Abstract
The hyperkinetic symptoms of Parkinson’s Disease (PD) are associated with the ensembles of interacting oscillators that cause excess or abnormal synchronous behavior within the Basal Ganglia (BG) circuitry. Delayed feedback stimulation is a closed loop technique shown to suppress this synchronous oscillatory activity. Deep Brain Stimulation (DBS) via delayed feedback is known to destabilize the complex intermittent synchronous states. Computational models of the BG network are often introduced to investigate the effect of delayed feedback high frequency stimulation on partially synchronized dynamics. In this study, we develop a reduced order model of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics. This model is able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential (LFP) recordings of the PD state. Train of high frequency pulses in a delayed feedback stimulation has shown deficiencies such as strengthening the synchronization in case of highly fluctuating neuronal activities, increasing the energy consumed as well as the incapability of activating all neurons in a large-scale network. To overcome these drawbacks, we propose a new feedback control variable based on the filtered and linearly delayed LFP recordings. The proposed control variable is then used to modulate the frequency of the stimulation signal rather than its amplitude. In strongly coupled networks, oscillations reoccur as soon as the amplitude of the stimulus signal declines. Therefore, we show that maintaining a fixed amplitude and modulating the frequency might ameliorate the desynchronization process, increase the battery lifespan and activate substantial regions of the administered DBS electrode. The charge balanced stimulus pulse itself is embedded with a delay period between its charges to grant robust desynchronization with lower amplitudes needed. The efficiency of the proposed Frequency Adjustment Stimulation (FAS) protocol in a delayed feedback method might contribute to further investigation of DBS modulations aspired to address a wide range of abnormal oscillatory behavior observed in neurological disorders.
Highlights
Parkinson’s disease (PD) is a neurodegenerative disorder associated with altered firing activity of the Basal Ganglia (BG) nuclei causing symptoms such as rigidity, tremor and akinesia
We developed a computational model of four nuclei within the basal ganglia according to the reduced order model of Izhikevich [39]
The changes of the Subthalamic Nucleus (STN) neuronal activity seen in PD does not completely reflect the Thalamo-Cortical level, which is difficult to be produced by models [25]
Summary
Parkinson’s disease (PD) is a neurodegenerative disorder associated with altered firing activity of the Basal Ganglia (BG) nuclei causing symptoms such as rigidity, tremor and akinesia. The effectiveness of DBS is argued to be related to the elimination of the rhythmic activity seen in PD by reducing the synchronization in the beta band (13–35 Hz) and by increasing it in the gamma band (35–70 Hz) [3,4,5]. Subthalamic Nucleus (STN) or Globus Pallidus interna (GPi) nuclei are the common targets for DBS [6], in which both targets have shown to yield great outcomes in the treatment of dyskinesia, motor fluctuation and rigidity [7]. Reducing the consumed energy of the DBS signal can increase the battery life and eliminate the costly replacement surgeries [12, 13]. Introducing a delay between the cathodic and anodic phases of the DBS pulse contributes to better desynchronization and energy efficiency and harvesting of the process [8, 14,15,16,17,18]
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