Insect nervous systems offer unique advantages for studying interactions between sensory systems and behaviour given that they are complex and yet highly tractable. By examining the neural coding of salient environmental stimuli and resulting behavioural output in the context of environmental stressors, we gain an understanding of the effects of these stressors on brain and behaviour and provide insight into normal function. The implication of neonicotinoid (neonic) pesticides in contributing to declines of non-target species, such as bees, has motivated development of new compounds, which could potentially mitigate putative resistance in target species (1, 2) and declines of non-target species. We used a neuroethological approach, including behavioural assays and multineuronal recording techniques, to investigate effects of imidacloprid and the novel insecticide sulfoxaflor on visual motion-detection circuits and related escape behaviour in the tractable locust system. Despite similar LD50 values, imidacloprid and sulfoxaflor evoked different behavioural and physiological effects. Imidacloprid significantly attenuated collision avoidance behaviours and impaired responses of neural populations, including a decrease in spontaneous firing and decreased neural habituation. In contrast, sulfoxaflor displayed no effect at a comparable sublethal dose. These are the first results to show that a neonic affects population responses and habituation of a visual motion detection system. We propose that differences in the sublethal effects of sulfoxaflor reflect an altered mode of action to imidacloprid. More broadly, we suggest that neuroethological assays for comparative neurotoxicology are a valuable tool to fully address current issues regarding proximal effects of environmental toxicity in non-target species.

We used a single hook electrode and multichannel tetrode arrays to record from descending visual neurons. Raw recordings were saved to disc using Dataview recording and analysis software (https://www.st-andrews.ac.uk/~wjh/dataview/) via a Datatranslation DT9818 acquistion board. Raw data were imported into Offline Sorter v 4.4 (Plexon Inc., Dallas, TX) and spikes were sorted using a semi-automatic method based on the K-means algorithm. Brodgar 2.7.9 (Highland Statistics Inc., Newburgh, UK) was used to run dynamic factor analysis. Spike trains were analyzed using Neuroexplorer v 5.0 spike train analysis software to create peristimulus time histograms and extract parameters from response profiles. Matlab r2019b was used to extract decay and rise times, calculate cummulative sums, and generate heatmaps for habituaiton data.