A synergistic core for human brain evolution and cognition

Abstract

A fundamental question in neuroscience is how brain organisation gives rise to humans' unique cognitive abilities. Although complex cognition is widely assumed to rely on frontal and parietal brain regions, the underlying mechanisms remain elusive: current approaches are unable to disentangle different forms of information processing in the brain. Here, we introduce a powerful framework to identify synergistic and redundant contributions to neural information processing and cognition. Leveraging multimodal data including functional MRI, PET, cytoarchitectonics and genetics, we reveal that synergistic interactions are the fundamental drivers of complex human cognition. Whereas redundant information dominates sensorimotor areas, synergistic activity is closely associated with the brain's prefrontal-parietal and default networks; furthermore, meta-analytic results demonstrate a close relationship between highlevel cognitive tasks and synergistic information. From an evolutionary perspective, the human brain exhibits higher prevalence of synergistic information than non-human primates. At the macroscale, we demonstrate that high-synergy regions underwent the highest degree of evolutionary cortical expansion. At the microscale, human-accelerated genes promote synergistic interactions by enhancing synaptic transmission. These convergent results provide critical insights that synergistic neural interactions underlie the evolution and functioning of humans' sophisticated cognitive abilities, and demonstrate the power of our widely applicable information decomposition framework..

Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition

“…Rather, the goal is to offer a crisp window into the types of insights that can emerge. We draw preferentially on our own work out of convenience, but this work in turn was inspired by, and complements, other exemplary lines of empirical work (for examples, see [ 5 , 21 , 35 , 41 , 42 , 43 , 44 , 45 , 46 ]).…”

“…A recent multi-target generalization of the PID, termed integrated information decomposition ( ID) [ 69 , 70 ] has been proposed, which opens the door to a much wider range of possible analyses than is possible with a single-target PID analysis. ID analysis of fMRI data has found changes in the flow of synergistic information associated with loss of consciousness [ 46 , 71 ] and the genetic architecture of the brain [ 44 ]. A recently introduced “temporal redundancy function” ( ), coupled with a ID analysis, led to the discovery of a set of sixteen distinct information dynamics associated with the temporal evolution of pairs of interacting neurons, many of which were previously unknown [ 45 ].…”

May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data

“…As an example of the former, ΦID has been used to show that existing quantifications of integrated information (and related quantities such as 'causal density' [90]) are not capturing a unique type of information dynamics, but rather conflating multiple ones. Following this finding, a revised version of Φ-denoted by Φ R -has been proposed, which has been shown to capture various important aspects of a broad range of complex systems [91,92], and be more precise than Φ in finding or detecting specific differences between conscious and unconscious dynamics of the human brain [93].…”

“…More broadly, ΦID is enabling novel ways to conceptualize brain function, by providing an information-resolved FC that complements the time-resolved perspective [93]. Empirical analyses based on fMRI data have shown a role for redundancy in ensuring robust input/output communication channels, as is especially prominent in somatomotor and sensory regions.…”

“…Empirical analyses based on fMRI data have shown a role for redundancy in ensuring robust input/output communication channels, as is especially prominent in somatomotor and sensory regions. By contrast, synergy is related to efficient communication in highorder association cortices, and supports humans' sophisticated cognitive functions, being more prominent in evolutionarily expanded regions of the cerebral cortex [93]. The further identification of specific molecular, cytoarchitectonic and metabolic profiles suggests that in the close future we may be able to embody information processing properties into tissues' biophysical properties, a critical step towards supporting the biological plausibility of intrinsic computation in the brain.…”