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Abstract
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OBJECTIVE: Impact kinematics are widely employed to investigate mechanisms of traumatic brain injury (TBI). However, they are susceptible to noise and artefacts; thus, require data filtering. Few studies have focused on how data filtering affects brain strain most relevant to TBI. Here, we report that impact-induced brain strains are much less sensitive to data filtering than kinematics based on three filtering methods: CFC180, lowpass 200Hz, and a new method called Head Exposure to Acceleration Database in Sport (HEADSport).
METHODS: Using mouthguard-measured head impacts in elite rugby (N=5694), average Euclidean distances between the three filtered angular velocity profiles and their unfiltered counterparts are used to identify three groups of impacts with large variations: 90-95th, 95-99th, and >99th percentile. From each group, 20 impacts are randomly selected for simulation using the anisotropic Worcester Head Injury Model (WHIM) V1.0.
RESULTS AND CONCLUSION: HEADSport and CFC180 are the most and least effective, respectively, in suppressing "unphysical artefacts" shown as sharp spikes with a rather short impulse duration (e.g., <3 ms) in angular velocity. However, maximum principal strain (MPS), especially that in the corpus callosum, is much less sensitive to data filtering compared to kinematic peaks (e.g., reduction of 3% vs. 47% and 90% for peak angular velocity and acceleration with HEADSport for impacts of >99th percentile). SIGNIFICANCE: These findings confirm that the brain acts as a low-pass filter, itself, to suppress high frequency noise in impact kinematics. Therefore, brain strain can serve as a common metric for TBI biomechanical studies to maximize relevance to the injury, as it is not sensitive to kinematic filters.
Language: en |