article: Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, ‘wake-like’ EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans.
  General anesthesia is a reversible, pharmaceutically induced state of unconsciousness, while sleep is internally generated and cycles between rapid (REM) and non-rapid eye movement sleep (NREM; Brown et al. , 2010; Franks and Zecharia, 2011). Both sleep stages and anesthesia are characterized by a behaviorally similar state of reduced physical arousal (Brown et al. , 2010; Franks and Zecharia, 2011; Murphy et al. , 2011). Definitions of arousal vary and include e. g. autonomic, behavioral or mental arousal. For this study, we followed an updated version of the framework by Laureys et al. that defined consciousness on two axes – content (awareness) and level (arousal; Boly et al. , 2013; Laureys, 2005). While the conscious content is low in NREM sleep and propofol anesthesia, it is high in wakefulness and dreaming states like REM. The arousal level is low during anesthesia and in all sleep states including REM. Both NREM sleep stage 3 (also called slow-wave sleep) and general anesthesia with propofol exhibit similar electrophysiological features, such as an increase in low frequency activity and the occurrence of prominent slow oscillations (<1. 25 Hz; Brown et al. , 2010; Franks and Zecharia, 2011; Murphy et al. , 2011; Prerau et al. , 2017; Purdon et al. , 2013). Moreover, propofol anesthesia has been linked to the emergence of a strong frontal alpha oscillation (8–12 Hz; Purdon et al. , 2013) whereas spindles (12–16 Hz) typically appear in NREM sleep stage 2 (Prerau et al. , 2017). In contrast, REM sleep is characterized by a desynchronized, active pattern in the electroencephalogram (EEG), which resembles wakefulness (Brown et al. , 2010; Prerau et al. , 2017). The additional defining features of REM sleep are therefore peripheral markers including muscle atonia as detected by electromyography (EMG) combined with rapid eye movements in the electrooculogram (EOG; Prerau et al. , 2017). To date, it has been challenging to differentiate REM sleep from wakefulness in humans solely from the electrophysiological brain state (Pal et al. , 2016). Recently, several lines of inquiry highlighted the importance of non-oscillatory, scale-free neural activity for brain physiology and behavior (Miller et al. , 2009a; Gao et al. , 2017; Voytek et al. , 2015; Voytek and Knight, 2015; Miller et al. , 2009b; He et al. , 2010). The electrophysiological power spectrum is characterized by a 1/f signal drop-off, i. e. higher frequency activity exhibits reduced power as compared to low frequency activity. This scaling law between power and frequency can be estimated from the exponential decay of the power spectrum (He et al. , 2010) and has previously been used to assess a variety of cognitive and EEG phenomena (Colombo et al. , 2019; Lina et al. , 2019; Miskovic et al. , 2019; Pereda et al. , 1998; Pritchard, 1992; Shen et al. , 2003; Susmáková and Krakovská, 2008). Notably, this decay function mainly captures non-oscillatory brain activity, which is not characterized by a defining temporal scale, such as band-limited oscillations (He et al. , 2010). Therefore, analyses of scale-free 1/f dynamics might prove especially helpful when analyzing brain states that are not characterized by prominent oscillations such as REM sleep in humans. We hypothesized that markers of 1/f activity, such as the spectral slope of the power spectrum, may provide an electrophysiological signature that distinguishes ‘paradoxical’ REM sleep (Siegel, 2011) from wakefulness. Importantly, 1/f dynamics can also be observed in a variety of other signals. For instance, long-range temporal correlations of neuronal oscillations (Linkenkaer-Hansen et al. , 2001) or the size and duration of neuronal avalanches (Beggs and Plenz, 2003; Palva et al. , 2013) also follow a power law but these scale-free behaviors likely have a different neurophysiological basis than the 1/f drop-off of the power spectrum (He et al. , 2010). Recent findings suggested that 1/f dynamics differentiate wakefulness from general anesthesia (Colombo et al. , 2019; Gao, 2016). For instance, using intracranial recordings in macaque monkeys, it had been shown that the spectral slope between 30 and 50 Hz reliably tracked changes in arousal level under propofol anesthesia from induction to emergence (Gao et al. , 2017). Moreover, it has been reported that that the spectral slope between 1 and 40 Hz in human scalp EEG recordings discriminated states with conscious content, namely wakefulness and ketamine anesthesia, from states where no conscious report was possible, i. e. Xenon and propofol anesthesia (Colombo et al. , 2019). Collectively, these studies implied that propofol anesthesia was accompanied by a steeper decay of the power spectrum (Colombo et al. , 2019; Gao et al. , 2017). With regard to sleep physiology, it had been observed that the spectral exponent of human scalp EEG becomes more negative during NREM sleep, when estimated e. g. in the 1 to 5 Hz (Shen et al. , 2003), 3 to 30 Hz (Pereda et al. , 1998) or 0. 5 to 35 Hz frequency range (Miskovic et al. , 2019). A similar pattern was observed in intracranial recordings with subdural grid electrodes in humans between 10 and 100 Hz (one subject; Freeman and Zhai, 2009) or 1 and 100 Hz (five subjects; He et al. , 2010). Note that the 1/f background activity was estimated from frequency bands that were potentially influenced by simultaneously occurring low frequency oscillation i. e. delta (<4 Hz) or slow waves (<1. 25 Hz) that might affect the degree of spectral tilt. General anesthetics like propofol, etomidate and barbiturates act on GABAergic receptors to enhance inhibition (Brown et al. , 2011). Recently, computational simulations indicated that the spectral slope might provide a surrogate marker for the excitation to inhibition (E/I) balance with more negative spectral slopes (esp. in the 30 to 50 Hz range) indexing enhanced inhibition (Gao et al. , 2017). This model was validated using intracranial recordings in macaques and rodents: A shift in E/I-balance towards inhibition by administrating propofol resulted in a steeper slope of the power spectrum. Likewise, the spectral slope in the rodent hippocampus varied across the depth of hippocampus, directly reflecting the ratio of excitatory to inhibitory cells in the underlying neuronal population. Moreover, a modulation of spectral slope was also observed as a function of the hippocampal theta cycle, likely reflecting rapid shifts in E/I-balance (Gao et al. , 2017). A recent study that employed two-photon calcium imaging in mice provided additional insight into putative changes in E/I-balance during the sleep cycle. Cortical activity in mice was reduced during NREM sleep compared to wakefulness and, notably, even further reduced during REM sleep (Niethard et al. , 2016). Crucially, the authors observed a selective increase in inhibitory interneuron activity (parvalbumin-positive interneurons; Niethard et al. , 2016) during REM but not NREM sleep revealing an overall shift towards inhibition during REM sleep. In the present study, we assessed if 1/f spectral dynamics, in particular in the 30 to 50 Hz range, which is devoid of prominent low-frequency oscillatory activity (Gao et al. , 2017), could track arousal states in humans both under anesthesia with propofol and during sleep in intracranial and scalp EEG recordings. Specifically, we hypothesized that the spectral slope should become more negative (i. e. the power spectrum steeper) in sleep and under anesthesia compared to wakefulness. Importantly, we also predicted that the spectral slope could discriminate wakefulness from NREM as well as REM sleep. Based on recent reports linking E/I-balance and electrophysiology (Gao et al. , 2017; Niethard et al. , 2016), we reasoned that the spectral slope, as a putative marker of E/I-balance, should facilitate the detection of REM sleep directly from the current brain state, without complementary information from additional EMG or EOG recordings. While previous studies that included lower frequency power in their slope estimates, found the slope of REM to be close to wakefulness (He et al. , 2010), we specifically investigated if the aperiodic background activity in the 30 to 50 Hz range could reliably discriminate REM sleep from wakefulness and NREM sleep.
  We first tested if the spectral slope discriminates wakefulness and propofol anesthesia in humans in two experiments. In the first study, we recorded scalp EEG during general anesthesia for orthopedic surgery in otherwise healthy adults (Study 1, n = 9). In the second study, we obtained intracranial EEG in epilepsy patients who were implanted with intracranial electrodes for seizure onset localization while they underwent general anesthesia for electrode explantation (Study 2, n = 12; subdural grid electrodes (electrocorticography; ECoG) and stereotactically placed depth electrodes (SEEG; coverage see Figure 1—figure supplement 1a). In Study 1 (n = 9), we found that the time-resolved spectral slope closely tracked changes in arousal levels while subjects underwent propofol anesthesia (Figure 1a). Specifically, we observed a significant decrease of the spectral slope from wakefulness (−1. 84 ± 0. 30; mean ± SEM) to anesthesia (−3. 10 ± 0. 20) when averaged across all electrodes (Figure 1b; permutation t-test: p<0. 0001, obs. t8 = 7. 09, dWake-Anesthesia = 1. 65). A cluster-based permutation test assessing the spatial extent of this effect on the scalp level resulted in a single large cluster that spanned all 25 electrodes without a clear peak (p<0. 001; Figure 1b). To further examine the spatial distribution of the observed scalp EEG pattern and to assess subcortical contributions, we utilized intracranial recordings in Study 2 (n = 12). Again, we observed that the spectral slope was higher during wakefulness (−2. 75 ± 0. 15) than during anesthesia (−4. 34 ± 0. 11) when averaged across all electrodes (Figure 1c; permutation t-test: p<0. 0001, obs. t11 = 9. 93, dWake-Anesthesia = 3. 57). This effect was present at the majority of recording sites (470 of 485 SEEG (96. 9%); Figure 1c, Table 1). Notably, recordings from subdural grid electrodes (n = 4) showed the same pattern: The spectral slope decreased from wakefulness to anesthesia in the majority of recording sites (129 of 147 ECoG (87. 75%); Figure 1—figure supplement 2a). Taken together, we observed a more negative spectral slope under anesthesia compared to wakefulness in both scalp as well as intracranial EEG (Figure 1b, c). Our results indicate that the spectral slope differentiates between wakefulness and general anesthesia in humans. This effect spanned all scalp and the majority of intracranial electrodes, hence, supporting the notion that propofol anesthesia induces a global, brain-wide state change (Brown et al. , 2010). Having established that the spectral slope differs significantly between wakefulness and propofol anesthesia, we next examined if this state-dependent modulation generalized to other forms of decreased arousal, such as sleep. We specifically sought to determine if the spectral slope could discern wakefulness from different sleep stages. We analyzed two datasets obtained during a full night of sleep. In Study 3, we obtained polysomnography recordings from 20 healthy subjects, which included scalp EEG, as well as electrocardiography (ECG), electromyography (EMG) and electrooculography (EOG). To determine the precise spatial extent and subcortical contributions, we again recorded intracranial EEG in a separate cohort for Study 4 (n = 10; electrode coverage see Figure 1—figure supplement 1b). Critically, we combined intracranial EEG with polysomnography (scalp EEG, ECG, EMG, EOG) to enable comparable sleep staging across both the scalp and intracranial studies. We observed that the time-resolved spectral slope closely tracked the technician-scored hypnogram (Figure 2a). To quantify this effect, we compared spectral slope estimates across wakefulness, N3 and REM sleep. In Study 3, we obtained a separate baseline eyes-closed recording during rest in 14 out of 20 subjects. In this subset, we observed prominent slope differences between quiescent rest (−1. 87 ± 0. 18; mean ± SEM), N3 sleep (−3. 46 ± 0. 16) and REM sleep (−4. 73 ± 0. 23; Figure 2b). These differences were significant when averaged across all scalp EEG channels (repeated-measures ANOVA permutation test: p<0. 0001, obs. F1. 94,25. 17 = 56. 05, dRest-Sleep = 3. 07). Notably, N2 sleep exhibited an average slope of −3. 67 ± 0. 10 that was also significantly below rest (Figure 2—figure supplement 1a; permutation t-test: pRest-N2 <0. 0001; obs. t13 = 7. 97; dRest-N2 = 3. 31). Permutation t-tests revealed a significant difference between rest and N3 (pRest-N3 <0. 0001, obs. t13 = 5. 69, dRest-N3 = 2. 49), between rest and REM (pRest-REM <0. 0001, obs. t13 = 11. 67, dRest-REM = 3. 71) and between N3 and REM sleep (pN3-REM = 0. 0001, obs. t13 = 4. 44, dN3-REM = 1. 70). Importantly, while some overlap of absolute spectral slope values between rest and sleep existed when comparing across individuals (Figure 2—figure supplement 1a), we observed a consistent individual decrease of – 2. 06 ± 0. 21 (mean ± SEM) between rest and all sleep stages (Figure 2—figure supplement 1b; Rest-N1 = −1. 95 ± 0. 26, Rest-N2 = −1. 81 ± 0. 23, Rest-N3 = −1. 59 ± 0. 28, Rest-REM = −2. 86 ± 0. 25). Including all available wake periods (before, during and after the night of sleep in all 20 subjects) increased the variance (Figure 2—figure supplement 1c), which can be explained by the fact that subjects were still drowsy and data during state transitions was included. However, the overall pattern was remarkably similar (Figure 2—figure supplement 1b, d). As this approach increased the available amount of data, we utilized all wake trials (referred to as wake) for subsequent analyses. Next, we assessed the spatial topography of where the slope tracks the hypnogram. Thus, we calculated the Mutual Information (MI) between the time-resolved spectral slope and the hypnogram. MI is ideal to assess the relationship between a discrete variable (hypnogram) and neurophysiologic data (Quian Quiroga and Panzeri, 2009). In addition, we also repeated all analyses based on linear rank correlations, which yielded comparable results (Figure 2—figure supplement 2a, b). We observed that the spectral slope closely tracked the hypnogram at all electrodes as indicated by a permutation test (n = 20; average z score = 4. 14 ± 0. 41 (mean ± SEM); all z > 2. 8 correspond to a Bonferroni-corrected p<0. 01; Figure 2b). This effect peaked over frontal electrodes F3, Fz and F4 (z = 4. 90 ± 0. 37; Figure 2b). Since frequencies of cranial muscle activity overlap with the frequency range used for spectral slope estimation, we controlled for possible muscle artifacts by repeating the analysis after local referencing (Laplacian; Fitzgibbon et al. , 2013). In addition, we utilized partial correlations that considered the slope of the EMG as a confounding variable. These control analyses indicated that excluding these confounds strengthened the observed relationship between the hypnogram and the spectral slope (Laplacian: pSpearman <0. 001, pMI <0. 0001; partial correlation: pSpearman <0. 001; Figure 2—figure supplement 2). We established that the spectral slope closely tracks the hypnogram. However, we observed pronounced differences between scalp electrodes (Figure 2b), thus, raising the question, which brain regions contribute most to the observed effects at the scalp level. In a source level analysis using an LCMV beamformer, prefrontal areas exhibited the strongest sleepstate-dependent modulation (Figure 2—figure supplement 3). To further investigate the contribution of cortical and subcortical regions, we obtained intracranial EEG recordings (Study 4, n = 10), which were combined with simultaneous scalp EEG recordings. First, we aimed to replicate the results from Study 3. Again, we found that the slope decreased from wakefulness (−2. 99 ± 0. 32; mean ± SEM) to N3 sleep (−3. 69 ± 0. 12) and REM sleep (−4. 15 ± 0. 29; Figure 2c). These three states were significantly different in a repeated-measures ANOVA permutation test (p=0. 0009; obs. F1. 97,17. 74 = 10. 79, dWake-Sleep = 1. 12), thus, directly replicating the pattern as observed in Study 3. Permutation t-tests revealed a significant difference between wakefulness and REM (p=0. 0002; obs. t9 = 4. 78; d = 1. 19) and wakefulness and N3 (p=0. 0136; obs. t9 = 2. 66; d = 0. 92) but only marginally between N3 and REM (p=0. 0431; obs. t9 = 1. 84; d = 0. 64). Second, we directly tested which intracranial SEEG contacts mirrored the observed scalp EEG pattern. We observed the same pattern - a more negative spectral slope in N3 and REM sleep as compared to wakefulness - in 155 of 352 SEEG (44. 03%; chi-squared test against chance-level (33%): X2 = 8. 20, p=0. 0042; Figure 2c, Figure 2—figure supplement 3). Importantly, this analysis revealed that medial prefrontal cortex (mPFC) and medial temporal lobe structures (MTL; details see Table 2, Figure 2—figure supplement 3) exhibit a significantly larger fraction of electrodes showing sleep state-dependent slope modulation compared to their lateral counterparts (chi-squared tests: mPFC - lateral PFC: p<0. 0001, X2 = 33. 56, MTL – lateral temporal cortex: p<0. 0001, X2 = 33. 12), hence, converging on the same brain regions known to be the most relevant for sleep-dependent memory consolidation (Dang-Vu et al. , 2008; Helfrich et al. , 2018; Mander et al. , 2013; Murphy et al. , 2009). Note that we did not specifically target any brain regions and in contrast to previous studies using subdural grid electrodes (Gao et al. , 2017; He et al. , 2010), the majority of our probes were stereotactically placed depth electrodes (for Wake - N3 and Wake - REM see Figure 2—figure supplement 4; subdural grid electrodes see Figure 1—figure supplement 2b, c). Given the spatial heterogeneity of intracranial responses (Parvizi and Kastner, 2018), there was a remarkable convergence on medial PFC that resembled the pattern observed at source level (Figure 2—figure supplement 3) and the overlying scalp EEG electrode Fz (Figure 2). Our findings provide evidence that the spectral slope reliably discriminates wakefulness from sleep. Multiple prior reports indicated that slow waves are a hallmark of decreased arousal states (Brown et al. , 2010; Franks and Zecharia, 2011; Murphy et al. , 2011). We directly compared how well slow wave activity and spectral slopes estimates differentiate arousal states using both a linear discriminant analysis (LDA) and a multivariate general linear Model (GLM) to discriminate different sleep states based on either the spectral slope or slow wave activity in 18 subjects (two subjects had to be excluded due to insufficient wake trials). Note that both the LDA classifier and the GLM were trained on the same values that were used in the univariate testing. Both the LDA classifier as well as the GLM output provide a quantitative metric, namely the accuracy of correctly classified trials for LDA and the unique explained variance quantified by eta squared for the GLM enabling a direct comparison between different conditions. The GLM offers the additional advantage of facilitating the assessment of the multivariate interaction of the spectral slope and slow wave power. Data were z-scored before modeling with GLM and LDA outputs were logit-transformed before comparison. First, we directly tested if the spectral slope is superior in discriminating REM sleep from wakefulness. We found that classifier performance was enhanced using the spectral slope compared to slow wave power (spectral slope: 76. 31 ± 3. 61% (mean ± SEM), slow wave power: 61. 50 ± 1. 93%; permutation t-test: p<0. 001, obs. t17 = 3. 73, d = 1. 25; Figure 3a). This finding indicates that the spectral slope constitutes a marker that successfully discriminates REM sleep from wakefulness solely from the electrophysiological brain state. Note that classification performance is bound by the accuracy of the underlying sleep scoring as a ground truth. Since the inter-rater reliability between sleep scoring experts is typically about 80% (Danker-Hopfe et al. , 2009), the classifier accurately predicts the experts' ratings in 80% of the time. Second, we repeated this analysis to discriminate wakefulness from N3 sleep. Classification performance using slow wave power or spectral slope did not differ significantly (slow wave power: 82. 09 ± 2. 13%, spectral slope: 73. 05 ± 2. 97%; permutation t-test: p=0. 054, obs. t17 = −1. 95, d = −0. 72; Figure 3b). This shows that the spectral slope successfully discriminates wakefulness from N3 sleep despite the fact that the defining criterion of N3 sleep is pronounced slow wave activity. If LDA was used to classify all three states simultaneously (wakefulness, N3, REM; chance level = 33%) then the classifier performance was comparable for the spectral slope and slow wave power (spectral slope: 58. 09 ± 2. 35%; slow wave power: 63. 94 ± 2. 04%; permutation t-test: p=0. 054, obs. t17 = −1. 77, d = −0. 62) potentially reflecting the respective advantageous classification of either REM or N3 from wakefulness. We repeated this analysis to discriminate anesthesia from wakefulness (n = 9). We found that classification based on the spectral slope performed better than the one based on slow wave power (spectral slope: 76. 56 ± 3. 56%, slow wave power: 52. 43 ± 1. 04%; permutation t-test: p<0. 001, obs. t8 = 6. 10, d = 2. 63; Figure 3c). Note, that slow wave power was already elevated during wakefulness, which may reflect a premedication with a sedative (see Figure 1a and Materials and Methods). When discerning wakefulness from REM sleep, information (as quantified by unique explained variance eta squared) about the underlying arousal state was significantly different between the spectral slope, SO power and their interaction (repeated-measures ANOVA permutation test: p<0. 001, F1. 16,19. 74 = 19. 69). Post hoc t-tests (p-values were Bonferroni-corrected for multiple testing) revealed that this effect was predominantly driven by the spectral slope (slope: 0. 29 ± 0. 05 (mean ± SEM), SO: 0. 05 ± 0. 01; interaction: 0. 06 ± 0. 03; post hoc permutation t-tests (Bonferroni-corrected): Slope-SO: p<0. 001, obs. t17 = 4. 29, d = 1. 50, Slope-Int. : p<0. 001, obs. t17 = 4. 84, d = 1. 62; SO-Int. : p=0. 63, obs. t17 = 0. 82, d = 0. 29). For NREM stage three sleep and wakefulness, there was no difference in explained variance between factors (slope: 0. 12 ± 0. 03, SO: 0. 17 ± 0. 03, interaction: 0. 08 ± 0. 02; repeated-measures ANOVA permutation test: p=0. 081, F1. 72,29. 28 = 2. 55; post hoc permutation t-tests (Bonferroni-corrected): Slope-SO: p=0. 406, obs. t17 = −1. 11, d = −0. 39; Slope-Int. : p=0. 422 obs. t17 = 1. 09, d = 0. 31; SO-Int. : p=0. 032, obs. t17 = 2. 44, d = 0. 88). Between anesthesia and wakefulness, information about the state was again significantly different between factors (slope: 0. 10 ± 0. 03, SO: 0. 01 ± 0. 003; interaction: 0. 002 ± 0. 001; repeated-measures ANOVA permutation test: p<0. 001, F1. 01,8. 09 = 14. 61). As in the wake-REM differentiation, this could mainly be attributed to the spectral slope (post hoc permutation t-tests (Bonferroni-corrected): Slope-SO: p<0. 001, obs. t8 = 3. 70, d = 1. 75), Slope-Int. : p<0. 001, obs. t8 = 3. 95, d = 1. 87, SO-Int. : p=0. 019, obs. t8 = 2. 67, d = 1. 07). Taken together, the results from the GLM mirrored the findings from the LDA approach: The spectral slope enabled an improved classification and contained more unique information about arousal state compared to slow wave power when differentiating wakefulness from both propofol anesthesia and REM sleep and was comparable when discerning wakefulness from N3 sleep. N3 sleep and propofol anesthesia are both characterized by the occurrence of prominent slow oscillations (Murphy et al. , 2011). Previous reports indicated that low frequency activity might serve as a marker to disentangle different arousal states (Brown et al. , 2010; Franks and Zecharia, 2011; Murphy et al. , 2011). Our results confirm and extend this observation. However, while slow wave activity (<1. 25 Hz) discriminated wakefulness from N3, it was less robust in separating wakefulness from REM sleep or propofol anesthesia Figure 3). We conducted several control analyses to investigate the relationship of slow wave activity and the spectral slope. First, we found that the interaction of spectral slope and slow wave activity did not explain more unique variance than the sum of the univariate metrics in a GLM, hence, indicating that the slope and slow wave activity provide complimentary information about arousal states (Figure 3). In addition, if lower frequencies (e. g. 1 to 20 Hz) were utilized for spectral slope estimation, MI between the hypnogram and the time-resolved spectral slope decreased (Figure 2—figure supplement 5d) suggesting that lower frequencies and the 30 to 45 Hz range may index distinct processes. Second, we analyzed the changes in spectral slope during the time course of a slow wave. At the scalp EEG level, the trough of a slow wave is associated with a cortical ‘down-state’, while the peak reflects an ‘up-state’ (Nir et al. , 2011; Valderrama et al. , 2012). We found, that the spectral slope mirrored up-/down-states during sleep with more negative slopes observed at slow wave troughs compared to peaks (Figure 4). This effect was most pronounced over frontal channels (cluster-based permutation test: p=0. 005, dTrough-Peak = −0. 65). Third, slow wave activity is also present to some degree during REM sleep (Funk et al. , 2016) as well as wakefulness (Vyazovskiy et al. , 2011). Here, we detected a significantly higher number of slow waves during N3 sleep (SON3 = 28. 79 ± 0. 79 per minute; mean ± SEM at electrode Fz) as compared to REM sleep (SOREM = 2. 16 ± 0. 89 per minute; permutation t-test: p<0. 0001, obs. t19 = 22. 64, d = 7. 05) and wakefulness (SOWake = 5. 05 ± 0. 51 per minute; permutation t-test: p<0. 0001, obs. t19 = 25. 32, d = 6. 92; Figure 4d). Interestingly, the averaged slope at the trough of the slow waves was significantly different between arousal states: −2. 26 ± 0. 12 in wakefulness, −3. 40 ± 0. 09 in N3 sleep and −4. 00 ± 0. 18 in REM sleep (mean ± SEM), mirroring our observation of the overall slope differences (Figure 4c; permutation t-tests: Wake-N3: p<0. 0001, obs. t18 = 7. 07, d = 2. 38; Wake-REM: p<0. 0001, obs. t18 = 9. 67, d = 2. 55, N3-REM: p=0. 007, obs. t19 = 2. 73, d = 0. 91). Taken together, our control analyses indicate that slow wave activity and the spectral slope may index two distinct processes. Rodent studies suggest that fronto-parietal theta and high-gamma network connectivity correlates with arousal levels in both sleep and general anesthesia (Pal et al. , 2018; Pal et al. , 2016). We tested this notion and directly compared connectivity estimates to the spectral slope metric: We found that the spectral slope was superior to fronto-parietal theta connectivity in tracking sleep stages and in reliably differentiating REM and N3 sleep (Figure 2—figure supplement 10). Note that our dataset did not have a sufficient number of intracranial electrodes in the parietal lobe to analyze fronto-parietal connectivity since the parietal lobe is an infrequent site for clinical exploration for epilepsy. Hence, we restricted our analyses to theta-band connectivity in scalp EEG. Future studies will be needed to address the relationship of high gamma-band connectivity and the spectral slope.
  Consciousness is commonly assessed on two axes – content (e. g. the experience) and level (e. g. vigilance; Boly et al. , 2013; Laureys, 2005). While conscious content is thought to fluctuate during sleep, mostly in the form of dreams during REM (Siclari et al. , 2017), the arousal level is generally reduced as compared to wakefulness. Both components are typically judged by verbal report of the research subject or patient leading to the approximation that content is equivalent to conscious experience as related by the subject whereas the arousal level corresponds to the subject’s ability to respond. Notably, there are some obvious restrictions to these definitions (e. g. the reduced arousal level in REM sleep prevents the subject from relating his experience to the experimenter unless awakened), which make more objective electrophysiological measures desirable. Several neurophysiological metrics of conscious content such as the Perturbational Complexity Index (PCI; Casali et al. , 2013) have been introduced. While PCI is decreased in N3 sleep and GABAergic (e. g. propofol) anesthesia, it resembles wakefulness during REM sleep and ketamine anesthesia, which are both associated with vivid dreams (Casali et al. , 2013; Pal et al. , 2015; Siclari et al. , 2017). A recent EEG study under anesthesia with propofol, xenon and ketamine found that the PCI correlated with the spectral exponent derived from the 1 to 40 Hz frequency range (Colombo et al. , 2019). A related study reported that the spectral slope derived from the 0. 5 to 35 Hz frequency range became progressively steeper from wakefulness to REM sleep, N2 and N3 sleep (Miskovic et al. , 2019). Critically, these metrics did not reliably differentiate arousal levels, i. e. they did not generalize to distinguishing wakefulness from REM sleep. The overall slowing of EEG activity and the occurrence of oscillations in lower frequency bands has previously been linked to reduced arousal levels (e. g. slow waves and spindles in sleep [Prerau et al. , 2017], delta waves and alpha oscillation under propofol anesthesia [Purdon et al. , 2013]). REM sleep, also called ‘paradoxical’ sleep (Siegel, 2011), is characterized by a ‘wake-like’ EEG without prominent oscillations in humans. Differentiating between wakefulness and REM solely from the electrophysiological brain state has been challenging and to date still requires simultaneous EMG and EOG recordings to detect muscle atonia and rapid eye movements (Iber et al. , 2007). Here, we demonstrate that the non-oscillatory, aperiodic part of the power spectrum, which is devoid of prominent low-frequency oscillatory components and can be approximated by the 1/f decay of the power spectrum estimated from the 30 to 45 Hz frequency range, reliably differentiates wakefulness from all three states of reduced arousal level, namely REM, N3 sleep and general anesthesia with propofol. 1/f dynamics are observed across a variety of tasks (He et al. , 2010; Miller et al. , 2009a; Miller et al. , 2009b; Voytek et al. , 2015), change with lifespan (Voytek et al. , 2015), and exhibit state-dependent variations during sleep (Freeman and Zhai, 2009; Leemburg et al. , 2018; Miskovic et al. , 2019; Robinson et al. , 2011) and anesthesia (Colombo et al. , 2019; Gao et al. , 2017). Critically, these dynamics can be observed irrespective of the employed recording modality and species (Colombo et al. , 2019; Freeman and Zhai, 2009; Gao et al. , 2017; He et al. , 2010; Leemburg et al. , 2018; Miskovic et al. , 2019). However, to date, the underlying neural mechanisms giving rise to the prominent 1/f decay of the electrophysiological power spectrum are not well understood (Buzsáki et al. , 2012; He et al. , 2010; Miller et al. , 2009a; Pesaran et al. , 2018). For instance, it had been observed that broadband activity (~2 to 150 Hz) and high frequency power (>80 Hz) correlate with population neuronal firing rates in macaques and humans (Ray and Maunsell, 2011; Manning et al. , 2009) as well as task performance across a range of behavioral experiments (Honey et al. , 2012; Miller et al. , 2009b; Miller et al. , 2014). Furthermore, several lines of research indicate that the spectral slope not only tracks the overall firing rate (Buzsáki et al. , 2012; Miller et al. , 2009a) but also correlates with a variety of related phenomena, including metrics that can be derived from electrophysiological time-series analysis such as entropy (Miskovic et al. , 2019), ensemble synchronization (Shen et al. , 2003) or signal complexity (Pereda et al. , 1998). Moreover, a link to the local balance between excitation and inhibition (E/I-balance; Gao et al. , 2017) as well as dendritic filtering (Buzsáki et al. , 2012) has been suggested previously. Notably, the EEG power spectrum in log-log space does not follow a straight line with a constant spectral exponent, but is characterized by the occurrence of so called ‘knees’ (bends). Different knee frequencies have been reported, such as around 1 ~ 2 Hz (He et al. , 2010), ~20 Hz (Robinson et al. , 2011; Robinson et al. , 2001) and ~75 Hz (Miller et al. , 2009a). The characteristic form of the power spectrum can be modeled by the multiplication of Lorentzian functions (He, 2014; He et al. , 2010; Miller et al. , 2009a; Miller et al. , 2014) where the knee frequencies are directly related to the time constants of the exponential decay (e. g. 2 ~ 3 ms for the 75 Hz knee and ~100 ms for the 1 ~ 2 Hz knee). Although the origin of these time constants remains unclear, it has been suggested that the 2 ~ 3 ms time constant originates from synaptic currents, while the 100 ms time constant reflects membrane leak (He, 2014; Miller et al. , 2009a). Other studies attributed the 20 Hz knee to the low pass filtering properties of dendrites (Robinson et al. , 2011; Robinson et al. , 2001). Critically, the 1/f dynamics in between those knee frequencies exhibit different spectral exponents: While an exponent of 2–3 is observed for center frequencies (~1 to 80 Hz; Freeman and Zhai, 2009; He et al. , 2010; Miller et al. , 2009a), higher frequencies (80 to 500 Hz) exhibited an exponent closer to 4 (Miller et al. , 2009a), thus, suggesting that the spectral slopes in different frequency ranges might be the consequence of different generative mechanisms. A recent study proposed that the shape of the power spectrum of human intracranial EEG is the product of local (fast) and distributed (slow) recurrent networks that are balancing excitation and inhibition in such a way that the network is tuned to the edge of dynamic instability, thus, maximizing information processing capacities (Chaudhuri et al. , 2018). Further in-silico modeling of E/I-balance suggested that the spectral slope in the 30 to 50 Hz range functions as an index of this balance where an increase in inhibition is accompanied by a decrease of spectral slope (Gao et al. , 2017). One testable hypothesis that arises from these observations is that cell-type-specific causal manipulations by e. g. optogenetics through selective targeting of excitatory pyramidal or inhibitory parvalbumin- or somatostatin-positive interneurons should bias the spectral slope in opposite directions. Future studies involving single neuron recordings and optogenetic manipulation will be needed to unravel the precise relationship between population firing statistics and changes in the spectral slope. Comparative studies in rodents (Gao et al. , 2017; Leemburg et al. , 2018), non-human primates (Gao et al. , 2017) and humans (Colombo et al. , 2019; He et al. , 2010; Miller et al. , 2009a; Miller et al. , 2009b; Miskovic et al. , 2019) combined with modeling work (Chaudhuri et al. , 2018; Robinson et al. , 2011; Robinson et al. , 2001) has the potential to integrate the divergent findings into a coherent framework, which is critical to further elucidate the neurophysiologic basis of 1/f dynamics and their relationship to arousal levels. Here, we found a decreased 1/f slope in N3 sleep, REM sleep and under general anesthesia compared to wakefulness in four independent studies. If the spectral slope reflects the local E/I-balance (Gao et al. , 2017), then our results would indicate that decreased arousal states are characterized by increased inhibition. In support of this consideration, several previous studies reported that propofol anesthesia and N3 sleep are associated with increased inhibition (Brown et al. , 2011; Gao et al. , 2017; Timofeev et al. , 2001) as well as prominent changes in neuronal firing rates (Lewis et al. , 2012; Timofeev et al. , 2001; Vyazovskiy et al. , 2009; Watson et al. , 2016). Notably, there is little evidence supporting a similar association for REM sleep. However, a recent two-photon calcium imaging study in rodents reported a reduction of the overall cortical firing rate during slow-wave and even further during REM sleep (Niethard et al. , 2016). Critically, this study also demonstrated that reduced firing rates during REM sleep were accompanied by a selective increase of inhibitory parvalbumin interneuron activity, thus, reflecting a relative shift towards inhibition (Niethard et al. , 2016). These results parallel the pattern as observed in this present study (Figure 2—figure supplement 11) where the steepest decay of the power spectrum (the most negative PSD slope) occurred during REM sleep. Jointly, these findings imply that REM sleep could be associated with the highest level of cortical inhibition, thus, resulting in a steeper decay of the power spectrum. This notion of increased inhibition during REM sleep offers a likely mechanistic explanation for certain REM-defining phenomena, such as muscle atonia (Scammell et al. , 2017) or the clinical observation that epileptic seizures during the night predominantly occur out of more excitable, highly synchronized NREM sleep and only rarely out of less excitable, desynchronized REM sleep (Ng and Pavlova, 2013). A wide array of settings has been employed to examine 1/f features in electrophysiological recordings. Practical guidelines on how to select parameters for spectral slope estimation remain scare. Here, we explored a range of different settings for a series of parameters to reliably estimate 1/f activity in arousal states. The estimation of 1/f background activity first requires a spectral decomposition of the underlying time-series. Here, we directly compared several algorithms (multiple slepian tapers, single Hanning window, Periodogram, Welch’s method) and found that the Multitaper approach yielded the highest signal-to-noise ratio (Figure 2—figure supplement 6). Critically, window length and spectral smoothing parameters need to be adjusted to a reasonable time window where stationarity of the time series can be assumed (e. g. 30 s segments for sleep; Figure 2—figure supplement 7). The choice of different reference schemes or slope fitting algorithms, namely a linear regression (see Methods), the eBOSC algorithm (Kosciessa et al. , 2020a) as well as the FOOOF algorithm (Haller et al. , 2018), resulted in a comparable overall spectral slope pattern with strongly correlated slope estimates (Figure 1—figure supplement 4, Figure 2—figure supplement 9). Additionally, we also explored a range of different fit frequencies (Figure 1—figure supplement 4, Figure 2—figure supplement 5). Collectively, our results indicate that the spectral slope should be approximated in a frequency range where no pronounced oscillatory activity and no prominent bend (‘knee’) are present. Therefore, the optimal choice of frequency range might differ between arousal states. In sleep, fitting the power spectrum to frequencies above 30 Hz (and <45 Hz to avoid the European line noise frequency at 50 Hz) shared the highest Mutual Information with the hypnogram (Figure 2—figure supplement 5). An inclusion of lower frequencies (e. g. 10 to 30 Hz or various fits length from 1 Hz onwards (compare Figure 2—figure supplement 5a, d) diminished this relationship, indicating a conflation of background and oscillatory activity. Critically, our results from a general linear model demonstrated that the slope in higher frequencies (above 30 Hz) carried independent information from slow oscillatory activity about the current brain state (Figure 3). Incorporating frequencies below 20 Hz resulted in a better separation between wakefulness and anesthesia (Figure 1—figure supplement 4), possibly due to the inclusion of the alpha range (~8–10 Hz) where prominent oscillations are observed under propofol anesthesia (Figure 1). Taken together, a fit to the 30 to 45 frequency range reliably differentiated wakefulness from anesthesia, NREM three and REM sleep. Collectively, our results from four independent studies provide five main advances: First, the spectral slope tracks changes in arousal levels in both propofol anesthesia and sleep in humans with high temporal precision and is observable on a wide range of timescales from sub-second epochs to full night recordings. Second, our results provide empirical evidence for the notion that the slope in the range from 30 to 45 Hz correlates well with all stages of arousal. Previous studies analyzing the spectral slope during states of reduced arousal have either employed different frequency ranges (Colombo et al. , 2019; Freeman and Zhai, 2009; He et al. , 2010; Miskovic et al. , 2019) or focused either on anesthesia (Colombo et al. , 2019; Gao et al. , 2017) or sleep (Miskovic et al. , 2019; Pereda et al. , 1998; Shen et al. , 2003). Here, we demonstrate that this frequency range is well suited to track arousal in both anesthesia and different sleep levels including REM and NREM sleep. Third, the spectral slope mirrored the rapid changes in excitability observed over the course of a slow wave directly tracking cortical ‘up- ‘and ‘down-states’, hence, providing a mechanistic link between the spectral slope and population synchrony during slow oscillations. Fourth, our observations support the premise that anesthesia is a brain-wide state (Brown et al. , 2010), whereas sleep exhibits network-specific activity patterns. Here, we observed that sleep dependent slope modulations were strongest in the medial temporal lobe and medial PFC, two key regions for sleep-dependent memory consolidation (Diekelmann and Born, 2010; Preston and Eichenbaum, 2013; Stickgold and Walker, 2013). Fifth, the spectral slope can be reliably estimated from scalp EEG recordings, thus, providing an accessible marker that can easily be incorporated into intraoperative neuromonitoring or automatic sleep stage classification algorithms. In the future, this marker could potentially be utilized to monitor other states of reduced arousal such as epileptic seizures, coma, the vegetative or minimally conscious state.
  We collected four independent datasets for this study to assess the neurophysiological basis of states of reduced arousal, namely general anesthesia and sleep. We recorded either non-invasive scalp electroencephalography (EEG) or intracranial EEG (electrocorticography; ECoG) using subdural grid and strip electrodes and stereotactically placed depth electrodes (SEEG; for coverage see Figure 1—figure supplement 1). The EEG and intracranial anesthesia studies were conducted at the University Hospital of Oslo. All participants or their parents provided informed written consent according to the local ethics committee guidelines (Regional Committees for Medical and Health Research Ethics in Oslo case number 2012/2015 and extension 2012/2015–8) and the Declaration of Helsinki. All patients received a premedication with 3. 75 to 7. 5 mg midazolam (Dormicum, Basel, Switzerland); the anesthesia scalp EEG group (Study 1) received additional 1 g oral paracetamol (Paracet, Weifa, Oslo, Norway) as well as 10 mg oxycodone sustained release tablet (OxyContin, Dublin, Ireland) for postoperative pain management. Propofol (Propolipid, Fresenius Kabi, Uppsala, Sweden) and remifentanil (Ultiva, GlaxoSmithKline, Parma, Italy) were administered by computer-controlled infusion pumps (B Braun Perfusor Space, Melsungen, Germany) using a target-controlled infusion (TCI) program (Schnider for propofol and Minto for remifentanil) in order to achieve plasma concentrations sufficient for anesthesia and analgesia. Prior to start of anesthesia all patients received an infusion of Ringer’s-Acetate (5 ml /kg) to prevent hypotension during anesthesia induction, as well as 3–5 ml 1% lidocaine intravenously to prevent pain during propofol injection. All patients were pre-oxygenated with 100% oxygen and received the non-depolarizing muscle relaxant cisatracurium for intubation (Nimbex, GlaxoSmithKline, Oslo, Norway). After intubation the inspiratory oxygen fraction was reduced to 40%; nitric oxide was not used. (1) To obtain average power spectra, after artifact removal the data were epoched into ten-second segments for anesthesia and 30 s segments for sleep. (2) Time-frequency decomposition was accomplished using a Fast Fourier Transformation (mtmfft, FieldTrip (Oostenveld et al. , 2011) from 0. 5 Hz to 45 Hz in 0. 5 Hz steps. The analysis was limited to 45 Hz due to line noise at 50 Hz in the Oslo recordings and then adopted to all consecutive studies for consistency. To obtain reliable spectral estimates we utilized a Multitaper approach (Prerau et al. , 2017) based on discrete prolate slepian sequences (dpss; anesthesia: 9 tapers for 10 s segments, no overlap, frequency smoothing of ± 0. 5 Hz; sleep: 29 tapers for 30 s segments, no overlap, frequency smoothing of ± 0. 5 Hz). (3) The power spectrum of each state was averaged over all samples of the state (wake and anesthesia or rest, wake, non-rapid eye movement sleep stage 3 (N3) and rapid eye movement sleep (REM) ), channels and subjects (Figure 1b, c and Figure 2b, c). For better comparison, we visualized the effect at the scalp level. For Study 2 simultaneous EEG recordings were not available. For the control analysis of the influence of segment length and number of tapers on the spectral slope estimate, anesthesia EEG recordings were epoched into 30 s segments, while sleep was epoched into 10 s segments. Time frequency decomposition was again done with FieldTrip’s mtmfft with a frequency smoothing of ± 0. 5 Hz and dpss tapers resulting in 29 for anesthesia and nine for sleep. Note that the number of tapers is a direct result of the choice of segment length and frequency smoothing (Prerau et al. , 2017): Numberofdpsstapers= (2∗ (Segment length (sec) ∗frequency smoothing (Hz) 2) ) −1 For the comparison of the signal-to-noise ratios of different power calculations (Figure 2—figure supplement 6), the Multitaper spectral decomposition (Prerau et al. , 2017) was calculated as outlined above. For all further computations, sleep data were epoched into 30 s segments. For the Single-taper analysis, power was calculated using the Fast Fourier Transformation mtmfft of FieldTrip (Oostenveld et al. , 2011) with no overlap after applying a Hanning taper. Spectral estimates were obtained between 0. 5 and 45 Hz in 0. 5 Hz steps. For the calculation of the Periodogram, we used MATLAB’s periodogram. m function (MATLAB and Signal Processing Toolbox Release R2018b, MathWorks, Inc, USA) and the default rectangular window and number of points as defined by the sampling rate of 400 Hz. To get the same number of observations as in the Multi- and Single-taper approaches, the frequencies closest to the ones chosen in the Multi- and Single-taper approach above (0. 5 to 45 Hz in 0. 5 Hz steps) were chosen for further analysis. For the Welch' s method, power was calculated with a 30 s window size and no overlap resulting in a single Hamming window employing MATLAB’s pwelch. m function (MATLAB and Signal Processing Toolbox Release R2018b, MathWorks, Inc, USA). The number of points were defined by the sampling rate. Again, the frequencies closest to the Multi-/Single-taper approach were selected for further analysis to enable a direct comparison of signal-to-noise ratios (SNR). SNR was calculated by dividing the average power of each frequency at every channel by the standard deviation of this frequency and channel. We calculated the spectral slope by fitting a linear regression line to the power spectrum in log-log space between 30 and 45 Hz, since it had been shown previously that this range correlates best with changes in arousal in rodents and monkeys, as well as with different excitation-inhibition ratios in simulations (Gao et al. , 2017). In line with previous reports, we excluded the low frequencies that contain strong oscillatory activity, which may distort the linear fit as well as the range over 50 Hz, which is confounded by both line noise (50 Hz in Europe, 60 Hz in the US) as well as broad-band muscle artifacts. We then adapted this range to the calculation of the slope in the other studies for consistency reasons. To compute a time resolved estimate of the spectral slope, we calculated the best line fit to the 10 (anesthesia) or 30 (sleep) second segments of the Multitapered power spectra (see above) in log-log space using polynomial curve fitting (polyfit. m, MATLAB and Curve Fitting Toolbox Release R2015a, The MathWorks, Inc, Natick, Massachusetts, United States). One subject in Study 3 (sleep EEG) exhibited an excessive noise level during wakefulness; therefore, his data had to be excluded from all slope comparisons to wakefulness. For the evaluation of different center frequencies for the linear regression, the spectral slope was estimated from intracranial sleep recordings (Study 4) as a function of different center frequencies (±10 Hz around center frequency starting from 20 up to 150 Hz) and different fit lengths (from 30 Hz onward with a 10 Hz increase of fit length up to 100 Hz) using the same procedure as outlined above (Figure 2—figure supplement 5). Moreover, the spectral slope was estimated from low frequencies in different recordings: (1) Anesthesia scalp EEG (Study 1) in the 1 to40 Hz range as previously reported (Colombo et al. , 2019) and (2) in Sleep scalp EEG (Study 3) from 1 to 5 Hz with an increasing length of additional 5 Hz per fit after discounting the oscillatory components from the power spectrum by means of irregular resampling (IRASA; Wen and Liu, 2016a; Figure 2—figure supplement 5c). The rationale for using this method was to exclude strong low frequency oscillations, such as slow waves, which could possibly distort the slope estimate. Furthermore, we estimated the spectral slope from the 30 to45 Hz range using two additional algorithms, the FOOOF package (Haller et al. , 2018) and the eBOSC algorithm (Kosciessa et al. , 2020a) in both anesthesia and sleep scalp EEG (Study 1 and 3). While eBOSC performs a linear regression in log-log space using MATLAB’s robustfit. m, FOOOF estimates both the oscillatory and aperiodical part of the power spectrum with a model fit: First an exponential fit is performed to the power spectrum in semi-log space, then this fit is subtracted from the power spectral density (PSD). The residual signal is treated as a combination of oscillations and noise and is repeatedly fit with multiple Gaussian fits to detect possible oscillatory peaks until the noise floor is reached. The detected peaks are then validated against the mixed oscillatory/noise signal and subtracted from the original PSD. The new residual signal is then fit again for a better estimate of the aperiodic signal. Both, the Multi-Gaussian fits and the improved aperiodic fit are then combined into a model (compare to Figure 3 (Haller et al. , 2018). When the PSD does not contain a bend, also called knee, in the aperiodic signal, then the algorithm is equivalent to fitting a line in log-log space (Haller et al. , 2018). Mutual Information (MI) is an information theoretical metric, which quantifies the mutual dependence of the two signals, specifically the amount of information gained about one variable when observing the other (Quian Quiroga and Panzeri, 2009). This is particularly useful for non-linear, binned signals. Mutual information between the two signals X and Y was defined asMIX; Y= ∑x ∈X∑y ∈ Ypx, y*log2⁡ (px, ypx*p (y) ) where p (x, y) depicts the joint probability function and p (x) and p (y) indicate the class probabilities. Probabilities were normalized by their sum. For MI analysis (Figure 2b, Figure 2—figure supplements 2,5, 8 and 10), we epoched the time-resolved slope into 30 s segments (the hypnogram was staged in 30 s epochs) and discretized it into five bins (Wake, REM, N1, N2, N3) using the discretize. m function of MATLAB Signal Processing Toolbox Release R2015a (MathWorks Inc, USA). Mutual Information was calculated using the MutualInformation. m function from MATLAB Central File Exchange (Dwinell, 2010). We source-localized the slope difference between wakefulness and sleep in scalp EEG (Study 3, n = 19; one patient had to be excluded due to insufficient wake trials; Figure 2—figure supplement 3). Cortical sources of the sensor-level EEG data were reconstructed by using a LCMV (linearly constraint minimum variance) beamforming approach (Van Veen et al. , 1997) to estimate the time series for every voxel on the grid. A standard T1 MRI template and a BEM (boundary element method) headmodel from the FieldTrip toolbox (Oostenveld et al. , 2011) were used to construct a 3D template grid at 1 cm spacing in standard MNI space. Electrode location was derived from a standard 10/20 template from the FieldTrip toolbox (Oostenveld et al. , 2011). Prior to source projection, sensor level data were common average referenced and epoched into 30 s segments. To minimize computational load, we selected a two second data segment from the center of every epoch to construct the covariance matrix. The LCMV spatial filter was then calculated using the covariance matrix of the sensor-level EEG data with 5% regularization. Spatial filters were constructed for each of the grid positions separately to maximally suppress activity from all other sources. The resulting time courses in source space then underwent spectral analysis using a Fourier transform after applying a Hanning window. The PSD slope of every segment was then estimated using linear regression in the range from 30 to 45 Hz as outlined before. Mean slope values during sleep were subtracted from the mean slope during wakefulness at every voxel in source space and then source-interpolated onto a standard template brain in MNI space. We employed a regions-of-interest (ROI) based approach focusing on prefrontal cortex (PFC) subregions since a reliable source localization of medial temporal lobe (MTL) activity using 19-channel EEG remains challenging. In line with our results from the intracranial sleep study (Study 4, Figure 2C and Figure 2—figure supplement 4), we defined the mPFC and dorsolateral PFC (dlPFC) as ROI. To identify ROI at source level, we used the Automated Anatomical Labeling (AAL) atlas as implemented in FieldTrip (Oostenveld et al. , 2011). The ROI mPFC encompassed the following regions: 23 and 24 – ‘Frontal_Sup_Medial_L and R’, 25 and 26 – ‘Frontal_Med_Orb_L and R’, 27 and 28 – ‘Rectus_L and R’, 31 and 32 – Cingulum_Ant_L and R’. The ROI dlPFC consisted of the following atlas tissue labels: 3 and 4 – ‘Frontal_Sup_L and R’, 7 and 8 – ‘Frontal_Mid_L and R’, 11 and 12 – ‘Frontal_Inf_Oper_L and R’, 13 and 14 – ‘Frontal_Inf_Tri_L and R’ and 15 and 16 ‘Frontal_Inf_Orb_L and R’. We employed a linear discriminant analysis (LDA) to assess if slow wave power or the spectral slope were a better predictor of wakefulness or sleep (Figure 1—figure supplement 4, Figure 3a–c). We utilized a leave-one-exemplar-out cross-validation approach that was repeated 50 times after randomly sampling an equal number of sleep and REM trials to equate the number of samples (classify. m, MATLAB and Statistics and Machine Learning Toolbox Release R2015a, MathWorks Inc, USA). Then every sample of the subsampled distribution was held out of the training dataset once. The LDA classifier was trained on the remaining samples and tested on the held-out test sample. The classifier performance was then assessed as percent correct. Two of the 20 sleep EEG participants had to be excluded due to insufficient number of wake trials. LDA performances were logit-transformed (logit. m from the BioSig toolbox (Schlogl and Brunner, 2008) and averaged across channels prior to statistical comparison. In order to model the unique contributions as well as the interaction between slow wave power (<1. 25 Hz) and spectral slope in the prediction of arousal states, we first scaled both parameters by means of a z-score and then calculated a general linear model using the MATLAB fitlm. m function (MATLAB and Statistics and Machine Learning Toolbox Release R2018b, MathWorks Inc, USA). This method offered the possibility to model both the main effects as well as the interaction between the two predictors (Figure 3d–f). Critically, we utilized an unbalanced design, which implicitly orthogonalizes the contribution of different factors and hence, distills non-overlapping unique explained variance (Siegel et al. , 2015), which was subsequently quantified by means of the effect size (eta squared). Slow wave events (Figure 4, Figure 2—figure supplement 10) were detected for each channel based on established algorithms (Helfrich et al. , 2018; Staresina et al. , 2015): The raw signal was bandpass-filtered between 0. 16 and 1. 25 Hz and zero crossings were detected. Events were then selected using a time (0. 8 to 2 s duration) and an amplitude criterion (75% percentile). The raw data were then epoched relative to the trough of the slow wave (±2. 5 s). Time-frequency decomposition was computed in 500 ms time windows with a 250 ms overlap using FieldTrip (Oostenveld et al. , 2011) (mtmfft, frequency smoothing of ±2 Hz and one dpss taper). The spectral slope was calculated by the best line fit in these time windows in log-log space between 30 and45 Hz using polynomial curve fitting (polyfit. m, MATLAB and Curve Fitting Toolbox Release R2015a, MathWorks Inc, USA). The spectral slope of the awake and anesthetized state was compared using Student’s t-test for paired samples (Figure 1b, c). The observed t-values were then compared against a surrogate distribution after re-calculating the test statistic 10,000 times with randomly shuffled labels (called permutation t-test in the manuscript). Post hoc t-tests were Bonferroni-corrected for multiple comparisons. To compare three states (awake, NREM and REM), we utilized Greenhouse-Geisser corrected 1-way repeated measures analysis of variance (Figure 2b and c; RM-ANOVA). We adopted an approach similar to the permutation t-test, where the original RM-ANOVA output was compared to a shuffled distribution after randomly flipping condition labels 10,000 times (repeated-measures ANOVA permutation test). Effect size was calculated using Cohen’s d. To assess the spatial extent of the observed effects in EEG, we calculated cluster-based permutation tests to correct for multiple comparisons as implemented in FieldTrip (Oostenveld et al. , 2011) (Monte-Carlo method; maxsize criterion; 1000 iterations). A permutation distribution was obtained by randomly shuffling condition labels and then compared to the actual distribution to obtain an estimate of significance. Spatial clusters are formed by thresholding independent t-tests of slope differences between wake and anesthesia (Figure 1b) or wake and sleep (Figure 2b) at a p value < 0. 05. All results were Bonferroni-corrected for multiple comparisons. In order to control for EMG as a potential confound in the sleep EEG (Study 3), we utilized a partial correlation (Spearman) that partialled the slope of the EMG out of the correlation before computing the cluster-based permutation test (Figure 2—figure supplement 2b). Correlation coefficients (r-values) were transformed into t-values using the following formula (N = number of subjects): t=r*sqrt (N-2) sqrt (1-r2) For statistical assessment of the Mutual Information (MI), we employed surrogate testing (Figure 2b, Figure 2—figure supplement 2a). To obtain a surrogate distribution from the observed data, we utilized a random block swapping procedure (Aru et al. , 2015; Canolty et al. , 2006). The number of repetitions was equal to the number of available sleep stages. On every iteration, we re-calculated the MI of these block swapped hypnograms with the discretized time-resolved slope to create a surrogate distribution against which we could compare our original observation. To compare the results across subjects, we z-scored the values by subtracting the mean of the surrogate distribution from the observed MI and dividing by the standard deviation of the surrogate distribution. Note that a z = 1. 96 reflects an uncorrected two-tailed p-value of 0. 05, while a z-score of >2. 8 indicates a Bonferroni-corrected significant p-value (p<0. 05/19 channels=0. 0026). The z-values were transformed into p-values for topographic depiction (Figure 2b; Figure 2—figure supplement 2a) based on a normal cumulative distribution function (two-tailed). For the analysis of fronto-parietal connectivity (Figure 2—figure supplement 10), we choose electrode Fz and Pz in our sleep EEG recordings (Study 3; n = 20) to calculate the magnitude squared coherence from frequencies of 0. 1 to 64 Hz in 0. 1 Hz steps using the mscohere. m function from the MATLAB Signal Processing Toolbox (Release R2015a, MathWorks, Inc, USA) as described previously (Pal et al. , 2016; Pal et al. , 2015). Note that coherence estimates reflect both power changes as well as changes in phase synchrony. Therefore, we also calculated the Phase-Locking Value (PLV) and amplitude correlations (rho) to disentangle the effects of phase and power, respectively. To discount the effects of volume spread, we calculated the imaginary PLV (Nolte et al. , 2004) (iPLV) and orthogonalized power correlations (Hipp et al. , 2012) (rhoortho). We then quantified the Mutual Information (MI; see above) to compare how well the results capture the changes between different sleep stages across the night (Quian Quiroga and Panzeri, 2009). For this analysis we only utilized the slope values of electrode Fz (as we were calculating the other measures in Fz-Pz) and defined theta from 4 to 10 Hz analog to Pal et al. , 2016; Pal et al. , 2015.
summary: Electroencephalogram (EEG for short) is a widespread technique that helps to monitor the electrical activity of the brain. In particular, it can be used to examine, recognize and compare different states of brain consciousness such as sleep, wakefulness or general anesthesia. Yet, during rapid eye movement sleep (the sleep phase in which dreaming occurs), the electrical activity of the brain is similar to the one recorded during wakefulness, making it difficult to distinguish these states based on EEG alone. EEG records brain activity in the shape of rhythmic waves whose frequency, shape and amplitude vary depending on the state of consciousness. In the EEG signal from the human brain, the higher frequency waves are weaker than the low-frequency waves: a measure known as spectral slope reflects the degree of this difference in the signal strength. Previous research suggests that spectral slope can be used to distinguish wakefulness from anesthesia and non-REM sleep. Here, Lendner et al. explored whether certain elements of the spectral slope could also discern wakefulness from all states of reduced arousal. EEG readings were taken from patients and volunteers who were awake, asleep or under anesthesia, using electrodes placed either on the scalp or into the brain. Lendner et al. found that the spectral slope could distinguish wakefulness from anesthesia, deep non-REM and REM sleep. The changes in the spectral slope during sleep could accurately track the degree of arousal with great temporal precision and across a wide range of time scales. This method means that states of consciousness can be spotted just from a scalp EEG. In the future, this approach could be embedded into the techniques used for monitoring sleep or anesthesia during operations; it could also be harnessed to monitor other low-response states, such as comas.
keywords: neuroscience
title: An electrophysiological marker of arousal level in humans