Method Development of Agreement Measures and Applications in Mental Health

协议措施的方法开发及其在心理健康中的应用

基本信息

批准号：
9144441
负责人：
Ying Guo
金额：
$ 38.79万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-04-01 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9144441
关键词：
Address Adopted Agreement Behavior Behavioral Behavioral Symptoms Biological Markers Biology Brain Brain imaging Calibration Communities Complex Data Data Reporting Diagnosis Dimensions Disease Evaluation Foundations Goals Health Image Institution Knowledge Measurement Measures Mental Health Mental disorders Methodology Methods Modality Motivation Multicenter Studies National Institute of Mental Health Network-based Noise Outcome Outcome Measure Post-Traumatic Stress Disorders Process Protocols documentation Research Research Project Grants Science Seminal Severities Signal Transduction Site Source Statistical Methods Symptoms Techniques Work accurate diagnosis analytical method base behavioral outcome behavioral study brain behavior image processing improved insight instrument interest method development neuroimaging novel psychologic sound symptom cluster tool treatment strategy user friendly software vector web site

项目摘要

DESCRIPTION: Understanding the underlying mechanisms of the brain and behavior is an essential requirement for improving the diagnosis of and treatments for mental health diseases. There is an intense interest in generating different modulates of neuroimaging and behavioral data to gain new insights into brain functionality and its connections with behavior outcomes. Challenges in studying the relationships, particularly the alignment, among brain- behavior outcomes from different sources include: (1) the outcomes measured from different modalities are diverse and complex, often in different scales (continuous, ordinal) and of different data representations(scalar, vector, matrix); (2) the neuroimaging data is high dimensional and reflects not only the desired signal but also the background noise; (3) there are obstacles to compare neuroimaging data from multi-center studies due to considerable between-center variability in brain images obtained from different scanners and processing protocols. Currently, there are very limited statistical methods available to address these issues. The overall objective of this proposal is to develop a unified statistical framework that fills in the aforementioned gaps. Our proposal of adopting agreement-based methodology provides a novel perspective for investigating the alignment between behavior outcomes and the biology of the brain (neuroimaging). While standard agreement methodology has been limited to the evaluation of outcomes that are made on the same scale, our seminal work on "broad sense agreement (BSA)" (Peng et al. 2011, a featured JASA article) that characterizes the agreement/alignment between a continuous and an ordinal variable lays the foundation for a promising framework proposed in this application. Specifically, we plan to fulfill our research goals by characterizing the alignment among outcomes with different scales and data-representations; incorporating covariates; assessing the strength of alignment between neuroimaging biomarkers and symptom domains; identifying relevant features in high-dimensional neuroimaging data that align with specific symptom clusters; and assessing agreement and calibrating images from multi-center studies. The proposed statistical methods will be applied to an ongoing PTSD study and a national multi-center imaging study, and user-friendly software will be developed and made available to general research communities. Our proposed method developments will directly benefit mental health research, and they are ubiquitous enough to be generally useful contributions to statistical practice.

描述：了解大脑和行为的潜在机制是改善精神健康疾病诊断和治疗的基本要求。人们对生成不同的神经影像和行为数据调制有着浓厚的兴趣，以获得对大脑功能及其与行为结果的联系的新见解。研究不同来源的大脑行为结果之间的关系（特别是一致性）的挑战包括：（1）从不同方式测量的结果是多样且复杂的，通常具有不同的尺度（连续的、有序的）和不同的数据表示（标量）、向量、矩阵）；（2）神经影像数据是高维的，不仅反映了所需信号，还反映了背景噪声； (3) 由于从不同扫描仪和处理协议获得的脑图像存在相当大的中心间差异，因此比较多中心研究的神经影像数据存在障碍。目前，可用于解决这些问题的统计方法非常有限。该提案的总体目标是制定一个统一的统计框架来填补上述空白。我们提出的采用基于协议的方法为研究行为结果与大脑生物学（神经影像学）之间的一致性提供了一个新颖的视角。虽然标准协议方法仅限于对相同规模的结果进行评估，但我们关于“广义协议 (BSA)”（Peng 等人，2011 年，JASA 的一篇特色文章）的开创性工作描述了协议/一致性的特征连续变量和序数变量之间的关系为本申请中提出的有前途的框架奠定了基础。具体来说，我们计划通过描述不同尺度和数据表示结果之间的一致性来实现我们的研究目标；纳入协变量；评估神经影像生物标志物和症状领域之间的一致性强度；识别高维神经影像数据中与特定症状群相符的相关特征；评估一致性并校准多中心研究的图像。拟议的统计方法将应用于正在进行的创伤后应激障碍研究和国家多中心影像研究，并且将开发用户友好的软件并向一般研究界提供。我们提出的方法开发将直接有利于心理健康研究，而且它们无处不在，足以对统计实践做出普遍有用的贡献。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Sequential multiple-assignment randomized trial design of neurobehavioral treatment for patients with metastatic malignant melanoma undergoing high-dose interferon-alpha therapy.

对接受高剂量干扰素-α 治疗的转移性恶性黑色素瘤患者进行神经行为治疗的序贯多任务随机试验设计。

DOI：
发表时间：
2009-10
期刊：
影响因子：
0
作者：
Auyeung, S Freda;Long, Qi;Royster, Erica Bruce;Murthy, Smitha;McNutt, Marcia D;Lawson, David;Miller, Andrew;Manatunga, Amita;Musselman, Dominique L
通讯作者：
Musselman, Dominique L

Use of high-resolution volumetric MR spectroscopic imaging in assessing treatment response of glioblastoma to an HDAC inhibitor.

使用高分辨率体积 MR 光谱成像评估胶质母细胞瘤对 HDAC 抑制剂的治疗反应。