MEG Analysis with Spatial Filtered Reconstruction

Shinpei OKAWA; Satoshi HONDA

doi:10.1093/ietfec/e89-a.5.1428

MEG Analysis with Spatial Filtered Reconstruction

Shinpei OKAWA, Satoshi HONDA

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Magnetoencephalography (MEG) is a method to measure a magnetic field generated by electrical neural activity in a brain, and it plays increasingly important role in clinical diagnoses and neurophysiological studies. However, in MEG analysis, the estimation of the brain activity, of the electric current density distribution in a brain which is represented by current dipoles, is problematic. A spatial filter and subsequent reconstruction of the current density distribution estimated by the spatial filter (spatial filtered reconstruction: SFR) are proposed. The spatial filter is designed to be used without prior or temporal information. The proposed spatial filter ensures that it concentrates the current distribution around the activated sources in the conductor. The current distribution estimated by the spatial filter is reconstructed by multiple linear regression. Redundant current dipoles are eliminated, and the current distribution is optimized in the sense of the Mallows C_p statistic. Numerical studies are demonstrated and show successful estimation by SFR in multiple-dipole cases. In single-dipole cases with SNRs of 10¹ and more, the location of the true dipole was successfully estimated for about 80% of the simulations. The reconstruction with multiple linear regression corrected the location of the maximum current density estimated by the proposed spatial filtering. The dipole on the correct position contributes to more than 70% of the total dipoles in the estimated current distribution in those cases. These results show that the current distribution is effectively localized by SFR. We also investigate the differences among SFR, the LCMV (linearly constrained minimum variance) beamformer and the SAM (synthetic aperture magnetometry), the representatives of spatial filters in MEG analyses. It is indicated that spatial resolution is improved by avoiding dependence on temporal information.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.5 pp.1428-1436

Publication Date: 2006/05/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e89-a.5.1428

Type of Manuscript: PAPER

Category: Digital Signal Processing

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Shinpei OKAWA, Satoshi HONDA, "MEG Analysis with Spatial Filtered Reconstruction" in IEICE TRANSACTIONS on Fundamentals, vol. E89-A, no. 5, pp. 1428-1436, May 2006, doi: 10.1093/ietfec/e89-a.5.1428.
Abstract: Magnetoencephalography (MEG) is a method to measure a magnetic field generated by electrical neural activity in a brain, and it plays increasingly important role in clinical diagnoses and neurophysiological studies. However, in MEG analysis, the estimation of the brain activity, of the electric current density distribution in a brain which is represented by current dipoles, is problematic. A spatial filter and subsequent reconstruction of the current density distribution estimated by the spatial filter (spatial filtered reconstruction: SFR) are proposed. The spatial filter is designed to be used without prior or temporal information. The proposed spatial filter ensures that it concentrates the current distribution around the activated sources in the conductor. The current distribution estimated by the spatial filter is reconstructed by multiple linear regression. Redundant current dipoles are eliminated, and the current distribution is optimized in the sense of the Mallows C_p statistic. Numerical studies are demonstrated and show successful estimation by SFR in multiple-dipole cases. In single-dipole cases with SNRs of 10¹ and more, the location of the true dipole was successfully estimated for about 80% of the simulations. The reconstruction with multiple linear regression corrected the location of the maximum current density estimated by the proposed spatial filtering. The dipole on the correct position contributes to more than 70% of the total dipoles in the estimated current distribution in those cases. These results show that the current distribution is effectively localized by SFR. We also investigate the differences among SFR, the LCMV (linearly constrained minimum variance) beamformer and the SAM (synthetic aperture magnetometry), the representatives of spatial filters in MEG analyses. It is indicated that spatial resolution is improved by avoiding dependence on temporal information.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.5.1428/_p

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@ARTICLE{e89-a_5_1428,
author={Shinpei OKAWA, Satoshi HONDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={MEG Analysis with Spatial Filtered Reconstruction},
year={2006},
volume={E89-A},
number={5},
pages={1428-1436},
abstract={Magnetoencephalography (MEG) is a method to measure a magnetic field generated by electrical neural activity in a brain, and it plays increasingly important role in clinical diagnoses and neurophysiological studies. However, in MEG analysis, the estimation of the brain activity, of the electric current density distribution in a brain which is represented by current dipoles, is problematic. A spatial filter and subsequent reconstruction of the current density distribution estimated by the spatial filter (spatial filtered reconstruction: SFR) are proposed. The spatial filter is designed to be used without prior or temporal information. The proposed spatial filter ensures that it concentrates the current distribution around the activated sources in the conductor. The current distribution estimated by the spatial filter is reconstructed by multiple linear regression. Redundant current dipoles are eliminated, and the current distribution is optimized in the sense of the Mallows C_p statistic. Numerical studies are demonstrated and show successful estimation by SFR in multiple-dipole cases. In single-dipole cases with SNRs of 10¹ and more, the location of the true dipole was successfully estimated for about 80% of the simulations. The reconstruction with multiple linear regression corrected the location of the maximum current density estimated by the proposed spatial filtering. The dipole on the correct position contributes to more than 70% of the total dipoles in the estimated current distribution in those cases. These results show that the current distribution is effectively localized by SFR. We also investigate the differences among SFR, the LCMV (linearly constrained minimum variance) beamformer and the SAM (synthetic aperture magnetometry), the representatives of spatial filters in MEG analyses. It is indicated that spatial resolution is improved by avoiding dependence on temporal information.},
keywords={},
doi={10.1093/ietfec/e89-a.5.1428},
ISSN={1745-1337},
month={May},}

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TY - JOUR
TI - MEG Analysis with Spatial Filtered Reconstruction
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1428
EP - 1436
AU - Shinpei OKAWA
AU - Satoshi HONDA
PY - 2006
DO - 10.1093/ietfec/e89-a.5.1428
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2006
AB - Magnetoencephalography (MEG) is a method to measure a magnetic field generated by electrical neural activity in a brain, and it plays increasingly important role in clinical diagnoses and neurophysiological studies. However, in MEG analysis, the estimation of the brain activity, of the electric current density distribution in a brain which is represented by current dipoles, is problematic. A spatial filter and subsequent reconstruction of the current density distribution estimated by the spatial filter (spatial filtered reconstruction: SFR) are proposed. The spatial filter is designed to be used without prior or temporal information. The proposed spatial filter ensures that it concentrates the current distribution around the activated sources in the conductor. The current distribution estimated by the spatial filter is reconstructed by multiple linear regression. Redundant current dipoles are eliminated, and the current distribution is optimized in the sense of the Mallows C_p statistic. Numerical studies are demonstrated and show successful estimation by SFR in multiple-dipole cases. In single-dipole cases with SNRs of 10¹ and more, the location of the true dipole was successfully estimated for about 80% of the simulations. The reconstruction with multiple linear regression corrected the location of the maximum current density estimated by the proposed spatial filtering. The dipole on the correct position contributes to more than 70% of the total dipoles in the estimated current distribution in those cases. These results show that the current distribution is effectively localized by SFR. We also investigate the differences among SFR, the LCMV (linearly constrained minimum variance) beamformer and the SAM (synthetic aperture magnetometry), the representatives of spatial filters in MEG analyses. It is indicated that spatial resolution is improved by avoiding dependence on temporal information.
ER -