Using neuroimaging data to map dysfunctional brain networks and predict symptom severity in Parkinson's disease progression.

使用神经影像数据绘制功能失调的大脑网络并预测帕金森病进展中的症状严重程度。

• 批准号: 2076906
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2076906
• 关键词: Using; neuroimaging; data; map; dysfunctional

A major challenge in Parkinson's disease (PD) research is heterogeneity in disease progression and severity of cognitive involvement. Conventional neuroimaging uses MRI to assess volume loss caused by neuronal cell death. However, grey matter atrophy is poorly sensitive in PD and fails to track cognitive change or motor progression. Techniques sensitive to brain tissue microstructure and to changes in network dynamics are better suited to do this. One such technique is quantitative susceptibility mapping (QSM), which estimates the absolute magnetic susceptibility of different brain tissues as a proxy for the distribution of brain iron. Oxidative stress secondary to excess brain iron accumulation is a pathomechanism in PD. Iron generates free radical species that interact with a-synuclein to promote Lewy-related pathology and produces neurotoxic by-products via catalysation of dopamine oxidation reactions. It is also linked with mitochondrial dysfunction, neurotoxicity and chronic inflammation. This PhD will use two complementary neuroimaging approaches to mapping disease activity in PD. The first section will use QSM data already acquired in 100 deeply phenotyped patients with Parkinson's disease to detect brain iron changes that correlate with disease activity in PD. An extension to this section will be to combine these maps of iron deposition with whole-brain transcriptional data to identify the relative expression of genes of interest in implicated regions. The second section will leverage resting state functional MRI data already acquired in the same cohort of patients. This will allow us to characterise the functional connectome, a detailed mapping of the functional integration of the brain, and how this changes in PD. An emerging approach to understanding the functional connectivity of resting state networks is spectral dynamic causal modelling (spectral DCM). This technique has the advantage of using Bayesian modelling to discover the most likely model from fully connected graphs, enabling us to characterise the connectivity patterns in different stages of PD. Together, this project aims to utilise novel neuroimaging techniques to track symptom severity in early stage PD and predict the rate of cognitive decline. By further relating our findings to whole-brain transcriptional data, we aim to shed light on the mechanisms underlying selective vulnerability and network dysfunction in PD.

帕金森氏病(PD)研究的一个主要挑战是疾病进展和认知受累严重程度的异质性。传统的神经成像使用MRI来评估神经细胞死亡造成的体积损失。然而，灰质萎缩对帕金森病的敏感度较低，无法跟踪认知变化或运动进展。对脑组织微结构和网络动态变化敏感的技术更适合这样做。一种这样的技术是定量磁化率图(QSM)，它估计不同脑组织的绝对磁化率，作为脑铁分布的替代。继发性氧化应激是帕金森病的一种发病机制。铁产生自由基物种，与α-突触核蛋白相互作用，促进路易氏相关的病理，并通过催化多巴胺氧化反应产生神经毒性副产物。它还与线粒体功能障碍、神经毒性和慢性炎症有关。这位博士将使用两种互补的神经成像方法来描绘帕金森病的疾病活动。第一部分将使用已经在100名帕金森氏病深表型患者中获得的QSM数据来检测与帕金森病疾病活动相关的脑铁变化。这一部分的延伸将是将这些铁沉积图与全脑转录数据相结合，以识别相关区域中感兴趣的基因的相对表达。第二部分将利用在同一组患者中已经获得的静息状态功能磁共振数据。这将使我们能够描述功能连接体的特征，大脑功能整合的详细图谱，以及这在帕金森病中是如何变化的。一种新的理解静息状态网络功能连通性的方法是谱动态因果模型(谱DCM)。这种技术的优势是使用贝叶斯建模从完全连通的图中发现最可能的模型，使我们能够表征PD不同阶段的连接模式。总之，该项目旨在利用新的神经成像技术来跟踪早期帕金森病的症状严重程度，并预测认知功能减退的速度。通过进一步将我们的发现与全脑转录数据联系起来，我们的目标是阐明帕金森病选择性脆弱性和网络功能障碍的潜在机制。