Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations

By Gustavo Deco, Adrián Ponce-Alvarez, Dante Mantini, Gian Luca Romani, Patric Hagmann, Maurizio Corbetta. Journal of Neuroscience, 2013

548 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/021cf8f2ae31731e079037dddc113c9b8d599fda

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Olaf Sporns
2. Gustavo Deco
3. Morten L. Kringelbach
4. Randy L. Buckner
5. D. Bassett
6. Vince D. Calhoun
7. B. T. T. Yeo
8. Joana Cabral

Citations in the corpus

• Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, methodology): “We also analyzed resting-state BOLD-MRI data collected in 24 human participants, which were also used in a number of our previous studies (Deco et al., 2013; Mantini et al., 2013; Deco et al., 2014; Ponce-Alvarez et al., 2015).”
• Colloquium: Criticality and dynamical scaling in living systems (2017, background): “Thus, resting state networks reflect structured/critical fluctuations among a set of possible attractors suggestive of a state of alertness facilitating rapid task-dependent shifts (Deco and Jirsa, 2012; Deco et al., 2013b; Ghosh et al., 2008).”
• Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, methodology): “The boundaries between suchblocks were determined by unsupervised clustering of the FC (t) (with the features for clustering provided by their upper triangular parts), using the K-means method (Hartigan and Wong, 1979).”
• Activity flow over resting-state networks shapes cognitive task activations (2016, methodology): “We see the present study as a precursor to more complex approaches that incorporate biophysical models of neuronal communication 27,28 to improve activation pattern predictions further.”
• Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics (2018, methodology): “Synaptic activity is used to simulate the BOLD signal using the mechanistic Balloon-Windkessel model for the hemodynamic response (Friston et al., 2003; Deco et al., 2013).”
• 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).”
• Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia (2014, background): “Dynamics are potentially evenmore prominent during resting-state (Deco et al., 2013), during which mental activity is unconstrained, than in task-activation studies.”
• Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, unknown): “…2005; Liley and Bojak, 2005; Foster et al., 2008; Spiegler and Jirsa, 2013; Liley and Walsh, 2013) to a whole cortical area (Ghosh et al., 2008a; Deco et al., 2009; Knock et al., 2009; Victor et al., 2011; Nakagawa et al., 2013; Deco et al., 2013; Honey et al., 2007, 2009; Cabral et al., 2012).”
• Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture (2014, background): “Our discussion of the implications of these observations is motivated by several recent papers that have also grappled with the observation of both stable and dynamic components of functional network configurations [48–53].”
• Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, methodology): “…focused in predicting the FC from the underlying SC and thus, led to the idea that model performance consists in optimizing the correlation between the simulated and empirical FC patterns (Honey et al., 2009, Cabral et al., 2011, Deco et al., 2013, Messe et al., 2015, Ponce-Alvarez et al., 2015).”
• Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, methodology): “The dynamical entrainment and correlations between different local brain region dynamics are shaped by the underlying structural connectivity (Deco et al., 2011b, 2013b; Deco et al., 2014a; Deco et al., 2014b; Ghosh et al., 2008; Honey et al., 2009).”