Method Development of Agreement Measures and Applications in Mental Health

描述：了解大脑和行为的潜在机制是提高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成像和行为数据的调节有着浓厚的兴趣，以获得对大脑功能及其与行为结果的联系的新见解。研究不同来源的脑行为结果之间的关系，特别是一致性，面临的挑战包括：(1)不同模式测量的结果是多样和复杂的，通常是不同的尺度（连续的、有序的）和不同的数据表示（标量、向量、矩阵）；(2)神经成像数据高维，既能反映期望信号又能反映背景噪声；(3)由于不同扫描仪和处理方案获得的脑图像在中心之间存在相当大的差异，因此对多中心研究的神经成像数据进行比较存在障碍。目前，可用于解决这些问题的统计方法非常有限。这项建议的总目标是制定一个统一的统计框架，以填补上述空白。我们建议采用基于协议的方法，为研究行为结果与大脑生物学（神经成像）之间的一致性提供了一个新的视角。虽然标准一致性方法仅限于对同一尺度上的结果进行评估，但我们在“广义一致性（BSA）”方面的开创性工作（Peng et al. 2011，一篇JASA专题文章）描述了连续变量和有序变量之间的一致性/一致性，为本应用中提出的有前景的框架奠定了基础。具体而言，我们计划通过描述不同尺度和数据表示的结果之间的一致性来实现我们的研究目标；将协变量;评估神经成像生物标志物与症状域之间的一致性；识别与特定症状群一致的高维神经影像学数据中的相关特征；评估一致性和校准来自多中心研究的图像。提出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成像研究，用户友好的软件将被开发并提供给一般研究团体。我们提出的方法发展将直接有利于心理健康研究，它们无处不在，足以对统计实践做出普遍有用的贡献。

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.