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

PLoS Comput Biol. 2015 Feb 26;11(2):e1003883. doi: 10.1371/journal.pcbi.1003883. eCollection 2015 Feb.

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Animals
  • Arousal / physiology*
  • Arousal / radiation effects*
  • Computational Biology
  • Deep Brain Stimulation*
  • Mice
  • Motor Activity / physiology*
  • Motor Activity / radiation effects*
  • Stochastic Processes