Detecting, tracking & modelling structural and functional brain imaging changes in Alzheimer's disease

痴呆症是一个重大的和日益增长的公共卫生挑战，也是发达国家的研究重点。根据英国阿尔茨海默氏症研究中心的数据，英国有2500万人的家人或亲密朋友患有痴呆症，每年给我们的经济造成的损失估计为230亿英镑。目前阿尔茨海默氏症（AD）只有对症治疗，没有“改善疾病”的药物。目前的许多研究工作都与寻找这些急需的疾病改善治疗方法有关。改善评估疾病进展的方法将增加显示干预减缓衰退进程的机会。脑扫描（磁共振成像; MRI）可测量的结构变化已被证明可以提前和预测症状发作，并与AD的临床衰退相关，并可能有助于评估潜在的疾病改善疗法。对专家来说视觉上正常的扫描可能具有通过计算分析可检测到的细微病理。有新的证据表明，功能性脑成像（fMRI）可以揭示疾病过程中甚至更早的变化，并且这些变化与记忆表现等临床指标有关。有一个强烈的需要，更精确的表征结构和功能的变化，并更好地了解结构和功能之间的相互作用。我将致力于解决这些具有挑战性的问题，在我的奖学金在威康信托中心神经影像学开发新的方法，并将这些方法应用于大型患者数据集与痴呆症研究中心合作。这两个中心都是世界著名的伦敦大学学院神经病学研究所的一部分，在伦敦。我的第一个目标是开发一个新的纵向结构成像数据的统计模型（多个大脑扫描随着时间的推移），并将其应用到AD的诊断和跟踪的问题。这将允许假设的区域定位的萎缩和其加速进行测试与更大的权力（即我们应该能够找到变化使用新的模型是无法检测到没有它）。我的第二个目标是调查的方法建模不同的大脑区域之间的连接，使用功能磁共振成像获得与受试者"休息“。这些数据以及它所揭示的大脑网络，正在引起科学和临床研究界越来越大的兴趣。即使在痴呆症的早期阶段，一个大脑区域与另一个大脑区域的交流方式也可能发生变化。这些变化的减少与潜在的药物治疗可能表明该药物的成功早于临床症状，如记忆丧失。功能磁共振成像中的这些“生物标志物”在痴呆症的诊断中的应用是目前研究不足的课题，它具有很大的潜力，可以用于早期发现变化，并为治疗试验提供新的结果指标。最后，我的目标是将这些新方法结合起来，研究结构和功能的相互关系及其在疾病中的变化。这将使我能够评估新的结构，功能和组合的多模式措施跟踪疾病进展的力量。这具有为临床试验提供更好的结果测量的显著潜力，在更稳健地检测变化方面，更少的重复性，或在疾病过程中更早地检测变化，从而对寻找疾病修饰治疗产生影响。

Dementia is a major and growing public health challenge and a research priority in the developed world. According to Alzheimer's Research UK, 25 million people in Britain have a family member or close friend with dementia, and the cost to our economy is estimated to be £23-billion per year.There are currently only symptomatic treatments for Alzheimer's disease (AD); no 'disease-modifying' drugs. A lot of current research work relates to the search for these much needed disease-modifying treatments. Improved methods for assessing disease progression would increase the chances of showing that an intervention slowed the course of decline.Structural changes, measurable on brain scans (magnetic resonance imaging; MRI) have been shown to predate and predict symptom onset and correlate with clinical decline in AD, and may help to evaluate potential disease-modifying therapies. Scans which appear visually normal to an expert might have subtle pathology detectable with computational analysis. There is emerging evidence that functional brain imaging (fMRI) can reveal changes even earlier in the course of the disease, and that these changes are associated with clinical measures like memory performance. There is a strong need for more precise characterisation of both structural and functional change, and for better understanding of the interactions between structure and function.I will work to address these challenging problems in my Fellowship by developing new methods at the Wellcome Trust Centre for Neuroimaging, and applying these methods to large patient data-sets in collaboration with the Dementia Research Centre. Both Centres are part of the world-renowned UCL Institute of Neurology, at University College London.My first objective is to develop a new statistical model for longitudinal structural imaging data (multiple brain scans over time), and to apply this to the problem of diagnosis and tracking of AD. This will allow hypotheses about the regional localisation of atrophy and its acceleration to be tested with greater power (i.e. we should be able to find changes using the new model that are undetectable without it).My second aim is to investigate methods for modelling the connectivity among different brain regions, using fMRI acquired with subjects 'at rest'. Such data, and the brain networks that it can reveal, are generating increasing interest in the scientific and clinical research communities. Even in the very early stages of dementia, there may be changes in the way one brain region communicates with another. Reductions in these changes with a potential drug therapy could indicate success of that drug much earlier than clinical symptoms like memory loss. The utility of such 'biomarkers' from fMRI for dementia is currently an under-researched topic with great potential for detecting early changes and providing new outcome measures for treatment trials.Finally, I aim to use the new methods together to investigate the inter-relation of structure and function and their changes in the disease. This will allow me to evaluate the power of the novel structural, functional and combined multi-modal measures for tracking disease progression. This has significant potential to provide better outcome measures for clinical trials, in terms of detecting change more robustly, less variably, or earlier in the disease course, with consequent impact on the search for disease-modifying treatments.