Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

Satohiro Tajima, Toru Yanagawa, Naotaka Fujii and Taro Toyoizumi

PLOS Computational Biology, 2015, vol. 11, issue 11, 1-28

Abstract: Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness.Author Summary: Advances in recording technologies have enabled the acquisition of neuronal dynamics data at unprecedented scale and resolution, but the increase in data complexity challenges reductionist model-based approaches. Motivated by generic theorems of dynamical systems, we characterize model-free, nonlinear embedding relationships for wide-field electrophysiological data from behaving monkeys. This approach reveals a universality of inter-areal interactions and complexity in conscious brain dynamics, demonstrating its wide application to deciphering complex neuronal systems.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004537

DOI: 10.1371/journal.pcbi.1004537

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