The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance

By James M. Shine, Patrick G. Bissett, Peter T. Bell, Oluwasanmi Koyejo, Joshua H. Balsters, Krzysztof J. Gorgolewski, Craig A. S. Moodie, Russell A. Poldrack. Neuron, 2015

831 citations recorded by the source corpus.

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions, however it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of functional magnetic resonance imaging data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/dab91b6faf1b613b99bc8cc00464fcef244a776e

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In Cognitive Neuroscience

Citations in the corpus

Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “, 2013a), and respond to different cognitive demands (Betti et al., 2013; Cole et al., 2014; Gratton et al., 2016; Krienen et al., 2014; Shine et al., 2016; Shirer et al., 2012).”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, neutral): “…for studying BOLD TVFC. Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention Shine et al., 2016; https://doi.org/10.1073/pnas.1604898113 A TVFC analysis of two large longitudinal single-subject datasets identiﬁed replicable…”
Estimating trial-wise modulation of functional connectivity using event-related fMRI (2026, unknown): “Although the task-dependent and dynamic nature of brain network interactions have long been recognized (Mill et al., 2017; Shine et al., 2016), much of the work has focused on task-free, resting-state connectivity (Buckner et al., 2013; Deco et al., 2011).”
Neural interactions in the human frontal cortex dissociate reward and punishment learning (2024, methodology): “To address this question, we performed model-based network-level analyses based on the Partial Information Decomposition (PID) framework (Williams and Beer, 2010; Wibral et al., 2017; Lizier et al., 2018).”
Oscillatory entrainment to our early social or physical environment and the emergence of volitional control (2022, background): “Recent research has made strides in understanding how the emergence of the capacity for active, volitional control is characterised by changes in how different systems within an individual (behaviour, physiology and brain) inter-relate – known as intra-individual entrainment (Grayson & Fair, 2017; H”
Resting-state EEG connectome topology and social norm processing in healthy young adults (2026, unknown): “Task-based studies demonstrate that large-scale networks dynamically reconfigure between more integrated and more segregated states depending on cognitive demands (Cole et al. 2013; Shine et al. 2016 ).”
Network-Targeted, Multi-site Direct Cortical Stimulation Enhances Working Memory by Modulating Phase Lag of Low-Frequency Oscillations (2019, methodology): “FDR was initially developed by Benjamini and Hochberg (1995) and has been commonly used to account for multiple comparisons in many fields including analysis of brain networks (Bassett et al., 2011; Shine et al., 2016; Accolla et al., 2016) as well as neuroimaging in general (Bennett et al.”