Analysis of Oscillatory Neural Activity in Series Network Models of Parkinson's Disease During Deep Brain Stimulation

Abstract

Parkinson's disease is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. It is associated with pathological, oscillatory neural activity in the basal ganglia. Deep brain stimulation (DBS) is often successfully used to treat medically refractive Parkinson's disease. However, the selection of stimulation parameters is based on qualitative assessment of the patient, which can result in a lengthy tuning period and a suboptimal choice of parameters. This study explores fourth-order, control theory-based models of oscillatory activity in the basal ganglia. Describing function analysis is applied to examine possible mechanisms for the generation of oscillations in interacting nuclei and to investigate the suppression of oscillations with high-frequency stimulation. The theoretical results for the suppression of the oscillatory activity obtained using both the fourth-order model, and a previously described second-order model, are optimized to fit clinically recorded local field potential data obtained from Parkinsonian patients with implanted DBS. Close agreement between the power of oscillations recorded for a range of stimulation amplitudes is observed ( R(2)=0.69-0.99 ). The results suggest that the behavior of the system and the suppression of pathological neural oscillations with DBS is well described by the macroscopic models presented. The results also demonstrate that in this instance, a second-order model is sufficient to model the clinical data, without the need for added complexity. Describing the system behavior with computationally efficient models could aid in the identification of optimal stimulation parameters for patients in a clinical environment.

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease

“…The beta-band power in the LFP power spectrum decreased non-linearly with increasing DBS amplitude, Figure 2C. This relationship is similar to the reduction in LFP beta-band activity with increasing amplitude observed in clinical data from parkinsonian patients which can be well-described by higher order models (Davidson et al, 2016). Low stimulation amplitudes had little influence on LFP beta-band activity with amplitudes less than 1.1 mA unable to suppress beta-band power in the LFP power spectrum regardless of the stimulation frequency.…”

“…The strength of synaptic connections between the cortex and STN, between the STN and GPe, and the thalamus cortex were increased to induce beta oscillations within the network and the STN LFP, similar to that observed experimentally (West et al, 2018). Previous simulation studies have similarly increased the strength of connections between nuclei to simulate the effects of dopamine depletion on basal ganglia network activity in PD resulting in the emergence of oscillatory activity in conductance-based models (Rubin and Terman, 2004;Humphries et al, 2006;Hahn and McIntyre, 2010;Kang and Lowery, 2013;Kumaravelu et al, 2016) and mean-field models (Nevado-Holgado et al, 2010;Davidson et al, 2016;Liu et al, 2017bLiu et al, , 2020.…”

“…Indirect evidence of antidromic activation and desynchronization of cortical neurons has also been observed in PD patients during STN DBS (Kuriakose et al, 2010;Weiss et al, 2015). The STN firing rate suppression and LFP beta-band power reduction during DBS were based observations in patient data (Davidson et al, 2016;Milosevic et al, 2018. In the model, antidromic propagation of cortical neurons was simulated and an equivalent proportion of GPe neurons were antidromically activated by the injection of an intracellular current. In practice, the level of antidromic activation of cortical and GPe neurons may differ, potentially altering DBS efficacy.…”

A Population Model of Deep Brain Stimulation in Movement Disorders From Circuits to Cells

“…Improved understanding of the physiological basis has been long sought, especially using computational modeling. Until recently however, the models used to study the impact of DBS have largely been focused on single neuron and single axon models with a high level of biophysical sophistication and detail (Davidson et al, 2016;Anderson et al, 2019;Howell et al, 2019). Our group has recently shown however, that the use of population or mean-field models can provide a great deal of insight into the mechanisms of both the underlying pathology as well as the mechanisms of DBS (Merrison-Hort et al, 2013;Yousif et al, 2017).…”

Simulation of closed-loop deep brain stimulation control schemes for suppression of pathological beta oscillations in Parkinson’s disease

“…in clinical data which can be well-described by higher order models (Davidson et al, 2016). Low 415 stimulation amplitudes had little influence on LFP beta-band activity with amplitudes less than 1.1 mA 416 unable to suppress LFP beta-band power regardless of the stimulation frequency.…”