Revisiting the Global Workspace: Orchestration of the functional hierarchical organisation of the human brain

Abstract

A central, unsolved challenge in neuroscience is how the brain orchestrates function by organising the flow of information necessary for the underlying computation. It has been argued that this whole-brain orchestration is carried out by a core subset of integrative brain regions, commonly referred to as the 'global workspace', although quantifying the constitutive brain regions has proven elusive. We developed a normalised directed transfer entropy (NDTE) framework for determining the pairwise bidirectional causal flow between brain regions and applied it to multimodal wholebrain neuroimaging from over 1000 healthy participants. We established the full brain hierarchy and common regions in a 'functional rich club' (FRIC) coordinating the functional hierarchical organisation during rest and task. FRIC contains the core set of regions, which similar to a 'club' of functional hubs are characterized by a tendency to be more densely functionally connected among themselves than to the rest of brain regions from where they integrate information. The invariant global workspace is the intersection of FRICs across rest and seven tasks, and was found to consist of the precuneus, posterior and isthmus cingulate cortices, nucleus accumbens, putamen, hippocampus and amygdala that orchestrate the functional hierarchical organisation based on information from perceptual, long-term memory, evaluative and attentional systems. We confirmed the causal significance and robustness of this invariant global workspace by systematically lesioning a generative whole-brain model accurately simulating the functional hierarchy defined by NDTE. Overall, this is a major step forward in understanding the complex choreography of information flow within the functional hierarchical organisation of the human brain.

Turbulent-like Dynamics in the Human Brain

“…(2016) have used neuroimaging to extend Mesulam’s seminal proposal that brain processing is shaped by a hierarchy of distinct unimodal areas to integrative transmodal areas ( Mesulam, 1998 ). More recently, we have added to this literature by identifying the “global workspace” of brain regions at the top of the hierarchy ( Deco et al., 2020 ).…”

Turbulent-like dynamics in the human brain

“…In particular, research by Margulies and colleagues (Margulies et al, 2016) have used neuroimaging to extend Mesulam’s seminal proposal that brain processing is shaped by a hierarchy of distinct unimodal areas to integrative transmodal areas (Mesulam, 1998). More recently, we have added to this literature by identifying the ‘global workspace’ of brain regions at the top of the hierarchy (Deco et al, 2020).…”

A Synergistic Workspace for Human Consciousness Revealed by Integrated Information Decomposition

“…Indeed, recent efforts have been carried out to obtain a datadriven characterisation of the brain's global workspace based on regions' involvement across multiple different tasks (Deco et al, 2019). This work is complementary to ours in two aspects: first, the focus of (Deco et al, 2019) is on the role of the workspace related to cognition, whereas here we focus primarily on consciousness. Second, by using transfer entropy as a measure of functional connectivity, (Deco et al, 2019) It is also worth bearing in mind is that our measure of integrated information between pairs of regions does not amount to measuring the integrated information of the brain as a whole, as formally specified in the context of Integrated Information Theory (Balduzzi and Tononi, 2008;Tononi, 2004) -although we do show that the average integrated information between pairs of regions is overall reduced across the whole brain.…”

“…This work is complementary to ours in two aspects: first, the focus of (Deco et al, 2019) is on the role of the workspace related to cognition, whereas here we focus primarily on consciousness. Second, by using transfer entropy as a measure of functional connectivity, (Deco et al, 2019) It is also worth bearing in mind is that our measure of integrated information between pairs of regions does not amount to measuring the integrated information of the brain as a whole, as formally specified in the context of Integrated Information Theory (Balduzzi and Tononi, 2008;Tononi, 2004) -although we do show that the average integrated information between pairs of regions is overall reduced across the whole brain. We also note that our revised measure of integrated information is based on IIT 2.0 (Balduzzi and Tononi, 2008), which relies on a conceptually distinct understanding of integrated information from the most recent IIT 3.0 (Oizumi et al, 2014), whose computation requires perturbing the system and all of its subsets, making it computationally intractable.…”