Skip to main content
Top
Published in: Brain Topography 2/2020

Open Access 01-03-2020 | Original Paper

Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth

Authors: A. Rezaei, A. Koulouri, S. Pursiainen

Published in: Brain Topography | Issue 2/2020

Login to get access

Abstract

We focus on electro-/magnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial and deep areas without specific a priori knowledge of the number or location of the activity. To enable source distinguishability for any depth, we introduce a randomized multiresolution scanning (RAMUS) approach in which the MAP estimate of the brain activity is varied during the reconstruction process. RAMUS aims to provide a robust and accurate imaging outcome for the whole brain, while maintaining the computational cost on an appropriate level. The inverse gamma (IG) distribution is applied as the primary hyperprior in order to achieve an optimal performance for the deep part of the brain. In this proof-of-the-concept study, we consider the detection of simultaneous thalamic and somatosensory activity via numerically simulated data modeling the 14-20 ms post-stimulus somatosensory evoked potential and field response to electrical wrist stimulation. Both a spherical and realistic model are utilized to analyze the source reconstruction discrepancies. In the numerically examined case, RAMUS was observed to enhance the visibility of deep components and also marginalizing the random effects of the discretization and optimization without a remarkable computation cost. A robust and accurate MAP estimate for the primary current density was obtained in both superficial and deep parts of the brain.
Appendix
Available only for authorised users
Literature
go back to reference Ary JP, Klein SA, Fender DH (1981) Location of sources of evoked scalp potentials: corrections for skull and scalp thicknesses. IEEE Trans Biomed Eng 6:447–452 Ary JP, Klein SA, Fender DH (1981) Location of sources of evoked scalp potentials: corrections for skull and scalp thicknesses. IEEE Trans Biomed Eng 6:447–452
go back to reference Attal Y, Schwartz D (2013) Assessment of subcortical source localization using deep brain activity imaging model with minimum norm operators: a MEG study. PLoS ONE 8(3):e59,856 Attal Y, Schwartz D (2013) Assessment of subcortical source localization using deep brain activity imaging model with minimum norm operators: a MEG study. PLoS ONE 8(3):e59,856
go back to reference Buchner H, Ferbert A, Hacke W (1988) Serial recording of median nerve stimulated subcortical somatosensory evoked potentials (SEPs) in developing brain death. Clin Neurophysiol 69(1):14–23 Buchner H, Ferbert A, Hacke W (1988) Serial recording of median nerve stimulated subcortical somatosensory evoked potentials (SEPs) in developing brain death. Clin Neurophysiol 69(1):14–23
go back to reference Buchner H, Adams L, Knepper A, Rüger R, Laborde G, Gilsbach JM, Ludwig I, Reul J, Scherg M (1994a) Preoperative localization of the central sulcus by dipole source analysis of early somatosensory evoked potentials and three-dimensional magnetic resonance imaging. J Neurosurg 80(5):849–856PubMed Buchner H, Adams L, Knepper A, Rüger R, Laborde G, Gilsbach JM, Ludwig I, Reul J, Scherg M (1994a) Preoperative localization of the central sulcus by dipole source analysis of early somatosensory evoked potentials and three-dimensional magnetic resonance imaging. J Neurosurg 80(5):849–856PubMed
go back to reference Buchner H, Fuchs M, Wischmann HA, Dössel O, Ludwig I, Knepper A, Berg P (1994b) Source analysis of median nerve and finger stimulated somatosensory evoked potentials: multichannel simultaneous recording of electric and magnetic fields combined with 3d-mr tomography. Brain Topogr 6(4):299–310PubMed Buchner H, Fuchs M, Wischmann HA, Dössel O, Ludwig I, Knepper A, Berg P (1994b) Source analysis of median nerve and finger stimulated somatosensory evoked potentials: multichannel simultaneous recording of electric and magnetic fields combined with 3d-mr tomography. Brain Topogr 6(4):299–310PubMed
go back to reference Buchner H, Adams L, Müller A, Ludwig I, Knepper A, Thron A, Niemann K, Scherg M (1995) Somatotopy of human hand somatosensory cortex revealed by dipole source analysis of early somatosensory evoked potentials and 3d-nmr tomography. Electroencephalogr Clin Neurophysiol 96(2):121–134PubMed Buchner H, Adams L, Müller A, Ludwig I, Knepper A, Thron A, Niemann K, Scherg M (1995) Somatotopy of human hand somatosensory cortex revealed by dipole source analysis of early somatosensory evoked potentials and 3d-nmr tomography. Electroencephalogr Clin Neurophysiol 96(2):121–134PubMed
go back to reference Calvetti D, Hakula H, Pursiainen S, Somersalo E (2009) Conditionally Gaussian hypermodels for cerebral source localization. SIAM J Imaging Sci 2(3):879–909 Calvetti D, Hakula H, Pursiainen S, Somersalo E (2009) Conditionally Gaussian hypermodels for cerebral source localization. SIAM J Imaging Sci 2(3):879–909
go back to reference Calvetti D, Pascarella A, Pitolli F, Somersalo E, Vantaggi B (2015) A hierarchical Krylov–Bayes iterative inverse solver for MEG with physiological preconditioning. Inverse Probl 31(12):125005 Calvetti D, Pascarella A, Pitolli F, Somersalo E, Vantaggi B (2015) A hierarchical Krylov–Bayes iterative inverse solver for MEG with physiological preconditioning. Inverse Probl 31(12):125005
go back to reference Calvetti D, Pascarella A, Pitolli F, Somersalo E, Vantaggi B (2018) Brain activity mapping from meg data via a hierarchical bayesian algorithm with automatic depth weighting. Brain Topogr 32(3):363–393PubMed Calvetti D, Pascarella A, Pitolli F, Somersalo E, Vantaggi B (2018) Brain activity mapping from meg data via a hierarchical bayesian algorithm with automatic depth weighting. Brain Topogr 32(3):363–393PubMed
go back to reference Clark I, Biscay R, Echeverría M, Virués T (1995) Multiresolution decomposition of non-stationary EEG signals: a preliminary study. Comput Biol Med 25(4):373–382PubMed Clark I, Biscay R, Echeverría M, Virués T (1995) Multiresolution decomposition of non-stationary EEG signals: a preliminary study. Comput Biol Med 25(4):373–382PubMed
go back to reference Cuffin BN, Schomer DL, Ives JR, Blume H (2001a) Experimental tests of EEG source localization accuracy in realistically shaped head models. Clin Neurophysiol 112(12):2288–2292PubMed Cuffin BN, Schomer DL, Ives JR, Blume H (2001a) Experimental tests of EEG source localization accuracy in realistically shaped head models. Clin Neurophysiol 112(12):2288–2292PubMed
go back to reference Cuffin BN, Schomer DL, Ives JR, Blume H (2001b) Experimental tests of EEG source localization accuracy in spherical head models. Clin Neurophysiol 112(1):46–51PubMed Cuffin BN, Schomer DL, Ives JR, Blume H (2001b) Experimental tests of EEG source localization accuracy in spherical head models. Clin Neurophysiol 112(1):46–51PubMed
go back to reference Fuchs M, Wagner M, Wischmann HA, Köhler T, Theißen A, Drenckhahn R, Buchner H (1998) Improving source reconstructions by combining bioelectric and biomagnetic data. Clin Neurophysiol 107(2):93–111 Fuchs M, Wagner M, Wischmann HA, Köhler T, Theißen A, Drenckhahn R, Buchner H (1998) Improving source reconstructions by combining bioelectric and biomagnetic data. Clin Neurophysiol 107(2):93–111
go back to reference Gavit L, Baillet S, Mangin JF, Pescatore J, Garnero L (2001) A multiresolution framework to MEG/EEG source imaging. IEEE Trans Biomed Eng 48(10):1080–1087PubMed Gavit L, Baillet S, Mangin JF, Pescatore J, Garnero L (2001) A multiresolution framework to MEG/EEG source imaging. IEEE Trans Biomed Eng 48(10):1080–1087PubMed
go back to reference Grover P (2016) Fundamental limits on source-localization accuracy of eeg-based neural sensing. In: Information Theory (ISIT), 2016 IEEE International Symposium on, IEEE, pp 1794–1798 Grover P (2016) Fundamental limits on source-localization accuracy of eeg-based neural sensing. In: Information Theory (ISIT), 2016 IEEE International Symposium on, IEEE, pp 1794–1798
go back to reference Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography: theory, instrumentation, and applications to invasive studies of the working human brain. Rev Mod Phys 65:413–498 Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography: theory, instrumentation, and applications to invasive studies of the working human brain. Rev Mod Phys 65:413–498
go back to reference Haueisen J, Leistritz L, Süsse T, Curio G, Witte H (2007) Identifying mutual information transfer in the brain with differential-algebraic modeling: evidence for fast oscillatory coupling between cortical somatosensory areas 3b and 1. NeuroImage 37(1):130–136PubMed Haueisen J, Leistritz L, Süsse T, Curio G, Witte H (2007) Identifying mutual information transfer in the brain with differential-algebraic modeling: evidence for fast oscillatory coupling between cortical somatosensory areas 3b and 1. NeuroImage 37(1):130–136PubMed
go back to reference He B, Sohrabpour A, Brown E, Liu Z (2018) Electrophysiological source imaging: a noninvasive window to brain dynamics. Annu Rev Biomed Eng 20:171–196PubMed He B, Sohrabpour A, Brown E, Liu Z (2018) Electrophysiological source imaging: a noninvasive window to brain dynamics. Annu Rev Biomed Eng 20:171–196PubMed
go back to reference Homa L, Calvetti D, Hoover A, Somersalo E (2013) Bayesian preconditioned CGLS for source separation in MEG time series. SIAM J Sci Comput 35(3):B778–B798 Homa L, Calvetti D, Hoover A, Somersalo E (2013) Bayesian preconditioned CGLS for source separation in MEG time series. SIAM J Sci Comput 35(3):B778–B798
go back to reference Jonmohamadi Y, Poudel G, Innes C, Weiss D, Krueger R, Jones R (2014) Comparison of beamformers for EEG source signal reconstruction. Biomed Signal Process Control 14:175–188 Jonmohamadi Y, Poudel G, Innes C, Weiss D, Krueger R, Jones R (2014) Comparison of beamformers for EEG source signal reconstruction. Biomed Signal Process Control 14:175–188
go back to reference Lee E, Duffy W, Hadimani R, Waris M, Siddiqui W, Islam F, Rajamani M, Nathan R, Jiles D (2016) Investigational effect of brain-scalp distance on the efficacy of transcranial magnetic stimulation treatment in depression. IEEE Trans Magn 52(7):1–4 Lee E, Duffy W, Hadimani R, Waris M, Siddiqui W, Islam F, Rajamani M, Nathan R, Jiles D (2016) Investigational effect of brain-scalp distance on the efficacy of transcranial magnetic stimulation treatment in depression. IEEE Trans Magn 52(7):1–4
go back to reference Liu J (2001) Monte Carlo strategies in scientific computing. Springer series in statistics. Springer, Berlin Liu J (2001) Monte Carlo strategies in scientific computing. Springer series in statistics. Springer, Berlin
go back to reference Liu J, Guenier B, Benard C (1995) A sensitivity decomposition for the regularized solution of inverse heat conduction problems by wavelets. Inverse Probl 11(6):1177 Liu J, Guenier B, Benard C (1995) A sensitivity decomposition for the regularized solution of inverse heat conduction problems by wavelets. Inverse Probl 11(6):1177
go back to reference Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 7:674–693 Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 7:674–693
go back to reference Miinalainen T, Rezaei A, Us D, Nüßing A, Engwer C, Wolters CH, Pursiainen S (2019) A realistic, accurate and fast source modeling approach for the EEG forward problem. NeuroImage 184:56–67PubMed Miinalainen T, Rezaei A, Us D, Nüßing A, Engwer C, Wolters CH, Pursiainen S (2019) A realistic, accurate and fast source modeling approach for the EEG forward problem. NeuroImage 184:56–67PubMed
go back to reference Nagarajan SS, Portniaguine O, Hwang D, Johnson C, Sekihara K (2006) Controlled support MEG imaging. NeuroImage 33(3):878–885PubMedPubMedCentral Nagarajan SS, Portniaguine O, Hwang D, Johnson C, Sekihara K (2006) Controlled support MEG imaging. NeuroImage 33(3):878–885PubMedPubMedCentral
go back to reference O’Hagan A, Forster JJ (2004) Kendall’s advanced theory of statistics, volume 2B: Bayesian inference, vol 2. Arnold O’Hagan A, Forster JJ (2004) Kendall’s advanced theory of statistics, volume 2B: Bayesian inference, vol 2. Arnold
go back to reference Pascual-Marqui RD (1999) Review of methods for solving the EEG inverse problem. Int J Bioelectromagn 1(1):75–86 Pascual-Marqui RD (1999) Review of methods for solving the EEG inverse problem. Int J Bioelectromagn 1(1):75–86
go back to reference Pascual-Marqui RD, Lehmann D, Koenig T, Kochi K, Merlo MC, Hell D, Koukkou M (1999) Low resolution brain electromagnetic tomography (loreta) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Res 90(3):169–179PubMed Pascual-Marqui RD, Lehmann D, Koenig T, Kochi K, Merlo MC, Hell D, Koukkou M (1999) Low resolution brain electromagnetic tomography (loreta) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Res 90(3):169–179PubMed
go back to reference Pascual-Marqui RD, Esslen M, Kochi K, Lehmann D et al (2002) Functional imaging with low-resolution brain electromagnetic tomography (loreta): a review. Methods Find Exp Clin Pharmacol 24(Suppl C):91–95PubMed Pascual-Marqui RD, Esslen M, Kochi K, Lehmann D et al (2002) Functional imaging with low-resolution brain electromagnetic tomography (loreta): a review. Methods Find Exp Clin Pharmacol 24(Suppl C):91–95PubMed
go back to reference Piana M, Bertero M (1997a) Projected landweber method and preconditioning. Inverse Probl 13(2):441 Piana M, Bertero M (1997a) Projected landweber method and preconditioning. Inverse Probl 13(2):441
go back to reference Piana M, Bertero M (1997b) Projected Landweber method and preconditioning. Inverse Probl 13(2):441–463 Piana M, Bertero M (1997b) Projected Landweber method and preconditioning. Inverse Probl 13(2):441–463
go back to reference Pizzo F, Roehri N, Villalon SM, Trébuchon A, Chen S, Lagarde S, Carron R, Gavaret M, Giusiano B, McGonigal A et al (2019) Deep brain activities can be detected with magnetoencephalography. Nat Communications 10(1):971 Pizzo F, Roehri N, Villalon SM, Trébuchon A, Chen S, Lagarde S, Carron R, Gavaret M, Giusiano B, McGonigal A et al (2019) Deep brain activities can be detected with magnetoencephalography. Nat Communications 10(1):971
go back to reference Pursiainen S (2008) Coarse-to-fine reconstruction in linear inverse problems with application to limited-angle computerized tomography. J Inv Ill Posed Probl 16(9):873–886 Pursiainen S (2008) Coarse-to-fine reconstruction in linear inverse problems with application to limited-angle computerized tomography. J Inv Ill Posed Probl 16(9):873–886
go back to reference Pursiainen S (2012a) Raviart–Thomas -type sources adapted to applied EEG and MEG: implementation and results. Inverse Probl 28(6):065,013 Pursiainen S (2012a) Raviart–Thomas -type sources adapted to applied EEG and MEG: implementation and results. Inverse Probl 28(6):065,013
go back to reference Pursiainen S (2012b) Raviart–Thomas-type sources adapted to applied EEG and MEG: implementation and results. Inverse Probl 28(6):065,013 Pursiainen S (2012b) Raviart–Thomas-type sources adapted to applied EEG and MEG: implementation and results. Inverse Probl 28(6):065,013
go back to reference Pursiainen S, Lucka F, Wolters CH (2012) Complete electrode model in EEG: relationship and differences to the point electrode model. Phys Med Biol 57(4):999–1017PubMed Pursiainen S, Lucka F, Wolters CH (2012) Complete electrode model in EEG: relationship and differences to the point electrode model. Phys Med Biol 57(4):999–1017PubMed
go back to reference Schmidt DM, George JS, Wood CC (1999) Bayesian inference applied to the electromagnetic inverse problem. Hum Brain Mapp 7(3):195–212PubMed Schmidt DM, George JS, Wood CC (1999) Bayesian inference applied to the electromagnetic inverse problem. Hum Brain Mapp 7(3):195–212PubMed
go back to reference Seeber M, Cantonas LM, Hoevels M, Sesia T, Visser-Vandewalle V, Michel CM (2019) Subcortical electrophysiological activity is detectable with high-density EEG source imaging. Nat Commun 10(1):753PubMedPubMedCentral Seeber M, Cantonas LM, Hoevels M, Sesia T, Visser-Vandewalle V, Michel CM (2019) Subcortical electrophysiological activity is detectable with high-density EEG source imaging. Nat Commun 10(1):753PubMedPubMedCentral
go back to reference Tarkiainen A, Liljeström M, Seppä M, Salmelin R (2003) The 3d topography of MEG source localization accuracy: effects of conductor model and noise. Clin Neurophysiol 114(10):1977–1992PubMed Tarkiainen A, Liljeström M, Seppä M, Salmelin R (2003) The 3d topography of MEG source localization accuracy: effects of conductor model and noise. Clin Neurophysiol 114(10):1977–1992PubMed
go back to reference Uutela K, Hämäläinen M, Somersalo E (1999) Visualization of magnetoencephalographic data using minimum current estimates. NeuroImage 10:173–180PubMed Uutela K, Hämäläinen M, Somersalo E (1999) Visualization of magnetoencephalographic data using minimum current estimates. NeuroImage 10:173–180PubMed
go back to reference Wang G, Yang L, Worrell G, He B (2009) The relationship between conductivity uncertainties and eeg source localization accuracy. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, IEEE, pp 4799–4802 Wang G, Yang L, Worrell G, He B (2009) The relationship between conductivity uncertainties and eeg source localization accuracy. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, IEEE, pp 4799–4802
Metadata
Title
Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth
Authors
A. Rezaei
A. Koulouri
S. Pursiainen
Publication date
01-03-2020
Publisher
Springer US
Published in
Brain Topography / Issue 2/2020
Print ISSN: 0896-0267
Electronic ISSN: 1573-6792
DOI
https://doi.org/10.1007/s10548-020-00755-8

Other articles of this Issue 2/2020

Brain Topography 2/2020 Go to the issue