Unknown

Dataset Information

0

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.


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.

SUBMITTER: Tajima S 

PROVIDER: S-EPMC4652869 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding.

Tajima Satohiro S   Yanagawa Toru T   Fujii Naotaka N   Toyoizumi Taro T  

PLoS computational biology 20151119 11


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  ...[more]

Similar Datasets

| S-EPMC10769245 | biostudies-literature
| S-EPMC1988856 | biostudies-literature
| S-EPMC10727951 | biostudies-literature
| S-EPMC4311826 | biostudies-other
| S-EPMC9065914 | biostudies-literature
| S-EPMC8421429 | biostudies-literature
| S-EPMC7429506 | biostudies-literature
| S-EPMC8127159 | biostudies-literature
| S-EPMC5428308 | biostudies-literature
| S-EPMC10514061 | biostudies-literature