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Single season changes in resting state network power and the connectivity between regions distinguish head impact exposure level in high school and youth football players
Author(s): Gowtham Murugesan; Behrouz Saghafi; Elizabeth Davenport; Ben Wagner; Jillian Urban; Mireille Kelley; Derek Jones; Alex Powers; Christopher Whitlow; Joel Stitzel; Joseph Maldjian; Albert Montillo
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Paper Abstract

The effect of repetitive sub-concussive head impact exposure in contact sports like American football on brain health is poorly understood, especially in the understudied populations of youth and high school players. These players, aged 9-18 years old may be particularly susceptible to impact exposure as their brains are undergoing rapid maturation. This study helps fill the void by quantifying the association between head impact exposure and functional connectivity, an important aspect of brain health measurable via resting-state fMRI (rs-fMRI). The contributions of this paper are three fold. First, the data from two separate studies (youth and high school) are combined to form a high-powered analysis with 60 players. These players experience head acceleration within overlapping impact exposure making their combination particularly appropriate. Second, multiple features are extracted from rs-fMRI and tested for their association with impact exposure. One type of feature is the power spectral density decomposition of intrinsic, spatially distributed networks extracted via independent components analysis (ICA). Another feature type is the functional connectivity between brain regions known often associated with mild traumatic brain injury (mTBI). Third, multiple supervised machine learning algorithms are evaluated for their stability and predictive accuracy in a low bias, nested cross-validation modeling framework. Each classifier predicts whether a player sustained low or high levels of head impact exposure. The nested cross validation reveals similarly high classification performance across the feature types, and the Support Vector, Extremely randomized trees, and Gradboost classifiers achieve F1-score up to 75%.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750F (27 February 2018); doi: 10.1117/12.2293199
Show Author Affiliations
Gowtham Murugesan, Univ. of Texas Southwestern Medical Ctr. (United States)
Behrouz Saghafi, Univ. of Texas Southwestern Medical Ctr. (United States)
Elizabeth Davenport, Univ. of Texas Southwestern Medical Ctr. (United States)
Ben Wagner, Univ. of Texas Southwestern Medical Ctr. (United States)
Jillian Urban, Wake Forest School of Medicine (United States)
Mireille Kelley, Wake Forest School of Medicine (United States)
Derek Jones, Wake Forest School of Medicine (United States)
Alex Powers, Wake Forest School of Medicine (United States)
Christopher Whitlow, Wake Forest School of Medicine (United States)
Joel Stitzel, Wake Forest School of Medicine (United States)
Joseph Maldjian, Univ. of Texas Southwestern Medical Ctr. (United States)
Albert Montillo, Univ. of Texas Southwestern Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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