Dixon Tanner C, Strandquist Gabrielle, Zeng Alicia, Frączek Tomasz, Bechtold Raphael, Lawrence Daryl, Ravi Shravanan, Starr Philip A, Gallant Jack L, Herron Jeffrey A, Little Simon J
Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Nat Biomed Eng. 2025 Jun 27. doi: 10.1038/s41551-025-01438-0.
Deep brain stimulation (DBS) has garnered widespread use as an effective treatment for advanced Parkinson's disease. Conventional DBS (cDBS) provides electrical stimulation to the basal ganglia at fixed amplitude and frequency, yet patients' therapeutic needs are often dynamic with residual symptom fluctuations or side effects. Adaptive DBS (aDBS) is an emerging technology that modulates stimulation with respect to real-time clinical, physiological or behavioural states, enabling therapy to dynamically align with patient-specific symptoms. Here we report an aDBS algorithm intended to mitigate movement slowness by delivering targeted stimulation increases during movement using decoded motor signals from the brain. Our approach demonstrated improvements in dominant hand movement speeds and study participant-reported therapeutic efficacy compared with an inverted control, as well as increased typing speed and reduced dyskinesia compared with cDBS. Furthermore, we demonstrate proof of principle of a machine learning pipeline capable of remotely optimizing aDBS parameters in a home setting. This work illustrates the potential of movement-responsive aDBS as a promising therapeutic approach and highlights how machine learning-assisted programming can simplify complex optimization to facilitate translational scalability.
深部脑刺激（DBS）作为晚期帕金森病的一种有效治疗方法已得到广泛应用。传统的DBS（cDBS）以固定的幅度和频率向基底神经节提供电刺激，但患者的治疗需求往往是动态变化的，存在残余症状波动或副作用。自适应DBS（aDBS）是一种新兴技术，它根据实时临床、生理或行为状态来调节刺激，使治疗能够动态地与患者特定症状相匹配。在此，我们报告一种aDBS算法，该算法旨在通过利用从大脑解码的运动信号在运动期间提供有针对性的刺激增加来减轻运动迟缓。与反向对照相比，我们的方法在优势手运动速度和研究参与者报告的治疗效果方面均有改善，并且与cDBS相比，打字速度提高，运动障碍减少。此外，我们展示了一种机器学习流程的原理证明，该流程能够在家庭环境中远程优化aDBS参数。这项工作说明了运动响应性aDBS作为一种有前景的治疗方法的潜力，并强调了机器学习辅助编程如何能够简化复杂的优化以促进转化可扩展性。
