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: 8518211
• 负责人: Dinggang Shen
• 金额: $ 37.61万
• 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 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8518211
• 关键词: Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Amyloid; Appearance; Autistic Disorder; Autopsy; Brain; Brain region; Cause of Death; Characteristics; Classification; Clinical; Cognitive; Complex; Computers; Data; Databases; Dementia; Diagnosis; Diagnostic; Discipline of Nuclear Medicine; Disease; Disease Progression; Early Diagnosis; Effectiveness; Elderly; Evaluation; Feedback; Goals; Individual; Intervention; Joints; Judgment; Label; Learning; Maps; Measures; Methods; Modality; Modeling; Molecular; Monitor; Multiple Sclerosis; Neurofibrillary Tangles; Neuropsychological Tests; Pathology; Patients; Pattern; Performance; Phase; Predisposition; Preventive Intervention; Process; Prophylactic treatment; Psychometrics; Public Health; Recording of previous events; Reporting; Research; Research Personnel; Schizophrenia; Scientist; Senile Plaques; Sensitivity and Specificity; Severity of illness; Staging; Techniques; Testing; Therapeutic; base; clinically relevant; comparison group; cost; design; disease diagnosis; effective therapy; imaging modality; improved; innovation; mild cognitive impairment; multimodality; multitask; nervous system disorder; neuroimaging; neuropsychological; novel; pre-clinical; public health relevance; screening; success

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.

阿尔茨海默病（AD）仅在美国就影响总共530万人，使其成为第七大死亡原因，并且每年花费约1720亿美元。目前，AD诊断主要基于临床和心理测量评估。然而，只有在尸检报告有进行性痴呆病史的个体在特定脑区存在特征性神经炎性淀粉样斑块和神经原纤维缠结时，诊断才能确定。因此，有一个重要的 对病理学的非侵入性客观诊断和量化以及疾病进展的一般评估的未满足的需求。该项目的目标是开发一种新的神经成像分析框架，该框架将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，从而促进早期检测以进行可能的治疗和预防。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）开发一种多模态多变量诊断技术，用于准确识别 有患AD的风险，（目标2：进展监测）设计一种新的多任务内核学习框架，用于预测和量化不同疾病阶段的大脑异常，以及（目标3：评估），以评估开发的方法，使用老年受试者的大型数据库，其在定量AD/MCI患者脑改变模式中的诊断能力，其对处于AD风险中的MCI患者的预测能力，以及它们在疾病进展时量化异常的能力。我们期望，在成功完成该项目后，由此产生的全面、综合和有效的诊断/监测框架将有助于提高MCI/AD以及其他神经系统疾病（包括精神分裂症、自闭症和多发性硬化症）的早期检测成功率。