Beamformer reconstruction of correlated sources using a modified source model

This paper introduces a lead field formulation for use in beamformer analysis of MEG data. This 'dual source beamformer' is a technique to image two temporally correlated sources using beamformer methodology. We show that while the standard, single source beamformer suppresses the reconstructed power of two spatially separate but temporally correlated sources, the dual source beamformer allows for their accurate reconstruction. The technique is proven to be accurate using simulations. We also show that it can be used to image accurately the auditory steady state response, which is correlated between the left and right auditory cortices. We suggest that this technique represents a useful way of locating correlated sources, particularly if a seed location can be defined a priori for one of the two sources. Such a priori information could be based on previous studies using similar paradigms, or from other functional neuroimaging techniques.

[1]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[2]  A. Rees,et al.  Steady-state evoked responses to sinusoidally amplitude-modulated sounds recorded in man , 1986, Hearing Research.

[3]  M-X Huang,et al.  Commonalities and Differences Among Vectorized Beamformers in Electromagnetic Source Imaging , 2003, Brain Topography.

[4]  Gareth R. Barnes,et al.  Imaging the dynamics of the auditory steady-state evoked response , 2005, Neuroscience Letters.

[5]  Samuel J. Williamson,et al.  Advances in Biomagnetism , 1990, Springer US.

[6]  David Poeppel,et al.  Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique , 2001, IEEE Transactions on Biomedical Engineering.

[7]  C Pantev,et al.  A high-precision magnetoencephalographic study of human auditory steady-state responses to amplitude-modulated tones. , 2000, The Journal of the Acoustical Society of America.

[8]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[9]  Se Robinson,et al.  Functional neuroimaging by Synthetic Aperture Magnetometry (SAM) , 1999 .

[10]  C Pantev,et al.  Right hemispheric laterality of human 40 Hz auditory steady-state responses. , 2005, Cerebral cortex.

[11]  J. Vrba.  Magnetoencephalography: the art of finding a needle in a haystack , 2002 .

[12]  Andrew W. Fitzgibbon,et al.  Robust Registration of 2D and 3D Point Sets , 2003, BMVC.

[13]  Wilkin Chau,et al.  Determination of activation areas in the human auditory cortex by means of synthetic aperture magnetometry , 2003, NeuroImage.

[14]  J. Hadamard,et al.  Lectures on Cauchy's Problem in Linear Partial Differential Equations , 1924 .

[15]  P. S. Lewis,et al.  Monte Carlo Analysis of Localization Errors in Magnetoencephalography , 1989 .

[16]  Krish D. Singh,et al.  A new approach to neuroimaging with magnetoencephalography , 2005, Human brain mapping.

[17]  H. Helmholtz.  Ueber einige Gesetze der Vertheilung elektrischer Ströme in körperlichen Leitern mit Anwendung auf die thierisch‐elektrischen Versuche , 1853 .

[18]  Shigeyuki Kuwada,et al.  Scalp potentials of normal and hearing-impaired subjects in response to sinusoidally amplitude-modulated tones , 1986, Hearing Research.

[19]  J. Sarvas.  Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[20]  David Poeppel,et al.  Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates , 2002, IEEE Transactions on Biomedical Engineering.

[21]  David Poeppel,et al.  Asymptotic SNR of scalar and vector minimum-variance beamformers for neuromagnetic source reconstruction , 2004, IEEE Transactions on Biomedical Engineering.

[22]  W. Drongelen,et al.  A spatial filtering technique to detect and localize multiple sources in the brain , 1996, Brain Topography.

[23]  J Gross,et al.  Properties of MEG tomographic maps obtained with spatial filtering , 2003, NeuroImage.

[24]  J. Vrba,et al.  Signal processing in magnetoencephalography. , 2001, Methods.

[25]  David Poeppel,et al.  Application of an MEG eigenspace beamformer to reconstructing spatio‐temporal activities of neural sources , 2002, Human brain mapping.

[26]  G. Barnes,et al.  Statistical flattening of MEG beamformer images , 2003, Human brain mapping.

[27]  Adrian L. Williams,et al.  Task-Related Changes in Cortical Synchronization Are Spatially Coincident with the Hemodynamic Response , 2002, NeuroImage.

[28]  Arjan Hillebrand,et al.  Beamformer analysis of MEG data. , 2005, International review of neurobiology.

[29]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[30]  Matthew J. Brookes,et al.  GLM-beamformer method demonstrates stationary field, alpha ERD and gamma ERS co-localisation with fMRI BOLD response in visual cortex , 2005, NeuroImage.

[31]  J.C. Mosher,et al.  Recursive MUSIC: A framework for EEG and MEG source localization , 1998, IEEE Transactions on Biomedical Engineering.

[32]  G. Barnes,et al.  Assessing interactions of linear and nonlinear neuronal sources using MEG beamformers: a proof of concept , 2005, Clinical Neurophysiology.