Time-resolved resting-state brain networks

By Andrew Zalesky, Alex Fornito, Luca Cocchi, Leonardo L. Gollo, Michael Breakspear. Proceedings of the National Academy of Sciences of the United States of America, 2014

728 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/4e56451ade1e7d23c312e1a7175a0b791da84703

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “Careful model construction facilitates the testing of specific hypotheses about underlying dynamics, as well as validation of findings from approaches that model the BOLD signal directly (Zalesky et al., 2014).”
Emotion and the Prefrontal Cortex: An Integrative Review (2017, methodology): “The framework provided here, in conjunction with recent advances in network neuroscience (Bassett et al., 2014; Calhoun, Miller, Pearlson, & Adali, 2014; Cole et al., 2016; Davison et al., 2015; Hutchison et al., 2013; Zalesky et al., 2014), may prove helpful in elucidating the dynamic nature of emo”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, background): “In recent years, there have been much interests in computing time-resolved connectivity measures and successful applications in identifying biomarkers from dynamic connectivity (Chang and Glover, 2010; Sakoglu et al., 2010; Allen et al., 2014; Zalesky et al., 2014; Du et al., 2015a; Sadaghiani et al”
Making big data open: data sharing in neuroimaging (2014, neutral): “Although the HCP is still collecting data, the early releases have already resulted in papers modeling functional segregation in the human cortex 30 , dynamic analyses of functional connectivity 31 and demonstrations of substantial vascular contributions to Granger causality measures 32 .”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “There is a growing focus in the imaging community on spontaneous (resting-state) fMRI data and the reproducible structures these reveal in health (Damoiseaux et al., 2006; Zalesky et al., 2014) and disease (Fornito et al., 2015).”
Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI (2019, neutral): “Time-resolved fMRI studies previously demonstrated that the RSNs spontaneously fluctuate in and out of spatially and temporally overlapping correlations (Cabral et al., 2017; Cole et al., 2014; Cole et al., 2013; de Pasquale et al., 2017; Hutchinson and Raff, 2014; Karahanoglu and Van De Ville, 2015”
Multi-scale brain networks (2016, neutral): “These fluctuations in network organization, especially over short timescales (< that of a single scan session), have become frequent topics of investigation (Bassett et al., 2011b; Allen et al., 2012; Bassett et al., 2015b; Zalesky et al., 2014; Betzel et al., 2016b).”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “Since then, this 3D non-linear oscillator model has been used as the underlying source of neural activity in simulation studies of brain network models for predicting functional connectivity during resting-state in the healthy (Honey et al., 2007, 2009; Goñi et al., 2014; Zalesky et al., 2014) and l”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, positive): “As in previous studies (Liao et al., 2017; Zalesky et al., 2014), fMRI acquisition with left-to-right phase-encoding was used.”
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2015, neutral): “To this end, recent experiments using fMRI data have demonstrated that global brain signals transition between states of high and low connectivity strength over time (Zalesky et al., 2014) and that these fluctuations are related to coordinated patterns of network topology (Betzel et al.”
Effects of aging on functional and structural brain connectivity☆ (2017, background): “…of more sophisticated analyses enabled by the advances in image acquisition are the examination of functional connectivity dynamics at a sub-second resolution (Zalesky et al., 2014), and the examination of neurite orientation dispersion and density using diffusion imaging (Zhang et al., 2012).”
An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI (2017, background): “However, the approach can distort the temporal properties of the data, precluding analysis of non-stationary dynamics (Hutchison et al., 2013; Zalesky et al., 2014) and other time series properties (Sethi et al., 2017).”