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Deep brain stimulation (DBS) is a highly promising therapy for Parkinson's disease (PD). However, most patients do not get full therapeutic benefit from DBS yet, due to its critical dependence on electrode location. For this reason, we believe that the investigation of a neural modeling, estimation and control framework for the STN is an interesting research problem. This would pave the way for the development of a novel surgical tool for the DBS placement standardization, i.e., an automated intraoperative closed-loop DBS localization system. A fundamental problem to be solved for the realization of a such framework is the neurophysiologic characterization of the STN activity. Indeed, this would allow to understand if the modeling of the sweet spot is feasible. In this paper an effort towards the modeling of the neuronal activity near the stimulation target is made: first we analyze single unit spiking activity of 120 STN neurons collected from four PD patients at different distances from the sweet spot and, for each neuron, we estimate a point process model (PPM). Then, we see that PPMs capture the stochastic effects of the distance from the sweet spot on the STN spiking activity, and characterize the impact of local neuronal networks on the single neurons. Our results suggest that PPMs might be an effective tool for modeling of the STN neuronal activities accounting for the depth within it.

High frequency Deep Brain Stimulation in the Sub-Thalamic Nucleus is a clinically recognized therapy for the treatment of motor disorders in Parkinson's Disease. Sub-thalamic Nucleus (STN) is a small lens-shaped nucleus in the brain where it is a part of the basal ganglia system and is currently thought to play a prominent role in Parkinson's Disease (Obeso et al., 1997). STN is the typical stimulation target in patients with symptoms like tremor and motor skills, typical in Parkinson's disease. The underlying mechanisms of Deep Brain Stimulation and how it impacts neighboring nuclei, however, are not yet completely understood and this is the reason because Deep Brain Stimulation is still debated. Electrophysiological data has been collected in Parkinson's Disease patients and primates to better understand the impact of Deep Brain Stimulation(DBS) on Sub-thalamic Nucleus and the entire Basal Ganglia motor circuit. We use single unit recordings from Globus Pallidus, both pars interna and externa segments in Basal Ganglia (GPi and GPe respectively), in a normal primate before and after DBS to reconstruct Local Field Potentials in the region. The aim of this paper is to understand, via system identification techniques, how GPe activity and the DBS signal applied to Sub-thalamic Nucleus influence GPi activity (see Fig 1 and 2), in order to clarify the impact of DBS on Basal Ganglia nuclei (in particular on GPi) analyzing Local Field Potentials. Our models suggest that when no stimulation is applied, the GPe has an inhibitory effect on GPi with a 2 ms delay on Local Field Potentials (LFPs), as is the case for single unit neuronal activity (4-5 ms delay in this instance). On the other hand, when DBS is ON the models suggest that stimulation has a dominant effect on GPi LFPs which masks the inhibitory effects of GPe.

Deep brain stimulation (DBS) is a clinically recognized electrical therapy for the treatment of several neural disorders, e.g., Parkinson's disease (PD), essential tremor, and compulsive disorders. High frequency (HF) STN DBS (Deep Brain Stimulation in SubThalamic Nucleus) is currently used to reduce the motor symptoms of PD, but the actual procedures for the selection of the stimulation parameters do not allow to cope with the disorder fluctuations while a poor knowledge of the cellular mechanisms of DBS has partially limited the development of new stimulation profiles. Popovych et al. [1] recently hypothesized that DBS works by reducing the level of synchronization among the neuronal firing patterns within the target site and proposed a desynchronization-based closed loop strategy for the generation of the DBS input (Nonlinear Delay Feedback Stimulation, NDFS). We review their work by introducing a Systems approach to the analysis of the stimulation performances, quantify the desynchronizing effects of the NDFS through the autoregressive (AR) model identification, and compare the results with those achieved by the actual HF DBS. Simulation results confirm the effectiveness of the approach by Popovych and coworkers [1].

This work presents experimental tests of a vehicle localization system. Based on data collected by a Global Positioning System (GPS), an odometer and a gyrometer, to measure respectively vehicle position, vehicle velocity and yaw velocity, an estimation strategy of the vehicle cover distance is designed. A prototype is then realized in order to test the strategy on urban and extra urban circuits in Benevento (Italy). The experiments are conducted on a Lancia Kappa 2.0 vehicle and the prototype is obtained connecting a dSpace MicroAutoBox electronic control unit with a TomTom Navigator, a gyrometer and the vehicle odometer. The experimental results are here analyzed.

Parkinson's disease (PD) is treated by means of deep brain stimulation (DBS), a therapy based on the injection of current on a regular basis into the basal ganglia, a set of small subcortical nervous system nuclei. DBS is effective in relief of motor symptoms and leads to a notable reduction of drug dose. From an engineering viewpoint, it also raises several optimization-related issues, like energy consumption minimization and stimulation effectiveness. How DBS really works, however, is still unknown, even though several hypotheses and experiments have been reported in literature. To shed some light on the function of DBS, we focused in [1] on those brain nuclei involved in the genesis of PD motor symptoms and developed for them conductance-based models to mimic quantitative data from different in vitro experiments. Now, we use such models in a network scheme to reproduce the main existing anatomical connections between and within the basal ganglia and the resulting macroscopic behaviors reported in literature both for normal and Parkinsonian state. The overall model is proposed as a simulatioParkinson's disease (PD) is treated by means of deep brain stimulation (DBS), a therapy based on the injection of current on a regular basis into the basal ganglia, a set of small subcortical nervous system nuclei. DBS is effective in relief of motor symptoms and leads to a notable reduction of drug dose. From an engineering viewpoint, it also raises several optimization-related issues, like energy consumption minimization and stimulation effectiveness. How DBS really works, however, is still unknown, even though several hypotheses and experiments have been reported in literature. To shed some light on the function of DBS, we focused in (S. Santaniello et al., 20007) on those brain nuclei involved in the genesis of PD motor symptoms and developed for them conductance-based models to mimic quantitative data from different in vitro experiments. Now, we use such models in a network scheme to reproduce the main existing anatomical connections between and within the basal ganglia and the resulting macroscopic behaviors reported in literature both for normal and Parkinsonian state. The overall model is proposed as a simulation instrument to better understand the functional mechanisms currently explaining the basal ganglia physiology and the cellular effects of DBS.n instrument to better understand the functional mechanisms currently explaining the basal ganglia physiology and the cellular effects of DBS.

The neural spiking activity of the Subthalamic Nucleus (STN) is devoted to modulate movement actuation and correct movement disorders (i.e. tremor at rest, rigidity, akynesia and postural instability) in Parkinson's Disease (PD) patients. Moreover, it has been recently revealed that an opportune electrical stimulation, called Deep Brain Stimulation (DBS), can annihilate, if associated with the most common L-dopa based pharmacological therapies, PD-related motor disorders. Currently, a great effort is made both in Medicine and Engineering for understanding and modeling in details how the STN works, how PD determines its pathological behavior and how DBS restores the correct motor function. The paper presents a nonlinear, stochastic, continuous-state model describing the global electrical behavior of the STN in PD patients: inspired by the fundamental physiologic features of subthalamic cells, a fictitious vector state is introduced to represent the main dynamics. Its numerical parameters and stochastic properties are chosen by fitting experimental data coming from two PD patients during the surgical implantation of stimulation electrodes in the brain.