Functional and Structural Neuroimaging Correlates of Repetitive Low-Level Blast Exposure in Career Breachers
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".