Key role of coupling, delay, and noise in resting brain fluctuations

Deco, Gustavo; Jirsa, Viktor K.; McIntosh, Anthony R.; Sporns, Olaf; Kötter, Rolf

doi:10.1073/pnas.0901831106

Cited by 738 publications

(782 citation statements)

References 36 publications

Supporting

Mentioning

755

Contrasting

Order By: Relevance

“…(Friston, 1997(Friston, , 2000), computational (e.g. (Breakspear et al, 2003, Honey et al, 2007, Ghosh et al, 2008, Deco et al, 2009 (Brookes et al, 2012b). Indeed this finding has been mirrored by other MEG studies (Hipp et al, 2012), and is in general agreement with findings from simultaneous electroencephalography (EEG) / fMRI.…”

supporting

confidence: 85%

“…(Friston, 1997(Friston, , 2000), computational (e.g. (Breakspear et al, 2003, Honey et al, 2007, Ghosh et al, 2008, Deco et al, 2009) and empirical (e.g. (Breakspear et al, 2004, Bassett et al, 2006) evidence suggests that complex and highly temporally variable neuronal dynamics underlie the coupling observed between spatially separate brain regions.…”

Section: ) Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2014

View full text Add to dashboard Cite

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...

show abstract

supporting

confidence: 85%

Section: ) Introductionmentioning

confidence: 99%

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

et al. 2014

View full text Add to dashboard Cite

show abstract

“…When variability is too low, there is little capacity for the system to achieve new states, yielding the potential for the system to remain rigidly and maladaptively in a single state (Ghosh et al 2008;Deco et al 2009;Deco et al 2011). When comparing fixation to task then, the requirement for kinetic energy may increase on task due to constantly changing external stimuli that, to be effectively processed, require a multitude of functional states.…”

Section: Why An Increase In Brain Variability During Internal-to-extementioning

confidence: 99%

“…Our recent work using functional magnetic resonance imaging (fMRI; Garrett et al 2010;Garrett et al 2011) suggests that in a variety of experimental conditions, older and poorer performing adults exhibit less variable brain activity overall and more dedifferentiation (i.e., more similarity) in signal variability across brain regions relative to younger and better performing adults. If this variability is important for state modulations (Ghosh et al 2008;Deco et al 2009;McIntosh et al 2010;Deco et al 2011), older and poorer performing adults may also exhibit more subtle state-to-state modulations in signal variability.…”

mentioning

confidence: 99%

“…been argued that, as a nonlinear dynamical system, the brain functions at the "edge of criticality" between a variety of possible "states" or functional network configurations (Ghosh et al 2008;Deco et al 2009;McIntosh et al 2010;Deco et al 2011). When variability is too low, there is little capacity for the system to explore these states, yielding the potential for the system to remain rigidly in a single state.…”

mentioning

confidence: 99%

See 1 more Smart Citation

The Modulation of BOLD Variability between Cognitive States Varies by Age and Processing Speed

et al. 2012

View full text Add to dashboard Cite

Increasing evidence suggests that brain variability plays a number of important functional roles for neural systems. However, the relationship between brain variability and changing cognitive demands remains understudied. In the current study, we demonstrate experimental condition-based modulation in brain variability using functional magnetic resonance imaging (fMRI). Within a sample of younger and older healthy adults, we found that BOLD variability was an effective discriminator between fixation and external cognitive demand. Across a number of regions, brain variability increased broadly on task compared to fixation, particularly in younger and faster performing adults. Conversely, older and slower performing adults exhibited fewer changes in brain variability within and across experimental conditions and brain regions, indicating a reduction in variability-based neural specificity. Increases in brain variability on task may represent a more complex neural system capable of greater dynamic range between brain states, as well as an enhanced ability to efficiently process varying and unexpected external stimuli. The current results help establish the developmental and performance correlates of state-to-state brain variability-based transitions, and offer a new line of inquiry in the study of rest vs. task modes in the human brain. Keywords brain variability; default mode; fMRI; noise Increasing evidence suggests that brain variability is a useful attribute for neural systems, perhaps reflecting greater network complexity, connectivity, increased dynamic range, heightened signal detection thresholds, and superior cognitive processing (Li et al.

show abstract