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Abstract

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

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2018-06-04
2024-06-25
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Literature Cited

  1. 1.  Herculano-Houzel S 2009. The human brain in numbers: a linearly scaled-up primate brain. Front. Hum. Neurosci. 3:1–11
    [Google Scholar]
  2. 2.  Pakkenberg B, Pelvig D, Marner L, Bundgaard MJ, Gundersen HJG et al. 2003. Aging and the human neocortex. Exp. Gerontol. 38:95–99
    [Google Scholar]
  3. 3.  Nunez PL, Srinivasan R 2006. Electric Fields of the Brain: The Neurophysics of EEG New York: Oxford Univ. Press
    [Google Scholar]
  4. 4.  He B, Ding L 2013. Electrophysiological mapping and neuroimaging. Neural Engineering B He 499–543 Berlin: Springer
    [Google Scholar]
  5. 5.  Plonsey R 1969. Bioelectric Phenomena New York: McGraw-Hill
    [Google Scholar]
  6. 6.  He B, Yang L, Wilke C, Yuan H 2011. Electrophysiological imaging of brain activity and connectivity—challenges and opportunities. Biomed. Eng. IEEE Trans. 58:1918–31
    [Google Scholar]
  7. 7.  Malmivuo J, Plonsey R 1995. Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields New York: Oxford Univ. Press
    [Google Scholar]
  8. 8.  Michel CM, He B 2011. EEG mapping and source imaging. Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields DL Schomer, FH Lopes da Silva 1179–202 Philadelphia: Lippincott Williams & Wilkins. , 6th ed..
    [Google Scholar]
  9. 9.  Lopes da Silva FH, Van Rotterdam AB 2011. Biophysical aspects of EEG and magnetoencephalography generation. Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields DL Schomer, FH Lopes da Silva 91–110 Philadelphia: Lippincott Williams & Wilkins. , 6th ed..
    [Google Scholar]
  10. 10.  Riera JJ, Ogawa T, Goto T, Sumiyoshi A, Nonaka H et al. 2012. Pitfalls in the dipolar model for the neocortical EEG sources. J. Neurophysiol. 108:956–75
    [Google Scholar]
  11. 11.  Jerbi K, Mosher JC, Baillet S, Leahy RM 2002. On MEG forward modelling using multipolar expansions. Phys. Med. Biol. 47:523–55
    [Google Scholar]
  12. 12.  Ding L, Zhang N, Chen W, He B 2009. Three-dimensional imaging of complex neural activation in humans from EEG. IEEE Trans. Biomed. Eng. 56:1980–88
    [Google Scholar]
  13. 13.  He B, Musha T, Okamoto Y, Homma S, Nakajima Y, Sato T 1987. Electric dipole tracing in the brain by means of the boundary element method and its accuracy. IEEE Trans. Biomed. Eng. 6:406–14
    [Google Scholar]
  14. 14.  Hamalainen MS, Sarvas J 1989. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. Biomed. Eng. IEEE Trans. 36:165–71
    [Google Scholar]
  15. 15.  Yan Y, Nunez PL, Hart RT 1991. Finite-element model of the human head: scalp potentials due to dipole sources. Med. Biol. Eng. Comput. 29:475–81
    [Google Scholar]
  16. 16.  von Helmholtz H 1853. Über einige Gesetze der Vertheilung elektrischer Ströme in körperlichen Leitern mit Anwendung auf die thierisch-elektrischen Versuche. Ann. Phys. 165:211–33
    [Google Scholar]
  17. 17.  Hansen PC 1992. Analysis of discrete ill-posed problems by means of the L-curve. SIAM Rev 34:561–80
    [Google Scholar]
  18. 18.  Lantz G, de Peralta RG, Spinelli L, Seeck M, Michel CM 2003. Epileptic source localization with high density EEG: How many electrodes are needed?. Clin. Neurophysiol. 114:63–69
    [Google Scholar]
  19. 19.  Sohrabpour A, Lu Y, Kankirawatana P, Blount J, Kim H, He B 2015. Effect of EEG electrode number on epileptic source localization in pediatric patients. Clin. Neurophysiol. 126:472–80
    [Google Scholar]
  20. 20.  Seeck M, Koessler L, Bast T, Leijten F, Michel C et al. 2017. The standardized EEG electrode array of the IFCN. Clin. Neurophysiol. 128:2070–77
    [Google Scholar]
  21. 21.  Scherg M, Von Cramon D 1985. Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model. Electroencephalogr. Clin. Neurophysiol. 62:32–44
    [Google Scholar]
  22. 76.  Ebersole JS 1991. EEG dipole modeling in complex partial epilepsy. Brain Topogr 4:113–23
    [Google Scholar]
  23. 22.  Dale AM, Sereno MI 1993. Improved localizadon of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cogn. Neurosci. 5:162–76
    [Google Scholar]
  24. 23.  Hämäläinen MS, Ilmoniemi RJ 1994. Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 32:35–42
    [Google Scholar]
  25. 24.  Wang J-Z, Williamson SJ, Kaufman L 1992. Magnetic source images determined by a lead-field analysis: the unique minimum-norm least-squares estimation. IEEE Trans. Biomed. Eng. 39:665–75
    [Google Scholar]
  26. 25.  Lawson CL, Hanson RJ 1995. Solving Least Squares Problems Philadelphia: SIAM
    [Google Scholar]
  27. 26.  Greenblatt RE 1993. Probabilistic reconstruction of multiple sources in the bioelectromagnetic inverse problem. Inverse Probl 9:271
    [Google Scholar]
  28. 27.  Fuchs M, Wischmann HA, Wagner M 1994. Generalized minimum norm least squares reconstruction algorithms. ISBET Newsl 5:8–11
    [Google Scholar]
  29. 28.  Pascual-Marqui RD, Michel CM, Lehmann D 1994. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int. J. Psychophysiol. 18:49–65
    [Google Scholar]
  30. 29.  Pascual-Marqui RD 2002. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol. 24:Suppl. D5–12
    [Google Scholar]
  31. 30.  Molins A, Stufflebeam SM, Brown EN, Hämäläinen MS 2008. Quantification of the benefit from integrating MEG and EEG data in minimum ℓ 2-norm estimation. NeuroImage 42:1069–77
    [Google Scholar]
  32. 31.  Van Veen BD, Buckley KM 1988. Beamforming: a versatile approach to spatial filtering. IEEE ASSP Mag 5:4–24
    [Google Scholar]
  33. 32.  Van Veen BD, Van Drongelen W, Yuchtman M, Suzuki A 1997. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng. 44:867–80
    [Google Scholar]
  34. 33.  Baillet S, Mosher JC, Leahy RM 2001. Electromagnetic brain mapping. IEEE Signal Process. Mag. 18:14–30
    [Google Scholar]
  35. 34.  Sekihara K, Nagarajan SS, Poeppel D, Marantz A, Miyashita Y 2001. Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique. IEEE Trans. Biomed. Eng. 48:760–71
    [Google Scholar]
  36. 35.  Gross J, Kujala J, Hämäläinen M, Timmermann L, Schnitzler A, Salmelin R 2001. Dynamic imaging of coherent sources: studying neural interactions in the human brain. PNAS 98:694–99
    [Google Scholar]
  37. 36.  Robinson SE, Vrba J 1998. Functional neuroimaging by synthetic aperture magnetometry (SAM). Recent Advances in Biomagnetism T Yoshimoto, M Kotani, S Kuriki, H Karibe, H Nahasato 302–5 Sandai, Jpn: Tokyo Univ. Press
    [Google Scholar]
  38. 37.  Mosher JC, Lewis PS, Leahy RM 1992. Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans. Biomed. Eng. 39:541–57
    [Google Scholar]
  39. 38.  Schmidt R 1986. Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34:276–80
    [Google Scholar]
  40. 39.  Mosher JC, Leahy RM 1999. Source localization using recursively applied and projected (RAP) MUSIC. IEEE Trans. Signal Process. 47:332–40
    [Google Scholar]
  41. 40.  Xu X-L, Xu B, He B 2004. An alternative subspace approach to EEG dipole source localization. Phys. Med. Biol. 49:327
    [Google Scholar]
  42. 41.  Wipf D, Nagarajan S 2009. A unified Bayesian framework for MEG/EEG source imaging. NeuroImage 44:947–66
    [Google Scholar]
  43. 42.  Bolstad A, Van Veen B, Nowak R 2009. Space-time event sparse penalization for magneto-/electroencephalography. NeuroImage 46:1066–81
    [Google Scholar]
  44. 43.  Friston K, Harrison L, Daunizeau J, Kiebel S, Phillips C et al. 2008. Multiple sparse priors for the M/EEG inverse problem. NeuroImage 39:1104–20
    [Google Scholar]
  45. 44.  Trujillo-Barreto NJ, Aubert-Vázquez E, Valdés-Sosa PA 2004. Bayesian model averaging in EEG/MEG imaging. NeuroImage 21:1300–19
    [Google Scholar]
  46. 45.  Trujillo-Barreto NJ, Aubert-Vázquez E, Penny WD 2008. Bayesian M/EEG source reconstruction with spatio-temporal priors. NeuroImage 39:318–35
    [Google Scholar]
  47. 46.  Henson RN, Flandin G, Friston KJ, Mattout J 2010. A parametric empirical Bayesian framework for fMRI-constrained MEG/EEG source reconstruction. Hum. Brain Mapp. 31:1512–31
    [Google Scholar]
  48. 47.  Wipf DP, Owen JP, Attias HT, Sekihara K, Nagarajan SS 2010. Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG. NeuroImage 49:641–55
    [Google Scholar]
  49. 48.  Grova C, Daunizeau J, Lina J-M, Bénar CG, Benali H, Gotman J 2006. Evaluation of EEG localization methods using realistic simulations of interictal spikes. NeuroImage 29:734–53
    [Google Scholar]
  50. 49.  Lamus C, Hämäläinen MS, Temereanca S, Brown EN, Purdon PL 2012. A spatiotemporal dynamic distributed solution to the MEG inverse problem. NeuroImage 63:894–909
    [Google Scholar]
  51. 50.  Gorodnitsky IF, George JS, Rao BD 1995. Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. Electroencephalogr. Clin. Neurophysiol. 95:231–51
    [Google Scholar]
  52. 51.  Ding L, He B 2008. Sparse source imaging in electroencephalography with accurate field modeling. Hum. Brain Mapp. 29:1053–67
    [Google Scholar]
  53. 52.  Haufe S, Nikulin VV, Ziehe A, Müller K-R, Nolte G 2008. Combining sparsity and rotational invariance in EEG/MEG source reconstruction. NeuroImage 42:726–38
    [Google Scholar]
  54. 53.  Gramfort A, Strohmeier D, Haueisen J, Hämäläinen MS, Kowalski M 2013. Time-frequency mixed-norm estimates: sparse M/EEG imaging with non-stationary source activations. NeuroImage 70:410–22
    [Google Scholar]
  55. 54.  Sohrabpour A, Lu Y, Worrell G, He B 2016. Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy. NeuroImage 142:27–42
    [Google Scholar]
  56. 55.  Ding L 2009. Reconstructing cortical current density by exploring sparseness in the transform domain. Phys. Med. Biol. 54:2683
    [Google Scholar]
  57. 56.  Donoho DL 2006. Compressed sensing. IEEE Trans. Inf. Theory 52:1289–306
    [Google Scholar]
  58. 57.  Matsuura K, Okabe Y 1995. Selective minimum-norm solution of the biomagnetic inverse problem. IEEE Trans. Biomed. Eng. 42:608–15
    [Google Scholar]
  59. 58.  Uutela K, Hämäläinen M, Somersalo E 1999. Visualization of magnetoencephalographic data using minimum current estimates. NeuroImage 10:173–80
    [Google Scholar]
  60. 59.  Wagner M, Wischmann H-A, Fuchs M, Köhler T, Drenckhahn R 2000. Current density reconstructions using the L1 norm. Biomag 96: Proceedings of the 10th International Conference on Biomagnetism CJ Aine, G Stroink, CC Wood, Y Okada, SJ Swithenby 393–96 Berlin: Springer
    [Google Scholar]
  61. 60.  Fuchs M, Wagner M, Köhler T, Wischmann H-A 1999. Linear and nonlinear current density reconstructions. J. Clin. Neurophysiol. 16:267–95
    [Google Scholar]
  62. 61.  Liao K, Zhu M, Ding L, Valette S, Zhang W, Dickens D 2012. Sparse representation of cortical current density maps using wavelets. Phys. Med. Biol. 57:6881–901
    [Google Scholar]
  63. 62.  Zhu M, Zhang W, Dickens DL, Ding L 2014. Reconstructing spatially extended brain sources via enforcing multiple transform sparseness. NeuroImage 86:280–93
    [Google Scholar]
  64. 63.  Chang W-T, Nummenmaa A, Hsieh J-C, Lin F-H 2010. Spatially sparse source cluster modeling by compressive neuromagnetic tomography. NeuroImage 53:146–60
    [Google Scholar]
  65. 64.  Bar M, Kassam KS, Ghuman AS, Boshyan J, Schmid AM et al. 2006. Top-down facilitation of visual recognition. PNAS 103:449–54
    [Google Scholar]
  66. 65.  Khan S, Gramfort A, Shetty NR, Kitzbichler MG, Ganesan S et al. 2013. Local and long-range functional connectivity is reduced in concert in autism spectrum disorders. PNAS 110:3107–12
    [Google Scholar]
  67. 66.  Sergent C, Baillet S, Dehaene S 2005. Timing of the brain events underlying access to consciousness during the attentional blink. Nat. Neurosci. 8:1391–400
    [Google Scholar]
  68. 67.  Jamison KW, Roy AV, He S, Engel SA, He B 2015. SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI. J. Neurosci. Methods 243:53–62
    [Google Scholar]
  69. 68.  Roy AV, Jamison KW, He S, Engel SA, He B 2017. Deactivation in the posterior mid-cingulate cortex reflects perceptual transitions during binocular rivalry: evidence from simultaneous EEG-fMRI. NeuroImage 152:1–11
    [Google Scholar]
  70. 69.  Zhang P, Jamison K, Engel S, He B, He S 2011. Binocular rivalry requires visual attention. Neuron 71:362–69
    [Google Scholar]
  71. 70.  Ahissar E, Nagarajan S, Ahissar M, Protopapas A, Mahncke H, Merzenich MM 2001. Speech comprehension is correlated with temporal response patterns recorded from auditory cortex. PNAS 98:13367–72
    [Google Scholar]
  72. 71.  He B, Gao S, Yuan H, Wolpaw JR 2013. Brain–computer interfaces. Neural Engineering B He 87–151 Berlin: Springer
    [Google Scholar]
  73. 72.  He B, Baxter B, Edelman BJ, Cline CC, Wenjing WY 2015. Noninvasive brain–computer interfaces based on sensorimotor rhythms. Proc. IEEE. 103:907–25
    [Google Scholar]
  74. 73.  Qin L, Ding L, He B 2004. Motor imagery classification by means of source analysis for brain-computer interface applications. J. Neural Eng. 1:65–72
    [Google Scholar]
  75. 74.  Edelman BJ, Baxter B, He B 2016. EEG source imaging enhances the decoding of complex right-hand motor imagery tasks. IEEE Trans. Biomed. Eng. 63:4–14
    [Google Scholar]
  76. 75.  Yuan H, Liu T, Szarkowski R, Rios C, Ashe J, He B 2010. Negative covariation between task-related responses in α/β-band activity and BOLD in human sensorimotor cortex: an EEG and fMRI study of motor imagery and movements. NeuroImage 49:2596–606
    [Google Scholar]
  77. 77.  Lai Y, Zhang X, van Drongelen W, Korhman M, Hecox K et al. 2011. Noninvasive cortical imaging of epileptiform activities from interictal spikes in pediatric patients. NeuroImage 54:244–52
    [Google Scholar]
  78. 78.  Brodbeck V, Spinelli L, Lascano AM, Wissmeier M, Vargas M-I et al. 2011. Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients. Brain 134:2887–97
    [Google Scholar]
  79. 79.  Shiraishi H, Ahlfors SP, Stufflebeam SM, Takano K, Okajima M et al. 2005. Application of magnetoencephalography in epilepsy patients with widespread spike or slow-wave activity. Epilepsia 46:1264–72
    [Google Scholar]
  80. 80.  Ossadtchi A, Baillet S, Mosher JC, Thyerlei D, Sutherling W, Leahy RM 2004. Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering. Clin. Neurophysiol. 115:508–22
    [Google Scholar]
  81. 81.  Kirsch HE, Robinson SE, Mantle M, Nagarajan S 2006. Automated localization of magnetoencephalographic interictal spikes by adaptive spatial filtering. Clin. Neurophysiol. 117:2264–71
    [Google Scholar]
  82. 82.  Ding L, Worrell GA, Lagerlund TD, He B 2007. Ictal source analysis: localization and imaging of causal interactions in humans. NeuroImage 34:575–86
    [Google Scholar]
  83. 83.  Yang L, Wilke C, Brinkmann B, Worrell GA, He B 2011. Dynamic imaging of ictal oscillations using non-invasive high-resolution EEG. NeuroImage 56:1908–17
    [Google Scholar]
  84. 84.  Lu Y, Yang L, Worrell GA, Brinkmann B, Nelson C, He B 2012. Dynamic imaging of seizure activity in pediatric epilepsy patients. Clin. Neurophysiol. 123:2122–29
    [Google Scholar]
  85. 85.  Yang L, Worrell GA, Nelson C, Brinkmann B, He B 2012. Spectral and spatial shifts of post-ictal slow waves in temporal lobe seizures. Brain 135:3134–43
    [Google Scholar]
  86. 86.  Hillebrand A, Barnes GR, Bosboom JL, Berendse HW, Stam CJ 2012. Frequency-dependent functional connectivity within resting-state networks: an atlas-based MEG beamformer solution. NeuroImage 59:3909–21
    [Google Scholar]
  87. 87.  Brookes MJ, Woolrich M, Luckhoo H, Price D, Hale JR et al. 2011. Investigating the electrophysiological basis of resting state networks using magnetoencephalography. PNAS 108:16783–88
    [Google Scholar]
  88. 88.  de Pasquale F, Penna SD, Snyder AZ, Lewis C, Mantini D et al. 2010. Temporal dynamics of spontaneous MEG activity in brain networks. PNAS 107:6040–45
    [Google Scholar]
  89. 89.  Coito A, Michel CM, van Mierlo P, Vulliémoz S, Plomp G 2016. Directed functional brain connectivity based on EEG source imaging: methodology and application to temporal lobe epilepsy. IEEE Trans. Biomed. Eng. 63:2619–28
    [Google Scholar]
  90. 90.  Haneef Z, Lenartowicz A, Yeh HJ, Levin HS, Engel J, Stern JM 2014. Functional connectivity of hippocampal networks in temporal lobe epilepsy. Epilepsia 55:137–45
    [Google Scholar]
  91. 91.  Friston KJ 2011. Functional and effective connectivity: a review. Brain Connect 1:13–36
    [Google Scholar]
  92. 92.  Friston KJ 1994. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2:56–78
    [Google Scholar]
  93. 93.  Horwitz B 2003. The elusive concept of brain connectivity. NeuroImage 19:466–70
    [Google Scholar]
  94. 94.  Friston KJ 2009. Modalities, modes, and models in functional neuroimaging. Science 326:399–403
    [Google Scholar]
  95. 95.  Granger CWJ 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–38
    [Google Scholar]
  96. 96.  Geweke J 1982. Measurement of linear dependence and feedback between multiple time series. J. Am. Stat. Assoc. 77:304–13
    [Google Scholar]
  97. 97.  Babiloni F, Cincotti F, Babiloni C, Carducci F, Mattia D et al. 2005. Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function. NeuroImage 24:118–31
    [Google Scholar]
  98. 98.  Kaminski MJ, Blinowska KJ 1991. A new method of the description of the information flow in the brain structures. Biol. Cybern. 65:203–10
    [Google Scholar]
  99. 99.  Ding M, Bressler SL, Yang W, Liang H 2000. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol. Cybern. 83:35–45
    [Google Scholar]
  100. 100.  Wilke C, Ding L, He B 2008. Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function. Biomed. Eng. IEEE Trans. 55:2557–64
    [Google Scholar]
  101. 101.  Astolfi L, Cincotti F, Mattia D, Fallani FDV, Tocci A et al. 2008. Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators. IEEE Trans. Biomed. Eng. 55:902–13
    [Google Scholar]
  102. 102.  Korzeniewska A, Mańczak M, Kamiński M, Blinowska KJ, Kasicki S 2003. Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J. Neurosci. Methods 125:195–207
    [Google Scholar]
  103. 103.  Baccalá LA, Sameshima K 2001. Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 84:463–74
    [Google Scholar]
  104. 104.  He B, Dai Y, Astolfi L, Babiloni F, Yuan H, Yang L 2011. eConnectome: a MATLAB toolbox for mapping and imaging of brain functional connectivity. J. Neurosci. Methods 195:261–69
    [Google Scholar]
  105. 105.  Sohrabpour A, Ye S, Worrell GA, Zhang W, He B 2016. Noninvasive electromagnetic source imaging and granger causality analysis: an electrophysiological connectome (eConnectome) approach. IEEE Trans. Biomed. Eng. 63:2474–87
    [Google Scholar]
  106. 106.  Lu Y, Yang L, Worrell GA, He B 2012. Seizure source imaging by means of FINE spatio-temporal dipole localization and directed transfer function in partial epilepsy patients. Clin. Neurophysiol. 123:1275–83
    [Google Scholar]
  107. 107.  Brookes MJ, Hale JR, Zumer JM, Stevenson CM, Francis ST et al. 2011. Measuring functional connectivity using MEG: methodology and comparison with fcMRI. NeuroImage 56:1082–104
    [Google Scholar]
  108. 108.  Elisevich K, Shukla N, Moran JE, Smith B, Schultz L et al. 2011. An assessment of MEG coherence imaging in the study of temporal lobe epilepsy. Epilepsia 52:1110–19
    [Google Scholar]
  109. 109.  Englot DJ, Hinkley LB, Kort NS, Imber BS, Mizuiri D et al. 2015. Global and regional functional connectivity maps of neural oscillations in focal epilepsy. Brain 138:2249–62
    [Google Scholar]
  110. 110.  Friston KJ, Harrison L, Penny W 2003. Dynamic causal modelling. NeuroImage 19:1273–302
    [Google Scholar]
  111. 111.  Friston K, Moran R, Seth AK 2013. Analysing connectivity with Granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23:172–78
    [Google Scholar]
  112. 112.  Daunizeau J, David O, Stephan KE 2011. Dynamic causal modelling: a critical review of the biophysical and statistical foundations. NeuroImage 58:312–22
    [Google Scholar]
  113. 113.  Baillet S 2017. Magnetoencephalography for brain electrophysiology and imaging. Nat. Neurosci. 20:327–39
    [Google Scholar]
  114. 114.  Logothetis NK 2008. What we can do and what we cannot do with fMRI. Nature 453:869–78
    [Google Scholar]
  115. 115.  He B, Liu Z 2008. Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG. IEEE Rev. Biomed. Eng. 1:23–40
    [Google Scholar]
  116. 116.  Ritter P, Villringer A 2006. Simultaneous EEG-fMRI. Neurosci. Biobehav. Rev. 30:823–38
    [Google Scholar]
  117. 117.  Liu Z, Ding L, He B 2006. Integration of EEG/MEG with MRI and fMRI. IEEE Eng. Med. Biol. Mag. 25:46–53
    [Google Scholar]
  118. 118.  Kim S-G, Ogawa S 2012. Biophysical and physiological origins of blood oxygenation level–dependent fMRI signals. J. Cereb. Blood Flow Metab. 32:1188–206
    [Google Scholar]
  119. 119.  Ogawa S, Lee TM, Kay AR, Tank DW 1990. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. PNAS 87:9868–72
    [Google Scholar]
  120. 120.  Bandettini PA, Wong EC, Hinks RS, Tikofsky RS, Hyde JS 1992. Time course EPI of human brain function during task activation. Magn. Reson. Med. 25:390–97
    [Google Scholar]
  121. 121.  Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM et al. 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. PNAS 89:5675–79
    [Google Scholar]
  122. 122.  Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG et al. 1992. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. PNAS 89:5951–55
    [Google Scholar]
  123. 123.  Thulborn KR, Waterton JC, Matthews PM, Radda GK 1982. Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high field. Biochim. Biophys. Acta 714:265–70
    [Google Scholar]
  124. 124.  Attwell D, Buchan AM, Charpak S, Lauritzen M, MacVicar BA, Newman EA 2010. Glial and neuronal control of brain blood flow. Nature 468:232–43
    [Google Scholar]
  125. 125.  Fox PT, Raichle ME 1986. Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. PNAS 83:1140–44
    [Google Scholar]
  126. 126.  Petzold GC, Murthy VN 2011. Role of astrocytes in neurovascular coupling. Neuron 71:782–97
    [Google Scholar]
  127. 127.  Devor A, Dunn AK, Andermann ML, Ulbert I, Boas DA, Dale AM 2003. Coupling of total hemoglobin concentration, oxygenation, and neural activity in rat somatosensory cortex. Neuron 39:353–59
    [Google Scholar]
  128. 128.  Otsu Y, Couchman K, Lyons DG, Collot M, Agarwal A et al. 2015. Calcium dynamics in astrocyte processes during neurovascular coupling. Nat. Neurosci. 18:210–18
    [Google Scholar]
  129. 129.  Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A 2001. Neurophysiological investigation of the basis of the fMRI signal. Nature 412:150–57
    [Google Scholar]
  130. 130.  Viswanathan A, Freeman RD 2007. Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nat. Neurosci. 10:1308–12
    [Google Scholar]
  131. 131.  Attwell D, Iadecola C 2002. The neural basis of functional brain imaging signals. Trends Neurosci 25:621–25
    [Google Scholar]
  132. 132.  Logothetis NK, Wandell BA 2004. Interpreting the BOLD signal. Annu. Rev. Physiol. 66:735–69
    [Google Scholar]
  133. 133.  Thomsen K, Offenhauser N, Lauritzen M 2004. Principal neuron spiking: neither necessary nor sufficient for cerebral blood flow in rat cerebellum. J. Physiol. 560:181–89
    [Google Scholar]
  134. 134.  Rauch A, Rainer G, Logothetis NK 2008. The effect of a serotonin-induced dissociation between spiking and perisynaptic activity on BOLD functional MRI. PNAS 105:6759–64
    [Google Scholar]
  135. 135.  Goense JB, Logothetis NK 2008. Neurophysiology of the BOLD fMRI signal in awake monkeys. Curr. Biol. 18:631–40
    [Google Scholar]
  136. 136.  Shmuel A, Leopold DA 2008. Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: implications for functional connectivity at rest. Hum. Brain Mapp. 29:751–61
    [Google Scholar]
  137. 137.  Schölvinck ML, Maier A, Frank QY, Duyn JH, Leopold DA 2010. Neural basis of global resting-state fMRI activity. PNAS 107:10238–43
    [Google Scholar]
  138. 138.  He BJ, Snyder AZ, Zempel JM, Smyth MD, Raichle ME 2008. Electrophysiological correlates of the brain's intrinsic large-scale functional architecture. PNAS 105:16039–44
    [Google Scholar]
  139. 139.  Wen H, Liu Z 2016. Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain Topogr 29:13–26
    [Google Scholar]
  140. 140.  Laufs H 2008. Endogenous brain oscillations and related networks detected by surface EEG-combined fMRI. Hum. Brain Mapp. 29:762–69
    [Google Scholar]
  141. 141.  Nunez PL, Silberstein RB 2000. On the relationship of synaptic activity to macroscopic measurements: Does co-registration of EEG with fMRI make sense?. Brain Topogr 13:79–96
    [Google Scholar]
  142. 142.  Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, de Peralta RG 2004. EEG source imaging. Clin. Neurophysiol. 115:2195–222
    [Google Scholar]
  143. 143.  Leopold DA, Maier A 2012. Ongoing physiological processes in the cerebral cortex. NeuroImage 62:2190–200
    [Google Scholar]
  144. 144.  Liu Z, de Zwart JA, Chang C, Duan Q, van Gelderen P, Duyn JH 2014. Neuroelectrical decomposition of spontaneous brain activity measured with functional magnetic resonance imaging. Cereb. Cortex 24:3080–89
    [Google Scholar]
  145. 145.  Ritter P, Schirner M, McIntosh AR, Jirsa VK 2013. The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connect 3:121–45
    [Google Scholar]
  146. 146.  Ahlfors SP, Simpson GV, Dale AM, Belliveau JW, Liu AK et al. 1999. Spatiotemporal activity of a cortical network for processing visual motion revealed by MEG and fMRI. J. Neurophysiol. 82:2545–55
    [Google Scholar]
  147. 147.  Bledowski C, Kadosh KC, Wibral M, Rahm B, Bittner RA et al. 2006. Mental chronometry of working memory retrieval: a combined functional magnetic resonance imaging and event-related potentials approach. J. Neurosci. 26:821–29
    [Google Scholar]
  148. 148.  Liu AK, Belliveau JW, Dale AM 1998. Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. PNAS 95:8945–50
    [Google Scholar]
  149. 149.  Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW et al. 2000. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26:55–67
    [Google Scholar]
  150. 150.  Liu Z, He B 2008. fMRI-EEG integrated cortical source imaging by use of time-variant spatial constraints. NeuroImage 39:1198–214
    [Google Scholar]
  151. 151.  Liu Z, Rios C, Zhang N, Yang L, Chen W, He B 2010. Linear and nonlinear relationships between visual stimuli, EEG and BOLD fMRI signals. NeuroImage 50:1054–66
    [Google Scholar]
  152. 152.  Liu Z, Zhang N, Chen W, He B 2009. Mapping the bilateral visual integration by EEG and fMRI. NeuroImage 46:989–97
    [Google Scholar]
  153. 153.  Shmuel A, Augath M, Oeltermann A, Logothetis NK 2006. Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1. Nat. Neurosci. 9:569–77
    [Google Scholar]
  154. 154.  Makeig S, Westerfield M, Jung T-P, Enghoff S, Townsend J et al. 2002. Dynamic brain sources of visual evoked responses. Science 295:690–94
    [Google Scholar]
  155. 155.  Raichle ME, Mintun MA 2006. Brain work and brain imaging. Annu. Rev. Neurosci. 29:449–76
    [Google Scholar]
  156. 156.  Sadaghiani S, Hesselmann G, Friston KJ, Kleinschmidt A 2010. The relation of ongoing brain activity, evoked neural responses, and cognition. Front. Syst. Neurosci. 4:20
    [Google Scholar]
  157. 157.  He B 2013. Spontaneous and task-evoked brain activity negatively interact. J. Neurosci. 33:4672–82
    [Google Scholar]
  158. 158.  Murta T, Leite M, Carmichael DW, Figueiredo P, Lemieux L 2015. Electrophysiological correlates of the BOLD signal for EEG-informed fMRI. Hum. Brain Mapp. 36:391–414
    [Google Scholar]
  159. 159.  Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M 2007. Electrophysiological signatures of resting state networks in the human brain. PNAS 104:13170–75
    [Google Scholar]
  160. 160.  Buzsáki G, Draguhn A 2004. Neuronal oscillations in cortical networks. Science 304:1926–29
    [Google Scholar]
  161. 161.  Olbrich S, Mulert C, Karch S, Trenner M, Leicht G et al. 2009. EEG-vigilance and BOLD effect during simultaneous EEG/fMRI measurement. NeuroImage 45:319–32
    [Google Scholar]
  162. 162.  Leopold DA, Murayama Y, Logothetis NK 2003. Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. Cereb. Cortex 13:422–33
    [Google Scholar]
  163. 163.  Ritter P, Moosmann M, Villringer A 2009. Rolandic α and β EEG rhythms’ strengths are inversely related to fMRI-BOLD signal in primary somatosensory and motor cortex. Hum. Brain Mapp. 30:1168–87
    [Google Scholar]
  164. 164.  Liu Y, Bengson J, Huang H, Mangun GR, Ding M 2014. Top-down modulation of neural activity in anticipatory visual attention: control mechanisms revealed by simultaneous EEG-fMRI. Cereb. Cortex 26:517–29
    [Google Scholar]
  165. 165.  Laufs H, Kleinschmidt A, Beyerle A, Eger E, Salek-Haddadi A et al. 2003. EEG-correlated fMRI of human α activity. NeuroImage 19:1463–76
    [Google Scholar]
  166. 166.  Liu Z, de Zwart JA, Yao B, van Gelderen P, Kuo L-W, Duyn JH 2012. Finding thalamic BOLD correlates to posterior α EEG. NeuroImage 63:1060–69
    [Google Scholar]
  167. 167.  Scheeringa R, Bastiaansen MC, Petersson KM, Oostenveld R, Norris DG, Hagoort P 2008. Frontal θ EEG activity correlates negatively with the default mode network in resting state. Int. J. Psychophysiol. 67:242–51
    [Google Scholar]
  168. 168.  He BJ, Zempel JM, Snyder AZ, Raichle ME 2010. The temporal structures and functional significance of scale-free brain activity. Neuron 66:353–69
    [Google Scholar]
  169. 169.  Jensen O, Colgin LL 2007. Cross-frequency coupling between neuronal oscillations. Trends Cogn. Sci. 11:267–69
    [Google Scholar]
  170. 170.  Wen H, Liu Z 2016. Broadband electrophysiological dynamics contribute to global resting-state fMRI signal. J. Neurosci. 36:6030–40
    [Google Scholar]
  171. 171.  Scheeringa R, Fries P, Petersson K-M, Oostenveld R, Grothe I et al. 2011. Neuronal dynamics underlying high-and low-frequency EEG oscillations contribute independently to the human BOLD signal. Neuron 69:572–83
    [Google Scholar]
  172. 172.  Lehmann D, Michel CM 2011. EEG-defined functional microstates as basic building blocks of mental processes. Clin. Neurophysiol. 122:1073–74
    [Google Scholar]
  173. 173.  Musso F, Brinkmeyer J, Mobascher A, Warbrick T, Winterer G 2010. Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks. NeuroImage 52:1149–61
    [Google Scholar]
  174. 174.  Britz J, Van De Ville D, Michel CM 2010. BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage 52:1162–70
    [Google Scholar]
  175. 175.  Yuan H, Zotev V, Phillips R, Drevets WC, Bodurka J 2012. Spatiotemporal dynamics of the brain at rest—exploring EEG microstates as electrophysiological signatures of BOLD resting state networks. NeuroImage 60:2062–72
    [Google Scholar]
  176. 176.  Eichele T, Specht K, Moosmann M, Jongsma ML, Quiroga RQ et al. 2005. Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI. PNAS 102:17798–803
    [Google Scholar]
  177. 177.  Debener S, Ullsperger M, Siegel M, Engel AK 2006. Single-trial EEG-fMRI reveals the dynamics of cognitive function. Trends Cogn. Sci. 10:558–63
    [Google Scholar]
  178. 178.  Allen PJ, Josephs O, Turner R 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage 12:230–39
    [Google Scholar]
  179. 179.  Neuner I, Arrubla J, Felder J, Shah NJ 2014. Simultaneous EEG-fMRI acquisition at low, high and ultra-high magnetic fields up to 9.4 T: perspectives and challenges. NeuroImage 102:71–79
    [Google Scholar]
  180. 180.  Liu Z, de Zwart JA, van Gelderen P, Kuo L-W, Duyn JH 2012. Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings. NeuroImage 59:2073–87
    [Google Scholar]
  181. 181.  Chowdhury ME, Mullinger KJ, Glover P, Bowtell R 2014. Reference layer artefact subtraction (RLAS): a novel method of minimizing EEG artefacts during simultaneous fMRI. NeuroImage 84:307–19
    [Google Scholar]
  182. 182.  Siegel M, Donner TH, Engel AK 2012. Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13:121
    [Google Scholar]
  183. 183.  Hipp JF, Siegel M 2015. BOLD fMRI correlation reflects frequency-specific neuronal correlation. Curr. Biol. 25:1368–74
    [Google Scholar]
  184. 184.  Van Kerkoerle T, Self MW, Dagnino B, Gariel-Mathis M-A, Poort J et al. 2014. α and γ oscillations characterize feedback and feedforward processing in monkey visual cortex. PNAS 111:14332–41
    [Google Scholar]
  185. 185.  Bastos AM, Vezoli J, Bosman CA, Schoffelen J-M, Oostenveld R et al. 2015. Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron 85:390–401
    [Google Scholar]
  186. 186.  Michalareas G, Vezoli J, Van Pelt S, Schoffelen J-M, Kennedy H, Fries P 2016. α-β and γ rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron 89:384–97
    [Google Scholar]
  187. 187.  Scheeringa R, Koopmans PJ, van Mourik T, Jensen O, Norris DG 2016. The relationship between oscillatory EEG activity and the laminar-specific BOLD signal. PNAS 113:6761–66
    [Google Scholar]
  188. 188.  Klink PC, Dagnino B, Gariel-Mathis M-A, Roelfsema PR 2017. Distinct feedforward and feedback effects of microstimulation in visual cortex reveal neural mechanisms of texture segregation. Neuron 95:209–20
    [Google Scholar]
  189. 189.  Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ 2012. Canonical microcircuits for predictive coding. Neuron 76:695–711
    [Google Scholar]
  190. 190.  Rao RP, Ballard DH 1999. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2:79–87
    [Google Scholar]
  191. 191.  Friston K 2010. The free-energy principle: a unified brain theory?. Nat. Rev. Neurosci. 11:127–38
    [Google Scholar]
  192. 192.  Lang J 2012. Clinical Anatomy of the Head: Neurocranium Orbit Craniocervical Regions Berlin: Springer
    [Google Scholar]
  193. 193.  Murphy M, Riedner BA, Huber R, Massimini M, Ferrarelli F, Tononi G 2009. Source modeling sleep slow waves. PNAS 106:1608–13
    [Google Scholar]
  194. 194.  Edelman BJ, Johnson N, Sohrabpour A, Tong S, Thakor N, He B 2015. Systems neuroengineering: understanding and interacting with the brain. Engineering 1:292–308
    [Google Scholar]
  195. 195.  Foxe JJ, Murray MM, Javitt DC 2005. Filling-in in schizophrenia: a high-density electrical mapping and source-analysis investigation of illusory contour processing. Cereb. Cortex 15:1914–27
    [Google Scholar]
  196. 196.  Zhang HC, Sohrabpour A, Lu Y, He B 2016. Spectral and spatial changes of brain rhythmic activity in response to the sustained thermal pain stimulation. Hum. Brain Mapp. 37:2796–91
    [Google Scholar]
  197. 197.  He B, Coleman T, Genin GM, Glover G, Hu X et al. 2013. Grand challenges in mapping the human brain: NSF workshop report. IEEE Trans. Biomed. Eng. 60:2983–92
    [Google Scholar]
  198. 198.  He B 2016. Focused ultrasound help realize high spatiotemporal brain imaging? A concept on acousto-electrophysiological neuroimaging. IEEE Trans. Biomed. Eng. 63:2654–56
    [Google Scholar]
  199. 199.  Oostenveld R, Fries P, Maris E, Schoffelen J-M 2011. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011:156869
    [Google Scholar]
  200. 200.  Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D et al. 2014. MNE software for processing MEG and EEG data. NeuroImage 86:446–60
    [Google Scholar]
  201. 201.  Dalal SS, Zumer JM, Agrawal V, Hild KE, Sekihara K, Nagarajan SS 2004. NUTMEG: a neuromagnetic source reconstruction toolbox. Neurol. Clin. Neurophysiol. 2004:52
    [Google Scholar]
  202. 202.  Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM 2011. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011:879716
    [Google Scholar]
  203. 203.  Delorme A, Makeig S 2004. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134:9–21
    [Google Scholar]
  204. 204.  Brunet D, Murray MM, Michel CM 2011. Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput. Intell. Neurosci. 2011:813870
    [Google Scholar]
  205. 205.  Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE 2011. Statistical Parametric Mapping: The Analysis of Functional Brain Images San Diego: Academic
    [Google Scholar]
  206. 206.  Ashburner J 2012. SPM: a history. NeuroImage 62:791–800
    [Google Scholar]
  207. 207.  Takahashi YD, Baccal AL, Sameshima K 2007. Connectivity inference between neural structures via partial directed coherence. J. Appl. Stat. 34:1259–73
    [Google Scholar]
  208. 208.  Delorme A, Mullen T, Kothe C, Acar ZA, Bigdely-Shamlo N et al. 2011. EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing. Comput. Intell. Neurosci. 2011:130714
    [Google Scholar]
  209. 209.  Fischl B 2012. FreeSurfer. NeuroImage 62:774–81
    [Google Scholar]
  210. 210.  Shattuck DW, Leahy RM 2002. BrainSuite: an automated cortical surface identification tool. Med. Image Anal. 6:129–42
    [Google Scholar]
  211. 211.  Rivière D, Geffroy D, Denghien I, Souedet N, Cointepas Y 2009. BrainVISA: an extensible software environment for sharing multimodal neuroimaging data and processing tools. NeuroImage 47:S163
    [Google Scholar]
  212. 212.  Gramfort A, Papadopoulo T, Olivi E, Clerc M 2010. OpenMEEG: opensource software for quasistatic bioelectromagnetics. Biomed. Eng. Online 9:45
    [Google Scholar]
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