Title | Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Forys, Brandon J., Dongsheng Xiao, Pankaj Gupta, and Timothy H. Murphy |
Journal | eNeuro |
Volume | 7 |
Issue | 3 |
Date Published | 2020 May/Jun |
ISSN | 2373-2822 |
Abstract | 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. |
DOI | 10.1523/ENEURO.0096-20.2020 |
Alternate Journal | eNeuro |
PubMed ID | 32409507 |
PubMed Central ID | PMC7307631 |
Grant List | FDN-143209 / CAPMC / CIHR / Canada |