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

多模态综合降维和统计建模及其在颞下颌关节 (TMJ) 形态测量和生物力学中的应用

• 批准号: 10196077
• 负责人: Elizabeth H. Slate
• 金额: $ 15.72万
• 依托单位: FLORIDA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-05 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10196077
• 关键词: 3-Dimensional; Address; Affect; Age; American; Anatomy; Biomechanics; Bone structure; Cadaver; Characteristics; Clinical; Clinical Treatment; Communities; Complex; Custom; Data; Dental; Dependence; Dimensions; Disease; Electromyography; Evaluation; Female; Genotype; Goals; Heterogeneity; Human; Image; Knowledge; Left; Literature; Magnetic Resonance Imaging; Measurement; Methods; Modality; Modeling; Modernization; Morphology; Multivariate Analysis; Muscle; Musculoskeletal; National Institute of Dental and Craniofacial Research; Operative Surgical Procedures; Oral; Oral health; Performance; Research; Research Personnel; Resources; Scanning; Series; Sex Differences; Shapes; Side; Skeletal Muscle; Statistical Data Interpretation; Statistical Methods; Statistical Models; Structure; Techniques; Technology; Temporomandibular Joint; Therapeutic; Three-Dimensional Image; Time; Tissues; Work; X-Ray Computed Tomography; complex data; computerized tools; cone-beam computed tomography; cost; craniofacial; craniofacial complex; craniofacial development; craniofacial structure; cranium; data dissemination; data visualization; design; feature selection; flexibility; frontier; genetic information; health data; high dimensionality; image processing; imaging modality; innovation; insight; joint function; male; morphometry; multimodal data; multimodality; novel; routine imaging; sex; skeletal; statistics; three dimensional structure; tool; treatment strategy; user friendly software; vector

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.

与颞下颌关节（TMJ）和监测组织相关的疾病影响5-12％的美国人，几乎是女性中普遍的两倍，是女性的两倍 尽管这些疾病的功能和生物力学 尤其是独特的解剖学，尤其是TMJ的生物力学，对UTS的理解不佳。 肌肉和骨骼特征相互作用，以影响功能RCH探索以发展安全的临床信任。 TMJ研究的最新创新研究产生了肌肉肌肉和骨骼结构的数据。 尺寸，多模式的多元数据和因性别，年龄和其他固有异质性的目标影响 因素。 成像，肌电图，基因分型和其他技术共同生成必须是的多种信息 有效整合以阐明颅面发展，异常和治疗策略。 hense提出了有效整合这些统计方法的需求 同时可以解释特征，因此可以指导治疗方法。 在TMJ形态计量学数据以及牙齿和颅面研究引起的多模式数据中。 三个相互联系的目的是：（1）开发两个新的统计模型，用于同时修订和 特征选择可以识别TMJ肌肉atchment和Skull测量的线性组合 大多数与男性和女性的差异相关； （3）使用新开发的统计方法分析TMJ形态计算数据，并开发和开发 传播用户软件。 TMJ中的肌肉骨骼关联，重要的是，是如何因性别和其他方式而变化 告知未来和临床治疗 多模式多元数据之间的有条件关系，并使这些方法广泛可用。