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

经颅磁刺激(TMS)是一种非侵入性刺激大脑回路的技术。TMS是一种神经科学的研究工具，有希望为临床治疗提供翻译机会。目前TMS的临床应用包括重度抑郁障碍、强迫症和偏头痛。正在探索的TMS疗法包括中风后康复、中枢疼痛、成瘾和耳鸣。然而，TMS的治疗方法还需要改进。例如，在抑郁症中，只有大约三分之一的患者能得到缓解。为了进一步提高TMS的实用价值，该项目将探索两个互补的技术领域。首先，我们认为扩大刺激参数空间是有益的。历史上，大多数TMS应用使用1-20赫兹的频率，连续或简单地重复突发。然而，新的范式正在出现；“Thetaburst刺激”最近显示出以比持续TMS更短的治疗间隔治疗抑郁症的前景(Blumberger，Lancet，2018)。此外，替代神经调节疗法，如脑深部刺激，使用频率明显更高的效果。脉冲形状是神经基质差动啮合的另一个自由度，但由于系统通常以单一共振频率运行，因此还没有得到充分的探索。这些数据表明，具有扩展参数功能的TMS可能会开启新的治疗用途或改善结果。其次，为了最大限度地利用这些扩展能力，我们认为研究人员需要一种系统地搜索参数空间的方法。即使在今天的选择中，也只有一小部分集群在实践中使用，而且这些设置的机制并不清楚(Klimjai，Annals Phys Rehab Med，2015)。与theta Burst类似，可能会出现其他提供额外好处的模式，但探索太空的途径必须是容易驾驭的。最后，TMS兼容的EEG系统是可用的，但还没有明确的算法来衡量TMS的潜在好处或负面影响。本论文项目将设计和测试一个增强的TMS系统来解决这些缺点。首先，该系统将为TMS提供扩展的参数集，包括更高的速率、扩展的模式能力和具有可调电路的脉冲整形变化。该项目的这一部分将需要电子学、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种用于搜索刺激参数空间的强化学习算法。生理传感器将被用来实时估计受试者的大脑状态。然后应用刺激的扰动，并使用对大脑状态的影响来调整下一次参数运行。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参数空间的探索，以寻找更优的刺激范例。为了展示新研究系统的实用性，本论文将包括对选定神经元网络的长时程增强和抑制的测量。我们将使用已建立的运动和语音中枢调制方法作为方法验证的客观标志。最终交付的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

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