Portoles, O. | bioRxiv Science

bioRxiv2021

Thalamic bursts modulate cortical synchrony locally to switch between states of global functional connectivity in a cognitive task

Performing a cognitive task requires going through a sequence of functionally diverse stages. Although it is typically assumed that these stages are characterized by distinct states of cortical synchrony that are triggered by sub-cortical events, little reported evidence supports this hypothesis. To test this hypothesis, we first identified cognitive stages in single-trial MEG data of an associative recognition task, showing with a novel method that each stage begins with local modulations of synchrony followed by a state of directed functional connectivity. Second, we developed the first whole-brain model that can simulate cortical synchrony throughout a task. The model suggests that the observed synchrony is caused by thalamocortical bursts at the onset of each stage, targeted at cortical synapses and interacting with the structural anatomical connectivity. These findings confirm that cognitive stages are defined by distinct states of cortical synchrony and explains the network-level mechanisms necessary for reaching stage-dependent synchrony states. Author summaryA novel machine-learning method was applied to unveil the dynamics of local and cortex-wide neural coordination underlying the fundamental cognitive processes involved in a memory task. To explain how neural activity - and ultimately behavior - was coordinated throughout the task, we developed a whole-brain model that incorporates cognitive mechanisms, anatomy, and neural biophysics. Similar models are used with resting state data, however, simulating a cognitive task remained elusive. The model showed that sub-cortical pulses at the onset of cognitive processes - as hypothesized by cognitive and neurophysiological theories - were sufficient to switch between the states of neural coordination observed. These findings have implications to understand goal-directed cognitive processing and the mechanisms to reach states of neural coordination.

neuroscience↗

bioRxiv2021

Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model

Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network. Author summaryHere we present and evaluate a parsimonious model of large-scale brain activity. The model represents the brain as a network-of-networks structure. The sub-networks describe the neural activity within a brain region, and the global network encodes interactions between brain regions. Unlike other models, it capture progressive changes of local synchrony and local dynamics can be tuned with one parameter. Therefore the model could be used not only to model resting-state, but also behavioural tasks. Furthermore, we describe a simple framework that can deal with the arduous task of identifying global and local parameters.

neuroscience↗