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

在发达国家，痴呆症是一个重大且日益严重的公共卫生挑战和研究重点。根据英国阿尔茨海默氏症研究公司的数据，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成本估计为230亿英镑。目前只有对症治疗阿尔茨海默氏症(AD)；没有“修饰疾病”的药物。目前的许多研究工作都与寻找这些亟需的疾病修正疗法有关。改进的疾病进展评估方法将增加显示干预减缓衰退进程的机会。脑部扫描(磁共振成像；MRI)可测量的结构变化已被证明可提前并预测症状出现，并与AD的临床下降相关，并可能有助于评估潜在的疾病修改疗法。在专家看来在视觉上正常的扫描可能有微妙的病理，可以通过计算分析来检测。越来越多的证据表明，脑功能成像(FMRI)可以更早地揭示疾病过程中的变化，并且这些变化与记忆表现等临床指标有关。我们迫切需要对结构和功能变化进行更精确的描述，并更好地了解结构和功能之间的相互作用。我将在我的研究员职位上努力解决这些具有挑战性的问题，方法是在惠康信托神经成像中心开发新方法，并与痴呆症研究中心合作将这些方法应用于大型患者数据集。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。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(随着时间的推移进行多次脑部扫描)，并将其应用于AD的诊断和跟踪问题。这将使有关萎缩及其加速的区域定位的假设得到更大的验证(即，我们应该能够使用新模型发现在没有新模型的情况下无法检测到的变化)。我的第二个目标是研究方法，使用在受试者“静止”时获得的功能磁共振成像来模拟不同大脑区域之间的连接。这些数据及其揭示的大脑网络正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，大脑区域与另一个区域的交流方式也可能会发生变化。通过一种潜在的药物治疗，这些变化的减少可能表明该药物在记忆力丧失等临床症状之前就取得了成功。从功能磁共振成像中获得的这些生物标记物在痴呆症中的应用目前是一个研究不足的课题，在发现早期变化和为治疗试验提供新的结果指标方面具有巨大的潜力。最后，我的目标是结合新的方法来研究结构和功能的相互关系以及它们在疾病中的变化。这将使我能够评估新的结构、功能和组合的多模式措施在跟踪疾病进展方面的力量。这极有可能为临床试验提供更好的结果衡量标准，在疾病过程中更有力、更少变化或更早地检测变化，从而对寻找疾病修改治疗方法产生影响。

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.