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TitleReal-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks.
Publication TypeJournal Article
Year of Publication2020
AuthorsForys, Brandon J., Dongsheng Xiao, Pankaj Gupta, and Timothy H. Murphy
Date Published2020 May/Jun

Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean=0.95, SD<0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut-a robust movement-tracking deep neural network framework-which enables real-time estimation of paw and digit movements of mice. Using this approach, we demonstrate movement-generated feedback by triggering a USB-GPIO (general-purpose input/output)-controlled LED when the movement of one paw, but not the other, selectively exceeds a preset threshold. The mean time delay between paw movement initiation and LED flash was 44.41 ms (SD=36.39 ms), a latency sufficient for applying behaviorally triggered feedback. We adapt DeepLabCut for real-time tracking as an open-source package we term DeepCut2RealTime. The ability of the package to rapidly assess animal behavior was demonstrated by reinforcing specific movements within water-restricted, head-fixed mice. This system could inform future work on a behaviorally triggered "closed loop" brain-machine interface that could reinforce behaviors or deliver feedback to brain regions based on prespecified body movements.

Alternate JournaleNeuro
PubMed ID32409507
PubMed Central IDPMC7307631
Grant ListFDN-143209 / CAPMC / CIHR / Canada