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Baskoylu, S. N.

Publications and source records attributed to Baskoylu, S. N..

2 recordsLinked to original sources

Deconstructing a behavioral state: parallel neural integrators control distinct features of an aversive behavioral state in C. elegans

Transient sensory stimuli can trigger long-lasting changes in behavior and cognition. For example, repeated aversive experiences can lead to persistent changes in arousal and mood. Specialized integrator circuits can store information about recent experiences, but how they are embedded within the architecture of the brain to control internal brain states remains poorly understood. We find that C. elegans accumulates evidence of past aversive experiences over minutes to generate a scalable behavioral state consisting of multiple behavioral changes. Using a brain-wide imaging screen, we identified a set of neurons that integrate aversive sensory stimuli over different timescales, from seconds to minutes. Neuronal perturbations show that these integrator neurons are critical for the aversive behavioral state. They control distinct behavioral features, such as changes in locomotion speed or increased responsiveness to subsequent aversive stimuli. Moreover, the integrator neurons function in parallel to one another, utilize different mechanisms to generate persistent neural activity, and signal through different transmitters. Our result show how a behavioral state can be decomposed into distinct behavioral features that map onto different neural integrators that act in parallel. Combined action of the integrators then generates the full set of behaviors that comprise the global state.

neuroscience↗

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↗