The contribution of electrophysiology to functional connectivity mapping

Schölvinck, Marieke L.; Leopold, David A.; Brookes, Matthew J.; Khader, Patrick H.

doi:10.1016/j.neuroimage.2013.04.010

Cited by 81 publications

(77 citation statements)

References 149 publications

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“…The temporal complexity of MEG signals means that a number of models to characterise functional connectivity have been proposed (Schölvinck et al, 2013 …”

Section: Meg Connectivity Metricsmentioning

confidence: 99%

“…no specific task paradigm is used) they are often termed resting state networks (RSNs) (Beckmann et al, 2005). Although predominantly measured using functional magnetic resonance imaging (fMRI), a growing body of work shows that network characterisation via magnetoencephalography (MEG) offers novel insights into the neural mechanisms that underlie functional connectivity O'Neill et al, 2015a;Schölvinck et al, 2013). MEG involves assessment of the magnetic fields that are induced outside the head by neural current flow in the brain.…”

Section: Introductionmentioning

confidence: 99%

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Optimising experimental design for MEG resting state functional connectivity measurement

et al. 2017

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The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of coregistration method and data recording duration. We show that the use of a foam head-cast, which is known to improve coregistration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies.3

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“…The temporal complexity of MEG signals means that a number of models to characterise functional connectivity have been proposed (Schölvinck et al, 2013 …”

Section: Meg Connectivity Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimising experimental design for MEG resting state functional connectivity measurement

et al. 2017

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“…(Scholvinck et al, 2013)). Throughout the remainder of this manuscript, we use the term functional connectivity to mean temporal correlation between the 6 amplitude envelopes of band limited neural oscillations (de Pasquale et al, 2010, Liu et al, 2010, Brookes et al, 2011a, Brookes et al, 2011b, Hipp et al, 2012, Luckhoo et al, 2012, Hall et al, 2013.…”

Section: ) Theorymentioning

confidence: 99%

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

et al. 2014

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ABSTRACT:The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i.e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe generation of time-frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial nonuniformity within short time-frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatiotemporal-spectral modes of covariation between the left and right sensorimotor areas. 3 1) INTRODUCTION:Traditional analysis of neuroimaging data has focussed on the identification of significant changes in some metric of interest that are time locked to a particular task. Such methodologies usually rely on knowledge of task timing, and in some cases accurate models of the temporal evolution of neuroimaging signals which are then compared to measured data. These techniques have proved effective in highlighting brain regions that are involved in sensory and cognitive tasks. However, the last decade has seen a 'paradigm shift' in functional brain imaging (Raichle, 2009), with traditional analyses increasingly complemented by analysis of functional connectivity (Biswal et al., 1995, Beckmann et al., 2005, Fox et al., 2005, Fox and Raichle, 2007, Deco and Corbetta, 2011. Here, researchers seek to elucidate spatial patterns of temporal covariation between brain regions.Significant statistical interdependency (e.g. assessed via temporal correlation (Biswal et al., 1995) or independent component analysis (Beckmann et al., 2005)) between signals originating in two or more spatially separate anatomical regions is usually taken to mean that those regions are 'connected'. Functional magnetic resonance imaging (fMRI) has...

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“…Functional connectivity metrics for MEG are complicated by the richness of the MEG signal. In fcMRI, functional connectivity assessment is largely confined to correlation between BOLD timecourses; however, in MEG there are many potential metrics for interaction (Schölvinck et al, 2013). Coupling between band limited oscillatory power envelopes is perhaps the simplest metric and has been used successfully to elucidate long range statistical dependency between MEG signals.…”

Section: Studies Of Meg Measures Of Connectivitymentioning

confidence: 99%

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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The contribution of electrophysiology to functional connectivity mapping

Cited by 81 publications

References 149 publications

Optimising experimental design for MEG resting state functional connectivity measurement

Optimising experimental design for MEG resting state functional connectivity measurement

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

The relationship between MEG and fMRI

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