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（由适用提供）：阿尔茨海默氏病（AD）在症状完全表现出来的时间未知且可变的时间中发展。但是，当症状在临床上可观察到时，已经发生了重要的神经变性。这在很大程度上对技术的需求在很大程度上是未满足的，可以在体内和更客观的方式中有效地帮助AD的有效诊断和预后。这个更新项目的目的是开发一组先进的机器学习技术，以精确的体内脑部的大脑变化，具有多峰性神经影像学，以期为AD的早期诊断和预后。 AD是一种具有复杂病理生理学的高度异质性神经退行性疾病，因此在没有先进计算技术的任何帮助的情况下非常挑战其微妙的病理。为此，我们提出了以下四个特定目的，以确定那些细微的疾病引起的改变，得出强大的诊断结论并预测未来的疾病轨迹。具体而言，在AIM 1中，我们将开发一种多视图特征表示技术，以与多个代表性地图的神经成像数据中鲁棒提取完整的信息，然后确定AD Diagnostics的最判别特征的一小部分。这种新型的多ATLAS技术将偏离特征表示中常规的单ATLA方法，这些方法通常容易受到主题间结构可变性，注册误差和ATLAS选择偏差的影响。在AIM 2中，我们将通过明确考虑从不同模态提取的不同类别的特征的分布异质性，进一步Devlop Devlop两种新颖的多视图映射技术，以与多模式信息进行协作融合。这将大大避免我们的协作融合后不必要的功能分布的复杂性，从而提高了效率 随后的诊断分类器。具体而言，将采用一种深度学习技术（具有深层的多层架构），用于层次上的多模式信息。 在每种模态内和不同模态之间的非线性。在AIM 3中，我们将开发一种新型的多任务稀疏学习技术，用于联合预测诊断状态和临床 通过考虑特征之间的继承相关性以及训练样本之间的继承相关性，得分（例如ADAS-COG和MMSE）。这也将使我们能够利用数据为基础的潜在结构，以对这些高度可变的临床分数进行稳健的估计。最后，在AIM 4中，我们将 通过开发，共同预测每个给定受试者的临床分数 耦合的随机森林可以利用所有具有完整甚至不完整的多模式数据的训练受试者，并进一步执行这些估计临床分数的暂时一致性。所有上述技术都将通过ADNI中的大型主题组合进行评估。我们预计，该更新项目的成功完成将导致一个全面有效的诊断/预后框架，以改善AD的早期检测。就像我们对Hammer软件所做的那样，相对软件工具将免费发布给研究社区，该软件已由> 20个国家 /地区的5200个用户下载。