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，它影响了大约5000万美国成年人。它造成了超过6000亿美元的医疗支出、残疾和生产力损失，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)批准的一种神经调节疗法，用于缓解慢性顽固性疼痛。虽然SCS可以有效地用于许多患有顽固性慢性疼痛的患者，但很大一部分患者接受的疼痛抑制效果不佳或不充分。考虑到每年有30,000多人因慢性疼痛而接受SCS植入，由于对最佳刺激参数缺乏共识而导致无法满意的疼痛缓解，每个患者每年的费用超过24,000美元，因此提高SCS在慢性顽固性疼痛的临床疗效至关重要。除了传统的刺激(强直)，使用新的刺激波形，如突发和高频刺激(HFS)是一种方法。这些波形不会引起感觉异常(刺痛感或麻木感)，它们的影响通常延迟24-48小时，不像紧张性刺激，后者的影响是立竿见影的。然而，这些波形的脊髓上效应仍然不是很清楚。同样不清楚的是，为什么一些患者对一种波形的反应好于另一种。另一个限制是，SCS对疼痛缓解的评估几乎完全依赖于患者的自我报告，缺乏对SCS植入物结果的客观测量可能会导致失败的永久放置的数量增加。因此，在清醒和麻醉的慢性疼痛患者中，研究SCS波形的神经模式，并使用脑电(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，阿尔法峰值频率和阿尔法与西塔波功率比率的变化可能与SCS诱导的疼痛缓解有关。这些图案的空间分布与所使用的波形密切相关。我们假设，术中脑电表现出与术后脑电相似的空间光谱模式。我们预测，疼痛缓解将与HFS的额叶和躯体感觉区以及躯体感觉皮质区的阿尔法波段功率增加最相关。在这项提案中，我们的目标是确定清醒患者的阿尔法频段特征与SCS诱导的疼痛缓解之间的关系(目标1)，确定“睡眠”和清醒状态脑电之间的空间频谱差异，并表征术中疼痛缓解的神经关联(目标2)，并检查当患者通过重新编程获得最小程度的止痛以体验疼痛缓解时阿尔法频段特征的变化(目标3)。具体地说，在目标3中，我们将根据这些神经特征开发一个数学模型，患者将被临时编程到这种脑电引导的设置。我们将研究评估的神经模式在选择最佳波形方面的预测能力。这个项目的直接目标是开发量化的、缓解疼痛的神经信号。这些客观标记物可以形成设备选择的客观指标，这可能会减少与失败植入物相关的医疗支出，并有助于指导SCS参数以更好地缓解疼痛，特别是在疼痛缓解程度最低的患者。总体而言，这些客观的神经生理指标可以用于更个性化的SCS治疗，具有更高的疗效，本研究有助于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.