An Enhanced Transcranial Magnetic Stimulator for Advancing Clinical Neuroscience and Minimally-Invasive Brain Therapies

用于推进临床神经科学和微创脑部治疗的增强型经颅磁刺激器

• 批准号: 2291403
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2291403
• 关键词: Enhanced; Transcranial; Magnetic; Stimulator; Advancing

Transcranial Magnetic Stimulation (TMS) is a non-invasive technology to stimulate brain circuits. TMS is a research tool for neuroscience, with promising translation opportunities for clinical treatments. Current TMS clinical applications include major depressive disorder, OCD and migraine. TMS therapies under exploration include post - stroke rehabilitation, central pain, addictions and Tinnitus. However, TMS therapy needs to be improved. For example, in depression, only about 1/3 of patients achieve remission. To further TMS utility, this project will explore complementary two areas of technology. First, we believe expanding the stimulation parameter space is beneficial. Historically, most TMS applications use frequencies of 1 - 20 Hz, continuously or in simple repetitive bursts. Yet new paradigms are emerging; "Thetaburst Stimulation" recently showed promise to treat depression with shorter treatment intervals than continuous TMS (Blumberger, Lancet, 2018). In addition, alternative neuromodulation therapies like Deep Brain Stimulation use significantly higher frequencies for effect. Pulse shape is another degree of freedom for differentially engaging neural substrates, but is not fully explored, as systems generally operate at a single resonance frequency. These data suggest TMS with expanded parameter capabilities may unlock novel therapeutic uses or improve outcomes. Second, to make the most of these extended capabilities, we believe researchers need a methodology for systematically searching the parameter space. Even with today's options, only a small set of clusters are used in practice, and the mechanisms of these settings are not clearly understood (Klimjai, Annals Phys Rehab Med, 2015). Similar to theta burst, other patterns might emerge that provide additional benefit, but the pathway for exploring the space must be tractable. Finally, TMS compatible EEG systems are available but no clear algorithms to measure potentially beneficial or negative effects of TMS have been validated.This thesis project will design and test an enhanced TMS system that addresses these shortcomings. First, the system will deliver TMS with an expanded parameter set, including higher rates, extended pattern capability, and variations in pulse shaping with tunable circuits. This portion of the project will require systems-level integration of electronics, magnetic coils, and neural activation models. The second component of the thesis will be to design a reinforcement learning algorithm for searching the stimulation parameter space. Physiological sensors will be used to estimate the subject's brain state in real-time. Perturbations of stimulation will then be applied, and the effect on brain state used to adjust the next parameter run. Using methods from reinforcement (machine) learning, we will explore both model-based and "black box" explorations of the TMS parameter space to in search of more optimal stimulation paradigms.To demonstrate the utility of the new research system, the thesis will include measurements of Long Term Potentiation and Inhibition of selected neuronal networks. We will use established methods for modulation of motor and speech centers as objective markers for method validation. The final deliverable will be a proof-of-concept instrument and parameter optimization framework for advancing TMS clinical neuroscience research and applications.Commercial partner: Magstim, Whitland, Wales

经颅磁刺激（TMS）是一种刺激大脑回路的非侵入性技术。TMS是神经科学的研究工具，具有用于临床治疗的有前途的翻译机会。目前TMS的临床应用包括重度抑郁症、强迫症和偏头痛。正在探索的TMS疗法包括中风后康复、中枢性疼痛、成瘾和耳鸣。然而，TMS治疗需要改进。例如，在抑郁症中，只有约1/3的患者获得缓解。为了进一步推动TMS的实用性，该项目将探索两个互补的技术领域。首先，我们认为扩展刺激参数空间是有益的。历史上，大多数TMS应用使用1 - 20 Hz的频率，连续或简单重复的突发。然而，新的范例正在出现;“Thetaburst Stimulation”最近显示出比连续TMS更短的治疗间隔治疗抑郁症的希望（Blumberger，Lancet，2018）。此外，替代神经调节疗法如脑深部电刺激使用更高的频率来产生效果。脉冲形状是用于差分接合神经基质的另一自由度，但未被充分探索，因为系统通常在单个谐振频率下操作。这些数据表明，具有扩展参数能力的TMS可以解锁新的治疗用途或改善结果。其次，为了充分利用这些扩展功能，我们认为研究人员需要一种系统搜索参数空间的方法。即使有了今天的选择，实践中也只使用了一小部分集群，并且这些设置的机制还没有被清楚地理解（Klimjai，Annals Phys Rehab Med，2015）。类似于θ波爆发，其他模式可能会出现，提供额外的好处，但探索空间的途径必须是易处理的。最后，TMS兼容的EEG系统是可用的，但没有明确的算法来衡量潜在的有益或负面影响的TMS已validated.This论文项目将设计和测试一个增强的TMS系统，解决这些缺点。首先，该系统将提供具有扩展参数集的TMS，包括更高的速率、扩展的模式能力以及可调谐电路的脉冲整形变化。该项目的这一部分将需要电子，磁线圈和神经激活模型的系统级集成。论文的第二部分将设计一个强化学习算法来搜索刺激参数空间。生理传感器将用于实时估计受试者的大脑状态。然后将施加刺激的扰动，并且对大脑状态的影响用于调整下一个参数运行。使用强化（机器）学习的方法，我们将探索基于模型和“黑盒”的TMS参数空间探索，以寻找更优化的刺激padigmas.To证明新的研究系统的效用，论文将包括测量的长期增强和抑制选定的神经元网络。我们将使用已建立的运动和语言中枢调制方法作为方法验证的客观标记。最终交付成果将是一个概念验证仪器和参数优化框架，用于推进TMS临床神经科学研究和应用。商业合作伙伴：威尔士惠特兰的Magstim