System identification of local field potentials under deep brain stimulation in a healthy primate

Variation in deep brain stimulation electrode impedance over years following electrode implantation

BACKGROUND

Deep brain stimulation (DBS) electrode impedance is a major determinant of current delivery to target tissues, but long-term variation in impedance has received little attention.

OBJECTIVES

To assess the relationship between electrode impedance and time in a large DBS patient population and characterize the relationship between contact activity and impedance.

METHODS

We collected retrospective impedance and programming data from 128 electrodes in 84 patients with Parkinson's disease, essential tremor or dystonia. Effects of time, contact activity, stimulation voltage and other parameters on impedance were assessed. We also examined impedance changes following contact activation and deactivation.

RESULTS

Impedance decreased by 73 Ω/year (p < 0.001), with 72% of contacts following a downward trend. Impedance was on average 163 Ω lower in active contacts (p < 0.001). Contact activation and inactivation were associated with a more (p < 0.001) and less (p = 0.016) rapid decline in impedance, respectively. Higher stimulation voltages were associated with lower impedance values (p < 0.001). Contact number and electrode model were also significant predictors of impedance.

CONCLUSIONS

Impedance decreases gradually in a stimulation-dependent manner. These trends have implications for long-term programming, the development of a closed-loop DBS device and current understanding of the electrode-tissue interface.

Point process modeling reveals anatomical non-uniform distribution across the subthalamic nucleus in Parkinson's disease

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, due to its critical dependence on electrode location in the Subthalamic Nucleus (STN). For this reason, we believe that the development of a novel surgical tool for DBS placement, i.e., an automated intraoperative closed-loop DBS localization system, is essential. In this paper, we analyze single unit spiking activity of 120 neurons in different STN locations collected from 4 PD patients. Specifically, for each neuron, we estimate a point process model (PPM) of the spiking activity for different depths within the STN by which we are able to detect pathological bursting and oscillations. Our results suggest that these signatures are more prominent in the dorsolateral part of the STN. Therefore, accurately placing the DBS electrode in this target may result in maximal therapeutic benefit with less power effort required by DBS. Furthermore, PPMs might be an effective tool for modeling of the STN neuronal activities as a function of location within the STN, which may pave the way towards developing a closed-loop navigation tool for optimal DBS electrode placement.

System Identification of mGluR-Dependent Long-Term Depression

Recent advances have started to uncover the underlying mechanisms of metabotropic glutamate receptor (mGluR)–dependent long-term depression (LTD). However, it is not completely clear how these mechanisms are linked, and it is believed that several crucial mechanisms remain to be revealed. In this study, we investigated whether system identification (SI) methods can be used to gain insight into the mechanisms of synaptic plasticity. SI methods have been shown to be an objective and powerful approach for describing how sensory neurons encode information about stimuli. However, to our knowledge, it is the first time that SI methods have been applied to electrophysiological brain slice recordings of synaptic plasticity responses. The results indicate that the SI approach is a valuable tool for reverse-engineering of mGluR-LTD responses. We suggest that such SI methods can aid in unraveling the complexities of synaptic function.