Interpretable Machine Intelligence for Biomarker Discovery in Concussion

About This Grant

Significance to the VA: Mild traumatic brain injuries (mTBIs) are a leading cause of chronic disability among service members and Veterans. The significance of this research lies in its potential to transform the molecular-level understanding of mTBI through innovative statistical and proteomic methodologies. This project directly supports the VA mission by aiming to improve the prognostication and understanding of the molecular underpinnings of mTBI for Veterans seeking care in the VA health system. Additionally, the creation of a large proteomics dataset for Veteran TBI patients represents a valuable resource for the VA and the broader scientific community, fostering further innovation and research in TBI and related fields. Innovation and Impact: This research introduces an advanced approach to mTBI biomarker analysis, utilizing cutting-edge multivariate statistical methodologies. By moving beyond traditional univariate analyses, the project employs tools such as non-linear principal component analysis (NLPCA), interpretable supervised classifiers, and topological data analysis to uncover intricate biomarker patterns. These innovative methods promise to enhance the understanding of molecular dynamics in mTBI, identify distinct patient subgroups, and enable the development of precision medicine strategies, potentially revolutionizing care for Veterans. Specific Aims: 1) To derive multidimensional protein signatures from existing biomarker data associated with specific clinical outcomes in mTBI, leveraging a novel machine learning workflow integrating NLPCA and interpretable supervised classifiers to analyze LIMBIC-CENC data. 2) To perform comprehensive proteomic analyses of blood samples using SomaSCAN technology, supplemented by topological data analysis, to identify patient subgroups with distinct symptomatic profiles and their unique protein signatures. 3) To externally validate biomarker panels and discover new panels related to long-term outcomes using the TRACTS dataset. Methodology: This study employs advanced statistical and proteomic approaches to analyze mTBI biomarkers. The first aim uses a novel machine learning workflow integrating non-linear principal component analysis (NLPCA) with interpretable supervised classifiers to analyze data from the LIMBIC-CENC cohort. The second aim involves comprehensive proteomic profiling using SomaSCAN technology and topological data analysis to identify symptomatic subgroups and their unique protein signatures. Finally, the third aim tests and validates biomarker panels using data from the TRACTS dataset, ensuring robustness and generalizability of findings. Path to Translation/Implementation: The insights gained from this study will provide actionable knowledge for developing targeted treatment strategies and precision medicine approaches for mTBI patients. By identifying distinct biomarker patterns and symptomatic subgroups, this research can inform clinical decision-making and personalized care plans within the VA healthcare system. The creation of a comprehensive proteomics dataset also supports future translational research and its potential clinical applications, facilitating the implementation of innovative diagnostic and therapeutic tools for mTBI in Veterans.