Sliding window analysis

The focus of this study was to delineate the state transitions as a function of drug effect, we adopted a sliding window approach in ESC, and the above fMRI measurements including autocorrelation index, functional connectivity (strength centrality, within-network, between-network, corticocortical and subcorticocortical). One concern with the sliding window approach is the choice of window size, as it has been varied from tens of seconds to several minutes in previous practices (Barttfeld et al., 2015; Hindriks et al., 2016; Hutchison et al., 2013; Laumann et al., 2017; Tagliazucchi et al., 2016). Smaller window size may capture more transient changes of the dynamics but it can reduce the statistical reliability and make it more difficult to perform group averaging of dynamic time courses (Hindriks et al., 2016). On the other hand, longer window size may capture changes that are more static with larger statistical reliability, but with the cost of losing transient information. We traded off this issue by choosing a moderate window size with 2 min length (150 fMRI frames; linear trend was removed per window) and 20 s time step (25 fMRI frames). To this end, the sliding window time-courses are presumably reflect the dynamic changes induced by the anesthetics, instead of intrinsic variabilities. It thus permitted us to average the time-course of a given measurement across subjects. Given the data length of PreLOR, LOR, PreROR and ROR varied across participants, we used the transition points of LOR and ROR as two reference time points in order to align all participants’ data within the same timeline. The sliding window was moving forward and backward centered at the reference time points, with a maximum data length of 15 min in each time direction.