Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

Abstract

A key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system. In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain led to a 'critical' transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain. Author summaryHigher brain function relies on a dynamic balance between functional integration and segregation. Previous work has shown that this balance is mediated in part by alterations in neural gain, which are thought to relate to projections from ascending neuromodulatory nuclei, such as the locus coeruleus. Here, we extend this work by demonstrating that the modulation of neural gain alters the information processing dynamics of the neural components of a biophysical neural model. Specifically, we find March 11, 2019 1/26 that low levels of neural gain are characterized by high Active Information Storage, whereas higher levels of neural gain are associated with an increase in inter-regional Transfer Entropy. Our results suggest that the modulation of neural gain via the ascending arousal system may fundamentally alter the information processing mode of the brain, which in turn has important implications for understanding the biophysical basis of cognition. IntroductionAlthough there is a long history relating individual brain regions to specific and specialized functions, regions in isolation cannot perform meaningful physiological or cognitive processes [1]. Instead, starting at a lower scale, a few prominent features of ...

Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing

“…Arguably, the storage-transfer decomposition reflects the segregation-integration dichotomy that has long characterized the interpretation of brain function (Sporns, 2010; Zeki & Shipp, 1988). Information theory has the potential to provide a quantitative definition of these fundamental but still unsettled concepts (Li et al, 2019). In addition, information theory provides a new way of testing fundamental computational theories in neuroscience, for example, predictive coding (Brodski-Guerniero et al, 2017).…”

It’s about time: Linking dynamical systems with human neuroimaging to understand the brain

“…Increasing neural gain at intermediate levels of excitability caused an abrupt, non-linear increase in inter-regional synchrony that overlapped with empirical network topological signatures observed when analyzing task-based fMRI data (Shine et al, 2016). This same model was used to demonstrate a gain-mediated increase in inter-regional transfer entropy (Li et al, 2019).…”

“…Brain, the input-output curve defining the activity of a slow variable was manipulated in two distinct ways: the sigmoid curve was steepened (left; neural gain) or amplified (right; excitability); b) varying neural gain and excitability caused an abrupt switch in systems-level dynamics --by increasing neural gain, the system shifted from a Segregated state ("S"; low phase synchrony) into an Integrated state ("I"; high phase synchrony); (c) Schematic diagram of functional brain networks in the Segregated (i.e., "S") and Integrated (i.e., "I") phases --in the Integrated state, there are increased connections present between otherwise isolated modules; d) upper panel: an energy landscape, which defines the energy required to move between different brain states: by increasing response gain, noradrenaline is proposed to flatten the energy landscape (red); whereas by increasing multiplicative gain, acetylcholine should deepen the energy wells (green); lower panel: empirical BOLD trajectory energies as a function of mean squared displacement (MSD) and sample time point (TR) of the baseline activity (black) and after phasic bursts in the locus coeruleus (a key noradrenergic hub in the brainstem; red) and the basal nucleus of Meynert (the major source of cortical acetylcholine; green)relative to the baseline energy landscape phasic bursts in the locus coeruleus (red) lead to a flattening or reduction of the energy landscape, whereas peaks in the basal nucleus of Meynert (green) lead to a raising of the energy landscape. Panels a-c adapted from (Li et al, 2019) and Panels d-e adapted from (Munn et al, 2021). Local-to-Global (z).…”

High-amplitude network co-fluctuations linked to variation in hormone concentrations over menstrual cycle

“…While the design of our study is not well-suited to support claims about causality, we speculate that by modulating receptor density during the menstrual cycle, hormones may shift the sensitivity of neural circuits, yielding brain states that are increasingly excitable and more likely to produce high-amplitude events [42][43][44]. Indeed, recent studies have demonstrated that the sex hormones estrogen and progesterone differentially influence the density of steroid hormone receptors across the brain throughout the menstrual cycle [45].…”