ABSTRACT
Optogenetic stimulation, an effective stimulation technique, is applied to the treatment of Parkinson's disease (PD) to compete with the current neuromodulation technology that focuses on the electrical stimulation. Using the cortical-thalamic-basal ganglia model, we systematically study the effect of optogenetic stimulation on pathological parkinsonian rhythmic neural activity. Based on the experimental studies, four types of neurons are selected as stimulation targets. Our results indicate that both the optogenetic excitatory stimulation of D1 medium spiny neurons and inhibitory stimulation of globus pallidus internal (GPi) can directly suppress the abnormal discharge of GPi neurons. The former stimulation pattern drives the model to health state with smaller stimulation parameters, suggesting that inhibiting the GPi abnormal discharge through synaptic action seems to be more effective. Compared with electrical stimulation, it is found that 120Hz optogenetic excitatory stimulation does not accurately activate the action potential of subthalamic nucleus (STN). In contrast, only optogenetic excitatory stimulation of globus pallidus externa (GPe) can reduce the firing rate of STN and GPi simultaneously. Finally, we study the difference between the effects of high-frequency low pulse width stimulation and low-frequency high pulse width stimulation while maintaining the same pulse duty cycle. For GPe, different stimulation patterns play a positive role as long as the stimulation frequency is not in the beta-band. Although the feasibility of optogenetic stimulation remains to be clinically explored, the results obtained help us understand the pathophysiology of PD.
ABSTRACT
The optimal consensus problem of asynchronous sampling single-integrator and double-integrator multiagent systems is solved by distributed model predictive control (MPC) algorithms proposed in this article. In each predictive horizon, the finite-time linear-quadratic performance is minimized distributively by the control input with consensus state optimization. The MPC technique is then utilized to extend the optimal control sequence to the case of an infinite horizon. Conditions depending only on each agent's weighting scalar and sampling step are derived to guarantee the stability of the closed-loop system. Numerical examples of rendezvous control of multirobot systems illustrate the efficiency of the proposed algorithm.
ABSTRACT
Biophysical computational models are complementary to experiments and theories, providing powerful tools for the study of neurological diseases. The focus of this review is the dynamic modeling and control strategies of Parkinson's disease (PD). In previous studies, the development of parkinsonian network dynamics modeling has made great progress. Modeling mainly focuses on the cortex-thalamus-basal ganglia (CTBG) circuit and its sub-circuits, which helps to explore the dynamic behavior of the parkinsonian network, such as synchronization. Deep brain stimulation (DBS) is an effective strategy for the treatment of PD. At present, many studies are based on the side effects of the DBS. However, the translation from modeling results to clinical disease mitigation therapy still faces huge challenges. Here, we introduce the progress of DBS improvement. Its specific purpose is to develop novel DBS treatment methods, optimize the treatment effect of DBS for each patient, and focus on the study in closed-loop DBS. Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.
