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Adaptive deep brain stimulation in advanced Parkinson disease.

文献信息

DOI10.1002/ana.23951
PMID23852650
期刊Annals of neurology
影响因子7.7
JCR 分区Q1
发表年份2013
被引次数526
关键词深脑刺激, 帕金森病, 脑机接口, 适应性调节, 运动效果
文献类型Journal Article
ISSN0364-5134
页码449-57
期号74(3)
作者Simon Little, Alex Pogosyan, Spencer Neal, Baltazar Zavala, Ludvic Zrinzo, Marwan Hariz, Thomas Foltynie, Patricia Limousin, Keyoumars Ashkan, James FitzGerald, Alexander L Green, Tipu Z Aziz, Peter Brown

一句话小结

本研究探讨了脑机接口(BCI)在晚期帕金森病患者中用于控制深脑刺激的有效性,结果显示自适应DBS(aDBS)相较于传统的连续刺激(cDBS)在运动评分上显著改善,同时刺激时间和能量需求大幅减少。该研究证明了BCI控制DBS的可行性,具有潜在的临床应用价值,有望提升帕金森病治疗的效果与效率。

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深脑刺激 · 帕金森病 · 脑机接口 · 适应性调节 · 运动效果

摘要

目标

脑机接口(BCI)可以用于与病理性脑信号互动,以干预并改善疾病状态下的效果。在此,我们通过使用BCI解读晚期帕金森病(PD)患者的病理性脑活动,并利用这种反馈控制治疗性深脑刺激(DBS)的实施时间,提供了这种方法的初步证明。我们的目标是通过实时个性化和优化刺激,提升传统连续DBS的疗效和效率。

方法

我们对8名PD患者进行了基于BCI控制的自适应DBS(aDBS)在丘脑下核的测试。通过直接从刺激电极记录的局部场电位进行处理提供反馈。结果与无刺激、传统连续刺激(cDBS)和随机间歇刺激进行了比较。使用统一帕金森病评级量表进行了不盲和盲法的临床评估,以评估运动效果。

结果

在aDBS期间,运动评分分别改善了66%(不盲)和50%(盲),相比于cDBS,分别提高了29%(p = 0.03)和27%(p = 0.005)。这些改善是在与cDBS相比刺激时间减少56%的情况下实现的,同时能量需求也相应减少(p < 0.001)。aDBS的效果也优于无刺激和随机间歇刺激。

解释

BCI控制的DBS是可行的,并且在帕金森病的治疗中比传统的连续神经调节更有效和高效。

英文摘要

OBJECTIVE Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.

METHODS We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.

RESULTS Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.

INTERPRETATION BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.

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主要研究问题

  1. 在适应性深脑刺激中,如何评估和优化反馈机制以提高治疗效果?
  2. 除了帕金森病,适应性深脑刺激技术在其他神经系统疾病中的应用前景如何?
  3. 适应性深脑刺激的能量消耗减少对长期治疗的影响是什么?
  4. 如何通过脑机接口技术进一步提升深脑刺激的个性化治疗效果?
  5. 在临床试验中,如何确保适应性深脑刺激的安全性和有效性评估?

核心洞察

研究背景和目的

深脑刺激(DBS)是治疗严重帕金森病(PD)的一种有效方法,但其广泛应用受到成本、副作用和疗效不足的限制。由于帕金森病的症状在短时间内可能波动,研究者希望通过实时反馈控制刺激,以提高治疗效果并减少副作用。本研究的目的是探索基于脑-计算机接口(BCI)的自适应深脑刺激(aDBS),以改善PD患者的症状。

主要方法/材料/实验设计

本研究共纳入8名患有严重PD的患者,采用BCI控制的自适应深脑刺激,主要针对下丘脑核。研究设计如下:

Mermaid diagram
  1. 患者选择和知情同意:招募8名有严重PD的患者,并获得知情同意。
  2. DBS手术和电极植入:患者接受下丘脑核的DBS手术,电极植入后通过CT或MRI确认位置。
  3. 记录局部场电位(LFP):在未用药状态下记录LFP,分析beta频段的活动。
  4. 处理LFP信号:实时处理LFP信号,提取beta活动以控制刺激。
  5. 设定刺激阈值:根据beta活动设定刺激阈值,并进行aDBS、cDBS和随机刺激的比较。
  6. 评估运动功能改善:使用统一帕金森病评定量表(UPDRS)评估运动功能的改善。

关键结果和发现

  • 运动改善:aDBS在无刺激状态下的UPDRS评分改善66%(非盲评)和50%(盲评),显著优于cDBS(54.3%和30.5%)和随机刺激(33.7%和6.7%)。
  • 能量消耗:aDBS的电能消耗为132μW,显著低于cDBS的270μW,刺激时间减少56%。
  • 刺激效率:aDBS模式下的刺激时间仅占44.2%,而随机刺激为43.3%。

主要结论/意义/创新性

本研究首次证明了基于BCI的自适应深脑刺激在PD患者中的有效性和高效性。aDBS不仅提高了临床效果,还显著减少了能量消耗,这可能延长植入设备的电池寿命,减少患者的手术风险和治疗成本。这一创新性的方法为未来PD的电刺激治疗提供了新的方向。

研究局限性和未来方向

  • 局限性:本研究仅评估了单侧刺激,且每个刺激块的评估时间较短,可能未能全面反映长期效果。
  • 未来方向:建议在更大规模的临床试验中验证aDBS的长期效果,优化刺激参数,并探索更复杂的算法以提高控制的精确性。此外,研究可以扩展到其他波动性运动和神经精神疾病的治疗中。

参考文献

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引用本文的文献

  1. Control of basal ganglia output by direct and indirect pathway projection neurons. - Benjamin S Freeze;Alexxai V Kravitz;Nora Hammack;Joshua D Berke;Anatol C Kreitzer - The Journal of neuroscience : the official journal of the Society for Neuroscience (2013)
  2. Bilateral functional connectivity of the basal ganglia in patients with Parkinson's disease and its modulation by dopaminergic treatment. - Simon Little;Huiling Tan;Anam Anzak;Alek Pogosyan;Andrea Kühn;Peter Brown - PloS one (2013)
  3. Closing the loop of deep brain stimulation. - Romain Carron;Antoine Chaillet;Anton Filipchuk;William Pasillas-Lépine;Constance Hammond - Frontiers in systems neuroscience (2013)
  4. Movement disorders in 2013: diagnosing and treating PD-the earlier the better? - François Tison;Wassilios G Meissner - Nature reviews. Neurology (2014)
  5. Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface. - Alik S Widge;Chet T Moritz - Journal of neural engineering (2014)
  6. Neuroscience: Tuning the brain. - Helen Shen - Nature (2014)
  7. Activity parameters of subthalamic nucleus neurons selectively predict motor symptom severity in Parkinson's disease. - Andrew Sharott;Alessandro Gulberti;Simone Zittel;Adam A Tudor Jones;Ulrich Fickel;Alexander Münchau;Johannes A Köppen;Christian Gerloff;Manfred Westphal;Carsten Buhmann;Wolfgang Hamel;Andreas K Engel;Christian K E Moll - The Journal of neuroscience : the official journal of the Society for Neuroscience (2014)
  8. Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders. - Frédéric D Broccard;Tim Mullen;Yu Mike Chi;David Peterson;John R Iversen;Mike Arnold;Kenneth Kreutz-Delgado;Tzyy-Ping Jung;Scott Makeig;Howard Poizner;Terrence Sejnowski;Gert Cauwenberghs - Annals of biomedical engineering (2014)
  9. Closed-loop neurostimulation: the clinical experience. - Felice T Sun;Martha J Morrell - Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics (2014)
  10. The highs and lows of beta activity in cortico-basal ganglia loops. - John-Stuart Brittain;Andrew Sharott;Peter Brown - The European journal of neuroscience (2014)

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