Quantifying Brain Abnormality by Multimodality Neuroimage Analysis,

描述(申请人提供)：阿尔茨海默病(AD)仅在美国就影响了530万人，使其成为第七大死因，每年也造成约1720亿美元的损失。目前，阿尔茨海默病的诊断主要基于临床和心理测量评估。然而，只有当尸检报告在有进行性痴呆史的个体的特定脑区存在特征性神经炎淀粉样斑块和神经纤颤缠结时，才能确定诊断。因此，有一个显著的 对非侵入性客观诊断和病理量化以及对疾病进展的一般评估的需求尚未得到满足。该项目的目标是开发一种新的神经成像分析框架，利用来自不同成像模式的互补信息来有效量化疾病引起的病理，从而促进早期发现可能的治疗和预防措施。实现这一目标需要在神经图像分析技术方面进行重大创新，以检测复杂而微妙的大脑变化模式。因此，本项目的具体目标是(目标1：疾病诊断)开发一种多模式多变量诊断技术，以准确识别符合以下条件的个人 为预测和量化不同疾病阶段的脑异常，(目标2：进展监测)设计新的多任务核心学习框架，并(目标3：评估)使用老年受试者的大型数据库来评估开发的方法，评估其在量化AD/MCI患者的脑变化模式方面的诊断能力，对面临AD风险的MCI患者的预测能力，以及它们量化随着疾病进展的异常的能力。我们预期，在这个项目成功完成后，由此产生的全面、集成和有效的诊断/监测框架将有助于提高对MCI/AD以及其他神经疾病(包括精神分裂症、自闭症和多发性硬化症)的早期发现的成功率。公共卫生相关声明：在临床症状出现之前，阿尔茨海默病经历了从几年到几十年的前驱阶段，其疾病病理或易感性在临床上无法检测或不确定。因此，如果疾病修正治疗要有效，确定谁不是阿尔茨海默病的风险是至关重要的。因此，在这个项目中开发的神经图像分析技术与公共卫生密切相关，因为它们将有助于提高患者识别和疾病监测的准确性 有效的治疗。

DESCRIPTION (provided by applicant): Alzheimer's disease (AD) affects a total of 5.3 million individuals in the U.S. alone, making it the 7th leading cause of death and also costing about 172 billion dollars annually. Currently, AD diagnosis is predominantly based on clinical and psychometric assessment. However, diagnosis can only be certain if an autopsy reports the presence of characteristic neuritic ¿-amyloid plaques and neurofibrilatory tangles in specific brain regions in an individual with a history of progressive dementia. Thus, there is a significant unmet need for non-invasive objective diagnosis and quantification of pathologies, as well as general assessment of disease progression. The goal of this project is to develop a novel neuroimaging analysis framework that will harness the complementary information from different imaging modalities for effective quantification of disease -induced pathologies, so as to promote early detection for possible treatment and prophylaxis. Achieving this goal requires significant innovation in neuroimage analysis techniques to detect sophisticated yet subtle brain alteration patterns. Accordingly, the specific aims of this project are (Aim 1: Disease Diagnosis) to develop a multimodality multivariate diagnosis technique for accurate identification of individuals who are at risk for AD, (Aim 2: Progress Monitoring) to design a novel multi-task kernel learning framework for prediction and quantification of brain abnormality at various disease stages, and (Aim 3: Evaluation) to assess the developed methods using a large database of elderly subjects, for their diagnostic power in quantifying brain alteration patterns in AD/MCI patients, their predictive power of MCI patients who are at risk for AD, and also their capability in quantifying abnormalities as the disease progresses. We expect, upon successful completion of this project, that the resulting comprehensive, integrated, and effective diagnosis/monitoring framework will be conducive to improving the success of early detection of MCI/AD, as well as other neurological disorders including schizophrenia, autism, and multiple sclerosis. Public Health Relevance Statement: Prior to the appearance of clinical symptomatology, AD undergoes a prodromal phase, lasting from years to decades, with disease pathology or predisposition that is clinically undetectable or uncertain. Thus, identifying individuals who are t risk for AD is critical if disease-modifying treatments are to be effective. For this reason, the neuroimage analysis techniques developed in this project are significantly relevant to public health in that they will help improve accuracy in patient identification and disease monitoring for effective treatment.