Analysis of Mouse Whisker Movement Synchronicity
Abstract: In active sensing, sensory organs are actively controlled by the motor system to optimize stimuli acquisition. Coupling behaviour of animal models with electrophysiological signals could give us important insights in the workings of this active sensing in health and disease. In this work, a contribution is made towards this aim. A modern machine learning method, called DeepLabCut, is evaluated and optimized for the use of tracking whiskers from videos of head-fixed, free whisking mice from two groups. Novel to the approach is the longer observation of the mice, without markers or trimming. This tracking is then used to investigate the natural statistics of whisking in healthy and Parkinsons Disease (PD) modeled animals. Two groups of mice (6 healthy versus 4 PD) were filmed for 5 minutes on 1-3 days per animal during head-fixed free air whisking, resulting in 21 filmed days in total. 6 Whiskers were tracked with 4 points per whisker using DeepLabCut and as few as 105 manually labeled images.A tracking accuracy of <3 pixels training error and <15 pixels test error was achieved. Qualitatively, tracking errors occurred both along and perpendicular to the whisker, but the perpendicular error reduced after 30 Hz low-pass filtering. The observed whisking was non-stationary and predominantly slow (<10 Hz). Whisking was also synchronized in time, as the cross-spectra were significantly higher than the random shuffled cross-values (pKS<0.05), but this synchronicity was lost on the right side in lesion animals with whisking frequencies between 15 and 25 Hz. In this PD-modeled group, a difference between left and right side co-synchronization was found (JSD=0.067, pKS<0.05) for higher frequencies (15-25 Hz). In total, control animals where whisking 34.1% of the time versus 35.6% for the lesion animals. Upon examining these active bouts, it was found that lesion animals have relatively more long bouts of co-activation than healthy controls across all subgroups (pKS<0.001). Furthermore, in lesion animals the activity distributions are laterally different (LL vs RR, JSD=0.178, pKS<0.001), but this cannot be concluded for healthy controls. Thus, it can be concluded that DeepLabCut is a promising method to speed up whisker tracking. Future improvements could be made in sharpness and contrast of the tips of the whiskers; incorporating information on the relation of whisker position in time; extending the tracking to 3D; and evaluating tracking by means of biomechanical models. These improvements will enable observation in a more realistic environment, and for a longer time. Finally, coupling of whisker position data to electrophysiological recordings will hopefully result in new insights in the relation between sensorimotor behaviour and global brain dynamics.
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