Estimation of dispersive properties of encapsulation tissue surrounding deep brain stimulation electrodes in the rat.

The aim of this study was to estimate the electrical properties of the encapsulation tissue surrounding chronically implanted electrodes for deep brain stimulation in the rat. The impedance spectrum of a concentric bipolar microelectrode implanted in the rat brain was measured immediately following surgery and after 8 weeks of implantation. The experimental impedance data were used in combination with a finite element model of the rat brain using a parametric sweep method to estimate the electrical properties of the tissue surrounding the electrode in acute and chronic conditions. In the acute case, the conductivity and relative permittivity of the peri-electrode space were frequency independent with an estimated conductivity of 0.38 S/m and relative permittivity of 123. The electrical properties of the encapsulation tissue in the chronic condition were fitted to a dispersive Cole-Cole model. The estimated conductivity and relative permittivity in the chronic condition at 1 kHz were 0.028 S/m and 2×105, respectively. The estimated tissue properties can be used in combination with computational modeling as a basis for optimization of chronically implanted electrodes to increase the efficacy of long-term neural recording and stimulation.

The Influence of Force Level and Motor Unit Coherence on Nonlinear Surface EMG Features Examined Using Model Simulation.

Nonlinear features extracted from surface EMG signals have been previously used to infer information on coherent or synchronous activity in the underlying motor unit discharges. However, it has not yet been assessed how these features are affected by the density of the surface EMG signal, and whether changes in the level of muscle activation can influence the effective detection of correlated motor unit firing. To examine this, a motoneuron pool model receiving a beta-band modulated cortical input was used to generate correlated motor unit firing trains. These firing trains were convolved with motor unit action potentials generated from an anatomically accurate electrophysiological model of the first dorsal interosseous muscle. The sample entropy (SampEn) and percentage determinism (%DET) of recurrence quantification analysis were calculated from the composite surface EMG signals, for signals comprised of both correlated and uncorrelated motor unit firing trains. The results show that although both SampEn and %DET are influenced by motor unit coherence, they are differentially affected by muscle activation and motor unit distribution. The results also suggest that sample entropy may provide a more accurate assessment of the underlying motor unit coherence than percentage determinism, as it is less sensitive to factors unrelated to motor unit synchrony.