Serre, T. | bioRxiv Science

bioRxiv2020

Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics

Following prolonged swimming, Caenorhabditis elegans cycle between active swimming bouts and inactive quiescent bouts. Swimming is exercise for C. elegans and here we suggest that inactive bouts are a recovery state akin to fatigue. Previously, analysis of exercise-induced quiescent (EIQ) bouts relied on laborious manual observation, as existing automated analysis methods for C. elegans swimming either cannot analyze EIQ bouts or fail to accurately track animal posture during these bouts. It is known that cGMP-dependent kinase (PKG) activity plays a conserved role in sleep, rest, and arousal. Using C. elegans EGL-4 PKG, we first validate a novel learning-based computer vision approach to automatically analyze C. elegans locomotory behavior and distinguish between activity and inactivity during swimming for long periods of time. We find that C. elegans EGL-4 PKG function predicts EIQ first bout timing, fractional quiescence, bout number, and bout duration, suggesting that previously described pathways are engaged during EIQ bouts. However, EIQ bouts are likely not sleep as animals are feeding during the majority of EIQ bouts. We find that genetic perturbation of neurons required for other C. elegans sleep states also does not alter EIQ dynamics. Additionally, we find that EIQ onset is sensitive to age and DAF-16 FOXO function. In summary, we have validated a new behavioral analysis software that enabled a quantitative and detailed assessment of swimming behavior, including EIQ. We found novel EIQ defects in aged animals and animals with mutations in a gene involved in stress tolerance. We anticipate that further use of this software will facilitate the analysis of genes and pathways critical for fatigue and other C. elegans behaviors.

animal behavior and cognition↗

bioRxiv2019

Differential involvement of EEG oscillatory components in sameness vs. spatial-relation visual reasoning tasks

The development of deep convolutional networks (DCNs) has recently led to great successes in computer vision and have become de facto computational models of vision. However, a growing body of work suggests that they exhibit critical limitations beyond image categorization. Here, we study a fundamental limitation of DCNs for judging whether two items are the same or different (SD) compared to a baseline assessment of their spatial relationship (SR). We test the prediction that SD tasks recruit additional cortical mechanisms which underlie critical aspects of visual cognition that are not explained by current computational models. We thus recorded EEG signals from 14 participants engaged in the same tasks as the computational models. Importantly, the two tasks were matched in terms of difficulty by an adaptive psychometric procedure: yet, on top of a modulation of evoked potentials, our results revealed higher activity in the low beta (13-20Hz) band in the SD compared to the SR conditions, which we surmise as reflecting the crucial involvement of recurrent mechanisms sustaining working memory and attention. Author SummaryDespite the impressive progress of deep convolutional networks (DCNs) in object recognition, recent studies demonstrated that state-of-the-art vision algorithms encounter severe limitations when performing certain visual reasoning tasks: for instance, convolutional networks can easily solve problems involving spatial relations, but fail in identifying whether two items are identical or different (same-different task). This conclusion led us to test the hypothesis that different computational mechanisms are needed to successfully perform these tasks also in the visual system. First, we confirmed in our simulations that DCNs can successfully perform spatial relationship tasks but struggle with same-different ones. Then, we tested 14 participants on the same experimental design while recording their EEG signals. Remarkably, our results revealed a significant difference between the tasks in the occipital brain regions both in evoked potentials and in the oscillatory dynamics. Specifically, an increase of activity was found when performing the SD over the SR condition. We interpret these results as reflecting the fundamental involvement of recurrent mechanisms implementing cognitive functions such as working memory and attention.

neuroscience↗