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

Abstract

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...

Optimising experimental design for MEG resting state functional connectivity measurement

“…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. 2) In addition to the technical limitation, the well characterised dynamic nature of functional connectivity, which changes over seconds, and even milliseconds Brookes et al, 2014;Chang and Glover, 2011;Hutchison et al, 2013;G C O'Neill et al, 2015 b;O'Neill et al, 2016) must be considered, since there is a question regarding how well a short time window can capture the canonical MEG networks, if those networks are constantly changing across multiple time-scales.…”

“…Furthermore, the excellent temporal precision afforded by MEG allows estimation of dynamic changes in network structure Brookes et al, 2014;O'Neill et al, 2015a). The importance of characterising electrophysiological connectivity is growing, with numerous demonstrations that connections are perturbed in pathologies Friston, 1998;Guggisberg et al, 2008;Kessler et al, 2014;Palaniyappan and Liddle, 2012;Schnitzler and Gross, 2005;Stufflebeam et al, 2011;Tewarie et al, 2014;van Dellen et al, 2012).…”

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

“…Most current connectivity studies assume stationarity to avoid the high complexity involved in modeling the dynamic signal information, which limits the ability to process connectomes with more than 10–20 regions (Smith, 2012). Nevertheless, the extension of our framework, using for example sliding-window correlations, to examine the dynamic complexity of the underlying signals is of particular interest (Brookes et al, 2014). Here, we examine brain connectivity based on the precision matrix, which is the inverse of the covariance matrix and it reflects partial correlation.…”

Increased Resting-State Gamma-Band Connectivity in First-Episode Schizophrenia

“…Measures based on temporal correlations between the amplitude envelopes of neural oscillations have been increasingly used to investigate functional connectivity. [56][57][58][59][60][61][62][63] It has been suggested that such connectivity measures are conceptually more similar to those obtained with fMRI than coherence-based methods. 56 It has indeed been shown that amplitude envelope correlations in various frequency bands (including the gamma band) adequately reflect interactions within and between resting-state networks depicted by fMRI.…”