Predicting errors from reconfiguration patterns in human brain networks

Abstract

Task preparation is a complex cognitive process that implements anticipatory adjustments to facilitate future task performance.Little is known about quantitative network parameters governing this process in humans. Using functional magnetic resonance imaging (fMRI) and functional connectivity measurements, we show that the large-scale topology of the brain network involved in task preparation shows a pattern of dynamic reconfigurations that guides optimal behavior. This network could be decomposed into two distinct topological structures, an error-resilient core acting as a major hub that integrates most of the network's communication and a predominantly sensory periphery showing more flexible network adaptations. During task preparation, core-periphery interactions were dynamically adjusted. Task-relevant visual areas showed a higher topological proximity to the network core and an enhancement in their local centrality and interconnectivity. Failure to reconfigure the network topology was predictive for errors, indicating that anticipatory network reconfigurations are crucial for successful task performance. On the basis of a unique network decoding approach, we also develop a general framework for the identification of characteristic patterns in complex networks, which is applicable to other fields in neuroscience that relate dynamic network properties to behavior. graph theory | attention | cognitive control T he human brain forms a highly complex network that is organized into a large number of specialized regions. During goal-directed behavior, like the preparation of an upcoming task, relevant cortical regions are anticipatorily modulated (1-5), which has been shown to facilitate the detection and analysis of task-relevant stimuli (6-13).However, little is known about how these task-specific adjustments are integrated across distinct brain regions and how preparatory mechanisms are reflected in a large-scale network topology (14-16). It has been shown that attention can modulate interarea correlations between distant cortical regions, independent from changes in regional blood flow (17-19). However, these studies were usually limited to a small selection of cortical regions (2,7,15,(18)(19)(20). With recent developments in functional connectivity analysis, it has become possible to study the role of large-scale networks for cognitive processing and to quantify network properties using global and local graph theoretical measures (21-26).On the one hand, task preparation involves dynamic adjustments in regions that carry out computations that are specific to a given task. On the other hand, it also requires the stable maintenance of task goals (7) and reconfigurations of the network based on these goals. Given these characteristics and the organization of brain networks into modules with distinct functional properties (27, 28), we hypothesized that task-specific processes, whose involvement varies from trial to trial, are reflected in dynamic adjustments of more peripheral components of the brain network. We...

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.…”

“…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.…”

On spurious and real fluctuations of dynamic functional connectivity during rest

“…Functional connectivity (FC), which is estimated by correlation of BOLD activity, identifies coherent brain activity in distributed and reproducible networks. FC has revealed reorganization of brain networks during cognitive tasks (Ekman et al, 2012;Lewis et al, 2009;Richiardi et al, 2011Richiardi et al, , 2013Shirer et al, 2012), but also at rest (Allen et al, 2014;Chang and Glover, 2010;Hutchison et al, 2013b;Kang et al, 2011;Leonardi et al, 2013;Majeed et al, 2011;Smith et al, 2012). To study changes in FC over time sliding-window correlation analysis, where the correlation is estimated for brain activity during multiple, possibly overlapping temporal segments (typically 30-60 s), has been widely deployed (Allen et al, 2014;Chang and Glover, 2010;Hutchison et al, 2013a;Sakoglu et al, 2010).…”