Quantification of Brain Macrostates Using Dynamical Nonstationarity of Physiological Time Series

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The brain shows complex, nonstationarity temporal dynamics, with abrupt micro- and macrostate transitions during its information processing. Detecting and characterizing these transitions in dynamical states of the brain is a critical issue in the field of neuroscience and psychiatry. In the current study, a novel method is proposed to quantify brain macrostates (e. g., sleep stages or cognitive states) from shifts of dynamical microstates or dynamical nonstationarity. A "dynamical microstate" is a temporal unit of the information processing in the brain with fixed dynamical parameters and specific spatial distribution. In this proposed approach, a phase-space-based dynamical dissimilarity map (DDM) is used to detect transitions between dynamically stationary microstates in the time series, and Tsallis time-dependent entropy is applied to quantify dynamical patterns of transitions in the DDM. We demonstrate that the DDM successfully detects transitions between microstates of different temporal dynamics in the simulated physiological time series against high levels of noise. Based on the assumption of nonlinear, deterministic brain dynamics, we also demonstrate that dynamical nonstationarity analysis is useful to quantify brain macrostates (sleep stages I, II, III, IV, and rapid eye movement (REM) sleep) from sleep EEGs with an overall accuracy of 77%. We suggest that dynamical nonstationarity is a useful tool to quantify macroscopic mental states (statistical integration) of the brain using dynamical transitions at the microscopic scale in physiological data.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2011-04
Language
English
Article Type
Article
Keywords

HUMAN SLEEP EEG; DETRENDED FLUCTUATION ANALYSIS; NONEXTENSIVE STATISTICS; CORRELATION DIMENSION; MICROSTATE DURATION; NONLINEAR STRUCTURE; SCALP EEG; ELECTROENCEPHALOGRAM; CLASSIFICATION; SEGMENTATION

Citation

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.58, no.4, pp.1084 - 1093

ISSN
0018-9294
DOI
10.1109/TBME.2009.2034840
URI
http://hdl.handle.net/10203/21471
Appears in Collection
BiS-Journal Papers(저널논문)
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