皮层-基底振荡指数在深度大脑刺激期间的自然主义运动
Daryl Lawrence1, Guy Avraham2,3, Jiaang Yao1
1University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA 94720, USA.
Brain : a journal of neurology
|December 16, 2025
概括
这项研究表明,结合来自皮质和基底质的大脑信号,可以改善帕金森病患者进行深度大脑刺激 (DBS) 的运动预测. 这些发现为适应性DBS疗法铺平了道路.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
背景情况:
- 基底和感觉运动皮质对于运动控制至关重要,但它们在帕金森病 (PD) 和深度大脑刺激 (DBS) 中的网络动态尚未完全理解.
- DBS有效地治疗PD的低动力症状,感应神经刺激器可以记录大脑活动,提供了改善运动解码的潜力.
研究的目的:
- 研究皮质和皮质下运动网络节点对接受DBS的PD患者自然运动的贡献.
- 评估DBS对皮质-基底电路动力学和运动相关的神经信号的影响.
- 开发和验证机器学习模型,用神经记录来预测自然主义运动.
主要方法:
- 记录了超过530小时的皮质和皮质下神经信号,来自15名PD患者,在每日活动中与下丘脑核 (STN) 或内球 (GPi) DBS.
- 使用同步的手腕加速度计来量化前臂速度和运动状态.
- 开发了机器学习模型,使用神经数据的光谱特征来预测运动和前臂速度.
主要成果:
- 皮层β振荡 (13-30 Hz) 有效地区分了移动状态和静止状态.
- 皮层下高β运动相关脱同步 (MRD) 和马运动相关同步 (MRS) 与运动动力学相关.
- 结合皮质 - 基底模型在预测运动状态和速度方面实现了高准确度 (AUC > 0.85) 和相关性 (r > 0.68),优于使用单个位置数据的模型.
- 较高的DBS电流幅度降低了皮质下βMRD和gamma MRS,对皮质下模型性能产生了负面影响,但对皮质或组合模型没有负面影响.
结论:
- 皮层-基底运动网络节点编码补充的动力学信息,这对于在DBS过程中准确和稳定的自然运动解码至关重要.
- 整合皮层和皮层下信号的机器学习模型提供了强大的运动预测,支持闭环,自适应DBS (aDBS) 系统的开发.
- 研究结果提供了关于运动控制神经生物标志物的见解,以及运动障碍的潜在治疗点.
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Flexion and extension motions are in the sagittal (anterior–posterior) plane of motion. These movements take place at the shoulder, hip, elbow, knee, wrist, metacarpophalangeal,...