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Magnetoencephalographic Imaging

  • Srikantan S. NagarajanEmail author
  • Kensuke Sekihara
Reference work entry

Abstract

Noninvasive and dynamic imaging of brain activity in the sub-millisecond timescale 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 have 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 estimating 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 discuss how functional connectivity approaches have evolved and are being applied in conjunction with MEG imaging.

Keywords

MEG/EEG source reconstruction Forward models Inverse algorithms Bayesian methods Functional connectivity Group statistics 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoUSA
  2. 2.Department of Systems Design and EngineeringTokyo Metropolitan UniversityHinoJapan
  3. 3.Signal Analysis Inc.HachiojiJapan
  4. 4.Department of Advanced Technology in MedicineTokyo Medical and Dental UniversityBunkyo-kuJapan

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

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