Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis
Abstract
This study applies principal component analysis (PCA) to investigate spatial co-variation patterns of traumatic brain injury metrics, including maximum principal strain (MPS), MPS rate (MPSR), and their product, across four types of head impacts: simulations, football, mixed martial arts, and car crashes. PCA was used to decompose element-wise injury metrics into principal components, with the first principal component (PC1) explaining over 80% of the variance in all datasets. High PC1 coefficients were consistently observed in regions such as the corpus callosum and midbrain. A PCA-based machine learning head model (PCA-MLHM) was then developed to predict PC1 and inverse-transform to reconstruct whole-brain injury metrics. Compared with the previous MLHM, PCA-MLHM achieved similar MPS estimation accuracy while reducing model parameters by 74%, improving interpretability and computational efficiency.
