Mechanism-Based Prediction of Chronic Post-Stroke Pain Using Advanced Neuroimaging Techniques

使用先进的神经影像技术对慢性中风后疼痛进行基于机制的预测

• 批准号: 2750291
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2750291
• 关键词: Mechanism; Based; Prediction; Chronic; Post

Central post-stroke pain (CPSP) is a chronic condition which develops in 8% to 18% of stroke patients (Andersen, et al., 1995; Liampass, et al., 2020). CPSP is heterogenous in onset, presentation, and intensity, and is, unfortunately, difficult to treat. Pharmacological trials have found mixed results: antidepressants, anticonvulsants, and anaesthetics have shown mild to moderate improvements but are limited by small effect sizes and poor reproducibility (Oh & Seo, 2015). Pharmacological treatments also have troublesome side-effects which negatively impact on patient quality of life. Nonpharmacological therapies such as Deep Brain Stimulation and repetitive Transcranial Magnetic Stimulation (rTMS) to primary motor cortex (M1) can improve CPSP but there is large inter-individual variability in pain relief. Some patients experience no benefit but others attain almost complete pain relief. The cause of this variability is unknown but may relate to generic targeting of M1. Treatments targeting brain regions that are mechanistically relevant to pain generation may represent a better option. However, this will require a detailed understanding of CPSP's pathophysiology.This PhD builds on the novel finding of primary somatosensory cortex's cytoarchitectonic areas 3A, 3B and 1 involvement in pain processing, and aims to identify novel targets to effectively treat CPSP (Whitsel, et al., 2019). 3A is hypothesised to contribute to abnormal pain processing, leading to chronic pain, when it is overactive. In CPSP this could happen when projections to 3A become hyperexcitable; when 3B/1 areas are damaged and no longer inhibit 3A through intracortical connections or because of stroke-induced alterations in glia and microglia cell function that cause abnormal network level excitability. As the location of 3A is directly adjacent to M1, rTMS stimulation of M1 could inadvertently target 3A, which could explain some therapeutic responses to rTMS. Interventions which more effectively inhibit 3A could lead to long-term chronic pain reduction in CPSP.There is currently a gap in literature examining the role of 3A in the development of CPSP. This project aims to determine whether disruption of brain circuits involving 3A are important in causing CPSP and also whether 3A represents a valid target for treating and preventing CPSP. The specific aims are to: (1) identify dysfunctional brain circuits that cause CPSP, (2) use lesion mapping and functionnectonomic analysis to find biomarkers for predicting the transition to CPSP and treatment response to rTMS, which will contribute to precision medicine approach to treatment, and (3) build a machine learning prediction algorithm to predict CPSP development and treatment response.This project and studentship is organised in stages: (1) training in R Project, Python and MatLab to undertake data-driven analyses of MRI and fMRI scans, as well as learning to build a machine learning algorithm to predict stroke patient outcomes in relation to CPSP, (2) conducting a systematic analysis of current CPSP literature, (3) data collection of CPSP patients, non-CPSP stroke patients, and healthy controls, (4) data-driven analysis of lesion mapping as it relates to development and outcomes of CPSP, (5) development of machine learning based biomarkers predicting development and outcomes of CPSP, (6) hypothesis-driven investigation of 3A afferents role in CPSP, (7) experimental rTMS targeting of 3A area to alleviate pain for stroke patients.Participants will be recruited through the local NHS Trusts (Aintree University Hospital and Royal Liverpool University Hospital) and potentially through other Trusts that are able to participate. As MRI or CT scanning is standard practice in stroke patients, some data will be used from patients' records.

中枢性中风后疼痛（CPSP）是在8%至18%的中风患者中发展的慢性病症（Andersen等人，1995; Liampass等人，2020年）。CPSP在发病、表现和强度方面具有异质性，并且不幸的是，难以治疗。药理学试验发现了混合结果：抗抑郁药、抗惊厥药和麻醉剂显示出轻度至中度的改善，但受限于较小的效应量和较差的重现性（Oh & Seo，2015）。药物治疗也有麻烦的副作用，对患者的生活质量产生负面影响。非药物治疗，如脑深部电刺激和对初级运动皮层（M1）的重复经颅磁刺激（rTMS），可以改善CPSP，但疼痛缓解的个体间差异很大。有些病人没有任何好处，但其他人几乎完全缓解疼痛。这种变异性的原因尚不清楚，但可能与M1的通用靶向有关。针对与疼痛产生机制相关的大脑区域的治疗可能是一个更好的选择。然而，这将需要对CPSP的病理生理学的详细理解。该博士学位建立在初级躯体感觉皮层的细胞结构区域3A、3B和1参与疼痛处理的新发现的基础上，旨在鉴定有效治疗CPSP的新靶点（Whitsel等人，2019年）。假设3A在过度活跃时有助于异常疼痛处理，导致慢性疼痛。在CPSP中，当3A的投射变得过度兴奋时，当3B/1区域受损并且不再通过皮质内连接抑制3A时，或者因为中风诱导的胶质细胞和小胶质细胞功能改变导致异常网络水平兴奋性时，可能会发生这种情况。由于3A的位置与M1直接相邻，M1的rTMS刺激可能无意中靶向3A，这可以解释对rTMS的一些治疗反应。更有效地抑制3A的干预措施可能会导致长期的慢性疼痛减少CPSP.There是目前的空白文献检查的作用，3A在CPSP的发展。该项目旨在确定涉及3A的脑回路中断是否在引起CPSP中重要，以及3A是否代表治疗和预防CPSP的有效靶点。具体目标是：（1）识别导致CPSP的功能失调的大脑回路，（2）使用病变映射和功能神经学分析来寻找预测向CPSP过渡和对rTMS的治疗反应的生物标志物，这将有助于精准医学治疗方法，以及（3）建立机器学习预测算法来预测CPSP的发展和治疗反应。该项目和学生资助分阶段组织：（1）在R Project、Python和MatLab中进行培训，以进行MRI和fMRI扫描的数据驱动分析，以及学习构建机器学习算法来预测与CPSP相关的卒中患者结局，（2）对当前CPSP文献进行系统分析，（3）收集CPSP患者、非CPSP卒中患者和健康对照的数据，（4）与CPSP的发展和结果相关的病变映射的数据驱动分析，（5）预测CPSP的发展和结果的基于机器学习的生物标志物的开发，（6）CPSP中3A传入作用的假设驱动调查，（7）针对3A区的实验性rTMS，以减轻中风患者的疼痛。参与者将通过当地NHS信托基金招募（安特里大学医院和皇家利物浦大学医院），并可能通过其他能够参与的信托基金。由于MRI或CT扫描是中风患者的标准做法，因此将使用患者记录中的一些数据。