A Network Model of Local Field Potential Activity in Essential Tremor and the Impact of Deep Brain Stimulation

Essential tremor (ET), a movement disorder characterised by an uncontrollable shaking of the affected body part, is often professed to be the most common movement disorder, affecting up to one percent of adults over 40 years of age. The precise cause of ET is unknown, however pathological oscillations of a network of a number of brain regions are implicated in leading to the disorder. Deep brain stimulation (DBS) is a clinical therapy used to alleviate the symptoms of a number of movement disorders. DBS involves the surgical implantation of electrodes into specific nuclei in the brain. For ET the targeted region is the ventralis intermedius (Vim) nucleus of the thalamus. Though DBS is effective for treating ET, the mechanism through which the therapeutic effect is obtained is not understood. To elucidate the mechanism underlying the pathological network activity and the effect of DBS on such activity, we take a computational modelling approach combined with electrophysiological data. The pathological brain activity was recorded intra-operatively via implanted DBS electrodes, whilst simultaneously recording muscle activity of the affected limbs. We modelled the network hypothesised to underlie ET using the Wilson-Cowan approach. The modelled network exhibited oscillatory behaviour within the tremor frequency range, as did our electrophysiological data. By applying a DBS-like input we suppressed these oscillations. This study shows that the dynamics of the ET network support oscillations at the tremor frequency and the application of a DBS-like input disrupts this activity, which could be one mechanism underlying the therapeutic benefit

The influence of reactivity of the electrode-brain interface on the crossing electric current in therapeutic deep brain stimulation

The use of deep brain stimulation (DBS) as an effective clinical therapy for a number of neurological disorders has been greatly hindered by the lack of understanding of the mechanisms which underlie the observed clinical improvement in patients. This problem is confounded by the difficulty of investigating the neuronal effects of DBS in situ, and the impossibility of measuring the induced current in vivo. In our recent computational work using a quasi-static finite element (FEM) model we have quantitatively shown that the properties of the depth electrode–brain interface (EBI) have a significant effect on the electric field induced in the brain volume surrounding the DBS electrode. In the present work, we explore the influence of the reactivity of the EBI on the crossing electric current using the Fourier-FEM approach to allow the investigation of waveform attenuation in the time domain. Results showed that the EBI affected the waveform shaping differently at different post-implantation stages, and that this in turn had implications on induced current distribution across the EBI. Furthermore, we investigated whether hypothetical waveforms, which were shown to have potential usefulness for neural stimulation but are not yet applied clinically, would have any advantage over the currently used square pulse. In conclusion, the influence of reactivity of the EBI on the crossing stimulation current in therapeutic DBS is significant, and affects the predictive estimation of current distribution around the implanted DBS electrode in the human brain