Can I read EEG source‐space synchrostate transitions and Markov modeling in the math‐gifted brain during a long‐chain reasoning task on EtoBox?

EEG source‐space synchrostate transitions and Markov modeling in the math‐gifted brain during a long‐chain reasoning task by Li Zhang; John Q. Gan; Yanmei Zhu; Jing Wang; Haixian Wang is a Neuroscience article available to read on EtoBox.

What is EEG source‐space synchrostate transitions and Markov modeling in the math‐gifted brain during a long‐chain reasoning task about?

## Abstract To reveal transition dynamics of global neuronal networks of math‐gifted adolescents in handling long‐chain reasoning, this study explores momentary phase‐synchronized patterns, that is, electroencephalogram (EEG) synchrostates, of intracerebral sources sustained in successive 50 ms time windows during a reasoning task and non‐task idle process. Through agglomerative hierarchical clustering for functional connectivity graphs and nested iterative cosine similarity tests, this study identifies seven general and one reasoning‐specific prototypical functional connectivity patterns from all synchrostates. Markov modeling is performed for the time‐sequential synchrostates of each trial to characterize the interstate transitions. The analysis reveals that default mode network, central executive network (CEN), dorsal attention network, cingulo‐opercular network, left/right ventral frontoparietal network, and ventral visual network aperiodically recur over non‐task or reasoning process, exhibiting high predictability in interactively reachable transitions. Compared to non‐gifted subjects, math‐gifted adolescents show higher fractional occupancy and mean duration in CEN and reaso

Who reads EEG source‐space synchrostate transitions and Markov modeling in the math‐gifted brain during a long‐chain reasoning task?

Author

Li Zhang; John Q. Gan; Yanmei Zhu; Jing Wang; Haixian Wang

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley; Research Square (ISSN 1065-9471)

Published

2020

Language

EN

Field

Neuroscience (Life Sciences)