Multimodal Integrative Dimension Reduction and Statistical Modeling with Applications to Temporomandibular Joint (TMJ) Morphometry and Biomechanics

与颞下颌关节（TMJ）和周围组织相关的疾病影响5-12%的美国人，每年花费40亿美元，并且由于不完全清楚的原因，女性的患病率几乎是男性的两倍。 男性。尽管这些疾病的衰弱效应，颞下颌关节的功能形态和生物力学 由于其独特而复杂的解剖结构，尚未得到很好的理解。特别是颞下颌关节的生物力学， 肌肉和骨骼特征相互作用影响功能，量化TMJ特征的性别差异是重要的科学研究问题，对于开发安全有效的临床治疗至关重要。 最近，TMJ研究中的一项创新性研究产生了描述肌肉形态测量学和骨骼结构的详细数据，但统计分析具有挑战性，因为不存在有效整合高水平的方法。 多维、多模态多变量数据和由于性别、年龄和其他因素导致的内在异质性的目标效应 因素这些复杂的数据现在遍布颅面研究，如计算机断层扫描，磁共振 成像、肌电图、基因分型和其他技术共同产生了各种信息， 有效整合，以阐明颅面发育，异常和治疗策略。有 因此迫切需要有效地整合这些高维多模态数据的统计方法 同时能够发现解释异质性的特征，因此可以指导治疗。本项目将开发新的统计模型和降维方法来应对挑战 在TMJ形态测量学数据和更普遍的来自牙齿和颅面研究的多模态数据中。 三个相互关联的目标是：（1）开发两个新的统计模型，同时减少维， 特征选择将识别TMJ肌肉附着和颅骨测量的线性组合 （2）将两个新模型从多变量扩展到张量变量（即多维阵列数据），以利用3D图像和肌肉附着的完整结构; 和（3）使用新开发的统计方法分析TMJ形态测量数据， 传播方便用户的软件。成功完成拟议目标将提供新的见解， 颞下颌关节的肌肉骨骼联系，重要的是，它们如何因性别和其他协变量而变化， 为未来的研究和临床治疗提供信息。这项工作将通过产生一系列新的统计模型，降维方法和分析计算工具来填补分析技术的重要空白。 多模态多变量数据之间的条件关系，并使这些方法广泛适用。

Disorders associated with the temporomandibular joint (TMJ) and surrounding tissues affect 5-12% of Americans, cost $4 billion/year and, for reasons not fully known, are nearly twice as prevalent among females as males. Despite debilitating effects of these disorders, the functional morphology and biomechanics of the TMJ are not well understood due to its unique and complex anatomy. In particular, the biomechanics of the TMJ, how the muscular and skeletal features interact to affect function, and quantifying sex differences in TMJ characteristics are important scientific research questions and crucial for developing safe and effective clinical treatments. A recent, innovative study in TMJ research generated detailed data describing muscle morphometry and skeletal structure, but statistical analysis is challenging because methods do not exist that effectively integrate high dimensional, multimodal multivariate data and target effects of intrinsic heterogeneity due to sex, age and other factors. Such complex data now pervade craniofacial research, as computed tomography, magnetic resonance imaging, electromyography, genotyping and other technologies jointly generate diverse information that must be efficiently integrated to elucidate craniofacial development, abnormalities, and treatment strategies. There is hence a pressing need for statistical methods that effectively integrate these high-dimensional multimodal data and simultaneously enable discovery of features that explain heterogeneity and, therefore, can guide therapeutics. This project will develop new statistical models and dimension reduction methods to address the challenges in the TMJ morphometry data and in multimodal data arising from dental and craniofacial studies more generally. Three interconnected aims are: (1) Develop two new statistical models for simultaneous dimension reduction and feature selection that will identify the linear combinations of TMJ muscle attachment and skull measurements most differentially correlated for males and females; (2) Extend the two new models from multivariate to tensor-variate (i.e. multidimensional array data) to exploit the full structure of the 3D images and muscle attachments; and (3) analyze the TMJ morphometry data using the newly developed statistical methods and develop and disseminate user-friendly software. Successful completion of the proposed aims will provide new insight about musculoskeletal associations in the TMJ and, importantly, how they vary by sex and other covariates, that can inform future research and clinical treatment. The work will fill important gaps in analysis techniques by producing a series of new statistical models, dimension reduction methods and computational tools for analyzing conditional relationships among multimodal multivariate data, and make these methods widely available.