Clustering Templates: A Data-Driven Framework for Dimensionality Reduction and Compression of Brain Strain in Head Impact

Abstract

Purpose: Finite element (FE) models are indispensable for studying the biomechanics of traumatic brain injury (TBI), and modern head FE models typically comprise thousands to millions of elements. The resulting brain strain is therefore high-dimensional, and when combined with dense temporal sampling and large numbers of head impact cases, this produces datasets of substantial data volume. Accordingly, this study investigates the effective dimensionality required to accurately represent brain strain and proposes a data-driven framework to reduce the dimensionality of FE simulation data while maintaining high spatiotemporal fidelity. Methods: We developed a series of "clustering templates" by applying K-means clustering to feature vectors derived from the maximum principal strain (MPS) time-history curves of 118,840 brain elements over 3,693 impact simulations using the THUMS head model. The performance of these templates, with cluster counts (K) ranging from 8 to 8192, was rigorously evaluated by quantifying the fidelity of data reconstruction. Results: The clustering templates demonstrated high-fidelity reconstruction of the original FE data. A template with K = 512 achieved a compression ratio of over 200:1 while maintaining a low mean element-wise peak MPS root mean square error (RMSE) of 0.027, and a template with K = 4096 (30:1 compression ratio) achieved an RMSE of 0.016. Conclusion: The proposed clustering templates provide an effective and automated method for the dimensionality reduction and compression of large-scale brain strain data. This framework mitigates critical data management bottlenecks and enables the development of subsequent data analysis and machine learning models, thereby accelerating research in TBI biomechanics.