Dynamic reconfiguration of human brain networks during learning

By D. Bassett, Nicholas F. Wymbs, M. A. Porter, Peter J. Mucha, Jean M. Carlson, Scott T. Grafton. Proceedings of the National Academy of Sciences of the United States of America, 2010

1,682 citations recorded by the source corpus.

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes—flexibility and selection—must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/5c21f7b97ddda1b2286d0f31d54b25cf64a2dbd6

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Citations in the corpus

Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “, modularity or community membership (Bassett et al., 2011; Jones et al., 2012; Kinnison et al., 2012)), or formal models to detect change points in connectivity, as introduced by Cribben et al.”
Altered Resting State Complexity in Schizophrenia (2011, methodology): “One potential method for examining the dynamics of resting state function is the construction of dynamic network models, as have recently been extracted from task data during a simple motor learning paradigm (Bassett et al., 2011).”
Structural and Functional Brain Networks: From Connections to Cognition (2013, background): “Some examples of the many topics reserved for future research include dynamic and transient-state functional networks (47), coordination of task-specific brain networks, reconfiguration of effective networks because of extrasynaptic neuromodulators (48), long-term modification of structural network ”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Researchers believe that such functional brain networks adapt ﬂexibly to their cognitive demands while preserving the modular structure (Bassett et al., 2011; Fornitoa et al., 2012; Braun et al., 2015; Liang et al., 2016).”
Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data (2018, methodology): “Additionally, using coherence avoids the need to remove negative correlation coefficients to calculate network measures (Achard & Bullmore, 2007; Bassett et al., 2011) and robustly extracts frequency-specific information from the time series without picking up on edge effects of band-pass filtering ”
Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks (2017, background): “Moreover, the network affiliation of a given brain region can shift across time and context (60, 96).”
Two’s company, three (or more) is a simplex (2016, background): “Such a complex is called an independence complex (Kozlov, 2007), and can be used to study properties of a system’s community structure such as dynamic ﬂexibility (Bassett et al, 2011, 2013).”
Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise (2017, background): “Medical imaging has already shown patterns of brain activation to predict learning rates [6] and memory tasks [60] in other domains.”
Decreased segregation of brain systems across the healthy adult lifespan (2014, background): “Consistent with this, there is evidence for a relationship between measures of modularity and changes in task performance and learning in healthy young adults (49, 50).”
Change-point detection in high-dimensional covariance structure (2016, background): “Analysing the dynamics of these networks is particularly important for the research on neural diseases and also in the context of brain development with emphasis on characterizing the re-configuration of the brain during learning [4].”
The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis (2012, background): “When a subject performs a task during fMRI, networks are known to reconﬁgure to some degree based on the speciﬁc cognitive demands of the task (Bassett et al., 2011b; Mennes et al., 2012; Shirer et al., 2012).”