Dynamic reconfiguration of frontal brain networks during executive cognition in humans

Abstract

The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of "dynamic network neuroscience" to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the "n-back" task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes "network flexibility," employs transient and heterogeneous connectivity between frontal systems, which we refer to as "integration." Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia.

The modular and integrative functional architecture of the human brain

“…Crucially, connector nodes are also not involved in all betweenmodule connectivity. Nonconnector node regions play a role as well (28,87,93,94), and, as noted above, increased betweenmodule connectivity that is not routed through connector nodes has been found during many different tasks, and differentiates task connectivity from resting-state connectivity (36). Although the current and previous findings suggest that connector node regions coordinate some of these between-module connections, it is likely that other mechanisms for between-module communication exist that are executed by nonconnector nodes.…”

“…Although the current and previous findings suggest that connector node regions coordinate some of these between-module connections, it is likely that other mechanisms for between-module communication exist that are executed by nonconnector nodes. In line with this, connector nodes are not the only nodes that change module membership during tasks (87,93). For example, an fMRI study of a 2-back task showed that, although many connector node regions changed module membership and this predicted higher performance, the regions with the most module membership changes were in ventromedial prefrontal cortex, where no connector nodes were located in our analysis (93).…”

“…In line with this, connector nodes are not the only nodes that change module membership during tasks (87,93). For example, an fMRI study of a 2-back task showed that, although many connector node regions changed module membership and this predicted higher performance, the regions with the most module membership changes were in ventromedial prefrontal cortex, where no connector nodes were located in our analysis (93). Finally, although connector node regions all exhibit the graph theoretic property of a high participation coefficient, these regions do not all have identical cytoarchitecture and their connectivity profiles are to different modules.…”

Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function

“…The architecture of the human brain is intrinsically organized into time-varying assemblies of functionally interacting nodes (16,18), a dynamic neural community structure allowing for high adaptability of the system to changing environmental demands (29). Schizophrenia is increasingly recognized as a heritable brain disorder with altered neural network dynamics, yet the presence of alterations in the time-varying configuration of the functional connectome and their genetic and pharmacological implications require clarification.…”

“…Unlike the "static" network approaches, these methods allow for the quantification of contextdependent changes of the network community structure over time (e.g., during learning or cognitive load), which is arguably a biologically more accurate description of normal and disordered brain network dynamics (16,18,19). Moreover, the temporal variability of the neural community structure predicts learning (16) and executive function (18) in normal volunteers, which are two established deficit areas in schizophrenia (20,21). The temporal dynamic thus appears to reflect the capacity of the brain connectome to configure flexibly in support of cognitive demands, a feature that may relate to the genetic risk architecture of schizophrenia.…”