Closed-Loop Deep Brain Stimulation Platform for Translational Research

• Published in: Neuromodulation (Malden, Mass.) Vol. 28; no. 3; pp. 464 - 471
• Main Authors: Li, Yan, Nie, Yingnan, Li, Xiao, Cheng, Xi, Zhu, Guanyu, Zhang, Jianguo, Quan, Zhaoyu, Wang, Shouyan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2025
• Subjects: Algorithms; Animals; Closed-loop deep brain stimulation; deep brain stimulation; Deep Brain Stimulation - instrumentation; Deep Brain Stimulation - methods; Humans; machine learning; real-time; translational research; Translational Research, Biomedical - instrumentation; Translational Research, Biomedical - methods; translational research; real-time; machine learning; Closed-loop deep brain stimulation; deep brain stimulation
• ISSN: 1094-7159; 1525-1403; 1525-1403
• DOI: 10.1016/j.neurom.2024.10.012

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. 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. The platform integrates hardware for both animal and clinical research. The closed-loop software framework handles the entire closed-loop stimulation, including data streaming, stimulation artifact removal, preprocessing, a closed-loop stimulation strategy, and stimulation control. It provides a unified programming interface for both C/C++ and Python, enabling secondary development to integrate new closed-loop stimulation strategies. Additionally, the platform includes an algorithm library with signal processing and machine learning methods to facilitate the development of new closed-loop DBS strategies. The platform can achieve low-latency feedback stimulation control with response times of 6.23 ± 0.85 ms and 6.95 ± 1.11 ms for animal and clinical experiments, respectively. It effectively removed stimulation artifacts and demonstrated flexibility in implementing new closed-loop DBS algorithms. The platform has integrated several typical closed-loop protocols, including threshold-adaptive DBS, amplitude-modulation DBS, dual-threshold DBS and neural state–dependent DBS. This work provides a research tool for rapidly deploying innovative closed-loop strategies for translational research in both animal and clinical studies. The platform’s capabilities in real-time data processing and low-latency control represent a significant advancement in translational DBS research, with potential implications for the development of more effective therapeutic interventions.