A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation
Deep brain stimulation (DBS) is an established therapy for Parkinson's Disease and is being investigated as a treatment for chronic depression, obsessive compulsive disorder and for facilitating functional recovery of patients in minimally conscious states following brain injury. For all of the...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Elsevier
2012
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| Online Access: | http://hdl.handle.net/1721.1/69923 https://orcid.org/0000-0003-2668-7819 |
| _version_ | 1826211187452280832 |
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| author | Smith, Anne C. Shah, Sudhin A. Hudson, Andrew E. Purpura, Keith P. Victor, Jonathan D. Brown, Emery N. Schiff, Nicholas D. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Smith, Anne C. Shah, Sudhin A. Hudson, Andrew E. Purpura, Keith P. Victor, Jonathan D. Brown, Emery N. Schiff, Nicholas D. |
| author_sort | Smith, Anne C. |
| collection | MIT |
| description | Deep brain stimulation (DBS) is an established therapy for Parkinson's Disease and is being investigated as a treatment for chronic depression, obsessive compulsive disorder and for facilitating functional recovery of patients in minimally conscious states following brain injury. For all of these applications, quantitative assessments of the behavioral effects of DBS are crucial to determine whether the therapy is effective and, if so, how stimulation parameters can be optimized. Behavioral analyses for DBS are challenging because subject performance is typically assessed from only a small set of discrete measurements made on a discrete rating scale, the time course of DBS effects is unknown, and between-subject differences are often large. We demonstrate how Bayesian state-space methods can be used to characterize the relationship between DBS and behavior comparing our approach with logistic regression in two experiments: the effects of DBS on attention of a macaque monkey performing a reaction-time task, and the effects of DBS on motor behavior of a human patient in a minimally conscious state. The state-space analysis can assess the magnitude of DBS behavioral facilitation (positive or negative) at specific time points and has important implications for developing principled strategies to optimize DBS paradigms. |
| first_indexed | 2024-09-23T15:01:58Z |
| format | Article |
| id | mit-1721.1/69923 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:01:58Z |
| publishDate | 2012 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/699232022-10-02T00:06:40Z A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation Smith, Anne C. Shah, Sudhin A. Hudson, Andrew E. Purpura, Keith P. Victor, Jonathan D. Brown, Emery N. Schiff, Nicholas D. Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brown, Emery N. Brown, Emery N. Deep brain stimulation (DBS) is an established therapy for Parkinson's Disease and is being investigated as a treatment for chronic depression, obsessive compulsive disorder and for facilitating functional recovery of patients in minimally conscious states following brain injury. For all of these applications, quantitative assessments of the behavioral effects of DBS are crucial to determine whether the therapy is effective and, if so, how stimulation parameters can be optimized. Behavioral analyses for DBS are challenging because subject performance is typically assessed from only a small set of discrete measurements made on a discrete rating scale, the time course of DBS effects is unknown, and between-subject differences are often large. We demonstrate how Bayesian state-space methods can be used to characterize the relationship between DBS and behavior comparing our approach with logistic regression in two experiments: the effects of DBS on attention of a macaque monkey performing a reaction-time task, and the effects of DBS on motor behavior of a human patient in a minimally conscious state. The state-space analysis can assess the magnitude of DBS behavioral facilitation (positive or negative) at specific time points and has important implications for developing principled strategies to optimize DBS paradigms. National Institutes of Health (U.S.)(R01 MH-071847) National Institutes of Health (U.S.) (DP1 OD003646) National Institutes of Health (U.S.)(NS02172) IntElect Medical (Firm) 2012-04-04T15:26:23Z 2012-04-04T15:26:23Z 2009-10 2009-06 Article http://purl.org/eprint/type/JournalArticle 0165-0270 http://hdl.handle.net/1721.1/69923 Smith, Anne C. et al. “A Bayesian Statistical Analysis of Behavioral Facilitation Associated with Deep Brain Stimulation.” Journal of Neuroscience Methods 183.2 (2009): 267–276. https://orcid.org/0000-0003-2668-7819 en_US http://dx.doi.org/10.1016/j.jneumeth.2009.06.028 Journal of Neuroscience Methods Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Elsevier PubMed Central |
| spellingShingle | Smith, Anne C. Shah, Sudhin A. Hudson, Andrew E. Purpura, Keith P. Victor, Jonathan D. Brown, Emery N. Schiff, Nicholas D. A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation |
| title | A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation |
| title_full | A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation |
| title_fullStr | A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation |
| title_full_unstemmed | A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation |
| title_short | A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation |
| title_sort | bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation |
| url | http://hdl.handle.net/1721.1/69923 https://orcid.org/0000-0003-2668-7819 |
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