Global and local complexity of intracranial EEG decreases during NREM sleep

Abstract

Key to understanding the neuronal basis of consciousness is the characterization of the neural signatures of changes in level of consciousness during sleep. Here we analysed three measures of dynamical complexity on spontaneous depth electrode recordings from 10 epilepsy patients during wakeful rest (WR) and different stages of sleep: (i) Lempel–Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability over time of the set of channels active above a threshold; (iii) synchrony coalition entropy, which measures the variability over time of the set of synchronous channels. When computed across sets of channels that are broadly distributed across multiple brain regions, all three measures decreased substantially in all participants during early-night non-rapid eye movement (NREM) sleep. This decrease was partially reversed during late-night NREM sleep, while the measures scored similar to WR during rapid eye movement (REM) sleep. This global pattern was in almost all cases mirrored at the local level by groups of channels located in a single region. In testing for differences between regions, we found elevated signal complexity in the frontal lobe. These differences could not be attributed solely to changes in spectral power between conditions. Our results provide further evidence that the level of consciousness correlates with neural dynamical complexity.

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

“…The amount of irregularity in time series patterns reflects linear stochastic effects as well as nonlinear deterministic correlations (Courtiol et al, 2016;Kaffashi, Foglyano, Wilson, & Lopario, 2008;Park, Kim, Kim, & Cichocki, 2007;Wang, McIntosh, Kovacevic, Karachalios, & Protzner, 2016). Surrogate data analyses, which involve preserving the linear properties of neural time series while simultaneously altering the underlying temporal dependencies via phase randomization, provide a more explicit test for the presence of nonlinearities (Theiler, Eubank, Longtin, Galdrikian, & Farmer, 1992), yet they have not been used frequently (for an exception, see Schartner, Pigorini, et al, 2017). The authors concluded that high frequency spectral components were entropy raising and lower frequencies entropy suppressing (see also Lee et al, 2013;Mizuno et al, 2010), and that overall, entropy reflects the balance between sleep and alertness promoting factors.…”

“…The rationale for use of the surrogates is the following: linear processes are completely accounted for by the second order statistics (power spectrum) of a time series. Surrogate sets were generated separately for every participant, electrode channel and sleep stage (using a maximum number of 100 iterations) and then, these surrogates were used to construct ratio scores of original relative to surrogate data, similar to what has been reported in other studies (Schartner, Carhart-Harris, et al, 2017;Schartner, Pigorini, et al, 2017). The surrogate signals are generated by keeping the power spectrum constant, while randomly shuffling phase (which destroys higher-order correlations) and in the final step, performing an inverse Fourier transform back into the time domain (Theiler et al, 1992).…”

“…The implication here is that entropy changes stem almost entirely from the linear properties of brain signals. Surrogate data analyses, which involve preserving the linear properties of neural time series while simultaneously altering the underlying temporal dependencies via phase randomization, provide a more explicit test for the presence of nonlinearities (Theiler, Eubank, Longtin, Galdrikian, & Farmer, 1992), yet they have not been used frequently (for an exception, see Schartner, Pigorini, et al, 2017).…”

“…The algorithm that we used to construct the surrogates was the iterated amplitude adjusted Fourier transform (IAAFT) which minimizes the spurious detection of nonlinearity (Schreiber & Schmitz, 1996). Surrogate sets were generated separately for every participant, electrode channel and sleep stage (using a maximum number of 100 iterations) and then, these surrogates were used to construct ratio scores of original relative to surrogate data, similar to what has been reported in other studies (Schartner, Carhart-Harris, et al, 2017;Schartner, Pigorini, et al, 2017). In brief, the normalized ratios were calculated as MDE original / MDE surrogate for each sleep stage.…”

“…The high amounts of variance in EEG entropy explained by variation in PSD 0.5-35 Hz slopes-between 60 and 70% at fine time scales and approximately 40% at coarse scales-indicates that a substantial portion of the state-dependent changes in scalp EEG complexity is driven by linear stochastic effects (see also Pereda, Gamundi, Rial, &González, 1998 andShen, Olbrich, Achermann, &Meier, 2003 for additional evidence of weak nonlinearity in human sleep EEG). However, higherorder dynamical properties of brain activity also accounted for some of the stage-dependent complexity changes (see also Schartner, Carhart-Harris, et al, 2017;Schartner, Pigorini, et al, 2017). This conclusion can be drawn from two separate lines of evidence.…”