Assessment of EEG Responses to Spinal Cord Stimulation Waveforms in Chronic Pain Patients

慢性疼痛是一个严重的问题，据估计，它影响着大约5000万美国成年人。它在医疗保健支出、残疾和生产力损失中占6000多亿美元，仍然是患者寻求医疗保健的最常见原因之一。脊髓刺激（SCS）是美国食品和药物管理局（FDA）批准的一种缓解慢性难治性疼痛的神经调节疗法。虽然SCS可以有效地用于许多难治性慢性疼痛患者，但很大一部分患者的疼痛抑制不理想或不充分。考虑到每年有超过30,000人接受SCS植入治疗慢性疼痛，并且由于对最佳刺激参数缺乏共识而导致缺乏令人满意的疼痛缓解，每位患者每年花费超过24,000美元，因此提高SCS治疗慢性难治性疼痛的临床疗效至关重要。在传统刺激（强音）的基础上，利用脉冲和高频刺激（HFS）等新型刺激波形是一种方法。这些波形不会引起感觉异常（刺痛或麻木的感觉），它们的作用通常延迟24- 48小时，不像紧张性刺激，其效果是立即的。然而，这些波形对脊柱上的影响尚不清楚。也不知道为什么有些病人对一种波形的反应比另一种好。另一个限制是，对SCS缓解疼痛的评估几乎完全取决于患者的自我报告，缺乏对SCS植入结果的客观测量可能导致永久性植入失败的数量增加。在这方面，在清醒和麻醉的慢性疼痛患者中，研究SCS波形的神经模式并利用脑电图（EEG）评估SCS诱导的疼痛缓解显得至关重要。我们的初步数据表明，α峰频率和α和θ波段功率比值的变化可能与scs诱导的疼痛缓解有关。这些模式的空间分布与所使用的波形密切相关。我们假设术中脑电图与术后脑电图具有相似的空间谱模式。我们预测，疼痛的缓解与突发刺激中额叶和体感区域以及HFS中体感皮质区域的α带功率的增加最相关。在这项提议中，我们的目标是确定清醒患者α带特征与scs诱导的疼痛缓解之间的关系（目的1），确定“睡眠”和清醒状态脑电图的空间光谱差异，并表征手术中疼痛缓解的神经相关因素（目的2），并研究当疼痛缓解最小的患者重新编程以体验疼痛缓解时α带特征的变化（目的3）。具体来说，在Aim 3中，我们将基于这些神经特征开发一个数学模型，患者将暂时被编程到这个脑电图引导的环境中。我们将研究评估的神经模式对选择最佳波形的预测能力。这个项目的直接目标是开发量化的疼痛缓解的神经信号。这些客观标记可以形成器械选择的客观指标，这可能会减少与植入失败相关的医疗保健支出，并有助于指导SCS参数以更好地缓解疼痛，特别是对于疼痛缓解最小的患者。综上所述，这些客观的神经生理指标可以用于更个性化的SCS治疗，并且具有更高的疗效，本研究可以促进神经技术和神经工程方法的发展。

Chronic pain is a significant problem and it has been estimated to affect about 50 million adult Americans. It accounts for more than 600 billion dollars in health care expenditures, disability and loss of productivity and remains one of the most common reasons that patients seek healthcare. Spinal cord stimulation (SCS) is a Food and Drug Administration (FDA)-approved neuromodulation treatment to relieve chronic refractory pain. While SCS can be used effectively in many patients with refractory chronic pain conditions, a significant portion of the patients receive suboptimal or inadequate pain suppression. Considering that over 30,000 individuals receive SCS implantation on an annual basis for chronic pain and an annual cost of over $24,000 per patient resulting from lack of satisfactory pain relief due to lack of consensus regarding optimal stimulation parameters, it is crucial to improve the clinical efficacy of SCS in chronic refractory pain. Beside the traditional stimulation (tonic), using novel stimulation waveforms such as burst and high frequency stimulation (HFS) is one approach. These waveforms do not induce paresthesia (sensation of tingling or numbness) and their effects are often delayed 24- 48 hours unlike tonic stimulation, where the effects are immediate. However, the supraspinal effects of these waveforms are still not very clear. It is also unknown why some patients respond well to one waveform over another. Another limitation is that assessment of the pain relief by SCS depends almost exclusively on patient self-reports and lack of objective measurements of SCS implants results might lead to increased number of failed permanent placements. In this regard, investigating neural patterns of SCS waveforms and assess SCS-induced pain relief by using electroencephalography (EEG) in awake and anesthetized chronic pain patients appears essential. Our preliminary data suggests that changes in alpha peak frequency and ratio of alpha and theta band power might be correlated to SCS-induced pain relief. The spatial distribution of these patterns is closely related to which waveform is used. We hypothesize that intra-operative EEG demonstrates similar spatio-spectral patterns to post-operative EEG. We predict that pain relief will correlate best with increased alpha band power in frontal and somatosensory regions in burst stimulation and somatosensory cortical regions in HFS. In this proposal, our objectives are to determine the relationship between alpha band features and SCS-induced pain relief in awake patients (Aim 1), determine the spatio-spectral differences between “sleep” and awake states EEG and characterize the intra-operative neural correlates of pain relief (Aim 2), and examine the changes in alpha band features when patients with minimal pain relief are reprogrammed to experience pain relief (Aim 3). Specifically, in Aim 3, we will develop a mathematical model based on these neural features and patients will be temporarily programmed to this EEG-guided setting. We will investigate the predictive power of the assessed neural patterns on selection of the optimal waveform. The immediate goal of this project is to develop quantified, neural signatures of pain relief. These objective markers could form an objective metric for device selection, which might reduce the healthcare expenditures associated with failed implants and help in guiding SCS parameters for better pain relief, especially in patients with minimal pain relief. Overall, these objective neurophysiological measures can be used for more personalized SCS therapy with higher efficacy, and, this study can contribute to development of neurotechnologies and neuroengineering methodologies.