bioRxivNeurosciencePreprint
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 whole-brain 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.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 16
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