Analyzing event-related EEG data with multivariate autoregressive parameters

Schlögl, Alois; Supp, Gernot G.

doi:10.1016/s0079-6123(06)59009-0

Cited by 156 publications

(156 citation statements)

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“…The utility of MEG to measure connectivity has been shown by a large number of previous studies (examples include (Tass et al, 1998, Ioannides et al, 2000, Gross et al, 2001a, Nolte et al, 2004a, Ramnania et al, 2004, Schlögl and Supp, 2006, Jerbi et al, 2007, Gow et al, 2008, Nolte et al, 2008, Schoffelen and Gross, 2009). However, the use of MEG to investigate the large scale distributed networks often observed in fMRI is relatively recent, with studies focussed on confirming that such networks are electrophysiological in origin, and are not a haemodynamic artefact (e.g.…”

Section: Studies Of Meg Measures Of Connectivitymentioning

confidence: 97%

The relationship between MEG and fMRI

et al. 2014

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In recent years functional neuroimaging techniques such as fMRI, MEG, EEG and PET have provided researchers with a wealth of information on human brain function. However none of these modalities can measure directly either the neuro-electrical or neuro-chemical processes that mediate brain function. This means that metrics directly reflecting brain 'activity' must be inferred from other metrics (e.g. magnetic fields (MEG) or haemodynamics (fMRI)). To overcome this limitation, many studies seek to combine multiple complementary modalities and an excellent example of this is the combination of MEG (which has high temporal resolution) with fMRI (which has high spatial resolution). However, the full potential of multi-modal approaches can only be truly realised in cases where the relationship between metrics is known. In this paper, we explore the relationship between measurements made using fMRI and MEG. We describe the origins of the two signals as well as their relationship to electrophysiology. We review multiple studies that have attempted to characterise the spatial relationship between fMRI and MEG, and we also describe studies that exploit the rich information content of MEG to explore differing relationships between MEG and fMRI across neural oscillatory frequency bands. Monitoring the brain at "rest" has become of significant recent interest to the neuroimaging community and we review recent evidence comparing MEG and fMRI metrics of functional connectivity. A brief discussion of the use of magnetic resonance spectroscopy (MRS) to probe the relationship between MEG/fMRI and neurochemistry is also given. Finally, we highlight future areas of interest and offer some recommendations for the parallel use of fMRI and MEG.

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Section: Studies Of Meg Measures Of Connectivitymentioning

confidence: 97%

The relationship between MEG and fMRI

et al. 2014

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“…Twenty-one of the components explained 99% of the variance so the remaining principal components were discarded. The MAR model was built on the principal components using the Yule-Walker method [Schlö gl and Supp, 2006]. Through Akaike's criterion [McQuarrie and Tsai, 1998] the model order was selected as six.…”

Section: Investigation 1:resultsmentioning

confidence: 99%

“…The statistical significance of the PDC causality results was accessed through a jackknife method, the trial based leaveone-out method (LOOM) [Schlö gl and Supp, 2006].…”

Section: Statistical Inference Of Pdcmentioning

confidence: 99%

“…The LOOM approach provides two main advantages [Schlö gl and Supp, 2006]. Firstly, LOOM obtains the leastbiased estimates over all other resampling methods.…”

Section: Statistical Inference Of Pdcmentioning

confidence: 99%

“…Only the (NÀ1)th part of each concatenated data vector was independent (one out of NÀ1 trials). Consequently, the true standard error is r u ffiffiffiffiffiffiffiffiffiffiffiffi ffi Supp, 2006]. The mean and the standard error are used in a simple ttest for testing whether the PDC at a specific frequency bin is significantly different from zero or not.…”

Section: Statistical Inference Of Pdcmentioning

confidence: 99%

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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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