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Inverse source imaging methods in recovering distributed brain sources

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Abstract

Over the past decades tremendous efforts have been made in developing functional neuroimaging techniques to better understand human brain functions in both normal and diseased states. Towards this goal, it is essential to develop a technique that can noninvasively image human brain activity with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are important tools for studying the human brain’s large-scale neuronal dynamics, thanks to their millisecond temporal resolution. However, EEG and MEG are limited in providing spatial information concerning the location of active sources in the brain. Localizing the sources of EEG/MEG dynamics can be achieved by the so-called electrophysiological source imaging techniques. Recently, there has been a growing interest in source imaging techniques in recovering distributed brain sources. Such distributed source imaging techniques have been advanced in many aspects, including the forward modeling and the inverse imaging, and have been shown promising in many neuroscience and clinical applications. This paper reviews the basic principles, recent advancements and applications of the distributed source imaging techniques.

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References

  1. Nunez PL. Neocortical dynamics and human EEG rhythms. New York: Oxford University Press; 1995.

    Google Scholar 

  2. Friston KJ. Modalities, modes, and models in functional neuroimaging. Science. 2009; 326(5951):399–403.

    Article  Google Scholar 

  3. Bandettini PA. What’s new in neuroimaging methods? Ann NY Acad Sci. 2009; 1156:260–293.

    Article  Google Scholar 

  4. He B, Yang L, Wilke C, Yuan H. Electrophysiological imaging of brain activity and connectivity-challenges and opportunities. IEEE T Bio-Med Eng. 2011; 58(7):1918–1931.

    Article  Google Scholar 

  5. Dale AM, Sereno MI. Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach. J Cognitive Neurosci. 1993; 5:162–176.

    Article  Google Scholar 

  6. Babiloni F, Carducci F, Cincotti F, Del Gratta C, Roberti GM, Romani GL, et al. Integration of high resolution EEG and functional magnetic resonance in the study of human movementrelated potentials. Method Inform Med. 2000; 39(2):179–182.

    Google Scholar 

  7. Malmivuo JA, Plonsey R. Bioelectromagnetism: Principles and applications of bioelectric and biomagnetic fields. New York: Oxford University Press; 1995.

    Google Scholar 

  8. Lin FH, Belliveau JW, Dale AM, Hämäläinen MS. Distributed current estimates using cortical orientation constraints. Hum Brain Mapp. 2006; 27:1–13.

    Article  MATH  Google Scholar 

  9. Hämäläinen MS, Sarvas J. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. IEEE T Bio-Med Eng. 1989; 36(2):165–71.

    Article  Google Scholar 

  10. He B, Musha T, Okamoto Y, Homma S, Nakajima Y, Sato T. Electric dipole tracing in the brain by means of the boundary element method and its accuracy. IEEE T Bio-Med Eng. 1987; 34(6):406–414.

    Article  Google Scholar 

  11. Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW, Lewine JD, et al. Dynamic statistical parametric mapping: Combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron. 2000; 26(1):55–67.

    Article  Google Scholar 

  12. Liu Z, He B. fMRI-EEG integrated cortical source imaging by use of time-variant spatial constraints. Neuroimage. 2008; 39(3):1198–1214.

    Article  Google Scholar 

  13. Hämäläinen M, Ilmoniemi R. Interpreting measured magnetic fields of the brain: Estimates of current distributions. Tech Rep, Helsinki Uni Tech. 1984:TKF-F-A559.

  14. Hämäläinen MS, Ilmoniemi RJ. Interpreting magnetic fields of the brain: Minimum norm estimates. Med Biol Eng Comput. 1994; 32(1):35–42.

    Article  Google Scholar 

  15. Wang JZ, Williamson SJ, Kaufman L. Magnetic source images determined by a lead-field analysis: The unique minimum-norm least-squares estimation. IEEE T Bio-Med Eng. 1992; 39(7):665–675.

    Article  Google Scholar 

  16. Pascual-Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography: A new method for localizing electrical activity in the brain. Int J Psychophysiol. 1994; 18(1):49–65.

    Article  Google Scholar 

  17. Ding L, He B. Sparse source imaging in electroencephalography with accurate field modeling. Hum Brain Mapp. 2008; 29(9):1053–1067.

    Article  Google Scholar 

  18. Fuchs M, Wagner M, Kohler T, Wischmann HA. Linear and nonlinear current density reconstructions. J Clin Neurophysiol. 1999; 16(3):267–295.

    Article  Google Scholar 

  19. Uutela K, Hämäläinen M, Somersalo E. Visualization of magnetoencephalographic data using minimum current estimates. NeuroImage. 1999; 10:173–180.

    Article  Google Scholar 

  20. Ding L. Reconstructing cortical current density by exploring sparseness in the transform domain. Phys Med Biol. 2009; 54(9):2683–2697.

    Article  Google Scholar 

  21. Baillet S, Garnero L. A bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem. IEEE T Bio-Med Eng. 1997; 44(5):374–385.

    Article  Google Scholar 

  22. Wipf D, Nagarajan S. A unified bayesian framework for MEG/ EEG source imaging. Neuroimage. 2009; 44(3):947–966.

    Article  Google Scholar 

  23. Huang MX, Dale AM, Song T, Halgren E, Harrington DL, Podgorny I, et al. Vector-based spatial-temporal minimum L1-norm solution for MEG. Neuroimage. 2006; 31(3):1025–1037.

    Article  Google Scholar 

  24. Ou W, Hämäläinen MS, Golland P. A distributed spatio-temporal EEG/MEG inverse solver. Neuroimage. 2009; 44(3):932–946.

    Article  Google Scholar 

  25. Tanaka N, Cole AJ, von Pechmann D, Wakeman DG, Hämäläinen MS, Liu H, et al. Dynamic statistical parametric mapping for analyzing ictal magnetoencephalographic spikes in patients with intractable frontal lobe epilepsy. Epilepsy Res. 2009; 85(2–-3):279–286.

    Article  Google Scholar 

  26. Galka A, Yamashita O, Ozaki T, Biscay R, Valdes-Sosa P. A solution to the dynamical inverse problem of EEG generation using spatiotemporal kalman filtering. Neuroimage. 2004; 23(2):435–453.

    Article  Google Scholar 

  27. Barton MJ, Robinson PA, Kumar S, Galka A, Durrant-Whyte HF, Guivant J, et al. Evaluating the performance of kalmanfilter-based EEG source localization. IEEE T Bio-Med Eng. 2009; 56(1):122–136.

    Article  Google Scholar 

  28. Michel CM, Grave de Peralta R, Lantz G, Gonzalez Andino S, Spinelli L, Blanke O, et al. Spatiotemporal EEG analysis and distributed source estimation in presurgical epilepsy evaluation. J Clin Neurophysiol. 1999; 16(3):239–266.

    Article  Google Scholar 

  29. Worrell GA, Lagerlund TD, Sharbrough FW, Brinkmann BH, Busacker NE, Cicora KM, et al. Localization of the epileptic focus by low-resolution electromagnetic tomography in patients with a lesion demonstrated by MRI. Brain Topogr. 2000; 12(4):273–282.

    Article  Google Scholar 

  30. Huiskamp G, van Der Meij W, van Huffelen A, van Nieuwenhuizen O. High resolution spatio-temporal EEG-MEG analysis of rolandic spikes. J Clin Neurophysiol. 2004; 21(2):84–95.

    Article  Google Scholar 

  31. Di Russo F, Martinez A, Hillyard SA. Source analysis of eventrelated cortical activity during visuo-spatial attention. Cereb Cortex. 2003; 13(5):486–499.

    Article  Google Scholar 

  32. Bar M, Kassam KS, Ghuman AS, Boshyan J, Schmid AM, Dale AM, et al. Top-down facilitation of visual recognition. P Natl Acad Sci USA. 2006; 103(2):449–454.

    Article  Google Scholar 

  33. Poghosyan V, Ioannides AA. Attention modulates earliest responses in the primary auditory and visual cortices. Neuron. 2008; 58(5):802–813.

    Article  Google Scholar 

  34. Yuan H, Liu T, Szarkowski R, Rios C, Ashe J, He B. Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: An EEG and fMRI study of motor imagery and movements. Neuroimage. 2010; 49(3):2596–2606.

    Article  Google Scholar 

  35. McDonald CR, Thesen T, Hagler DJ, Jr, Carlson C, Devinksy O, Kuzniecky R, et al. Distributed source modeling of language with magnetoencephalography: Application to patients with intractable epilepsy. Epilepsia. 2009; 50(10):2256–2266.

    Article  Google Scholar 

  36. Osipova D, Takashima A, Oostenveld R, Fernandez G, Maris E, Jensen O. Theta and gamma oscillations predict encoding and retrieval of declarative memory. J Neurosci. 2006; 26(28):7523–7531.

    Article  Google Scholar 

  37. Jensen O, Kaiser J, Lachaux JP. Human gamma-frequency oscillations associated with attention and memory. Trends Neurosci. 2007; 30(7):317–324.

    Article  Google Scholar 

  38. Ding L, Yuan H. Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging. Hum Brain Mapp. 2011; doi:10.1002/hbm.21473.

  39. Wang AL, Mouraux A, Liang M, Iannetti GD. The enhancement of the N1 wave elicited by sensory stimuli presented at very short inter-stimulus intervals is a general feature across sensory systems. PLoS One. 2008; 3(12):e3929.

    Article  Google Scholar 

  40. Greffrath W, Baumgartner U, Treede RD. Peripheral and central components of habituation of heat pain perception and evoked potentials in humans. Pain. 2007; 132(3):301–311.

    Article  Google Scholar 

  41. Jin SH, Lin P, Hallett M. Reorganization of brain functional small-world networks during finger movements. Hum Brain Mapp. 2011.

  42. Yuan H, Doud A, Gururajan A, He B. Cortical imaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain. IEEE T Neural Syst Rehabil Eng. 2008; 16(5):425–431.

    Article  Google Scholar 

  43. Yuan H, Perdoni C, Yang L, He B. Differential electrophysiological coupling for positive and negative BOLD responses during unilateral hand movements. J Neurosci. 2011; 31(26):9585–9593.

    Article  Google Scholar 

  44. Babiloni C, Carducci F, Cincotti F, Rossini PM, Neuper C, Pfurtscheller G, et al. Human movement-related potentials vs desynchronization of EEG alpha rhythm: A high-resolution EEG study. Neuroimage. 1999; 10(6):658–665.

    Article  Google Scholar 

  45. de Pasquale F, Della Penna S, Snyder AZ, Lewis C, Mantini D, Marzetti L, et al. Temporal dynamics of spontaneous MEG activity in brain networks. P Natl Acad Sci USA. 2010; 107(13):6040–6045.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA

    Lei Ding

  2. Center for Biomedical Engineering, University of Oklahoma, Norman, OK, USA

    Lei Ding

  3. Han Yuan Laureate Institute for Brain Research, Tulsa, OK, USA

    Han Yuan

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  1. Lei Ding
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  2. Han Yuan
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Correspondence to Lei Ding.

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Ding, L., Yuan, H. Inverse source imaging methods in recovering distributed brain sources. Biomed. Eng. Lett. 2, 2–7 (2012). https://doi.org/10.1007/s13534-012-0047-x

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  • DOI: https://doi.org/10.1007/s13534-012-0047-x

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