4D Software Tools for Longitudinal Prediction of Brain Disease

描述（由申请人提供）：神经影像学允许对大脑结构和功能及其随时间的变化进行安全、无创的测量。这导致了许多纵向研究，以发现成像生物标志物，以更好地预测大脑疾病。然而，相关的纵向变化往往在很短的随访时间内很小，因此很难用常规方法检测到，因为测量误差可能大于实际变化。此外，随着纵向随访数据的显著增加，从大数据中捕获一小组有效的成像生物标志物以进行准确的疾病预测变得具有挑战性。当纵向研究中存在数据缺失时，这一问题变得更加关键，这在临床应用中是不可避免的。这个更新项目的目标是创建一套创新的4D软件工具，致力于通过纵向数据更有效地早期诊断和预测大脑疾病。这些工具将允许阐明微妙的异常变化，否则现有工具将无法检测到这些变化。具体而言，在Aim 1中，我们将创建一种新的多图谱引导的4D脑标记方法，用于在同一受试者的纵向图像（4D图像）中一致和准确地标记感兴趣区域（roi）。每个受试者的所有纵向图像将首先通过一种新颖的分组四维配准算法进行对齐，该算法可以更准确地估计纵向变形。然后，这些对齐的纵向图像可以进一步与多个地图集进行配准，并通过一个局部连接所有（受试者和地图集）图像的图来引导，从而对同一受试者的纵向图像获得更准确/一致的配准和ROI标记。在Aim 2中，我们将进一步创建一个多模态、稀疏的纵向预测模型，以有效地整合序列成像和非成像生物标志物，用于早期诊断和预测大脑状态。此外，为了进一步提取有效的生物标志物，一种新的机器学习技术，称为深度学习，将被用于学习高级特征，以帮助预测一种新的时间约束组稀疏学习方法，该方法能够预测未来时间点的临床评分。最后，在Aim 3中，我们将创建新的方法来处理临床应用中不可避免的纵向研究中的数据缺失。特别地，我们将首先开发几种数据补全方法（包括矩阵）

DESCRIPTION (provided by applicant): Neuroimaging allows safe, non-invasive measurement of brain structures and functions and their changes over time. This has led to many longitudinal studies to discover imaging biomarkers for better prediction of brain disorders. However, the associated longitudinal changes are often tiny within a short follow-up time, and are thus difficult to detect by conventional methods since the measurement errors could be larger than the actual changes. Also, with the significant increase of data with longitudinal follow-ups, it becomes challenging to capture a small set of effective imaging biomarkers from large data for accurate disease prediction. This issue becomes even more critical when there is missing data in the longitudinal study, which is unavoidable in clinical application. The goal of this renewal project is to create a set of innovative 4D software tools that are dedicated to more effective early diagnosis and prediction of brain disorders with longitudinal data. These tools wil allow elucidating subtle abnormal changes that would be otherwise left undetected with existing tools. Specifically, in Aim 1, we will create a novel multi-atlas guided 4D brain labelling method for consistent and accurate labeling of Regions of Interest (ROIs) across longitudinal images (4D image) of the same subject. All longitudinal images of each subject will be first aligned by a novel groupwise 4D registration algorithm that can more accurately estimate longitudinal deformations. Then, these aligned longitudinal images can be further registered with multiple atlases guided by a graph that locally connects all (subject and atlas) images, thus obtaining more accurate/consistent registration and ROI labeling for the longitudinal images of same subject. In Aim 2, we will further create a multimodal, sparse longitudinal prediction model to effectively integrate serial imaging and non-imaging biomarkers for early diagnosis and prediction of brain status. Also, to further extract effective biomarkers, a new machine learning technique, called deep learning, will be adapted to learn high-level features for helping prediction with a novel temporally-constrained group sparse learning method, which is able to predict clinical scores consistently for future time-points. Finally, in Aim 3, we will create nove methods to deal with missing data in longitudinal study, which is unavoidable in clinical application. In particular, we will first develop several data completion methods (including matrix completion) to complete the missing data. Then, instead of designing a single predictor that may be limited, we will design multiple diverse predictors (by multi-task learning) for ensemble prediction, thus significantly increasing the overall prediction performance. Also, considering that the individual's future images are not available at early time-points, to improve the clinical utility of the proposed methods, we will apply our models to various cases with different numbers of longitudinal images and then further train them jointly to achieve the overall best performance. Note that the performance of all proposed methods will be evaluated in this project for Alzheimer's Disease (AD) study, although they are also applicable to studies of other brain disorders.