Wakefulness can be distinguished from general anesthesia and sleep in flies using a massive library of univariate time series analyses.

The neural mechanisms of consciousness remain elusive. Previous studies on both human and non-human animals, through manipulation of level of conscious arousal, have reported that specific time-series features correlate with level of consciousness, such as spectral power in certain frequency bands. However, such features often lack principled, theoretical justifications as to why they should be related with level of consciousness. This raises two significant issues: firstly, many other types of times-series features which could also reflect conscious level have been ignored due to researcher biases toward specific analyses; and secondly, it is unclear how to interpret identified features to understand the neural activity underlying consciousness, especially when they are identified from recordings which summate activity across large areas such as electroencephalographic recordings. To address the first concern, here we propose a new approach: in the absence of any theoretical priors, we should be maximally agnostic and treat as many known features as feasible as equally promising candidates. To apply this approach, we use highly comparative time-series analysis (hctsa), a toolbox which provides over 7,700 different univariate time-series features originating from different research fields. To address the second issue, we employ hctsa to high-quality neural recordings from a relatively simple brain, the fly brain (Drosophila melanogaster), extracting features from local field potentials during wakefulness, general anesthesia, and sleep. At Stage 1 of this registered report, we constructed a classifier for each feature, for discriminating wakefulness and anesthesia in a discovery group of flies (N = 13). At Stage 2, we assessed their performances on four independent groups of evaluation flies, from which recordings were made during anesthesia and sleep, and which were originally blinded to the data analysis team (N = 49). We found only 47 time-series features, applied to recordings obtained from the center of the fly brain, to also significantly classify wake from anesthesia or sleep in all 4 of these evaluation datasets. Most of these were related to autocorrelation, and they indicated that signals during wakefulness remained correlated to their past for a longer timescale than during anesthesia and sleep. Meanwhile, time-series features related to well-known potential markers of consciousness, such as those related to complexity or spectral power, failed to generalize across all the flies. However, many of these complexity and spectral features have a consistent direction of effect due to anesthesia or sleep across flies, suggesting that even slight variations in experiment setup can reduce generalizability of classifiers. These results caution the current state of frequent discoveries of new potential consciousness markers, which may not generalize across datasets, and point to autocorrelation as a class of dynamical properties which does.