20140117
Deep brain stimulation (DBS) has evolved as a widely employed procedure to treat the symptoms of motor skill disorders such as Parkinson’s disease, essential tremor and dystonia. Although successfully employed across various clinical fields, the fundamental mechanisms of action of DBS remain uncertain. Starting in the last decade, many computational models to gain insight into these mechanisms have been developed. One branch of these computational models focuses on the prediction of the volume of tissue activated (VTA) occurring during DBS. However, the parameters of these volume conductor models are subject to uncertainty and knowledge on how this uncertainty influences the predicted neural activation is scarce. This additional information on the probability distribution of the VTA could help engineers as well as clinicians in evaluating the actual activated area and rating the likelihood of undesired activation, but is computational intensive if classical methods such as Monte Carlo simulations are applied.
The polynomial chaos technique (PCT) provides a surrogate model based on a multivariate polynomial expansion, which expansion coefficients are determined by multidimensional numerical integration. The PCT combined with the application of sparse grids for the numerical integration can substantially reduce the computational expense for the analysis of the probabilistic VTA. In addition, the implemented PCT is nonintrusive, which means that the deterministic model remains unchanged and can be used as kind of a "blackbox". The talk will present the implementation of the PCT in combination with a generated finite element model of the human brain to quantify the influence of uncertain model parameters on the uncertainty in the probabilistic VTA.
Category: CE SeminarTechnische Universität Darmstadt
Graduate School CE
Dolivostraße 15
D64293 Darmstadt

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