A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation
Name
Brown-A Bayesian statistical analysis.pdf
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1.17 MB
Format
Adobe PDF
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Author(s)
Smith, Anne C.
Shah, Sudhin A.
Hudson, Andrew E.
Purpura, Keith P.
Victor, Jonathan D.
Brown, Emery N.
Schiff, Nicholas D.
Date Issued
October 2009
Journal
Journal of Neuroscience Methods
Publisher
Elsevier
Citation
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.
Version
Author's final manuscript
Abstract
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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.jneumeth.2009.06.028
