Optimising experimental design for MEG beamformer imaging

Brookes, Matthew J.; Vrba, Jan; Robinson, Stephen E.; Stevenson, Claire; Peters, Andrew M.; Barnes, Gareth R.; Hillebrand, Arjan; Morris, Peter G.

doi:10.1016/j.neuroimage.2007.09.050

Cited by 222 publications

(202 citation statements)

References 18 publications

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“…1) There is a technical limitation generated when reconstructing data and measuring functional connectivity using a reduced number of data points. This manifests firstly as an error in the data covariance matrix used for beamformer weights generation (Brookes et al, 2008); fewer data points mean a larger covariance matrix error, and ultimately a lower beamformer projected power. Second, when estimating functional connectivity itself, most correlative or coherence based measures are highly sensitive to the number of degrees of freedom in the timecourses used to generate them.…”

Section: Discussionmentioning

confidence: 99%

“…We concentrate on two parameters; recording duration and coregistration method. It is clear that longer recordings will maximise SNR, and previous work (Brookes et al, 2010(Brookes et al, , 2008 shows that source reconstruction accuracy and spatial resolution are improved as recording duration (or bandwidth) is increased. Here, using simulated and experimental MEG data, we investigate the effect that changing recording duration has on network connectivity.…”

Section: Introductionmentioning

confidence: 92%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Optimising experimental design for MEG resting state functional connectivity measurement

et al. 2017

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“…Here, source space projection is achieved via beamforming (Van Drongelen et al, 1996, Van Veen et al, 1997, Robinson and Vrba, 1998, Gross et al, 2001, Sekihara et al, 2001, Brookes et al, 2008; a popular methodology that has been well characterised in previous papers. Briefly, using a beamformer, an estimate of electrical source strength is made at some predetermined location in the brain, using a weighted sum of MEG sensor measurements.…”

Section: ) Source Localisation and Selection Of Voxels Clustersmentioning

confidence: 99%

“…With this in mind, it is noteworthy that electrophysiological metrics such as MEG have significant advantages over fMRI: increased time resolution offers advantages in characterising temporal non-stationarity whilst the direct nature of MEG allows a non-invasive window on neural oscillations, and therefore spectral structure. In this paper, we introduce a novel technique to characterise functional connectivity, based upon beamforming (Van Veen et al, 1997, Robinson and Vrba, 1998, Gross et al, 2001, Sekihara et al, 2006, Brookes et al, 2008 and canonical correlation analysis (CCA) (Soto et al, 2010, Barnes et al, 2011, Brookes et al, 2012b. We extend work presented in our previous papers (Brookes et al, 2011a, Brookes et al, 2012b, Hall et al, 2013 by developing a method capable of measuring the temporal, spectral and spatial variation in functional connectivity, assessed by band limited envelope correlation.…”

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confidence: 92%

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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“…Even though other beamforming techniques exist that are optimized for the detection of correlated sources (e.g., Diwakar et al, 2011) we decided to use the LCMV beamformer for this study because we were interested in the reconstruction of large-scale functional brain networks in source space rather than on 2 correlated sources. As suggested by Brookes and colleagues (Brookes et al, 2008) we used a broad frequency band of 1-100 Hz for the beamforming technique in order to minimize distortions of the source reconstruction, time course, and estimation of the signal strength.…”

Section: Source Projectionmentioning

confidence: 99%

Development of large-scale functional networks over the lifespan

Schlee¹,

Leirer²,

Kolassa³

et al. 2012

Neurobiology of Aging

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Abstract:We investigated the development of large-scale functional networks over the lifespan by examining resting MEG of 53 healthy participants aged 18-89. Results show differential developments across the lifespan for low and high frequency networks. Delta frequency (2-4 Hz) networks decrease in size, while beta/gamma frequency (16 Hz) networks increase in size with age. The right frontal lobe and bilateral temporal areas were found to be important relay stations in high frequency networks. Normal age-related declines in visual memory and visuoconstructive capabilities were associated with the age-related increase of functional connectivity in both temporal lobes.

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Optimising experimental design for MEG beamformer imaging

Cited by 222 publications

References 18 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

Development of large-scale functional networks over the lifespan

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