Doctoral thesis defense by Sofia Karamintziou
On Wednesday 18/11/2015, Sofia karamintziou successfully defended her doctoral thesis, titled " - "Development of Stochastic Dynamical Models for Optimization of Deep Brain Stimulation in Movement and Neuropsychiatric Disorders".
In the framework of the doctoral dissertation, stochastic dynamical models for the optimization of the clinical outcome of deep brain stimulation (DBS) in movement and neuropsychiatric disorders are being developed. The ultimate goal is to algorithmically design a closed-loop DBS system for advanced Parkinson’s disease (PD) and treatment-refractory obsessive-compulsive disorder (OCD), ensuring optimal performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. On grounds of a stochastic phase model fitted to subthalamic microelectrode recordings (MERs) acquired during surgical interventions for PD and OCD, it is first proven that the desynchronizing and probably also the therapeutic effect of low-frequency stochastic DBS waveforms may be significantly stronger compared with the effect of standard stimulation. Subsequently, it is demonstrated that the presented modeling approach is able to identify, at a relatively low computational cost, stimulation settings associated with a significantly higher efficiency and selectivity compared with stimulation settings determined during post-operative clinical management of patients with advanced PD and treatment-refractory OCD. Together, the data provide strong evidence for the applicability of computational neurostimulation to real-time, closed-loop DBS systems for movement and neuropsychiatric disorders.