Stochastic modeling of mouse motor activity under deep brain stimulation: the extraction of arousal information.

Abstract

In the present paper, we quantify, with a rigorous approach, the nature of motor activity in response to Deep Brain Stimulation (DBS), in the mouse. DBS is currently being used in the treatment of a broad range of diseases, but its underlying principles are still unclear. Because mouse movement involves rapidly repeated starting and stopping, one must statistically verify that the movement at a given stimulation time was not just coincidental, endogenously-driven movement. Moreover, the amount of activity changes significantly over the circadian rhythm, and hence the means, variances and autocorrelations are all time varying. A new methodology is presented. For example, to discern what is and what is not impacted by stimulation, velocity is classified (in a time-evolving manner) as being zero-, one- and two-dimensional movement. The most important conclusions of the paper are: (1) (DBS) stimulation is proven to be truly effective; (2) it is two-dimensional (2-D) movement that strongly differs between light and dark and responds to stimulation; and, (3) stimulation in the light initiates a manner of movement, 2-D movement, that is more commonly seen in the (non-stimulated) dark. Based upon these conclusions, it is conjectured that the above patterns of 2-D movement could be a straightforward, easy to calculate correlate of arousal. The above conclusions will aid in the systematic evaluation and understanding of how DBS in CNS arousal pathways leads to the activation of behavior.