Itch is an aversive somatic sense that elicits the desire to scratch. In animal models of itch, scratching behavior is frequently used as a proxy for itch, and this behavior is typically assessed through visual quantification. However, manual scoring of videos has numerous limitations, underscoring the need for an automated approach. Here, we propose a novel automated method for acoustic detection of mouse scratching. Using this approach, we show that chloroquine-induced scratching behavior in C57BL/6 mice can be quantified with reasonable accuracy (85% sensitivity, 75% positive predictive value). This report is the first method to apply supervised learning techniques to automate acoustic scratch detection.

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Multivariate time series from neural electrophysiological recordings are a rich source of information about neural processing systems and require appropriate methods for proper analysis. Current methods for mapping brain function in these data using neural decoding aggregate information across space and time in limited ways, rarely incorporating spatial dependence across recording locations. We propose Shrinkage Classification for Overlapping Time Series (SCOTS), a neural decoding method that maps brain function, while accounting for spatio-temporal dependence, through interpretable dimensionality reduction and classification of multivariate neural time series. SCOTS has two components: first, overlapping clustering from sparse semi-nonnegative matrix factorization gives a data-driven aggregation of neural information across space; second, wavelet-transformed nearest shrunken centroids with sparse group lasso performs multi-class classification with selection of informative clusters and time intervals. We demonstrate use of SCOTS by applying it to human intracranial electrophysiological and MEG data collected while participants viewed visual stimuli from a range of categories. The method reveals the dynamic activation of brain regions with sensitivity to different object categories, giving insight into spatio-temporal contributions of these neural processing systems.

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