A general linear model for MEG beamformer imaging.
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).
Brookes
Matthew J
MJ
Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
Gibson
Andrew M
AM
Hall
Stephen D
SD
Furlong
Paul L
PL
Barnes
Gareth R
GR
Hillebrand
Arjan
A
Singh
Krish D
KD
Holliday
Ian E
IE
Francis
Sue T
ST
Morris
Peter G
PG
D000465:Algorithms; D003198:Computer Simulation; D003625:Data Collection; D005189:False Positive Reactions; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D009010:Monte Carlo Method; D012607:Scotoma / Q000503:physiopathology