In addition to providing novel insights into the efficiency of low-frequency nonregular patterns of STN-DBS for advanced PD and treatment-refractory OCD, this work points to a possible correlation of a model-based outcome measure with clinical effectiveness of stimulation and may have significant implications for an energy- and therapeutically-efficient configuration of a closed-loop neuromodulation system.
We present a novel closed-loop subthalamic nucleus (STN) deep brain stimulation (DBS) scheme for Parkinson's disease (PD) and obsessive-compulsive disorder (OCD). The algorithm is designed to effectuate real-time, adaptive stimulation employing the outcome of the 0-1 test for chaos as a state-specific biomarker. In case of a null outcome, the system identifies optimal patterns of stimulation desynchronizing pathologic neuronal activity with minimal energy consumption, on grounds of a stochastic dynamical model and an appropriately formulated cost function. Simulations are performed utilizing microelectrode recordings (MERs) acquired during 8 and 2 DBS surgical interventions for PD and OCD, respectively.
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