Empowering Diffusion MRI Measures by Integrating White and Grey Matter Morphology

通过整合白质和灰质形态来增强扩散 MRI 测量

• 批准号: 8808684
• 负责人: Yalin Wang
• 金额: $ 18.2万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8808684
• 关键词: Accounting; Adopted; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Anatomy; Anisotropy; Architecture; Area; Atrophic; Biological Markers; Bipolar Disorder; Brain; Brain imaging; Brain scan; Characteristics; Clinical; Clinical assessments; Communities; Computer software; Data Set; Dementia; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Early Intervention; Elderly; Genetic; Genetic Research; Geometry; Health Benefit; Image; Image Analysis; Individual; Institution; Magnetic Resonance Imaging; Major Depressive Disorder; Maps; Mathematics; Measures; Mental disorders; Meta-Analysis; Methods; Microscopic; Morphology; Multimodal Imaging; Nerve Degeneration; Parkinson Disease; Pathway Analysis; Pattern; Population; Prevalence; Public Health; Publishing; Qualifying; Research; Research Project Grants; Resolution; Risk Factors; Schizophrenia; Source Code; Structure; Surface; System; Targeted Research; Techniques; Twin Multiple Birth; Variant; Work; age related; base; cerebral atrophy; cohort; comparison group; computer framework; design; disease diagnosis; empowered; experience; geometric methodologies; gray matter; high risk; image processing; imaging biomarker; imaging system; mild cognitive impairment; morphometry; nervous system disorder; neuroimaging; novel; open source; population based; pre-clinical; public health relevance; software development; statistics; tool; white matter; white matter change; working group

DESCRIPTION (provided by applicant): We will develop and apply novel geometric algorithms to brain diffusion MRI images obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian twin datasets. Despite major advances in brain imaging measures on grey matter, there is still a lack of efficient brain multimodal imaging algorithms by integrating brain white matter and grey matter morphology. In the current project, we will develop novel diffusion MRI analysis methods using quasiconformal geometry and volumetric harmonic map. Specifically, we will develop 1) multimodal brain surface parcellation for tractography and network analysis, and 2) brain white matter multivariate tensor-based morphometry to measure white matter morphometry. The proposed system will focus on developing a coherent computational framework to integrate both white and grey matter morphology and compute their complete geometric features to capture subtle brain white matter changes. This may provide an effective way to pinpoint unique brain network structure or subregional areas for diagnostic group difference comparison and identify genetic effects that might account for some of the structural variations in a large population. To investigate the reliability and practicality of our method, we seek to 1) construct structural brain network with tractography on multimodal surface parcellation results, and 2) compute brain white matter multivariate tensor-based morphometry (WM-mTBM) features. The computed connectivity network measures and the WM-mTBM features will be validated by 1) between-diagnostic-group comparisons in the ADNI dataset, and 2) genetic influence analysis in the Australian twin dataset.

描述（由申请人提供）：我们将开发和应用新的几何算法来获得来自阿尔茨海默病神经成像倡议（ADNI）和澳大利亚双胞胎数据集的脑弥散MRI图像。尽管对灰质的脑成像测量取得了重大进展，但仍然缺乏有效的脑白质和灰质形态学相结合的脑多模态成像算法。在当前的项目中，我们将开发新的扩散MRI分析方法，使用准共形几何和体积谐波图。具体来说，我们将开发1)用于神经束造影和网络分析的多模态脑表面包裹，以及2)基于脑白质多元张量的形态测量来测量白质形态。该系统将专注于开发一个连贯的计算框架，以整合白质和灰质形态，并计算其完整的几何特征，以捕捉细微的脑白质变化。这可能提供一种有效的方法来确定独特的大脑网络结构或分区域，以进行诊断组差异比较，并确定可能导致大量人群中某些结构变化的遗传效应。探讨了本系统的可靠性和实用性