Role of local network oscillations in resting-state functional connectivity

By Joana Cabral, Etienne Hugues, Olaf Sporns, Gustavo Deco. NeuroImage, 2011

547 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e1003721b13e6b394a579e265713ba5ce19bd190

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Citations in the corpus

The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core (2016, unknown): “This is motivated by previous research which has shown that this variability can be linked to the underlying metastable cluster synchronization 25 .”
Colloquium: Criticality and dynamical scaling in living systems (2017, result): “…correlations similar to those of the empirically-measured in the resting state are reproduced only if the models operate close to criticality (Cabral et al., 2011; Fraiman et al., 2009; Haimovici et al., 2013; Plenz, 2013), suggesting that resting-state spatio-temporal patterns of activity…”
Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics (2018, result): “The conductance velocity was defined as v = 6m/s, which is consistent with empirically observed values (Horowitz et al., 2015) and with previous modeling studies (Cabral et al., 2011).”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, methodology): “Here we used the SC matrix of 66 regions derived from Diffusion Spectrum Imaging (DSI) as previously published and detailed in Hagmann et al. (2008) with the modi ﬁ cations introduced by Cabral et al. (2011), which made this matrix slightly asymmetrical.”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, methodology): “…mostly descriptive but whole-brain computational models have started to make inroads in understanding the link between structural and functional brain connectivity and their potential breakdown in disease (Cabral et al., 2014a; Deco and Corbetta, 2011; Deco et al., 2011a; Honey and Sporns, 2008).”
The Development of Human Amygdala Functional Connectivity at Rest from 4 to 23 Years: a cross-sectional study (2014, unknown): “2014), and a recent sophisticated simulation of neural activity patterns at rest coupled with empirically-measured structural connectivity suggest that such negative connectivity is biologically meaningful and may represent complex patterns between regions within a network (Cabral et al. 2011).”
Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations (2013, background): “…several experimental and network-modeling studies to understand structure–function relationships (Honey et al., 2007, 2009; Ghosh et al., 2008; Bullmore and Sporns, 2009; Deco et al., 2009, 2011; Cabral et al., 2011; Freyer et al., 2011; Pernice et al., 2011; Deco and Jirsa, 2012; Robinson, 2012).”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “These observations may suggest that local synchronization of low-frequency spontaneous activity in the intrinsic brain architecture plays a fundamental role (Cabral et al., 2011; Singer, 1993).”
The role of the posterior cingulate cortex in cognition and disease. (2014, background): “One approach is to view the brain as a complex dynamic network (Deco et al., 2009; Kitzbichler et al., 2009; Chialvo, 2010; Shanahan, 2010b; Cabral et al., 2011).”
The human connectome: Origins and challenges (2013, background): “, 2007), rich temporal structure emerges in models of the resting human brain as well (Cabral et al., 2011; Deco et al., 2009; Ghosh et al., 2008).”
The Metastable Brain (2014, unknown): “Both the duration of transient functional groupings and the presence of escape tendencies depend entirely on the dynamical structure of the system (see also Briggman and Kristan, 2008; Prinz et al., 2004; Rabinovich et al., 2008; Cabral et al., 2011).”
Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity (2012, unknown): “It is only recently that the temporal dynamics of resting state fMRI connectivity have been explored with computational models (Cabral et al., 2011; Honey et al., 2007) and with real data using sliding time window correlation (Majeed et al., 2011), sliding time window ICA (Kiviniemi et al., 2011),…”
Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations (2014, unknown): “At the node level, the spontaneous dynamics is represented using either neuralmassmodels (Honey et al., 2007, 2009), mean field models (Cabral et al., 2011; Deco et al., 2009, 2013; Ghosh et al., 2008a, 2008b) or even realistic spiking/synaptic models (Deco and Jirsa, 2012).”
The Kuramoto model in complex networks (2015, methodology): “Model (150) with different natural frequencies ωi can be derived from neural networks to investigate the effect of the large-scale structural connectivity on neural dynamics at the neural population level [205].”