Quantifying Brain Abnormality by Multimodality Neuroimage Analysis

通过多模态神经图像分析量化大脑异常

• 批准号: 9246415
• 负责人: Dinggang Shen
• 金额: $ 35.09万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9246415
• 关键词: Address; Adopted; Algorithms; Alzheimer disease detection; Alzheimer' s Disease; Amyloid; Appearance; Architecture; Atlases; Brain; Categories; Clinical; Communities; Complex; Computer software; Computers; Country; Coupled; Data; Diagnosis; Diagnostic; Discipline of Nuclear Medicine; Disease; Early Diagnosis; Elderly; Functional disorder; Future; Goals; Heterogeneity; Image; Individual; Joints; Learning; Machine Learning; Maps; Methods; Modality; Modeling; Molecular; Nature; Nerve Degeneration; Neurodegenerative Disorders; Neurologist; Neuropsychological Tests; Pathologic; Pathology; Pattern; Preventive Intervention; Research; Sampling; Scientist; Selection Bias; Software Tools; Staging; Structure; Symptoms; Techniques; Technology; Therapeutic; Time; Training; Work; brain abnormalities; clinical predictors; comparison group; design; diagnostic accuracy; disease diagnosis; forest; imaging modality; improved; in vivo; innovation; learning strategy; multimodality; multitask; neuroimaging; novel; outcome forecast; pre-clinical; public health relevance; success; symptomatology; vector

 DESCRIPTION (provided by applicant): Alzheimer's disease (AD) develops for an unknown and variable amount of time before its symptoms fully manifest. But, when the symptoms become clinically observable, a significant neurodegeneration has already taken place. Thus, there is a largely unmet need for technologies that can aid the effective early diagnosis and prognosis of AD in an in vivo and more objective manner. The goal of this renewal project is to develop a set of advanced machine-learning techniques for precise in vivo quantification of pathological changes of brains with multimodality neuroimaging for both early diagnosis and prognosis of AD. AD is a highly heterogeneous neurodegenerative disorder with complex pathophysiology, thus very challenging to pinpoint its subtle pathologies without any aid from advanced computational technologies. To this end, we propose the following four specific aims to identify those subtle disease-induced alterations, derive robust diagnostic conclusions, and predict future disease trajectories. Specifically, in Aim 1, we will develop a multi-view feature representation technique to robustly extract complementary information from neuroimaging data with multiple representative atlases, and then identify a small subset of most discriminative features for AD diagnosis. This novel multi-atlas technique will deviate from the conventional single-atlas approaches in feature representation, which are often susceptible to inter-subject structural variability, registration error, and atlas selection bias. In Aim 2, we will further devlop two novel multi-view feature mapping techniques for collaborative fusion of multimodality information by explicitly considering the distribution heterogeneity of different categories of features extracted from different modalities. This will significantly avoid the unnecessary complexity of feature distributions after our collaborative fusion, thus increasing the efficacy of subsequent diagnostic classifiers. Specifically, a deep learning technique (with deep multi-layered architecture) will be adopted to hierarchically mine multimodality information that resides nonlinearly both within each modality and between different modalities. In Aim 3, we will develop a novel multi-task sparse learning technique for joint prediction of diagnostic status and clinical scores (e.g., ADAS-Cog and MMSE) by considering the inherent correlations between features and between training samples. This will also allow us to exploit the latent structure underlying the data for robust estimation of these highly variable clinical scores. Finally, in Aim 4, we will jointly predict clinical scores of each given subject in multiple future time points, by developing coupled random forests that can take advantage of all training subjects with complete or even incomplete multimodality data and further enforce temporal consistency of those estimated clinical scores. All the above-proposed techniques will be evaluated by a large image set of elderly subjects in ADNI. We expect that the successful completion of this renewal project will result in a comprehensive and effective diagnosis/prognosis framework for improving early detection of AD. The respective software tools will be released freely to the research community, as we have done with our HAMMER software, which has been downloaded by >5200 users from >20 countries.

 描述（由申请人提供）：阿尔茨海默氏病（AD）在其症状完全显现之前的发展时间未知且可变。但是，当症状变得临床可观察时，已经发生了显著的神经变性。因此，对于能够以体内和更客观的方式帮助AD的有效早期诊断和预后的技术存在很大程度上未满足的需求。该更新项目的目标是开发一套先进的机器学习技术，用于精确的体内定量大脑病理变化，多模态神经成像，用于AD的早期诊断和预后。AD是一种高度异质性的神经退行性疾病，具有复杂的病理生理学，因此在没有先进计算技术的任何帮助的情况下精确定位其细微病理非常具有挑战性。为此，我们提出了以下四个具体目标，以确定这些微妙的疾病引起的变化，得出可靠的诊断结论，并预测未来的疾病轨迹。具体而言，在目标1中，我们将开发一种多视图特征表示技术，以从具有多个代表性图谱的神经影像数据中稳健地提取互补信息，然后识别用于AD诊断的最具鉴别力特征的一个小子集。这种新的多图谱技术将偏离传统的单图谱方法的特征表示，这往往是容易受到受试者间的结构变异性，配准误差和图谱选择偏差。在目标2中，我们将进一步开发两种新的多视图特征映射技术，通过明确考虑从不同模态中提取的不同类别特征的分布异质性，用于多模态信息的协同融合。这将显著避免在我们的协作融合之后特征分布的不必要的复杂性，从而提高 后续诊断分类器。具体而言，将采用深度学习技术（具有深度多层架构）来分层挖掘驻留在 在每个模态内和不同模态之间都是非线性的。在目标3中，我们将开发一种新的多任务稀疏学习技术，用于联合预测诊断状态和临床 分数（例如，ADAS-Cog和MMSE）通过考虑特征之间和训练样本之间的固有相关性。这也将使我们能够利用数据的潜在结构，对这些高度可变的临床评分进行稳健估计。最后，在目标4中， 联合预测每个给定受试者在多个未来时间点的临床评分， 耦合的随机森林可以利用具有完整或甚至不完整的多模态数据的所有训练对象，并进一步加强那些估计的临床分数的时间一致性。 所有上述提出的技术将通过ADNI中的老年受试者的大型图像集进行评估。我们预期，这项重建计划的成功完成，将为改善阿尔茨海默病的早期发现，提供一个全面和有效的诊断/预后框架。相关软件工具将免费向研究界发布，正如我们对HAMMER软件所做的那样，该软件已被来自20多个国家的5200多名用户下载。