• James R Stone, Brian B Avants, Nicholas J Tustison, Eric M Wassermann, Jessica Gill, Elena Polejaeva, Kristine C Dell, Walter Carr, Angela M Yarnell, Matthew L LoPresti, Peter Walker, Meghan O'Brien, Natalie Domeisen, Alycia Quick, Claire M Modica, John D Hughes, Francis J Haran, Carl Goforth, and Stephen T Ahlers.
• Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
• J. Neurotrauma. 2020 Dec 1; 37 (23): 2468-2481.

AbstractCombat military and civilian law enforcement personnel may be exposed to repetitive low-intensity blast events during training and operations. Persons who use explosives to gain entry (i.e., breach) into buildings are known as "breachers" or dynamic entry personnel. Breachers operate under the guidance of established safety protocols, but despite these precautions, breachers who are exposed to low-level blast throughout their careers frequently report performance deficits and symptoms to healthcare providers. Although little is known about the etiology linking blast exposure to clinical symptoms in humans, animal studies demonstrate network-level changes in brain function, alterations in brain morphology, vascular and inflammatory changes, hearing loss, and even alterations in gene expression after repeated blast exposure. To explore whether similar effects occur in humans, we collected a comprehensive data battery from 20 experienced breachers exposed to blast throughout their careers and 14 military and law enforcement controls. This battery included neuropsychological assessments, blood biomarkers, and magnetic resonance imaging measures, including cortical thickness, diffusion tensor imaging of white matter, functional connectivity, and perfusion. To better understand the relationship between repetitive low-level blast exposure and behavioral and imaging differences in humans, we analyzed the data using similarity-driven multi-view linear reconstruction (SiMLR). SiMLR is specifically designed for multiple modality statistical integration using dimensionality-reduction techniques for studies with high-dimensional, yet sparse, data (i.e., low number of subjects and many data per subject). We identify significant group effects in these data spanning brain structure, function, and blood biomarkers.

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