A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation

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 statespace 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.

Statistical modeling of behavioral dynamics during propofol-induced loss of consciousness

“…The current state-space model is an extension of previous models used to analyze learning and behavioral performance (Smith et al, 2004, 2005, 2009). In previous applications of the state-space paradigm, the study needed only to consider whether the response was correct or incorrect on each trial because the subject was certain to respond on each trial with probability one.…”

“…Under the EM framework, a new algorithm has to be written for each model making its use less feasible when model selection is central to the analysis. By using the Bayesian Monte Carlo methods implemented in WinBUGS to conduct the analysis we only needed to specify the observation and state equations for each new model (Lunn et al, 2000; Smith et al, 2009), WinBUGS used a Bayesian Monte Carlo scheme to conduct the model analysis thus, obviating the need to write a fitting algorithm for each model.…”

“…We used the WinBUGS software (Lunn et al, 2000; Smith et al, 2009) to compute by Bayesian Monte Carlo methods the posterior densities f ( X , Θ | M ) and the marginal posterior densities of the form…”

“…We develop a family of state-space models analogous to those used in learning and behavioral studies to characterize the responses of each individual subject to the behavioral tasks during induction and emergence (Wirth et al, 2003; Smith et al, 2004, 2005). We use a Bayesian Monte Carlo approach to fit the models to the experimental data and the Deviance Information Criterion (DIC) to guide model selection (Lunn et al, 2000; Spiegelhalter et al, 2002; Smith et al, 2009). Our analysis allows us to define precisely the time points of loss and recovery of consciousness for each subject in terms of stimulus saliency.…”

Chemical and Physical Approaches for the Treatment of Alzheimer's Disease

“…Anterior caudate nucleus is in charge of implementing selective adjustments to the 'associative weights' between sensory cues and motor responses during the learning period [148]. DBS in central thalamus could lead to a complicated situation in arousal, behavior, and cognition [149][150][151]. With regard to the lateral hypothalamus, the post-training ICSS (the intracranial self-stimulation) could enhance the hippocampus-dependent memories via improving molecular and cellular mechanisms associated with synaptic plasticity, neurogenesis, and neuroprotection [152][153][154][155].…”