Closed-Loop Deep Brain Stimulation Platform for Translational Research.

Objective: This study aims to facilitate the translation of innovative closed-loop deep brain stimulation (DBS) strategies from theory to practice by establishing a research platform. The platform addresses the challenges of real-time stimulation artifact removal, low-latency feedback stimulation, and rapid translation from animal to clinical experiments.

Materials And Methods: The platform comprises hardware for neural sensing and stimulation, a closed-loop software framework for real-time data streaming and computation, and an algorithm library for implementing closed-loop DBS strategies.

Brain-machine interactive neuromodulation research tool with edge AI computing.

Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application.

Subthalamic and pallidal oscillations and their couplings reflect dystonia severity and improvements by deep brain stimulation.

Background: Deep brain stimulation (DBS) targeting the globus pallidus internus (GPi) and subthalamic nucleus (STN) is employed for the treatment of dystonia. Pallidal low-frequency oscillations have been proposed as a pathophysiological marker for dystonia. However, the role of subthalamic oscillations and STN-GPi coupling in relation to dystonia remains unclear.

Multi-timescale neuromodulation strategy for closed-loop deep brain stimulation in Parkinson's disease.

Beta triggered closed-loop deep brain stimulation (DBS) shows great potential for improving the efficacy while reducing side effect for Parkinson's disease. However, there remain great challenges due to the dynamics and stochasticity of neural activities. In this study, we aimed to tune the amplitude of beta oscillations with different time scales taking into account influence of inherent variations in the basal ganglia-thalamus-cortical circuit.

Amplitude Adaptive Modulation of Neural Oscillations Over Long-Term Dynamic Conditions: A Computational Study.

Closed-loop deep brain stimulation (DBS) shows great potential for precise neuromodulation of various neurological disorders, particularly Parkinson's disease (PD). However, substantial challenges remain in clinical translation due to the complex programming procedure of closed-loop DBS parameters. In this study, we proposed an online optimized amplitude adaptive strategy based on the particle swarm optimization (PSO) and proportional-integral-differential (PID) controller for modulation of the beta oscillation in a PD mean field model over long-term dynamic conditions.

Real-time removal of stimulation artifacts in closed-loop deep brain stimulation.

Closed-loop deep brain stimulation (DBS) with neural feedback has shown great potential in improving the therapeutic effect and reducing side effects. However, the amplitude of stimulation artifacts is much larger than the local field potentials, which remains a bottleneck in developing a closed-loop stimulation strategy with varied parameters.We proposed an irregular sampling method for the real-time removal of stimulation artifacts.