Multi-Modal Imaging

多模态成像

• 批准号: 8531790
• 负责人: Michael A Weiner
• 金额: $ 12.09万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8531790
• 关键词: Alzheimer' s Disease; Amyloid; Amyloid beta-Protein; Anatomy; Autopsy; Behavioral; Boxing; Brain; Brain imaging; Brain region; Cerebrovascular Circulation; Classification; Clinical; Clinical Treatment; Clinical Trials; Cognition; Cognitive; Data; Detection; Development; Diagnosis; Diagnosis Clinical Trials; Diagnostic; Differential Diagnosis; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Early Diagnosis; Emotional; Emotions; Fiber; Frontotemporal Dementia; Functional Magnetic Resonance Imaging; Functional disorder; Genes; Goals; Gold; Grant; Image; Impairment; Individual; Instruction; Language; Lead; Linguistics; Magnetic Resonance Imaging; Measures; Methodology; Methods; Modality; Monitor; Motor; Multivariate Analysis; Nature; Nerve Degeneration; Neurodegenerative Disorders; Outcome; Output; Pathology; Patients; Pattern; Perception; Positron-Emission Tomography; Predictive Value; Primary Progressive Aphasia; Process; Progressive Supranuclear Palsy; Sample Size; Sensitivity and Specificity; Speech; Spin Labels; Staging; Statistical Methods; Structure; Sum; Syndrome; System; Testing; Variant; Work; amyloid pathology; base; brain behavior; cognitive function; executive function; fluorodeoxyglucose positron emission tomography; imaging modality; improved; innovation; interest; multimodality; neuroimaging; neuropsychological; spatial relationship; statistics; treatment trial; white matter

The overall long-term goal of this project is to assess the value of multimodality imaging for (1) differential diagnosis and early detection of FTD subtypes and related disorders, (2) for understanding the changes in the brain responsible for cognitive, linguistic, and emotional dysfunction in FTD and AD, and (3) to predict longitudinal changes in cognition and function in FTD. These goals will be accomplished by utilizing an integrated processing framework for multimodality neuroimages as well as modern multivariate statistical methods that allow simultaneous testing of variations across image modalities and across brain regions for maximally exploiting information from multimodality brain images. Specific Aims are to: (1) Use multimodality neuroimaging to distinguish those subjects with non-AD clinical syndromes caused by Alzheimer's amyloid pathology from those without amyloid pathology. We hypothesize that although the changes of sMRI will be the dominant imaging features for differential diagnosis at later stages of disease, adding ASL, DTI, ICN fMRI, FDG PET will improve single subject prediction of subjects with FTD clinical syndromes associated with amyloid pathology determined by PIB imaging versus those associated with other pathologies. (2) Explore the brain-behaviour association of multimodality neuroimaging for the following cognitive and behavioral profiles: (a) motor speech impairment, (b) executive control, and (c) emotion. (3) Explore the predictive value of baseline brain-behavior associations for longitudinal decline and identify a combination of multimodality brain-behavior associations that best predicts the decline. The predictors will be various brain regions in the different imaging modalities and the outcomes will be the rate of change of cognitive function measured by CDR sum of boxes. The innovative nature of this project is the use of multimodality-multivariate analysis methods to investigate FTD and related neurodegenerative diseases. The long term significance of this project is that as these methods are developed, they will be used to explore improved methods for early detection, diagnosis, and monitoring of change, and ultimately will lead to improved patient assessment and development of improved treatments.

该项目的总体长期目标是评估（1）多模式成像对（1）FTD亚型及相关疾病的差异诊断和早期检测，（2）用于理解负责FTD和AD的认知，语言和情感功能障碍的大脑的变化，以及预测纵向纵向变化的认知和（3）。这些目标将通过利用集成的处理框架来实现多模式神经图像以及现代的多元统计方法，该方法允许同时测试跨图像模态和整个大脑区域的变化，以最大程度地利用来自多模态性脑图像的信息。具体目的是：（1）使用多模式神经影像学来区分阿尔茨海默氏症淀粉样蛋白病理学引起的非AD临床综合征的受试者与没有淀粉样病理学的受试者。我们假设，尽管SMRI的变化将是疾病后期差异诊断的主要成像特征，但添加ASL，DTI，ICN FMRI，FDG PET将改善对FTD临床综合症受试者的单一受试者预测，该受试者与PIB Imaging与PIB Imaging相关的与其他病理学相关的疗程相关。 （2）探索以下认知和行为概况的多模式神经影像的大脑行为关联：（a）运动语音障碍，（b）执行控制，以及（c）情绪。 （3）探讨基线脑行为相关性的预测价值，以纵向下降，并确定多模式性脑行为关联的组合，可以最好地预测这种下降。预测因素将是各种大脑 不同成像方式和结果中的区域将是通过盒子的CDR总和来测量认知功能的变化率。该项目的创新性质是使用多模式 - 多型分析方法来研究FTD和相关的神经退行性疾病。该项目的长期意义在于，随着这些方法的开发，它们将用于探索改进的早期检测，诊断和监测变化的方法，并最终会改善患者的评估和改进的治疗方法。