Revealing a multiplex brain network through the analysis of recurrences.
Nikita S FrolovVladimir A MaximenkoAlexander E HramovPublished in: Chaos (Woodbury, N.Y.) (2021)
A multilayer approach has recently received particular attention in network neuroscience as a suitable model to describe brain dynamics by adjusting its activity in different frequency bands, time scales, modalities, or ages to different layers of a multiplex graph. In this paper, we demonstrate an approach to a frequency-based multilayer functional network constructed from nonstationary multivariate data by analyzing recurrences in application to electroencephalography. Using the recurrence-based index of synchronization, we construct intralayer (within-frequency) and interlayer (cross-frequency) graph edges to model the evolution of a whole-head functional connectivity network during a prolonged stimuli classification task. We demonstrate that the graph edges' weights increase during the experiment and negatively correlate with the response time. We also show that while high-frequency activity evolves toward synchronization of remote local areas, low-frequency connectivity tends to establish large-scale coupling between them.
Keyphrases
- resting state
- functional connectivity
- high frequency
- white matter
- transcranial magnetic stimulation
- convolutional neural network
- machine learning
- deep learning
- wastewater treatment
- neural network
- working memory
- multiple sclerosis
- big data
- real time pcr
- cerebral ischemia
- free survival
- blood brain barrier
- single cell
- artificial intelligence