Using connectomics to characterize risk for Alzheimer's Disease

项目描述 阿尔茨海默病（AD）的患病率预计在未来30年内将显著增加。而 研究工作继续集中在AD的原因和开发有效的医学治疗， 也迫切需要在疾病诊断之前确定AD的风险。可能会有微妙的 大脑和认知功能的变化，在主要症状出现之前就可以检测到。的一种方法 对这些脆弱性的描述是使用功能和结构神经成像来识别风险状况 对于AD。本研究提出并测试了一个模型的AD病理学使用神经影像网络分析 和机器学习方法，以提供对信息处理中广泛变化的洞察力， AD大脑。该模型假设网络信息处理的某些方面反映了 与AD病理学相关的神经变性和认知衰退（例如，枢纽连接和全球 连接性），而其他网络属性反映了补偿受损信息的尝试 处理（例如，信息传播）。此外，该提案还将模型的功效与 关于区分诊断类别（例如，AD和临床正常的机器学习分类 受试者）与隔离AD认知衰退的潜在维度（例如，机器学习预测 来自网络特征的记忆和语言得分）。最后，本研究将确定AD的特征是否 病理学存在于受试者的风险样本中;即，被诊断患有轻度遗忘症的个体 认知障碍（aMCI）。这将通过将AD网络模型转移到aMCI受试者来检查 以及测试该模型是否可以将aMCI与临床正常匹配对照区分开， 模型可以预测认知测试的分数。分析方法将使用静息状态fMRI数据作为 网络完整性的主要分析，但扩散成像和任务功能磁共振成像数据也将在一个 探索性目标一般方法将招募患有AD、aMCI和临床正常匹配的个体。 每个诊断组的对照。直接比较的组将匹配淀粉样蛋白状态， 如Florbetapir PET成像所示。该项目的新贡献包括（a）测试网络 AD病理学模型，其统一了网络功能的各种测量，（B）比较 关于描绘诊断类别的建模与捕获潜在的认知维度 AD，以及（c）将AD模型转移到风险群体以评估疾病脆弱性。后一种创新 可以在未来的研究中应用于任何一组被定义为有风险的受试者，例如那些遗传 脆弱性或积极的家族史。本研究还为随后的纵向研究奠定了基础。 后续研究，以验证使用网络建模确定的风险个体是否真的转化为AD。 最终，这种类型的网络建模可以被用作相对较便宜且侵入性较小的方法。 生物标志物比PET成像更重要，这可能导致更早的治疗和干预。

PROJECT DESCRIPTION The prevalence of Alzheimer’s Disease (AD) is expected to increase significantly in the next 30 years. While research efforts continue to focus on the causes of AD and to develop effective medical treatments, there is also a pressing need to characterize risk for AD before the disease is diagnosed. There may be subtle changes in brain and cognitive function that are detectable before major symptoms emerge. One approach for characterizing these vulnerabilities is the use of functional and structural neuroimaging to identify risk profiles for AD. The present study proposes and tests a model of AD pathology using neuroimaging network analysis and machine learning approaches to provide insight on widespread changes in information processing in the AD brain. The proposed model hypothesizes that some aspects of network information processing reflect neurodegeneration and cognitive decline associated with AD pathology (e.g., hub connectivity and global connectivity) whereas other network properties reflect attempts to compensate for compromised information processing (e.g., diffusion of information). In addition, this proposal compares the efficacy of models with respect to discriminating diagnostic categories (e.g., machine learning classification of AD and clinically normal subjects) versus isolating underlying dimensions of AD cognitive decline (e.g., machine learning prediction of memory and language scores from network features). Finally, this study will determine whether features of AD pathology are present in an at-risk sample of subjects; namely, individuals diagnosed with amnestic mild cognitive impairment (aMCI). This will be examined by transferring the AD network models to aMCI subjects and testing whether the model can discriminate aMCI from clinically normal matched controls and whether the model can predict scores on cognitive tests. The analytic approach will using resting state fMRI data as a primary assay of network integrity, but diffusion imaging and task fMRI data will also be examined in an exploratory aim. The general approach will recruit individuals with AD, aMCI and clinically normal matched controls for each diagnostic group. The groups that are compared directly will be matched for amyloid status, as indicated by florbetapir PET imaging. The novel contributions of this project include (a) testing a network model of AD pathology that unifies various measures of network functioning, (b) comparing efficacy of modeling with respect to delineating diagnostic categories versus capturing underlying cognitive dimensions of AD, and (c) transferring the AD model to an at-risk group to assess disease vulnerability. This latter innovation can be applied in future studies to any group of subjects that is defined at risk, such as those with genetic vulnerability or positive family history. The present study also sets the stage for a subsequent longitudinal follow-up study to validate whether individuals identified at risk using network modeling actually convert to AD. Ultimately, network modeling of this sort may be used as a relatively less expensive and less invasive biomarker than PET imaging, which could lead to earlier treatments and interventions.