Quantifying the association between white matter integrity changes and subconcussive head impact exposure from a single season of youth and high school football using 3D convolutional neural networks

Behrouz Saghafi; Gowtham Murugesan; Elizabeth Davenport; Ben Wagner; Jillian Urban; Mireille Kelley; Derek Jones; Alexander Powers; Christopher Whitlow; Joel Stitzel; Joseph Maldjian; Albert Montillo

doi:10.1117/12.2293023

27 February 2018 Quantifying the association between white matter integrity changes and subconcussive head impact exposure from a single season of youth and high school football using 3D convolutional neural networks

Behrouz Saghafi, Gowtham Murugesan, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Alexander Powers, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, Albert Montillo

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Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105750E (2018) https://doi.org/10.1117/12.2293023
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

The effect of subconcussive head impact exposure during contact sports, including American football, on brain health is poorly understood particularly in young and adolescent players, who may be more vulnerable to brain injury during periods of rapid brain maturation. This study aims to quantify the association between cumulative effects of head impact exposure from a single season of football on white matter (WM) integrity as measured with diffusion MRI. The study targets football players aged 9-18 years old. All players were imaged pre- and post-season with structural MRI and diffusion tensor MRI (DTI). Fractional Anisotropy (FA) maps, shown to be closely correlated with WM integrity, were computed for each subject, co-registered and subtracted to compute the change in FA per subject. Biomechanical metrics were collected at every practice and game using helmet mounted accelerometers. Each head impact was converted into a risk of concussion, and the risk of concussion-weighted cumulative exposure (RWE) was computed for each player for the season. Athletes with high and low RWE were selected for a two-category classification task. This task was addressed by developing a 3D Convolutional Neural Network (CNN) to automatically classify players into high and low impact exposure groups from the change in FA maps. Using the proposed model, high classification performance, including ROC Area Under Curve score of 85.71% and F1 score of 83.33% was achieved. This work adds to the growing body of evidence for the presence of detectable neuroimaging brain changes in white matter integrity from a single season of contact sports play, even in the absence of a clinically diagnosed concussion.

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Behrouz Saghafi, Gowtham Murugesan, Elizabeth Davenport, Ben Wagner, Jillian Urban, Mireille Kelley, Derek Jones, Alexander Powers, Christopher Whitlow, Joel Stitzel, Joseph Maldjian, and Albert Montillo "Quantifying the association between white matter integrity changes and subconcussive head impact exposure from a single season of youth and high school football using 3D convolutional neural networks", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750E (27 February 2018); https://doi.org/10.1117/12.2293023

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