Stochastic modeling of mouse motor activity under deep brain stimulation: the extraction of arousal information.
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 invo...
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Public Library of Science (PLoS)
2015-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003883&type=printable |
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| author | Daniel M Keenan Amy W Quinkert Donald W Pfaff |
| author_facet | Daniel M Keenan Amy W Quinkert Donald W Pfaff |
| author_sort | Daniel M Keenan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a37149f7f40d4155a149169f08b4edac |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2015-02-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Computational Biology |
| spelling | doaj-art-a37149f7f40d4155a149169f08b4edac2025-08-20T02:15:15ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-02-01112e100388310.1371/journal.pcbi.1003883Stochastic modeling of mouse motor activity under deep brain stimulation: the extraction of arousal information.Daniel M KeenanAmy W QuinkertDonald W PfaffIn 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003883&type=printable |
| spellingShingle | Daniel M Keenan Amy W Quinkert Donald W Pfaff Stochastic modeling of mouse motor activity under deep brain stimulation: the extraction of arousal information. PLoS Computational Biology |
| title | Stochastic modeling of mouse motor activity under deep brain stimulation: the extraction of arousal information. |
| title_full | Stochastic modeling of mouse motor activity under deep brain stimulation: the extraction of arousal information. |
| title_fullStr | Stochastic modeling of mouse motor activity under deep brain stimulation: the extraction of arousal information. |
| title_full_unstemmed | Stochastic modeling of mouse motor activity under deep brain stimulation: the extraction of arousal information. |
| title_short | Stochastic modeling of mouse motor activity under deep brain stimulation: the extraction of arousal information. |
| title_sort | stochastic modeling of mouse motor activity under deep brain stimulation the extraction of arousal information |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003883&type=printable |
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