Skip to main content
SpringerLink
Log in

Magnetoencephalographic Imaging

  • Chapter
  • First Online:
Magnetoencephalography

Abstract

Non-invasive and dynamic imaging of brain activity in the sub-millisecond time-scale is enabled by measurements on or near the scalp surface using an array of sensors that measure magnetic fields (magnetoencephalography (MEG)) or electric potentials (electroencephalography (EEG)). Algorithmic reconstruction of brain activity from MEG data is referred to as magnetoencephalographic imaging (MEGI). Reconstructing the actual brain response to external events and distinguishing unrelated brain activity has been a challenge for many existing algorithms in this field. Furthermore, even under conditions where there is very little interference, accurately determining the spatial locations and timing of brain sources from MEG data is a challenging problem because it involves solving for unknown brain activity across thousands of voxels from just a few sensors (~300). In recent years, our research group has developed a suite of novel and powerful algorithms for MEGI that we have shown to be considerably superior to existing benchmark algorithms. Specifically, these algorithms can solve for many brain sources, including sources located far from the sensors, in the presence of large interference from unrelated brain sources. Our algorithms efficiently model interference contributions to sensors, accurately estimate sparse brain source activity using fast and robust probabilistic inference techniques. Here, we review some of these algorithms and illustrate their performance in simulations and real MEG/EEG data. We also briefly how functional connectivity approaches have evolved and are being applied in conjunction with MEG imaging.

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

References

  • Aine CJ, Bryant JE, Knoefel JE, Adair JC, Hart B, Donahue CH, Montano R, Hayek R, Qualls C, Ranken D, Stephen JM (2010) Different strategies for auditory word recognition in healthy versus normal aging. Neuroimage 49:3319–3330

    Article  Google Scholar 

  • Astolfi L, Cincotti F, Mattia D, Babiloni C, Carducci F, Basilisco A, Rossini PM, Salinari S, Ding L, Ni Y, He B, Babiloni F (2005) Assessing cortical functional connectivity by linear inverse estimation and directed transfer function: simulations and application to real data. Clin Neurophysiol 116:920–932

    Article  Google Scholar 

  • Bartolomei F, Bosma I, Klein M, Baayen JC, Reijneveld JC, Postma TJ, Heimans JJ, Van Dijk BW, De Munck JC, De Jongh A, Cover KS, Stam CJ (2006a) Disturbed functional connectivity in brain tumour patients: evaluation by graph analysis of synchronization matrices. Clin Neurophysiol 117:2039–2049

    Article  Google Scholar 

  • Bartolomei F, Bosma I, Klein M, Baayen JC, Reijneveld JC, Postma TJ, Heimans JJ, Van Dijk BW, De Munck JC, De Jongh A, Cover KS, Stam CJ (2006b) How do brain tumors alter functional connectivity? A magnetoencephalography study. Ann Neurol 59:128–138

    Article  Google Scholar 

  • Belliveau JW, Kwong KK, Kennedy DN, Baker JR, Stern CE, Benson R, Chesler DA, Weisskoff RM, Cohen MS, Tootell RB et al (1992) Magnetic resonance imaging mapping of brain function. Human visual cortex. Invest Radiol 27(Suppl 2):S59–S65

    Article  Google Scholar 

  • Bosma I, Douw L, Bartolomei F, Heimans JJ, Van Dijk BW, Postma TJ, Stam CJ, Reijneveld JC, Klein M (2008a) Synchronized brain activity and neurocognitive function in patients with low-grade glioma: a magnetoencephalography study. Neuro Oncol 10:734–744

    Article  Google Scholar 

  • Bosma I, Stam CJ, Douw L, Bartolomei F, Heimans JJ, Van Dijk BW, Postma TJ, Klein M, Reijneveld JC (2008b) The influence of low-grade glioma on resting state oscillatory brain activity: a magnetoencephalography study. J Neurooncol 88:77–85

    Article  Google Scholar 

  • Campi C, Pascarella A, Sorrentino A, Piana M (2011) Highly automated dipole estimation (HADES). Comput Intell Neurosci 2011:982185

    Article  Google Scholar 

  • Dalal SS, Guggisberg AG, Edwards E, Sekihara K, Findlay AM, Canolty RT, Berger MS, Knight RT, Barbaro NM, Kirsch HE, Nagarajan SS (2008) Five-dimensional neuroimaging: localization of the time-frequency dynamics of cortical activity. Neuroimage 40:1686–1700

    Article  Google Scholar 

  • Dalal SS, Sekihara K, Nagarajan SS (2006) Modified beamformers for coherent source region suppression. IEEE Trans Biomed Eng 53:1357–1363

    Article  Google Scholar 

  • Darvas F, Pantazis D, Kucukaltun-Yildirim E, Leahy RM (2004) Mapping human brain function with MEG and EEG: methods and validation. Neuroimage 23(Suppl 1):S289–S299

    Article  Google Scholar 

  • De Pasquale F, Della Penna S, Snyder AZ, Lewis C, Mantini D, Marzetti L, Belardinelli P, Ciancetta L, Pizzella V, Romani GL, Corbetta M (2010) Temporal dynamics of spontaneous MEG activity in brain networks. Proc Natl Acad Sci U S A 107:6040–6045

    Article  Google Scholar 

  • De Pasquale F, Della Penna S, Snyder AZ, Marzetti L, Pizzella V, Romani GL, Corbetta M (2012) A cortical core for dynamic integration of functional networks in the resting human brain. Neuron 74:753–764

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Douw L, Baayen H, Bosma I, Klein M, Vandertop P, Heimans J, Stam K, De Munck J, Reijneveld J (2008) Treatment-related changes in functional connectivity in brain tumor patients: a magnetoencephalography study. Exp Neurol 212:285–290

    Article  Google Scholar 

  • Douw L, Baayen JC, Klein M, Velis D, Alpherts WC, Bot J, Heimans JJ, Reijneveld JC, Stam CJ (2009) Functional connectivity in the brain before and during intra-arterial amobarbital injection (Wada test). Neuroimage 46:584–588

    Article  Google Scholar 

  • Douw L, Van Dellen E, Baayen JC, Klein M, Velis DN, Alpherts WC, Heimans JJ, Reijneveld JC, Stam CJ (2010) The lesioned brain: still a small-world? Front Hum Neurosci 4:174

    Article  Google Scholar 

  • Gross J, Kujala J, Hamalainen M, Timmermann L, Schnitzler A, Salmelin R (2001) Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proc Natl Acad Sci U S A 98:694–699

    Article  Google Scholar 

  • Guggisberg AG, Honma SM, Findlay AM, Dalal SS, Kirsch HE, Berger MS, Nagarajan SS (2007) Mapping functional connectivity in patients with brain lesions. Ann Neurol 63(2):193–203

    Google Scholar 

  • Hillebrand A, Barnes GR (2002) A quantitative assessment of the sensitivity of whole-head MEG to activity in the adult human cortex. Neuroimage 16:638–650

    Article  Google Scholar 

  • Hipp JF, Engel AK, Siegel M (2011) Oscillatory synchronization in large-scale cortical networks predicts perception. Neuron 69:387–396

    Article  Google Scholar 

  • Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK (2012) Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat Neurosci 15:884–890

    Article  Google Scholar 

  • Huang M, Aine CJ, Supek S, Best E, Ranken D, Flynn ER (1998) Multi-start downhill simplex method for spatio-temporal source localization in magnetoencephalography. Electroencephalogr Clin Neurophysiol 108:32–44

    Article  Google Scholar 

  • Jerbi K, Mosher JC, Baillet S, Leahy RM (2002) On MEG forward modelling using multipolar expansions. Phys Med Biol 47:523–555

    Article  Google Scholar 

  • Kiebel SJ, Daunizeau J, Phillips C, Friston KJ (2008) Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG. Neuroimage 39:728–741

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Makeig S, Jung TP, Bell AJ, Ghahremani D, Sejnowski TJ (1997) Blind separation of auditory event-related brain responses into independent components. Proc Natl Acad Sci U S A 94:10979–10984

    Article  Google Scholar 

  • Martino J, Honma SM, Findlay AM, Guggisberg AG, Owen JP, Kirsch HE, Berger MS, Nagarajan SS (2011) Resting functional connectivity in patients with brain tumors in eloquent areas. Ann Neurol 69:521–532

    Article  Google Scholar 

  • Marzetti L, Della Penna S, Snyder AZ, Pizzella V, Nolte G, De Pasquale F, Romani GL, Corbetta M (2013) Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure. Neuroimage 79:172–183

    Article  Google Scholar 

  • Mosher JC, Baillet S, Leahy RM (1999a) EEG source localization and imaging using multiple signal classification approaches. J Clin Neurophysiol 16:225–238

    Article  Google Scholar 

  • Mosher JC, Leahy RM, Lewis PS (1999b) EEG and MEG: forward solutions for inverse methods. IEEE Trans Biomed Eng 46:245–259

    Article  Google Scholar 

  • Mosher JC, Lewis PS, Leahy RM (1992) Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans Biomed Eng 39:541–557

    Article  Google Scholar 

  • Mosher JC, Spencer ME, Leahy RM, Lewis PS (1993) Error bounds for EEG and MEG dipole source localization. Electroencephalogr Clin Neurophysiol 86:303–321

    Article  Google Scholar 

  • Nagarajan SS, Attias HT, Hild KE 2nd, Sekihara K (2006) A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity. Neuroimage 30:400–416

    Article  Google Scholar 

  • Nagarajan SS, Attias HT, Hild KE 2nd, Sekihara K (2007) A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26(21):3886–3910

    Google Scholar 

  • Niessing J, Ebisch B, Schmidt KE, Niessing M, Singer W, Galuske RA (2005) Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 309:948–951

    Article  Google Scholar 

  • Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, Hallett M (2004) Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol 115:2292–2307

    Article  Google Scholar 

  • Nolte G, Curio G (2000) Current multipole expansion to estimate lateral extent of neuronal activity: a theoretical analysis. IEEE Trans Biomed Eng 47:1347–1355

    Article  Google Scholar 

  • Nunez PL, Srinivasan R (2006) A theoretical basis for standing and traveling brain waves measured with human EEG with implications for an integrated consciousness. Clin Neurophysiol 117:2424–2435

    Article  Google Scholar 

  • Nunez PL, Srinivasan R, Westdorp AF, Wijesinghe RS, Tucker DM, Silberstein RB, Cadusch PJ (1997) EEG coherency. I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalogr Clin Neurophysiol 103:499–515

    Article  Google Scholar 

  • Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle H, Ugurbil K (1992) Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc Natl Acad Sci U S A 89:5951–5955

    Article  Google Scholar 

  • Owen JP, Wipf DP, Attias HT, Sekihara K, Nagarajan SS (2012) Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data. Neuroimage 60:305–323

    Article  Google Scholar 

  • Parra L, Alvino C, Tang A, Pearlmutter B, Yeung N, Osman A, Sajda P (2002) Linear spatial integration for single-trial detection in encephalography. Neuroimage 17:223–230

    Article  Google Scholar 

  • Parra LC, Spence CD, Gerson AD, Sajda P (2005) Recipes for the linear analysis of EEG. Neuroimage 28:326–341

    Article  Google Scholar 

  • Phillips JW, Leahy RM, Mosher JC, Timsari B (1997) Imaging neural activity using MEG and EEG. IEEE Eng Med Biol Mag 16:34–42

    Article  Google Scholar 

  • Porcaro C, Zappasodi F, Rossini PM, Tecchio F (2009) Choice of multivariate autoregressive model order affecting real network functional connectivity estimate. Clin Neurophysiol 120:436–448

    Article  Google Scholar 

  • Quraan MA, Cheyne D (2010) Reconstruction of correlated brain activity with adaptive spatial filters in MEG. Neuroimage 49:2387–2400

    Article  Google Scholar 

  • Roberts TP, Ferrari P, Perry D, Rowley HA, Berger MS (2000) Presurgical mapping with magnetic source imaging: comparisons with intraoperative findings. Brain Tumor Pathol 17:57–64

    Article  Google Scholar 

  • Salmelin R, Hari R, Lounasmaa OV, Sams M (1994) Dynamics of brain activation during picture naming. Nature 368:463–465

    Article  Google Scholar 

  • Sekihara K, Nagarajan SS (2008) Adaptive spatial filters for electromagnetic brain imaging Springer, Heidelberg

    Google Scholar 

  • Singh KD, Barnes GR, Hillebrand A (2003) Group imaging of task-related changes in cortical synchronisation using nonparametric permutation testing. Neuroimage 19:1589–1601

    Article  Google Scholar 

  • Singh KD, Barnes GR, Hillebrand A, Forde EM, Williams AL (2002) Task-related changes in cortical synchronization are spatially coincident with the hemodynamic response. Neuroimage 16:103–114

    Article  Google Scholar 

  • Sorrentino A, Parkkonen L, Pascarella A, Campi C, Piana M (2009) Dynamical MEG source modeling with multi-target Bayesian filtering. Hum Brain Mapp 30:1911–1921

    Article  Google Scholar 

  • Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28:1178–1193

    Article  Google Scholar 

  • Susac A, Ilmoniemi RJ, Pihko E, Nurminen J, Supek S (2009) Early dissociation of face and object processing: a magnetoencephalographic study. Hum Brain Mapp 30:917–927

    Article  Google Scholar 

  • Tang AC, Pearlmutter BA, Malaszenko NA, Phung DB (2002a) Independent components of magnetoencephalography: single-trial response onset times. Neuroimage 17:1773–1789

    Article  Google Scholar 

  • Tang AC, Pearlmutter BA, Malaszenko NA, Phung DB, Reeb BC (2002b) Independent components of magnetoencephalography: localization. Neural Comput 14:1827–1858

    Article  MATH  Google Scholar 

  • Tank DW, Ogawa S, Ugurbil K (1992) Mapping the brain with MRI. Curr Biol 2:525–528

    Article  Google Scholar 

  • Tarapore PE, Martino J, Guggisberg AG, Owen J, Honma SM, Findlay A, Berger MS, Kirsch HE, Nagarajan SS (2012) Magnetoencephalographic imaging of resting-state functional connectivity predicts postsurgical neurological outcome in brain gliomas. Neurosurgery 71:1012–1022

    Article  Google Scholar 

  • Van Dellen E, Douw L, Hillebrand A, Ris-Hilgersom IH, Schoonheim MM, Baayen JC, De Witt Hamer PC, Velis DN, Klein M, Heimans JJ, Stam CJ, Reijneveld JC (2012) MEG network differences between low- and high-grade glioma related to epilepsy and cognition. PLoS ONE 7:e50122

    Article  Google Scholar 

  • Von Ellenrieder N, Muravchik CH, Nehorai A (2005) MEG forward problem formulation using equivalent surface current densities. IEEE Trans Biomed Eng 52:1210–1217

    Article  Google Scholar 

  • Vrba J, Robinson SE (2002) SQUID sensor array configurations for magnetoencephalography applications. Supercond Sci Technol 15:51–89

    Article  Google Scholar 

  • Westlake KP, Hinkley LB, Bucci M, Guggisberg AG, Byl N, Findlay AM, Henry RG, Nagarajan SS (2012) Resting state alpha-band functional connectivity and recovery after stroke. Exp Neurol 237:160–169

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 49:641–655

    Article  Google Scholar 

  • Wolters CH, Beckmann RF, Rienacker A, Buchner H (1999) Comparing regularized and non-regularized nonlinear dipole fit methods: a study in a simulated sulcus structure. Brain Topogr 12:3–18

    Article  Google Scholar 

  • Zumer JM, Attias HT, Sekihara K, Nagarajan SS (2007) A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data. Neuroimage 37(1):102–115

    Google Scholar 

  • Zumer JM, Attias HT, Sekihara K, Nagarajan SS (2008) Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data. Neuroimage 41:924–940

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology and Biomedical Imaging, University of California, 513 Parnassus Avenue, S362, San Francisco, CA, 94143, USA

    Srikantan Nagarajan

  2. Department of Systems Design & Engineering, Tokyo Metropolitan University, Asahigaoka 6-6, Hino, Tokyo, 191-0065, Japan

    Kensuke Sekihara

Authors
  1. Srikantan Nagarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kensuke Sekihara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srikantan Nagarajan .

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

Nagarajan, S., Sekihara, K. (2014). Magnetoencephalographic Imaging. In: Supek, S., Aine, C. (eds) Magnetoencephalography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33045-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33045-2_6

  • 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