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

帕金森病（PD）研究的一个主要挑战是疾病进展的异质性和认知参与的严重性。传统的神经成像使用MRI来评估由神经元细胞死亡引起的体积损失。然而，灰质萎缩在PD中的敏感性较差，无法跟踪认知变化或运动进展。对脑组织微观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映射（QSM），其估计不同脑组织的绝对磁化率作为脑铁分布的代理。继发于过量脑铁蓄积的氧化应激是PD的病理机制。铁产生与α-突触核蛋白相互作用的自由基物质，以促进路易相关的病理学，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。该博士将使用两种互补的神经影像学方法来绘制PD中的疾病活动。第一部分将使用已经在100名深度表型帕金森病患者中获得的QSM数据来检测与PD疾病活动相关的脑铁变化。本节的扩展将是将这些铁沉积图与全脑转录数据结合联合收割机，以确定相关区域中感兴趣基因的相对表达。第二部分将利用在同一患者队列中已经获得的静息状态功能MRI数据。这将使我们能够识别功能性连接体，大脑功能整合的详细映射，以及PD中的变化。一个新兴的方法来理解静息态网络的功能连接是光谱动态因果建模（光谱DCM）。这种技术的优点是使用贝叶斯建模来从全连接图中发现最可能的模型，使我们能够在PD的不同阶段识别连接模式。总之，该项目旨在利用新的神经影像学技术来跟踪早期PD的症状严重程度，并预测认知能力下降的速度。通过进一步将我们的研究结果与全脑转录数据联系起来，我们的目标是阐明PD中选择性脆弱性和网络功能障碍的潜在机制。

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.