Research ArticleDEVELOPMENTAL BIOLOGY

Neuronal noise as an origin of sleep arousals and its role in sudden infant death syndrome

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Science Advances  25 Apr 2018:
Vol. 4, no. 4, eaar6277
DOI: 10.1126/sciadv.aar6277
  • Fig. 1 Schematic representation of our hypothesis that neuronal noise originating from subthreshold voltage fluctuations in WPN triggers spontaneous arousals during sleep.

    (A) The superposition of the uncorrelated currents from n WPN (connected in parallel) results in a neuronal current that has Gaussian distribution with SD σ (due to central limit theorem; see Materials and Methods). If this superposed neuronal current and its associated voltage exceed an excitability threshold Δ, then it triggers an arousal in the cortex and simultaneously stimulates sleep-promoting neurons (SPNs) in the brainstem. As a consequence, the excited SPNs inhibit the WPN (negative feedback) through an inhibitory current, which ensures sleep inertia and restoring force back to sleep, and thus keeps the arousal brief. (B) Top: Two biased random walk model simulations of the superposed neuronal voltage (that is, the integrated neuronal current) with different values of σ corresponding to low temperature (high σ) and high temperature (low σ). Note that the SD σ is temperature-dependent because high temperatures reduce subthreshold voltage fluctuations leading to lower values of σ, as shown by empirical studies (10, 31). Although the random walk freely moves within the interval [VK,VTh] (corresponding to “sleep state”), there is an inhibitory current once the random walk is above VTh (“wake state”). Bottom: Shown are arousals/wake and sleep bouts corresponding to whether the random walk is above or below the threshold voltage VTh, respectively. Note that at low temperature, wake bouts occur more frequently and have longer durations as compared to high temperature. In our model simulations, the threshold VTh and the potassium Nernst potential VK are approximately constant for different temperatures in agreement with experiments (50); the voltage difference (excitability threshold) is defined as Δ = VThVK.

  • Fig. 2 Sleep and wake characteristics of zebrafish larvae (red circles) compared to model simulations (blue squares).

    (A) The percent sleep time for zebrafish larvae (blue squares) increases with increasing water temperature—from 20% at 25°C to 50% at 34°C. The same temperature dependence is also obtained for our model simulations, where increasing temperatures are modeled by decreasing neuronal noise levels (as measured by the noise SD σ; for more details, see Materials and Method). (C) The mean sleep bout duration shows similar behavior for increasing temperatures, whereas the number of arousals during sleep [shown in (B)] is decreasing with increasing temperature, with most arousals at low temperatures. Thus, the monotonous increase of total sleep time with increasing temperatures in zebrafish larvae is associated with longer sleep bouts. (D) The mean wake bout duration of zebrafish larvae decreases with increasing temperature, and this trend is also reproduced by our model. The great similarity in (A) to (D) between the experimental measurements obtained for zebrafish larvae under different water temperatures and the model results for different noise levels strongly supports our hypothesis that arousals/brief awakenings from sleep originate from neuronal noise. We examine for each larva the temperature dependence of the sleep/wake parameters in (A) to (D), applying to all 48 larvae in the database a one-way analysis of variance (ANOVA) with repeated measures (comparisons between all temperatures), and we obtain a P < 10−9 (all pairwise multiple comparisons by Tukey’s test yield P < 0.05, except at 25°C versus 28°C for the mean sleep bout duration and at 25°C versus 28°C for number of arousals per sleep hour). In all panels, for each temperature, we present the group average and SE of the 48 larvae during two dark periods (when zebrafish predominantly sleep) with 10-hour duration each, and the model results for each σ are obtained from 48 20-hour independent model simulations matching the duration of the 20-hour dark period. All model parameters are the same for each temperature, only the neuronal noise level σ is changed: For 25°C, we use Embedded Image; for 28°C, Embedded Image; for 31°C, Embedded Image; and for 34°C, Embedded Image, where Δt = 0.08 s is the time resolution of each simulation.

  • Fig. 3 Cumulative probability distributions of sleep and wake bout durations for zebrafish larvae at different water temperatures compared to model simulations with different neuronal noise levels.

    Consistent with previous findings (20, 24), sleep bout durations are exponentially distributed with characteristic time scale τ, whereas wake bout durations show a power-law distribution characterized by the scaling exponent α. However, the characteristics of these distributions, τ and α, change with temperature. (A) The characteristic sleep time monotonically increases from τ = 1.06 min at 25°C to τ = 2.21 min at 34°C. (B) The power-law scaling exponent of the wake distributions increases with increasing temperatures from α = 0.82 at 25°C to α = 1.35 at 34°C. These empirically obtained sleep and wake distributions for zebrafish larvae are well reproduced by our model simulations with different temperatures (that is, different values of the neuronal noise level σ: for 25°C Embedded Image; for 28°C, Embedded Image; for 31°C, Embedded Image; and for 34°C, Embedded Image) that yield the same type of distributions and temperature dependence as the experimental data. Moreover, we obtain practically matching values for τ and α between zebrafish measurements and the model simulations for each temperature and the corresponding noise level σ, consistent with our conclusions from Fig. 2. For all simulations in (C) and (D), we choose the same parameters for the inhibitory current (sleep inertia) coefficient b = 20 mV2/Δt and the excitability threshold (sleep depth) parameter Δ = 10 mV to best match our empirical observations in (A) and (B). Both α and τ values and their SDs (which are of the order 10−2 or lower) were calculated using the maximum likelihood estimation (MLE) method (see Materials and Methods) (41) on the pooled data of all larvae at a given temperature, and these estimates for α and τ are not affected by particular bin selection for the distribution.

  • Fig. 4 The possible effect of ambient temperatures on apnea of prematurity and SIDS.

    Asphyxia and brain hypoperfusion that can occur in infants during sleep may result in a life-threatening event (5). Depending on the level of arousability, this event may lead to either arousal and rebreathing or to failure of arousal and hypoxic coma. High neuronal noise generated at low ambient temperature yields higher arousability that in turn increases the probability for arousal and rebreathing. In contrast, low neuronal noise at high ambient temperature yields lower probability of arousal and can therefore result in hypoxic coma for the vulnerable infant. Note that very young infants show certain ectothermic traits (similar to fish) because their thermoregulation is not fully developed until the age of 6 months (52), and thus, they are more susceptible to higher ambient temperature and in higher risk for SIDS. Notably, after age of 6 months, the percent of SIDS occurrence drastically decreases (19). The proposed mechanism here indicates that the clinically observed correlation between high ambient temperature and high risk for SIDS is associated with decline in the arousability of sleep-regulatory neuronal circuits that is due to reduction in neuronal noise at high temperature.

  • Fig. 5 Schematic presentation of the physiological importance of the degree of arousability for a group of healthy infants and infants with low neuronal noise levels.

    (A and B) Circadian temperature variations play a role for increased risk of SIDS events. Circadian rhythms in infants lead to increasing body temperature in the early morning hours (29, 30). These independent empirical observations combined with the mechanism we propose indicate that the circadian phase associated with the early morning hours is characterized by reduced arousability in infants. For vulnerable infants that are characterized by low level of neuronal noise, identical circadian temperature influences as observed in the healthy infants could further suppress neuronal noise and thus shift the arousability toward a “critical low point,” contributing to increased risk for SIDS events in the morning hours. We note that the superposition of the circadian temperature peak combined with elevated ambient temperature can lead to an even higher risk for SIDS (Fig. 4). Taking into account the intrinsic risk factors for SIDS (5), low levels of neuronal noise in the vulnerable infant [as in (B)] may be due to genetic or developmental factors. Elevated room temperature, extensive crib bedding, bed sharing, infections, and prone sleeping position, all factors that can contribute to higher body temperature (and hence to lower levels of neuronal noise and arousability), are known to be extrinsic risk factors for SIDS (5). Our empirical analyses (fig. S2) show a similar strong dependence of arousability on core body temperature also in adults with higher arousability between 4 a.m. and 6 a.m., when core body temperature reaches a minimum (53, 54).

  • Fig. 6 Simulation results for the effect of the excitability threshold Δ (related to sleep depth), b (related to sleep inertia) on the cumulative probability distributions P of sleep and wake bouts, and different neuronal noise levels σ (related to different temperatures).

    (A) Increasing the excitability threshold Δ leads to longer sleep bouts as reflected by higher values of τ. In contrast, wake bout durations remain unaffected and show similar distributions for different values of Δ as shown in (B). (C) Increasing b leads to longer sleep bouts (that is, larger values of τ) and (D) to larger values of the power-law exponent α for wake bout durations. Changing the noise level σ affects both the sleep bout durations (E) and the wake bout durations (F), however in different ways. Although higher values of σ (for example, due to lower temperatures) decrease the mean sleep bout duration and the exponent τ, wake bout durations increase and α decreases. For all simulations, we use b = 1.1 and Δ = 4.0, and the results are consistent with Eqs. 14 and 16.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/4/eaar6277/DC1

    section S1. Sleep behavior of zebrafish larvae

    section S2. Sleep behavior of healthy adult humans under different circadian temperatures

    section S3. Occurrence of SIDS and heat loss versus infant age

    section S4. Model parameters to simulate the sleep/wake statistics of zebrafish larvae

    fig. S1. Sleep behavior of zebrafish larvae across 48 hours under 14-hour light/10-hour dark cycles at different temperatures.

    fig. S2. Experimentally obtained sleep and wake characteristics for adult subjects as a function of core body temperature during the night (red circles) compared to our model simulations (blue squares).

    fig. S3. Occurrence of SIDS and heat loss versus infant age.

    References (5558)

  • Supplementary Materials

    This PDF file includes:

    • section S1. Sleep behavior of zebrafish larvae
    • section S2. Sleep behavior of healthy adult humans under different circadian temperatures
    • section S3. Occurrence of SIDS and heat loss versus infant age
    • section S4. Model parameters to simulate the sleep/wake statistics of zebrafish larvae
    • fig. S1. Sleep behavior of zebrafish larvae across 48 hours under 14-hour light/10-hour dark cycles at different temperatures.
    • fig. S2. Experimentally obtained sleep and wake characteristics for adult subjects as a function of core body temperature during the night (red circles) compared to our model simulations (blue squares).
    • fig. S3. Occurrence of SIDS and heat loss versus infant age.
    • References (55–58)

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