Participants and experiment

Five male astronauts (53 ± 1.6 years old) took part in the Neurospat experiment (AO-2004, 118). Written informed consent was obtained prior to participation. The experimental protocol was approved by the European Space Agency’s Medical Board (ESA-MB) and the NASA Johnson Space Centre Institutional Review Board (NASA-IRB). To ensure comparable levels of sleep quantity the night before the recordings, a sleep questionnaire was filled out by astronauts. Astronauts were allocated 8.5 h for sleep the night before the experiment and we excluded all recordings where astronauts reported sleeping <5 h. Neurospat experiments were not performed in the 48 h following air travel that involved a change of >4 time zones, nor following work shifts inducing >4 h of time shift, nor the day after imposed sleep deprivation, nor after a highly strenuous physical or mental activity such as extravehicular activities, centrifuge training, vestibular counter-measures experiments. Astronauts were instructed to maintain their normal consumption of caffeine but were not allowed alcohol nor medication 16 h before the experiment. Although Neurospat’s principal investigators asked to perform the experiments at the same time of day, preferably the morning, recordings took place at variable times along the day, between 2 and 10 h after awakening. In this study, we defined the recording conditions with the label Earth for the recordings on Earth and space1/space2 for the recordings on the ISS. During each Neurospat recording session, the astronauts had a resting state period, a visual orientation task and a visuomotor task to perform. Prior to Earth recordings, the astronauts had two training sessions on Earth to get familiar with the tasks. These two training sessions were not used in this analysis. Then, they had three recordings on Earth before the mission for each astronaut. Out of the three Earth recordings available in the Neurospat experiment, we included in our analysis only the Earth recording with a minimum difference in time after awakening compared to the space recordings. Finally the astronauts had two recordings on the ISS. We discarded post-flight recordings because they were recorded on another EEG system (Advanced Neuro Technology) at the Johnson Space Centre (Houston, USA).

Wake EEG recordings

Each participant, for each session, was recorded with 58 EEG electrodes with the multi-electrode electroencephalogram mapping module (MEEMM) from the European physiology module placed on the ISS Columbus module, at the European Astronaut Centre (Köln, Germany) or at Star City (Moscow, Russia). In addition to the 58 EEG electrodes (10–20 electrode system EEG cap), three electrooculogram (EOG) (allowing horizontal and vertical EOGs), one electrocardiogram (ECG) and one electromyogram (EMG) (recorded at the first interosseous muscle of the right hand) were recorded. Continuous wake EEG was recorded for 70 min during each Neurospat session at a sampling rate of 1116 Hz (0.01–558 Hz band width). Scalp electrodes’ impedance were measured and kept below 5 KΩ. For all recordings, the reference was placed on the right earlobe. EOG, EMG, ECG and derivation P5 and P6 were excluded from further analysis. On Earth, the astronauts performed the experiment seated at a table. On the ISS Columbus module they were free floating with a secured loose-fitting leash around the waist and attached to the European Physiology Module rack.

EEG pre-processing

EEG data pre-processing was performed in Matlab (Version R2017b) using EEGLAB toolbox scripts (Version 14)33 and additional custom made scripts. EEG data were pass-band filtered [0.1–48 Hz] and down sampled to 512 Hz. The signal was recorded as the difference of potential between the electrodes of interest and the right earlobe (i.e., earlobe referencing), which dampened the amplitudes of all oscillations close to the reference point and ultimately induced an asymmetry towards the left hemisphere. To correct for this effect, we transformed the data by subtracting the average activity across all electrodes (i.e., average referencing). A first Independent Component Analysis34 was performed to remove ocular, muscular, and electrocardiographic artefacts (Earth: 2.6 ± 0.7, space1: 1.0 ± 0.0, space2: 1.8 ± 0.5 components rejected) as defined by Hulse et al.35. Using the EEGLAB graphical user interface, all movement artefacts in the signal were marked by visual inspection and removed (Earth: 55.3 ± 2.2, space1: 60.6 ± 2.8, space2: 64.7 ± 4.4 min of recording remaining). The power spectrum was computed for each channel and outliers containing high muscle artefacts (20–30 Hz) were excluded from the dataset (Earth: 2.0 ± 0.7, space1: 1.6 ± 0.8, space2: 0.8 ± 0.3 channels rejected)22. A second independent component analysis was performed on cleaned data to further remove ocular, muscular, and electrocardiographic artefacts (Earth: 4.2 ± 0.9, space1: 4.0 ± 0.7, space2: 4.4 ± 0.8 components rejected). For each subject, rejected channels were interpolated. To the best of our knowledge, the prospect of an impedance difference at the electrodes between Earth and space has never been studied. With the assumption that such a difference could occur due to microgravity (e.g., electrical conduction differences), we prevented any effects of the recording montage on the signal by using a z-score transformation. Moreover, to improve the signal to noise ratio in the power spectrum analysis, we performed a phase-rectified signal averaging (PRSA).36 PRSA allows superimposing of the oscillations to create interference and hence reduce the weight of acute noise generators in the signal. The power spectral density was estimated using the Welch’s averaged periodograms with a four second Hamming window and a frequency resolution of 0.125 Hz. In each frequency bin, the power at each channel was normalised by the average power over the scalp. The theta power band was computed between 5 and 7 Hz, to stay distant from astronauts’ alpha peak (8–10 Hz).37 As in previous work,23 we define a global increase when >50% of the electrodes were involved.

Local sleep-like events detection

To look for evidence of local sleep-like events during wakefulness we combined existing detection methods.22,23,24,38,39 We used the SWA-Matlab toolbox developed by Mensen and colleagues with the following parameters39,40 (Fig. 5). The EEG channels within three non-overlapping areas (frontal electrodes: Fp1, Fp2, AF3, AFz, AF4, F1, Fz, F2, central electrodes: FC1, FCz, FC2, C1, CPz, CP2 and parietal electrodes: CP1, CPz, CP2, P1, Pz, P2, PO3, POz, PO4) were averaged and the three outcomes were filtered within the theta band using a second order Butterworth band pass filter. A threshold was set at two times the median deviation from the median signal for each area reference signal. To correct for potential remaining slow drifts in the EEG signals, we chose to detect local sleep-like events using the minimum negative point between two maximum peaks oscillating within theta oscillations, instead of the commonly used minimum negative point between two consecutive zero crossings. All negative peaks on the reference signal below this relative threshold were detected and marked as a local sleep-like event (7019 ± 488 events per recording session). To study the size of each local sleep-like event over the scalp, the event’s globality was computed by cross correlating the reference signal with each channel across the scalp within the theta range, looking for similar oscillations within a 50 ms time window. For each correlation above 95%, the corresponding channel was marked as involved in the event. As an additional layer of security, we unmarked isolated channels, which could represent artefacts by applying a cluster test.41 By defining a 50 ms time window to assess how many electrodes are involved in a local sleep-like event, we assumed that the theta waves are travelling over the scalp at least twice faster than slow waves.22,38 The number of areas of interest over the scalp defined the sensitivity of our detection algorithm. By choosing three areas of interest, we targeted only events within one of these areas and by averaging the signals within these areas, we targeted events involving at least a few electrodes. To refine the detection of events in specific cortical areas, we would need higher density EEG recordings, exceeding the current 58 electrodes. Moreover, average referencing will subtract the signal of the neighbouring electrodes, which might prevent our algorithm from detecting local sleep-like events involving only one electrode. Eventually, average referencing will induce a bias towards the detection of more global events compared to earlobe referencing. We further assessed the density of local sleep-like events per minute of recording and the amplitude from the negative peak to the following positive peak, measured at the channel with the median slope across all channels involved in the corresponding event.

Fig. 5 Local sleep-like event detection method. a Difference of potential at the derivation C3. b Signal average within three non-overlapping areas (frontal, central and parietal) and filtered within the theta band. c Detection threshold (red horizontal line) at two times the median deviation from the median signal within each area. All negative peaks on the reference signal below this relative threshold were detected and marked as a local sleep-like event. d Red circles mark the local sleep-like events involving the derivation C3 Full size image

Visuomotor task

In the last part of the experimental protocol, the astronauts had to perform a visuomotor task that lasted 25 min (Fig. 6). The astronauts were told to look straight ahead at a laptop screen through a facemask to remove any external visual cues (Fig. 7). The astronauts were looking at a virtual display on a computer screen, simulating randomly two scenarios: one piloting the Soyuz vehicle, preparing a docking to the ISS (ISS being the target in this case) or the second while being within the ISS, preparing remotely the docking of the Soyuz vehicle (the Soyuz being the target in this second case). There were 80 trials per session (40 for each scenario). The 80 trials were divided in four blocks, which allowed the astronauts to take a break before starting the next block of 20 trials. At the beginning of each trial and for 2 s the astronauts saw first their own spaceship and then their target. Following this, the target deviated from its nominal straight-ahead position for another 2 s. Throughout this first period, the astronauts were asked to observe their target (ISS or Soyuz vehicle) without performing any movement. Six seconds after the beginning of the trial, the centre of the target changed from white to grey, which meant that the astronauts were required to take control of the spaceship and perform, as quickly as possible and in <7 s, the recovery manoeuvre towards the target by controlling a joystick with their right index finger. Once the docking position was reached, the astronauts were asked to confirm their attempt by pressing a button with their right thumb. The centre of the spaceship changed from white to blue if they successfully (S) docked the spaceship or to yellow if they failed (F). The next trial would start 2 s later. The astronauts had to perform the same experiment repeatedly during each recording session, bringing the total number of artefact free trials to 455 (Fail: 10.80 ± 1.00, Succeed: 21.27 ± 1.35 trials per session and per astronaut). The time point from which the astronauts were allowed to recover the trajectory of the spacecraft until the first movement recorded with the joystick was defined as reaction time. Only 15 reaction times were above 500 ms. They were considered as outliers and discarded from our analysis. Reaction times below 100 ms were due to anticipated movements (i.e., false start) and also discarded, reducing the total number of trials to 237.27 The time interval to look for local sleep-like events was defined as 250 ms before stimulus (i.e., motor action planning) and 500 ms after stimulus presentation (i.e., maximal reaction time).42

Fig. 6 Visuomotor task experimental protocol. Rendez-vous with ISS (first 6 s) and recovery manoeuvre (following 7 s). The time when the astronauts are allowed to recover the simulated Soyuz vehicle until they take control of the spacecraft with their first movement is defined as reaction time. Photographs used from ESA, ESA-David Ducros and ESA/NASA with permission (photographs from left to right) Full size image

Fig. 7 Astronaut performing the Neurospat experiment on the ISS Columbus module. Photograph used from ESA/NASA with permission Full size image

Statistics

Data points were reported as mean ± sem. For topographical analysis we plotted t-values for the two-tailed paired Student’s t-tests (uncorrected p-values) and we used a non-parametric permutation test (coefficient of variation = 2.757, 25 permutations, n = 5 astronauts) for cluster correction,43,44 defining the minimum cluster size of significant neighbouring electrodes for a pattern to be significant. In our analysis at least three neighbouring electrodes needed to be significant to be reported as a significant effect. For repeated measures time series, we used mixed-effects analysis to model repeated measures. First we assessed normal distribution with a quantile-quantile plot. For normal distributions, we performed a linear mixed-effects analysis of the relationship between the response variable and fixed/random effects. For non-normal distributions (e.g., binomial distributions), we used a generalised linear mixed-effects analysis. We used the restricted maximum likelihood estimate method to fit the model and choose the best model based on Bayesian information criterion results. Visual inspection by quantile-quantile plot of the residuals confirmed that homoscedasticity and normality were respected. The influence of the fixed effects on the model were determined by F-tests (F(degrees of freedom in the numerator, degrees of freedom in the denominator) = F-value, p = p-value). We finally reported the estimated differences (mean ± sem) between repeated measures for each fixed effect, together with the two-tailed paired Student’s t-tests results (t = t-values, df = degrees of freedom, p = p-values). When no fixed effects could help to fit the model (i.e., best model is the intercept only model), we reported the non-significant results for the F-test assessing the influence of each fixed effect candidate on the model. If the variable of interest is best modelled by a unique fixed effect without random effects, we used a linear regression model to obtain a R2 adjusted value, which indicates how much of the total variation can be explained by the fixed effect. Then, we completed a F-test (F-value, df = degrees of freedom, p = p-value) with the null hypothesis that the slope of the model is equal to zero. All statistical analysis were performed in Matlab.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.