Identifying neuromodulation targets for pain in the human brain

识别人脑中疼痛的神经调节靶点 神经性疼痛在许多不同的神经和精神疾病中普遍存在，并且具有其中一种 对生活质量的最大负面影响。药物疼痛治疗通常具有有限的功效， 不良副作用，包括依赖和成瘾。神经调节治疗，如经颅 磁刺激显示出对疼痛的承诺，同时避免了与止痛药相关的副作用。一个 改善神经调节治疗的障碍是识别负责疼痛的人脑区域。 传统的功能性神经影像学研究只能识别可能导致症状的疼痛相关性， 补偿症状，或者仅仅作为症状的标记。这种模糊性是一个问题， 寻找治疗靶点与功能性神经影像学不同，病变研究允许偶然的联系 症状和人类神经解剖学之间的联系PI开发并验证了一种新的方法来识别 基于脑损伤和人脑连接图，神经调节靶点在人脑中。 称为病变网络映射，这种方法已被用于映射许多神经和精神疾病 症状到大脑网络（Fox 2018，NEJM）。使用这种方法识别的大脑网络已被证明 是诸如震颤、帕金森病、肌张力障碍等症状的有效神经调节靶标， 萧条我们的初步数据表明，这种方法对于映射疼痛同样有价值，包括 确定治疗靶点。在这里，我们将使用这种方法来映射疼痛到人类大脑回路， 关于先前报告的导致疼痛的病变位置（目标1），一个专注于丘脑病变的专用数据集 引起或不引起疼痛（目标2），以及纵向评估的患者前瞻性数据集， 中风后疼痛的发展（目标3）。我们假设：（1）引起疼痛的病变将映射到一个共同的 脑网络;（2）该网络将显示中风后疼痛患者的代谢异常，（3）该网络 将预测谁会患上中风后疼痛;（4）这个疼痛网络将包括初级运动皮层，我们的 最有效的疼痛神经调节靶点。完成这些目标将确定一个候选人的大脑 与疼痛有因果关系的网络。一旦确定，该网络可以在未来的试验中进行验证， 中风后疼痛的表征，并用作神经调节的改进目标，可以在 临床试验

Identifying neuromodulation targets for pain in the human brain Neuropathic pain is prevalent across many different neurological and psychiatric disorders and has one of the largest adverse effects on quality of life. Pharmacological pain treatments often have limited efficacy and undesirable side effects, including dependence and addiction. Neuromodulation treatments such as transcranial magnetic stimulation show promise for pain while avoiding the side effects associated with pain medications. An obstacle to improving neuromodulation treatment is identifying the human brain regions responsible for pain. Traditional functional neuroimaging studies only identify correlates of pain which could be causing symptoms, compensating for symptoms, or simply serving as a marker of symptoms. This ambiguity is a problem when seeking to identify therapeutic targets. Unlike functional neuroimaging, lesion studies allow for casual links between symptoms and human neuroanatomy. The PI has developed and validated a new method to identify neuromodulation targets in the human brain based on brain lesions and a map of human brain connectivity. Called lesion network mapping, this approach has been used to map numerous neurological and psychiatric symptoms to brain networks (Fox 2018, NEJM). Brain networks identified using this approach have proven to be effective neuromodulation targets for symptoms such as tremor, Parkinson’s disease, dystonia, and depression. Our preliminary data suggests this method is equally valuable for mapping pain, including identification of therapeutic targets. Here, we will use this approach to map pain to a human brain circuit based on lesion locations previously reported to cause pain (Aim 1), a dedicated dataset focused on thalamic lesions causing or not causing pain (Aim 2), and a prospective dataset of patients longitudinally assessed for development of post-stroke pain (Aim 3). We hypothesize that; (1) lesions causing pain will map to a common brain network; (2) this network will show abnormal metabolism in patients with post stroke pain, (3) this network will predict who will develop post-stroke pain; and (4) this pain network will include primary motor cortex, our best validated neuromodulation target for pain. Completion of these aims will identify a candidate human brain network causally linked to pain. Once identified, this network can be validated in future trials with more detailed characterization of post stroke pain and used as an improved target for neuromodulation that can be tested in clinical trials.