Dynamic Brain States During Sleep: Initial Studies


Andrew A. Marino1, Simona Carrubba1, David McCarty1, Andrew L. Chesson, Jr.1, and Clifton Frilot II2
1Department of Neurology, LSU Health Sciences Center, Shreveport, LA, United States;
2School of Allied Health Professions, LSU Health Sciences Center, Shreveport, LA, United States

Presented at SLEEP 2011, the 25th Anniversary Meeting of the Associated Professional Sleep Societies, June 11–15, 2011, Minneapolis, MN.


Abstract

Introduction: Nonlinear analysis of EEG signals using the RQA method quantifies deterministic (nonrandom) changes in brain states of arbitrary length. RQA has been used to study cognitive processing in normality and disease, but not to characterize sleep microstates. We sought evidence indicating RQA’s usefulness for this purpose.

Methods: Polysomnograms scored based on the ASSM manual were analyzed using published computational procedures (implementing LabView code available). Employing the RQA variable %R, which measures amount of law-governed activity whether or not visually perceivable, we quantified EEGs from frontal, central, and occipital electrodes, resulting in approximately 25,000 sequential values in successive 1-s intervals (time series). Additionally, phasic events (EEG arousals, K complexes, spindles, delta bursts) were analyzed by calculating %R for 100-ms intervals using a step of 2 ms (500 values of %R per sec). Additional calculations were performed using the RQA variable %D (independent measure of determinism). Spectral power analysis was employed as a control procedure.

Results: In each case (6 patients × 6 electrodes), the %R time series consisted of 3 relative maxima with periods of 200–400 min; as expected, for each patient, the patterns for all derivations were essentially identical. The patterns were not detected using power analyses. Visible phasic events consistently resulted in localized increases in %R; similar changes occurred in the absence of visually-scored phasic EEG events. Averaged over 30-s epochs, increased %R correlated strongly with increased sleep-state depth as assessed by standard scoring (80–90% agreement); the extent of the agreement was increased when %D was included in the RQA sleep-stage classification.

Conclusion: Nonlinear EEG analysis reliably permitted quantification of brain states lasting 0.1–1 s and reproduced the scoring of gold-standard-scored PSGs, suggesting the possibility of an integrated approach to the study of brain dynamical changes that includes both background signals and superimposed phasic changes.

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