Functional and Structural Neuroimaging Correlates of Repetitive Low-Level Blast Exposure in Career Breachers

Abstract

Combat 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.

Keywords: SiMLR; breachers; cortical thickness; diffusion tensor imaging; functional MRI; perfusion imaging.

FIG. 1.

Illustration of the template-based processing performed for each subject. Each modality is used to produce one or more derived images (indicated by yellow arrows), which are then transformed to the group template for statistical analysis by each subject's T₁-weighted image. “↔” denotes a linear mapping whereas “” denotes a diffeomorphic deformable mapping. DWI, diffusion-weighted imaging; FA, fractional anisotropy; fALFF, fractional amplitude of low-frequency fluctuations; rs-fMRI, resting-state functional magnetic resonance imaging; T1-w, T₁-weighted; T2-w, T₂-weighted. CBF, cerebral blood flow.

FIG. 2.

The various effects observed in traumatic brain injury overlap, but the signals used for assessing the various components are often noisy, incomplete, and/or small. SiMLR leverages the covariation between imperfect data sources (along with dimensionality reduction techniques) to infer the underlying signal(s) of interest in a focused, statistically powerful way. PCA, principal component analysis; SiMLR, similarity-driven multi-view linear reconstruction.

FIG. 3.

Illustration of inferential design using SiMLR. Multiple modalities are projected into a regularized, low-dimensional space for linear regression analysis (similar to PCA regression). In the imaging context, for a specific modality, each of these low-dimensional projections comprise a similarly varying (across subjects) spatial region in the space of the template. DMN, default mode network; FA, fractional anisotropy; fALFF, fractional amplitude of low-frequency fluctuations; PCA, principal component analysis; RD, radial diffusivity; SiMLR, similarity-driven multi-view linear reconstruction.

FIG. 4.

Group differences in cortical thickness (top) and perfusion (bottom) imaging. On the left are box plots showing the subject distribution based on the SiMLR-based imaging-specific decompositions. Along the x-axis are the control versus breacher groups with the raw p value in the upper left of the plots. Cortical thickness projections showed a general cortical thickening (driven largely by occipital lobe and default mode network regions). No group differences in relative perfusion survived multiple comparisons correction. SiMLR, similarity-driven multi-view linear reconstruction.

FIG. 5.

White matter group differences illustrated with FA and RD projections derived from DWI. On the left are box plots showing the subject distribution based on the SiMLR-based imaging-specific decompositions. Along the x-axis are the controls and breacher groups with the raw p values in the upper left of the plots. Whereas fractional anisotropy is decreased in the breacher group (vs. controls), radial diffusivity is increased. Both measurements are driven primarily by frontal lobe white matter. DWI, diffusion-weighted imaging; FA, fractional anisotropy; RD, radial diffusivity; SiMLR, similarity-driven multi-view linear reconstruction.

FIG. 6.

Functional connectivity group differences illustrated with DMN correlation maps and fALFF projections derived from resting-state fMRI. On the left are box plots showing the subject distribution based on the SiMLR-based imaging-specific decompositions. Along the x-axis are the controls and breacher groups with raw p values in the upper left of the plots. Connectivity between default mode network and other networks is increased in breachers whereas resting-state activity, largely within the default mode regions, is higher in controls. DMN, default mode network; fALFF, fractional amplitude of low-frequency fluctuations; fMRI, functional magnetic resonance imaging; SiMLR, similarity-driven multi-view linear reconstruction.

FIG. 7.

Summary statistical findings, in terms of mean modality-specific projection values, over all modalities used in the study, demonstrating statistically significant differences between breachers and controls. Eight modalities × two bases per modality resulted in 16 statistical tests. All axes of the spider plot represent significant effects where the limits of each axis represent the minimum and maximum of the modality-specific projections. Blood biomarker results, though important to this study, will be detailed in a separate publication where greater specificity about the role of each exosome measurement will be reported. DMN, default mode network.