuroImage 42(3):1078–1093PubMedPubMedCentralCrossRefGoogle Scholar
  37. 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):1639–1651PubMedCrossRefPubMedCentralGoogle Scholar
  38. Heeger DJ, Ress D (2002) What does fMRI tell us about neuronal activity? Nat Rev Neurosci 3:142–150PubMedCrossRefPubMedCentralGoogle Scholar
  39. Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New YorkCrossRefGoogle Scholar
  40. Ilhalainen T, Kuusela L, Turunen S, Heikkinen S, Savolainen S, Sililä (2015) Data quality in fMRI and simultaneous EEG-fMRI. Magn Reson Mater Phy 28(1):23–31. doi: Scholar
  41. Klein C, HÄnggi J, Luechinger R, JÄncke (2015) MRI with and without a high-density EEG cap-what makes the difference? NeuroImage 106:189–197. doi: Scholar
  42. Klimesch W, Sauseng P, Hanslmayr S (2007) EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res Rev 53(1):63–88. Scholar
  43. Labounek R, Bridwell DA, Mareček R, Lamoš M, Mikl M, Slavíček T, Bednařík P, Baštinec J, Hluštik P, Brázdil M, Jan J (2018) Stable scalp EEG spatiospectral patterns across paradigms estimated by group ICA. Brain Topogr 31(1):76–89. Scholar
  44. Labounek R, Bridwell DA, Mareček R, Lamoš M, Mikl M, Bednařík P, Baštinec J, Slavíček T, Hluštik P, Brázdil M, Jan J (2019) EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions. J Neurosci Methods 318:34–46. Scholar
  45. Lamme VAF, Roelfsema PR (2000) The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci 23(11):571–579PubMedCrossRefPubMedCentralGoogle Scholar
  46. 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 U S A 100(19):11053PubMedPubMedCentralCrossRefGoogle Scholar
  47. Lin FH, Belliveau JW, Dale AM, Hämäläinen MS (2005) Distributed current estimates using cortical orientation constraints. Hum Brain Mapp 27(1):1–13CrossRefGoogle Scholar
  48. Logothetis NK (2008) What we can do and what we cannot do with fMRI. Nature 453(7197):869–878PubMedCrossRefPubMedCentralGoogle Scholar
  49. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843):150–157CrossRefPubMedPubMedCentralGoogle Scholar
  50. Luo Q, Glover GH (2012) Influence of dense-array EEG cap on FMRI signal. Magn Reson Med 68(3):807–815PubMedCrossRefPubMedCentralGoogle Scholar
  51. Makeig S, Debener S, Onton J, Delorme A (2004) Mining event-related brain dynamics. Trends Cogn Sci 8(5):204–210PubMedCrossRefPubMedCentralGoogle Scholar
  52. Malonek D, Grinvald A (1996) Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science 272(5261):551–554PubMedCrossRefPubMedCentralGoogle Scholar
  53. 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):13170PubMedCrossRefPubMedCentralGoogle Scholar
  54. 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 6:160–629PubMedCrossRefPubMedCentralGoogle Scholar
  55. 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. Scholar
  56. Mo J, Schroeder CE, Ding M (2011) Attentional modulation of alpha oscillations in macaque inferotemporal cortex. J Neurosci 31(3):878–882. Scholar
  57. 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. Scholar
  58. Niedermeyer E (1997) Alpha rhythms as physiological and abnormal phenomena. Int J Psychophysiol 26(1–3):31–49PubMedCrossRefGoogle Scholar
  59. Nunez PL (2000) Toward a quantitative description of large-scale neocortical dynamic function and EEG. Behav Brain Sci 23(3):371–398PubMedCrossRefPubMedCentralGoogle Scholar
  60. Nunez P, Srinivasan R (2006) Electric fields of the brain: the neurophysics of EEG, 2nd edn. Oxford University Press, New YorkCrossRefGoogle Scholar
  61. 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):125–164PubMedCrossRefPubMedCentralGoogle Scholar
  62. 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):604–609PubMedPubMedCentralCrossRefGoogle Scholar
  63. 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. Scholar
  64. 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 1:112–123CrossRefGoogle Scholar
  65. 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):1059–1071PubMedPubMedCentralCrossRefGoogle Scholar
  66. Ritter P, Villringer A (2006) Simultaneous EEG-fMRI. Neurosci Biobehav Rev 30:823–838PubMedCrossRefPubMedCentralGoogle Scholar
  67. 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. Scholar
  68. 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. Scholar
  69. 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):242–251PubMedCrossRefPubMedCentralGoogle Scholar
  70. 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. Scholar
  71. Scheeringa R, Petersson KM, Kleinschmidt A, Jensen O, Bastiaansen MCM (2012) EEG alpha power modulation of fMRI resting state connectivity. Brain Connect 2:254–264PubMedPubMedCentralCrossRefGoogle Scholar
  72. 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):365–368PubMedPubMedCentralCrossRefGoogle Scholar
  73. Siegel M, Donner TH, Engel AK (2012) Spectral fingerprints of large-scale neuronal interactions. Nat Rev Neurosci. Scholar
  74. Srinivasan R (2005) High-resolution EEG: theory and practice. In: Handy T (ed) Event-related potentials: a methods handbook. The MIT Press, CambridgeGoogle Scholar
  75. 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):355–366PubMedCrossRefPubMedCentralGoogle Scholar
  76. 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. Scholar
  77. Stone JV (2004) Independent component analysis: a tutorial introduction. MIT Press, CambridgeCrossRefGoogle Scholar
  78. Varela F, Lachaux JP, Rodriguez E, Martinerie J (2001) The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2(4):229–239CrossRefGoogle Scholar
  79. 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):1252–1260PubMedPubMedCentralCrossRefGoogle Scholar
  80. Yu Q, Wu L, Bridwell DA, Erhardt EB, Du Y, He H, Chen J, Liu P, Sui J, Pearlson D, Calhoun VD (2016) Building an EEG-fMRI multi-modal brain graph: a concurrent EEG-fMRI study. Front Hum Neurosci 10:476. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Mind Research NetworkAlbuquerqueUSA
  2. 2.Electrical and Computer EngineeringUniversity of New MexicoAlbuquerqueUSA

Section editors and affiliations

  • Selma Supek
    • 1
  • Cheryl J. Aine
    • 2
    • 3
  1. 1.Faculty of Science, Department of PhysicsUniversity of ZagrebZagrebCroatia
  2. 2.The Mind Research Network ,AlbuquerqueUSA
  3. 3.School of Medicine Department of RadiologyUniversity of New MexicoAlbuquerqueUSA

Personalised recommendations