Detection of slow waves and spindles
Individual events of slow waves and sleep spindles, two prominent brain oscillations during NREM sleep, were detected using previously validated and published algorithms
(Ferrarelli et al.
, 2010). To detect slow waves, EEG signal containing NREM epochs only was band-pass filtered at 0.5~4 Hz using 4th-order Butterworth filter in both forward and reverse directions to ensure no phase shift. Slow waves were detected as surface-negative deflections between two consecutive zero-crossings whose intervals were between 0.25 to 1 second. For each slow wave, peak amplitude, down slope (average slope from first zerocrossing point to negative peak), and up slope (average slope from negative peak to second zero-crossing point) were quantified. Incidence of slow waves (per minute) was also calculated. We chose to analyze a frontal electrode (F3-A2 channel) because slow waves are most prominent in frontal cortex (Ferrarelli et al.
, 2010)
To detect sleep spindles, NREM EEG epochs from C3-A2 channel were first band-pass filtered from 10 to 16 Hz. Notice that we did not make any distinction between slow frontocentral and fast parieto-central spindles, which may differ in functions or generating mechanisms (Andrillon et al., 2011). Central channels were sensitive in detecting both types of spindles. Band-passed signal was Hilbert-transformed and absolute value was taken to obtain instantaneous amplitude. Threshold for detection was set to be mean plus 2.5 times standard deviation (Z-score of 2.5) of the entire amplitude of time series. Spindles were identified if amplitude exceeded the threshold. To determine the duration of spindles, we extracted the points when the band-passed signal first exceeded and dropped mean plus standard deviation (Z-score of 1) around each detection. Duration should be longer than 0.25 second and shorter than 3 seconds. Spindle incidence (waves per minute) was calculated. In addition, peak amplitude, duration, and integrated spindle activity (integrating absolute amplitude values of each spindle divided by NREM sleep duration) were quantified, and submitted to statistical test for comparison between conditions.
Results on slow waves and spindles
Detailed analysis on individual slow waves and sleep spindles demonstrated that, as well as frequency band power, some specific parameters were altered with light on during sleep. For slow waves (Supplementary Fig. 1A), there was slight decrease in incidence of slow waves per minute with light on, which was not statistically different. However, amplitude, up slope and down slope were significantly reduced with light ( P <0.05). Sleep spindle analysis (Supplementary Fig. 1B) revealed decrease in the number of individual spindles per minute ( P <0.05), but no significant difference in amplitude and duration.
Integrated spindle activity, which is the integration of amplitude of spindles divided by
NREM duration, were significantly decreased ( P <0.05).
Reference
Andrillon, T., Nir, Y., Staba, R.J., et al . Sleep spindles in humans: insights from intracranial
EEG and unit recordings. J. Neurosci.
, 2011, 31:17821017834

Ferrarelli, F., Peterson, M. J., Sarasso, S., et al . Thalamic dysfunction in schizophrenia suggested by whole-night deficits in slow and fast spindles. Am. J Psychiatry , 2010,
167:1339-1348

Supplementary Figure 1: Comparison of parameters from individual (A) slow waves and (B) sleep spindles detected from automated algorithms Black bar represents light off condition while white bar represents light on condition. Bars scale up to the mean and black solid lines stand for standard error of the mean. Stars (*) correspond to statistically significant results.