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 andnon-human animals, through manipulation of level of conscious arousal, have reported thatspecific time-series features correlate with level of consciousness, such as spectral power incertain frequency bands. However, such features often lack principled, theoreticaljustifications as to why they should be related with level of consciousness. This raises twosignificant issues: firstly, many other types of times-series features which could also reflectconscious level have been ignored due to researcher biases towards specific analyses; andsecondly, it is unclear how to interpret identified features to understand the neural activityunderlying consciousness, especially when they are identified from recordings whichsummate activity across large areas such as electroencephalographic recordings. To addressthe 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 equallypromising candidates. To apply this approach we use highly comparative time-series analysis(hctsa), a toolbox which provides over 7,700 different univariate time-series featuresoriginating from different research fields. To address the second issue, we employ hctsa tohigh-quality neural recordings from a relatively simple brain, the fly brain (Drosophilamelanogaster), extracting features from local field potentials during wakefulness, generalanesthesia and sleep. At Stage 1 of this registered report, we constructed a classifier for eachfeature, 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 originallyblinded to the data analysis team (N = 49). We found only 47 time-series features, applied torecordings obtained from the center of the fly brain, to also significantly classify wake fromanesthesia or sleep in all 4 of these evaluation datasets. Most of these were related toautocorrelation, and they indicated that signals during wakefulness remained correlated totheir past for a longer timescale than during anesthesia and sleep. Meanwhile, time-seriesfeatures related to well-known potential markers of consciousness, such as those related tocomplexity or spectral power, failed to generalize across all the flies. However, many of thesecomplexity and spectral features have a consistent direction of effect due to anesthesia orsleep across flies, suggesting that even slight variations in experiment setup can reducegeneralizability of classifiers. These results caution the current state of frequent discoveriesof new potential consciousness markers, which may not generalize across datasets, and pointto autocorrelation as a class of dynamical properties which does.