Dynamic reconfiguration of human brain networks during learning

Abstract

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.complex network | time-dependent network | fMRI | motor learning | community structure T he brain is a complex system, composed of many interacting parts, which dynamically adapts to a continually changing environment over multiple temporal scales. Over relatively short temporal scales, rapid adaptation and continuous evolution of those interactions or connections form the neurophysiological basis for behavioral adaptation or learning. At small spatial scales, stable neurophysiological signatures of learning have been best demonstrated in animal systems at the level of individual synapses between neurons (1-3). At a larger spatial scale, it is also well-known that specific regional changes in brain activity and effective connectivity accompany many forms of learning in humans-including the acquisition of motor skills (4, 5).Learning-associated adaptability is thought to stem from the principle of cortical modularity (6). Modular, or nearly decomposable (7), structures are aggregates of small subsystems (modules) that can perform specific functions without perturbing the remainder of the system. Such structure provides a combination of compartmentalization and redundancy, which reduces the interdependence of components, enhances robustness, and facilitates behavioral adaptation (8, 9). Modular organization also confers evolvability on a system by reducing constraints on change (8,(10)(11)(12). Indeed, a putative relationship between modularity and adaptability in the context of human neuroscience has recently been posited (13,14). To date, however, the existence of modularity in large-scale cortical connectivity during learning has not been tested directly.Based on the aforementioned theoretical and empirical grounds, we hypothesized that the principle of modularity would characterize the fundamental organiz...

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

“…These networks of regions with coherent activity during rest are consistent across subjects and closely resemble the brain's functional organization of evoked responses (Damoiseaux et al, 2006;Fox and Raichle, 2007;Laird et al, 2011;Smith et al, 2009). Coherent BOLD activity persists during sleep and in anesthetized monkeys, suggesting that it reflects a fundamental property of the brain's functional organization (Larson-Prior et al, 2009;Vincent et al, 2007).Coherent BOLD activity, known as "functional connectivity" (FC), is modulated by learning (Bassett et al, 2011), cognitive and affective states (Cribben et al, 2012;Ekman et al, 2012;Eryilmaz et al, 2011;Richiardi et al, 2011;Shirer et al, 2012) and also spontaneously Chang and Glover, 2010; Kitzbichler et al, 2009). Chang andGlover (2010) showed that FC between the posterior cingulate cortex, a key region of the default mode network, and various other brain regions was highly dynamic over time.…”

“…Previous studies reported large-scale reorganizations of whole-brain functional brain networks during learning (Bassett et al, 2011), task preparation (Ekman et al, 2012), or rest (Allen et al, in press). Bassett et al (2011) studied dynamic community organization, Ekman et al (2012) predicted upcoming tasks and errors from network measures (e.g.…”

“…Bassett et al (2011) studied dynamic community organization, Ekman et al (2012) predicted upcoming tasks and errors from network measures (e.g. degree), and Allen et al (in press) employed clustering to identify stable FC topologies.…”

“…The sliding-window technique has previously been used by several other groups to investigate dynamic FC with window lengths varying from 15 to 120 TRs (Allen et al, in press;Bassett et al, 2011;Chang and Glover, 2010;Handwerker et al, 2012;Hutchison et al, in press) and similar step sizes (Allen et al, in press;Handwerker et al, 2012;Hutchison et al, in press). We used window lengths of 30, 40, 60 and 120 TRs, covering 33, 44, 66 and 132 s respectively, to investigate the effect of the window length.…”

“…Coherent BOLD activity, known as "functional connectivity" (FC), is modulated by learning (Bassett et al, 2011), cognitive and affective states (Cribben et al, 2012;Ekman et al, 2012;Eryilmaz et al, 2011;Richiardi et al, 2011;Shirer et al, 2012) and also spontaneously Chang and Glover, 2010; Kitzbichler et al, 2009). Chang andGlover (2010) showed that FC between the posterior cingulate cortex, a key region of the default mode network, and various other brain regions was highly dynamic over time.…”