TR-猫眼课题宝

批准号：

9535307

负责人：

JAMES J. PEKAR

金额：

$16.36万

依托单位：

HUGO W. MOSER RES INST KENNEDY KRIEGER

依托单位国家：

美国

项目类别：

财政年份：

资助国家：

美国

项目状态：

未结题

起止时间：

至

来源：

https://reporter.nih.gov/project-details/9535307

关键词：

Address Alzheimer&apos s Disease Atlases Attention deficit hyperactivity disorder Autistic Disorder Bayesian Analysis Bayesian Method Behavioral Biological Biological Markers Biological Neural Networks Biometry Brain Brain imaging Clinical Clinical Research Collaborations Computer software Data Data Set Databases Development Diagnosis Disease Doctor of Philosophy Excision Exhibits Functional Magnetic Resonance Imaging Goals Heart Rate Human Huntington Disease Image Individual Differences Magnetic Resonance Imaging Measures Methods Modeling Morphologic artifacts Multiple Sclerosis Noise Outcome Measure Physiological Population Primary Progressive Aphasia Radiology Specialty Reproducibility Research Research Personnel Resources Respiration Rest Sampling Sampling Studies Schizophrenia Sensitivity and Specificity Signal Transduction Slice Specificity Statistical Data Interpretation Statistical Methods Structure Techniques Testing Therapeutic Therapeutic Intervention Time Work autism spectrum disorder base blood oxygen level dependent design improved mid-career faculty non-invasive imaging novel outcome forecast physiologic model professor task analysis temporal measurement two-dimensional

中文摘要

翻译

研发3. 功能磁共振成像的先进统计方法。主要调查人员： James J.Pekar博士，放射学副教授布莱恩·S·卡福博士，生物统计学教授摘要将大脑描述为分布式神经网络的进化集成的生物学描述是功能连通性的影像测量在临床研究中的意义。我们的协作项目使用血氧水平依赖的功能磁共振成像(BOLD FMRI)来评估大脑的变化自闭症、ADHD、阿尔茨海默病、多发性硬化症、精神分裂症、原发进展性疾病的网络失语症和亨廷顿病，寻求开发基于非侵入性成像的生物标记物，以便揭示疾病机制，改善诊断和预后，并评估治疗措施。他们的研究受到fMRI大胆采集的敏感性和特异性的限制。这件事的首要目标是研发是与我们的合作者合作，增强他们功能连接的敏感性和特异性通过开发新的经验贝叶斯分析方法来衡量这些措施，这些方法利用了两个正在进行的显著提高fMRI数据的获取和可用性的转换，即同步多层(SMS)磁共振成像，以及大型公共数据集的可用性。因此，我们有制定了三个具体目标：1.开发时间不变的自回归建模方法，以及针对短信功能磁共振数据对其进行优化。2.开发时不变的滋扰回归方法，以及针对短信功能磁共振数据对其进行优化。3.设计、实施和评估经验贝叶斯方法将来自大型公共数据库的信息与从单一受试者/小样本获得的数据结合起来学习。

英文摘要

TR&D 3. Advanced Statistical Methods for Functional MRI. Principle Investigators: James J. Pekar, PhD., Associate Professor of Radiology Brian S. Caffo, Ph.D., Professor of Biostatistics SUMMARY The biological description of the brain as an evolved ensemble of distributed neural networks underlies the significance of applying imaging measures of functional connectivity to clinical research. Our collaborative projects use blood oxygenation level dependent functional MRI (BOLD fMRI) to assess changes in brain networks in autism, ADHD, Alzheimer's disease, multiple sclerosis, schizophrenia, primary progressive aphasia, and Huntington's disease, seeking to develop noninvasive imaging-based biomarkers in order to reveal disease mechanisms, improve diagnosis and prognosis, and assess therapeutic interventions. Their studies are limited by the sensitivity and specificity of BOLD fMRI acquisitions. The overarching goal of this TR&D is to work with our collaborators to enhance the sensitivity and specificity of their functional connectivity measures by developing novel empirical Bayesian analysis approaches that exploit two ongoing transformations that are dramatically improving the acquisition and availability of fMRI data, namely simultaneous multi-slice (SMS) MRI, and the availability of large public datasets. Accordingly, we have developed three specific aims: 1. To develop time-invariant approaches to autoregressive modeling, and optimize them for SMS fMRI data. 2. To develop time-invariant approaches to nuisance regression, and optimize them for SMS fMRI data. 3. To design, implement, and assess empirical Bayesian methods for combining information from large public databases with data obtained from single subject/small sample studies.

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