Canonical correlation analysis applied to functional connectivity in MEG

Soto, Juan L.P.; Pantazis, Dimitrios; Jerbi, Karim; Baillet, Sylvain; Leahy, Richard M.

doi:10.1109/isbi.2010.5490400

Cited by 12 publications

(18 citation statements)

References 7 publications

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“…The columns of Following computation of β , it is possible to apply previously established CCA methods (Soto et al, 2009, Soto et al, 2010, Barnes et al, 2011, Brookes et al, 2012b. We first compute the covariance explained by the estimate β W Xo as:…”

Section: 3)mentioning

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: 91%

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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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Section: 3)mentioning

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

et al. 2014

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“…Canonical correlation [2] is one way to determine relationships between sets of channels. Canonical correlation finds the most related components of y1 and y2 by maximizing a correlation measure between projections of each vector:…”

Section: Canonical Correlationmentioning

confidence: 99%

“…When M1 = M2 = 1, optimization reduces to bivariate Granger causality. Since the calculation of variances is a bivariate model independent of M1 and M2, the number of parameters is O(M1 + M2), an order lower than multivariate causality (2). In comparison to very recent independent work on regional causality [4] we maximize causality rather than residual correlation.…”

Section: Proposed: Canonical Granger Causalitymentioning

confidence: 99%

“…Granger causality [1] can measure the effect of the past of one signal on the future of another, but is hard to extend to signals recorded in regions of interest. On the other hand, canonical correlation [2] can measure the relationship between regions of interest but does not find the direction of inference between participating regions. This work was supported by the NIH under grants R01EB009048, R01EB000473 and the T32EB00438 Training Grant as well as the NSF under grant BCS-1028389.…”

Section: Introductionmentioning

confidence: 99%

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Canonical Granger causality applied to functional brain data

Ashrafulla

Haldar

Joshi

et al. 2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

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Dynamic images of functional activity in the brain offer the potential to measure connectivity between regions of interest. We want to measure causal activity between regions of interest (ROIs) with signals recorded from multiple channels or voxels in each ROI. Previous methods, such as Granger causality, look for causality between individual time series; hence, they suffer from local interactions or interferers obscuring signals of interest between two ROIs. We propose a metric that reduces the effect of interference by taking weighted sums of sensors in each ROI, as is done with canonical correlation. Hence, we measure region-to-region, rather than channel-to-channel or point-to-point, Granger causality. We show in simulation that our "canonical Granger causality" accurately mimics the underlying structure with few samples, unlike current methods of multivariate Granger causality. We then use anatomically relevant regions of interest in a visuomotor task in a multichannel intracortical EEG study to infer the direction of transmission in visual processing.

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Granger Causality for Ill‐Posed Problems: Ideas, Methods, and Application in Life Sciences

Hlaváčková‐Schindler¹,

Naumova²,

Pereverzyev³

2016

Statistics and Causality

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Granger causality, based on a vector autoregressive model, is one of the most popular methods for uncovering the temporal dependencies between time series. The application of Granger causality to detect inference among a large number of variables (such as genes) requires a variable selection procedure. To address the lack of informative data, so-called regularization procedures are applied. In this chapter, we review current literature on Granger causality with Lasso regularization techniques for ill-posed problems (i.e., problems with multiple solutions). We discuss regularization procedures for inverse and ill-posed problems and present our recent approaches. These approaches are evaluated in a case study on gene regulatory networks reconstruction.

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Canonical correlation analysis applied to functional connectivity in MEG

Cited by 12 publications

References 7 publications

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

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

Canonical Granger causality applied to functional brain data

Granger Causality for Ill‐Posed Problems: Ideas, Methods, and Application in Life Sciences

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