Linear transformations of data space in MEG

Groß, Joachim; Ioannides, A.A.

doi:10.1088/0031-9155/44/8/317

Cited by 83 publications

(62 citation statements)

References 16 publications

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“…The choice of dipole along one of the orthogonal axes is purely for convenience, as was done in other papers (Van Veen et al 1997;Gross and Ioannides 1999;Sekihara et al 2001;Barnes and Hillebrand 2003). To confirm this principle, we rotated the dipole orientations in the simulation 45 degrees along the z-axis, and obtained results that were virtually identical to the findings before the rotation, for all simulated cases shown previously in this section.…”

Section: Source Orientations and The Order Of Covariance Matrixsupporting

confidence: 56%

“…The dimension of the signal subspace used in the regularization of the type 2 beamformer was 2. The Backus-Gilbert regularization parameter µ used in the type 3 beamformer was determined by the method proposed by Gross and Ioannides (1999). A third-order covariance matrix (n=3) was used in our type 4 beamformer (figure 4d).…”

Section: Signals With Very Low Correlationmentioning

confidence: 99%

“…Recently, a variety of beamformer approaches have been introduced to study neuronal activity using electroencephalography (EEG) or magnetoencephalography (MEG) (Van Veen et al 1997;Robinson and Vrba 1999;Gross and Ioannides 1999;Sekihara et al 2001;Gross et al 2001;Barnes and Hillebrand 2003). A beamformer in EEG/MEG is a set of spatial filters based on the EEG/MEG lead fields, the covariance matrix of the signal evaluated for an epoch wherein the number of time points is several times as many as the number of measurement channels (Van Veen et al 1997), and sometimes the covariance matrix of the noise.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Commonalities and Differences Among Vectorized Beamformers in Electromagnetic Source Imaging

Huang¹,

Shih²,

Lee³

et al. 2003

Brain Topogr

131

117

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Summary: A number of beamformers have been introduced to localize neuronal activity using magnetoencephalography (MEG) and electroencephalography (EEG). However, currently available information about the major aspects of existing beamformers is incomplete. In the present study, detailed analyses are performed to study the commonalities and differences among vectorized versions of existing beamformers in both theory and practice. In addition, a novel beamformer based on higher-order covariance analysis is introduced. Theoretical formulas are provided on all major aspects of each beamformer; to examine their performance, computer simulations with different levels of correlation and signal-to-noise ratio are studied. Then, an empirical data set of human MEG median-nerve responses with a large number of neuronal generators is analyzed using the different beamformers. The results show substantial differences among existing MEG/EEG beamformers in their ways of describing the spatial map of neuronal activity. Differences in performance are observed among existing beamformers in terms of their spatial resolution, false-positive background activity, and robustness to highly correlated signals. Superior performance is obtained using our novel beamformer with higher-order covariance analysis in simulated data. Excellent agreement is also found between the results of our beamformer and the known neurophysiology of the median-nerve MEG response.

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Section: Source Orientations and The Order Of Covariance Matrixsupporting

confidence: 56%

Section: Signals With Very Low Correlationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Commonalities and Differences Among Vectorized Beamformers in Electromagnetic Source Imaging

Huang¹,

Shih²,

Lee³

et al. 2003

Brain Topogr

131

117

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“…By minimizing the corresponding Lagrange function, the frequency-dependent solution can be derived in analogy with ref. 41:…”

Section: Appendixmentioning

confidence: 99%

Dynamic imaging of coherent sources: Studying neural interactions in the human brain

Groß

Kujala

Hämäläinen

et al. 2001

Proc. Natl. Acad. Sci. U.S.A.

1,644

1,349

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Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept (''binding'') is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto-or electroencephalography (MEG͞EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.oscillations ͉ functional connectivity ͉ coherence ͉ magnetoencephalography ͉ synchronization T he hypothesis that relevant information in the brain is coded by accurate timing of neuronal discharges has received strong support from recent reports of synchronization of neuronal firing within and across areas of the cat visual cortex (1). The synchronization of neural activity, which was modulated by gammaband oscillations, was shown to depend on stimulus properties like continuity, vicinity, and common motion, and on receptive field constellations (for review, see ref.2). This and similar findings seem to support the concept that synchronized rhythmic neural firing has a role in solving the binding problem, i.e., the integration of distributed information into a unified representation (1-4).To investigate cortico-cortical synchrony noninvasively in the human brain, new analysis tools must be developed. In functional magnetic resonance imaging (fMRI) studies, structural equation models have been used to estimate connectivities between brain areas (5, 6). Although this is a very promising approach, it lacks the temporal resolution required to measure oscillatory activity and to observe the expected transient formation of neuronal assemblies (7).Magnetoencephalography (MEG) and electroencephalography (...

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“…These methods are typically used to perform the linear transformation of sensor time series into r Michalareas et al r r 892 r source space [Baillet et al, 2001;Gross and Ioannides, 1999;Hämäläinen, 1992]. However, these linear transformations can also be applied to other measures such as the cross-spectral density to perform tomographic mapping of power or coherence [Gross et al, 2001].…”

Section: Methodsmentioning

confidence: 99%

Investigating causality between interacting brain areas with multivariate autoregressive models of MEG sensor data

Michalareas¹,

Schoffelen²,

Gross³

2009

NeuroImage

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In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements. We first demonstrate the theoretical feasibility of estimating source level causal interactions after projection of the sensor-level model coefficients onto the locations of the neural sources. Next, we show with simulated MEG data that causality, as measured by partial directed coherence (PDC), can be correctly reconstructed if the locations of the interacting brain areas are known. We further demonstrate, if a very large number of brain voxels is considered as potential activation sources, that PDC as a measure to reconstruct causal interactions is less accurate. In such case the MAR model coefficients alone contain meaningful causality information. The proposed method overcomes the problems of model nonrobustness and large computation times encountered during causality analysis by existing methods. These methods first project MEG sensor time-series onto a large number of brain locations after which the MAR model is built on this large number of source-level time-series. Instead, through this work, we demonstrate that by building the MAR model on the sensor-level and then projecting only the MAR coefficients in source space, the true casual pathways are recovered even when a very large number of locations are considered as sources. The main contribution of this work is that by this methodology entire brain causality maps can be efficiently derived without any a priori selection of regions of interest. Hum Brain Mapp 34:890-913, 2013.V C 2012 Wiley Periodicals, Inc.

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Linear transformations of data space in MEG

Cited by 83 publications

References 16 publications

Commonalities and Differences Among Vectorized Beamformers in Electromagnetic Source Imaging

Commonalities and Differences Among Vectorized Beamformers in Electromagnetic Source Imaging

Dynamic imaging of coherent sources: Studying neural interactions in the human brain

Investigating causality between interacting brain areas with multivariate autoregressive models of MEG sensor data

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