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）：阿尔茨海默病（AD）在其症状完全显现之前会持续一段未知且可变的时间。但是，当临床上可以观察到症状时，就已经发生了严重的神经退行性变。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的需求在很大程度上尚未得到满足。该更新项目的目标是开发一套先进的机器学习技术，通过多模态神经影像对大脑病理变化进行精确的体内量化，以实现 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帮助下查明其微妙的病理学非常具有挑战性。为此，我们提出以下四个具体目标，以识别疾病引起的细微变化，得出可靠的诊断结论，并预测未来的疾病轨迹。具体来说，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集的神经影像数据中稳健地提取互补信息，然后识别用于 AD 诊断的最具辨别力的一小部分特征。这种新颖的多图集技术将偏离特征表示方面的传统单图集方法，后者通常容易受到主体间结构变异、配准误差和图集选择偏差的影响。在目标 2 中，我们将通过明确考虑从不同模态提取的不同类别特征的分布异质性，进一步开发两种新颖的多视图特征映射技术，用于多模态信息的协作融合。这将显着避免我们的协作融合后特征分布不必要的复杂性，从而提高了效率 随后的诊断分类器。具体来说，将采用深度学习技术（具有深度多层架构）来分层挖掘驻留的多模态信息 在每种模态内以及不同模态之间都是非线性的。在目标 3 中，我们将开发一种新颖的多任务稀疏学习技术，用于联合预测诊断状态和临床 通过考虑特征之间以及训练样本之间的内在相关性来计算分数（例如，ADAS-Cog 和 MMSE）。这也将使我们能够利用数据背后的潜在结构来稳健地估计这些高度可变的临床评分。最后，在目标 4 中，我们将 通过开发，共同预测每个给定受试者在多个未来时间点的临床分数 耦合随机森林，可以利用具有完整甚至不完整多模态数据的所有训练对象，并进一步增强这些估计临床评分的时间一致性。 所有上述技术将通过 ADNI 中老年受试者的大型图像集进行评估。我们期望这一更新项目的成功完成将产生一个全面有效的诊断/预后框架，以改善 AD 的早期检测。相应的软件工具将免费向研究界发布，就像我们对 HAMMER 软件所做的那样，该软件已被来自超过 20 个国家的超过 5200 名用户下载。