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: 8373964
• 负责人: Dinggang Shen
• 金额: $ 41.34万
• 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/8373964
• 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; Screening procedure; 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 neurocognitive impairment; multimodality; multitask; nervous system disorder; neuroimaging; neuropsychological; novel; pre-clinical; public health relevance; 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. PUBLIC HEALTH RELEVANCE: Description of Project This project aims to develop an individual-based diagnosis method for early detection and progression monitoring of brain disease by using multimodality imaging and non-imaging data. This is significantly different from the conventional methods that focus on group comparison of brain disease using a single imaging modality or simple combination of multimodality data. These group comparison methods are not able to diagnose and predict brain disease for an individual patient, although they may help identify the effect of disease on brain structures and functions at a group level.

描述（由申请人提供）：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，每年造成约 1,720 亿美元的损失。目前，AD 诊断主要基于临床和心理测量评估。然而，只有当尸检报告有进行性痴呆病史的个体的特定大脑区域存在特征性神经炎β-淀粉样斑块和神经纤维缠结时，诊断才能确定。因此，有一个显着的 对非侵入性客观诊断和病理量化以及疾病进展一般评估的需求未得到满足。该项目的目标是开发一种新颖的神经影像分析框架，该框架将利用来自不同成像方式的补充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。实现这一目标需要神经图像分析技术的重大创新，以检测复杂而微妙的大脑改变模式。因此，该项目的具体目标是（目标 1：疾病诊断）开发一种多模态多变量诊断技术，以准确识别患有以下疾病的个体： （目标 2：进展监测）设计一种新颖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；（目标 3：评估）使用老年受试者的大型数据库来评估所开发的方法，以评估其在量化 AD/MCI 患者大脑改变模式方面的诊断能力、对有 AD 风险的 MCI 患者的预测能力以及量化的能力 随着疾病的进展出现异常。我们期望，在该项目成功完成后，由此产生的全面、综合、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）早期检测的成功率。公共卫生相关性声明：在出现临床症状之前，AD 会经历一个持续数年至数十年的前驱期，其疾病病理或倾向在临床上无法检测或不确定。因此，如果疾病缓解治疗想要有效，识别没有 AD 风险的个体至关重要。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。 公共健康相关性：项目描述该项目旨在开发一种基于个体的诊断方法，通过使用多模态成像和非成像数据来早期检测和监测脑部疾病的进展。这与传统方法显着不同，传统方法侧重于使用单一成像模式或多模态数据的简单组合对脑部疾病进行分组比较。这些群体比较方法无法诊断和预测个体患者的脑部疾病，尽管它们可能有助于在群体水平上确定疾病对脑部结构和功能的影响。