A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain

Pasquale, Francesco de; Penna, Stefania Della; Snyder, Abraham Z.; Marzetti, Laura; Pizzella, Vittorio; Romani, Gian Luca; Corbetta, Maurizio

doi:10.1016/j.neuron.2012.03.031

Cited by 411 publications

(475 citation statements)

References 47 publications

(78 reference statements)

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“…This so-called "spontaneous" or "resting-state" brain activity has been shown to covary among functionally related brain regions, for example, those involved with sensorimotor (Biswal et al 1995;Lowe et al 1998), attention (Fox et al 2006), and default-mode (Raichle et al 2001;Greicius et al 2003) functions. The patterns that have been observed with resting-state fMRI are similar to those activated by various tasks (Smith et al 2009), and generally consistent with those based on intracranial electro-optical and electrical recordings in animals (Kenet et al 2003;Leopold et al 2003), and on electrocorticography Nir et al 2008) and magnetoencephalography (MEG;de Pasquale et al 2010;Liu et al 2010;Brookes et al 2011;de Pasquale et al 2012;Hipp et al 2012) in humans. For this reason, fMRI covariation patterns have been interpreted as representing covarying electrical activity in so-called "functional networks.…”

Section: Introductionsupporting

confidence: 78%

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Li¹,

Zwart²,

Chang³

et al. 2013

Cerebral Cortex

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Spontaneous activity in the human brain occurs in complex spatiotemporal patterns that may reflect functionally specialized neural networks. Here, we propose a subspace analysis method to elucidate large-scale networks by the joint analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. The new approach is based on the notion that the neuroelectrical activity underlying the fMRI signal may have EEG spectral features that report on regional neuronal dynamics and interregional interactions. Applying this approach to resting healthy adults, we indeed found characteristic spectral signatures in the EEG correlates of spontaneous fMRI signals at individual brain regions as well as the temporal synchronization among widely distributed regions. These spectral signatures not only allowed us to parcel the brain into clusters that resembled the brain's established functional subdivision, but also offered important clues for disentangling the involvement of individual regions in fMRI network activity.

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Section: Introductionsupporting

confidence: 78%

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Li¹,

Zwart²,

Chang³

et al. 2013

Cerebral Cortex

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“…Furthermore, application within multiple frequency bands enables effective measurement of the spectral signature of temporal correlation. Multiple previous studies (Chang and Glover, 2010, de Pasquale et al, 2010, Brookes et al, 2011a, Baker et al, 2012, de Pasquale et al, 2012 have shown that functional connectivity is dynamic and that temporal correlation between spatially separate brain areas exhibits large changes in time; this observation has been made using both fMRI and MEG. The results presented in Figures 7 and 8 are in agreement with this, showing large dynamic changes in canonical correlation between the left and right motor clusters.…”

Section: ) Discussionmentioning

confidence: 96%

“…The method may be extended to other networks, e.g. the default mode network, where previous literature (de Pasquale et al, 2010, de Pasquale et al, 2012 has shown that non-stationarity may be of great importance. In terms of the methodology, this may also be expanded.…”

Section: ) Conclusionmentioning

confidence: 99%

“…Pasquale and colleagues have published multiple papers (de Pasquale et al, 2010, de Pasquale et al, 2012 showing that accounting for temporal non-stationarity aids in the detection of several resting state networks, suggesting that networks transiently engage with other networks during periods of high internal correlation, with the default mode network acting as a hub of cross network interaction. Also using MEG, Baker et al (Baker et al, 2012) show evidence of a bi-state nature to band limited power correlation, with periods of zero functional connectivity interspersed with periods of high transient functional connectivity.…”

Section: ) Introductionmentioning

confidence: 99%

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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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“…To project the channel signals into the source space, approaches based on 153 extended source models such as minimum--norm estimates, low--resolution tomographies, and 154 beam--forming methods (Brookes et al., 2007;Hamalainen and Ilmoniemi, 1994; Pascual--Marqui, 155 2002) could be applied on the raw data after removing the artefact components. However, we 156 preferred to use an approach already applied to the study of brain interactions at rest (de 157 Pasquale et al, 2010;de Pasquale et al, 2012;Mantini et al, 2011), briefly described in the 158 following. …”

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confidence: 99%

Being an agent or an observer: Different spectral dynamics revealed by MEG

et al. 2014

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14Several neuroimaging studies reported that a common set of regions are recruited during 15 action observation and execution and it has been proposed that the modulation of the µ rhythm, 16 in terms of oscillations in the alpha and beta bands might represent the electrophysiological 17 correlate of the underlying brain mechanisms. However, the specific functional role of these bands 18 within the µ rhythm is still unclear. Here, we used magnetoencephalography (MEG) to analyze the 19 spectral and temporal properties of the alpha and beta bands in healthy subjects during an action 20 observation and execution task. 21We associated the modulation of the alpha and beta power to a broad action observation 22 network comprising several parieto--frontal areas previously detected in fMRI studies. Of note, we 23 observed a dissociation between alpha and beta bands with a slow--down of beta oscillations 24 ACCEPTED FOR PUBLICATION -NEUROIMAGE2 compared to alpha during action observation. We hypothesize that this segregation is linked to a 25 different sequence of information processing and we interpret these modulations in terms of 26 internal models (forward and inverse). In fact, these processes showed opposite temporal 27 sequences of occurrence: anterior--posterior during action (both in alpha and beta bands) and 28 roughly posterior--anterior during observation (in the alpha band). The observed differentiation 29 between alpha and beta suggests that these two bands might pursue different functions in the 30 action observation and execution processes. 32Keywords: action observation and execution, alpha and beta rhythms, Event--Related 33Desynchronization (ERD), magnetoencephalography (MEG), internal models 34 35 36 INTRODUCTION 37Several studies report that our sensorimotor system is activated when we observe an 38 action performed by other people. This putative mirror--like activity in humans was found in the 39 precentral gyrus (vPM), the inferior frontal gyrus (IFG), the inferior parietal lobule (IPL), and 40 regions within the intraparietal sulcus (for a review see Rizzolatti and Sinigaglia, 2010). In addition 41 to this limited number of regions, neuroimaging studies observed a broader action observation 42 network (AON) which seems to be involved during action observation (OBS) and execution (EXE) 43 (Avenanti et al., 2012;Buccino et al., 2004;Gazzola and Keysers, 2009). A proposed theoretical 44 framework postulates that such AON implements forward and inverse internal models (Wolpert 45 and Ghahramani, 2000), which should be engaged during EXE and OBS. The internal models can 46 account both for how we link our actions to sensory consequences and how others' actions match 47 our own actions and sensations (Gazzola and Keysers, 2009;Iacoboni, 2005). It has been proposed 48 that the fronto--parietal AON has a predictive nature and according to this hypothesis, during 49 action observation the inverse and forward models are integrated to achieve a prediction of 50 ACCEPTED FOR PUBLICATION -NEUROIMA...

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A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain

Cited by 411 publications

References 47 publications

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

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

Being an agent or an observer: Different spectral dynamics revealed by MEG

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