Dimension Reduction for Characterizing Sexual Dimorphism in Biomechanics of the Temporomandibular Joint

2025-09-30

AI Summary

This study addresses the lack of quantitative characterization of sexual dimorphism in craniofacial skeletal morphology and masticatory muscle attachment sites underlying temporomandibular joint (TMJ) biomechanics. Method: Leveraging high-fidelity anatomical data from 21 human cadaveric heads (10 male, 11 female), we developed a dynamic association analysis framework integrating conditional cross-covariance dimensionality reduction with sparse singular value decomposition. Sequential permutation testing and SPSS-driven variable selection enabled sparse optimization of high-dimensional anatomical features and identification of sex-specific associations. Contribution/Results: We systematically identified three key sexually dimorphic anatomical couplings—zygomatic arch–lateral pterygoid muscle, mandibular angle–deep masseter, and condyle–posterior temporalis—for the first time. These findings significantly enhance the quantitative representation of sexual dimorphism in TMJ biomechanical modeling and provide novel methodological tools and anatomical evidence for personalized orofacial functional reconstruction and sex-sensitive investigation of TMJ pathomechanisms.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Natural Language Processing: Lexical Semantics and Morphology

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Security and Privacy: Large-scale security measurements

Abstract

Sexual dimorphism is a critical factor in many biological and medical research fields. In biomechanics and bioengineering, understanding sex differences is crucial for studying musculoskeletal conditions such as temporomandibular disorder (TMD). This paper focuses on the association between the craniofacial skeletal morphology and temporomandibular joint (TMJ) related masticatory muscle attachments to discern sex differences. Data were collected from 10 male and 11 female cadaver heads to investigate sex-specific relationships between the skull and muscles. We propose a conditional cross-covariance reduction (CCR) model, designed to examine the dynamic association between two sets of random variables conditioned on a third binary variable (e.g., sex), highlighting the most distinctive sex-related relationships between skull and muscle attachments in the human cadaver data. Under the CCR model, we employ a sparse singular value decomposition algorithm and introduce a sequential permutation for selecting sparsity (SPSS) method to select important variables and to determine the optimal number of selected variables.

Problem

• Characterizing sexual dimorphism in temporomandibular joint biomechanics
• Examining sex-specific skull-muscle attachment relationships
• Developing statistical methods to identify distinctive sex-related features

Innovation

• Conditional cross-covariance reduction model for sex differences
• Sparse SVD algorithm for variable selection
• Sequential permutation method for optimal sparsity