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: 8688869
• 负责人: Dinggang Shen
• 金额: $ 39.8万
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8688869
• 关键词: Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Amyloid; Appearance; Autistic Disorder; Autopsy; Brain; Brain Diseases; 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; Image; 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; Structure; 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. 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.

描述（由适用提供）：仅在美国，阿尔茨海默氏病（AD）就会影响530万人，使其成为第七大死亡原因，每年造成约1720亿美元。目前，AD诊断主要基于临床和心理测量评估。但是，诊断只能确定尸检在特定的脑部特定脑区域中存在特定的淀粉样蛋白斑块和神经成6的特征性神经性斑块和神经成6的存在。那是一个重要的 未满足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。该项目的目的是开发一种新型的神经影像学分析框架，该框架将利用不同成像方式的完整信息来有效地量化疾病诱导的病理，以促进早期检测，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中进行大量创新，以检测复杂而微妙的大脑改变模式。彼此之间，该项目的具体目的是（目标1：疾病诊断）开发多模式的多元诊断技术，以准确鉴定为个人 在AD的风险中（目标2：进度监测）设计一种新型的多任务内核学习框架，用于预测和量化各种疾病阶段的大脑异常，并（AIM 3：评估）使用旧受试者的大型数据库评估开发的方法，以量化其预测患者的诊断量，以量化其诊断性，并在其预测的患者中量化大脑的诊断，并且是MCI的强大功能，这些功能是有能力的，这些功能是有能力的，这些功能是有效的，这些功能是有效的，这些功能是有能力的，这些功能是有能力的，这些功能是有能力的，这些功能是有效的，这些功能是有能力的，这些功能是有能力的，这些功能是有能力的，这些功能是有效的，这些功能是有效的。随着疾病的进展，异常。我们希望，在成功完成该项目后，将进行由此产生的全面，综合和有效的诊断/监测框架，以改善MCI/AD的早期检测以及其他神经系统疾病的成功，包括精神分裂症，自闭症，自闭症和多发性硬化症。公共卫生相关性陈述：在出现临床症状学之前，AD经历了前驱阶段，持续了数年到几十年，疾病病理学或易感性在临床上不确定或不确定。如果要有效，确定t风险t风险的人至关重要。因此，该项目中开发的神经图像分析技术与公共卫生非常相关，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。