A general linear model for MEG beamformer imaging

Matthew J Brookes; Andrew M Gibson; Stephen D Hall; Paul Lawrence Furlong; Gareth R Barnes; Arjan Hillebrand; Krish D Singh; Ian E Holliday; Sue T Francis; Peter G Morris

doi:https://doi.org/10.1016/j.neuroimage.2004.06.031

A general linear model for MEG beamformer imaging

Matthew J Brookes, Andrew M Gibson, Stephen D Hall, Paul Lawrence Furlong, Gareth R Barnes, Arjan Hillebrand, Krish D Singh, Ian E Holliday, Sue T Francis, Peter G Morris

Research output: Contribution to journal › Article › Academic › peer-review

45 Citations (Scopus)

Abstract

A new general linear model (GLM) beamformer method is described for processing magnetoencephalography (MEG) data. A standard nonlinear beamformer is used to determine the time course of neuronal activation for each point in a predefined source space. A Hilbert transform gives the envelope of oscillatory activity at each location in any chosen frequency band (not necessary in the case of sustained (DC) fields), enabling the general linear model to be applied and a volumetric T statistic image to be determined. The new method is illustrated by a two-source simulation (sustained field and 20 Hz) and is shown to provide accurate localization. The method is also shown to locate accurately the increasing and decreasing gamma activities to the temporal and frontal lobes, respectively, in the case of a scintillating scotoma. The new method brings the advantages of the general linear model to the analysis of MEG data and should prove useful for the localization of changing patterns of activity across all frequency ranges including DC (sustained fields).

Original language	English
Pages (from-to)	936-46
Number of pages	11
Journal	NEUROIMAGE
Volume	23
Issue number	3
DOIs	https://doi.org/10.1016/j.neuroimage.2004.06.031
Publication status	Published - Nov 2004
Externally published	Yes

Keywords

Algorithms
Computer Simulation
Data Collection
False Positive Reactions
Fourier Analysis
Humans
Image Processing, Computer-Assisted/statistics & numerical data
Linear Models
Magnetic Resonance Imaging
Magnetoencephalography/statistics & numerical data
Monte Carlo Method
Scotoma/physiopathology

Access to Document

https://doi.org/10.1016/j.neuroimage.2004.06.031

Cite this

@article{7c1bef988c784dd9bc9c310ab2ca46fb,

title = "A general linear model for MEG beamformer imaging",

abstract = "A new general linear model (GLM) beamformer method is described for processing magnetoencephalography (MEG) data. A standard nonlinear beamformer is used to determine the time course of neuronal activation for each point in a predefined source space. A Hilbert transform gives the envelope of oscillatory activity at each location in any chosen frequency band (not necessary in the case of sustained (DC) fields), enabling the general linear model to be applied and a volumetric T statistic image to be determined. The new method is illustrated by a two-source simulation (sustained field and 20 Hz) and is shown to provide accurate localization. The method is also shown to locate accurately the increasing and decreasing gamma activities to the temporal and frontal lobes, respectively, in the case of a scintillating scotoma. The new method brings the advantages of the general linear model to the analysis of MEG data and should prove useful for the localization of changing patterns of activity across all frequency ranges including DC (sustained fields).",

keywords = "Algorithms, Computer Simulation, Data Collection, False Positive Reactions, Fourier Analysis, Humans, Image Processing, Computer-Assisted/statistics & numerical data, Linear Models, Magnetic Resonance Imaging, Magnetoencephalography/statistics & numerical data, Monte Carlo Method, Scotoma/physiopathology",

author = "Brookes, {Matthew J} and Gibson, {Andrew M} and Hall, {Stephen D} and Furlong, {Paul Lawrence} and Barnes, {Gareth R} and Arjan Hillebrand and Singh, {Krish D} and Holliday, {Ian E} and Francis, {Sue T} and Morris, {Peter G}",

year = "2004",

month = nov,

doi = "https://doi.org/10.1016/j.neuroimage.2004.06.031",

language = "English",

volume = "23",

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journal = "NEUROIMAGE",

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AU - Brookes, Matthew J

AU - Gibson, Andrew M

AU - Hall, Stephen D

AU - Furlong, Paul Lawrence

AU - Barnes, Gareth R

AU - Hillebrand, Arjan

AU - Singh, Krish D

AU - Holliday, Ian E

AU - Francis, Sue T

AU - Morris, Peter G

PY - 2004/11

Y1 - 2004/11

N2 - A new general linear model (GLM) beamformer method is described for processing magnetoencephalography (MEG) data. A standard nonlinear beamformer is used to determine the time course of neuronal activation for each point in a predefined source space. A Hilbert transform gives the envelope of oscillatory activity at each location in any chosen frequency band (not necessary in the case of sustained (DC) fields), enabling the general linear model to be applied and a volumetric T statistic image to be determined. The new method is illustrated by a two-source simulation (sustained field and 20 Hz) and is shown to provide accurate localization. The method is also shown to locate accurately the increasing and decreasing gamma activities to the temporal and frontal lobes, respectively, in the case of a scintillating scotoma. The new method brings the advantages of the general linear model to the analysis of MEG data and should prove useful for the localization of changing patterns of activity across all frequency ranges including DC (sustained fields).

AB - A new general linear model (GLM) beamformer method is described for processing magnetoencephalography (MEG) data. A standard nonlinear beamformer is used to determine the time course of neuronal activation for each point in a predefined source space. A Hilbert transform gives the envelope of oscillatory activity at each location in any chosen frequency band (not necessary in the case of sustained (DC) fields), enabling the general linear model to be applied and a volumetric T statistic image to be determined. The new method is illustrated by a two-source simulation (sustained field and 20 Hz) and is shown to provide accurate localization. The method is also shown to locate accurately the increasing and decreasing gamma activities to the temporal and frontal lobes, respectively, in the case of a scintillating scotoma. The new method brings the advantages of the general linear model to the analysis of MEG data and should prove useful for the localization of changing patterns of activity across all frequency ranges including DC (sustained fields).

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KW - False Positive Reactions

KW - Fourier Analysis

KW - Humans

KW - Image Processing, Computer-Assisted/statistics & numerical data

KW - Linear Models

KW - Magnetic Resonance Imaging

KW - Magnetoencephalography/statistics & numerical data

KW - Monte Carlo Method

KW - Scotoma/physiopathology

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DO - https://doi.org/10.1016/j.neuroimage.2004.06.031

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