This project focuses on analyzing microstructural changes in the brain during aging using statistical methods and machine learning techniques applied to quantitative MRI (qMRI) data. The project develops a novel multi-parameter method to assess brain region convergence and variability during aging. It leverages data science, machine learning, and statistical methods to uncover insights into brain aging.
- qMRI Data Processing: Preprocessing of quantitative MRI data from multiple brain regions for analysis.
- Machine Learning: Application of advanced algorithms like XGBoost and t-SNE to classify brain regions by age and uncover patterns in brain aging.
- Statistical Analysis: Use of statistical methods to explore correlations between brain regions and variability across subjects.
- Visualization: Creation of visualizations such as brain region clustering plots and correlation matrices.