|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|
|Date Published||2020 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.
|PubMed Central ID||PMC7307631|
|Grant List||FDN-143209 / CAPMC / CIHR / Canada|