The Journal of head trauma rehabilitation · 2026
Lobar Brain Volume Associations With Brain-Age Gap in Canadian Special Operations Forces Breachers.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Fernandez-Ruiz Juan, Coverdale Nicole S, Skinner Christopher, Schwarz Betty Anne, Glikstein Rafael, Melkus Gerd, Cook Douglas J
Аннотация
OBJECTIVE: To determine whether military breachers show significant positive raw brain-age gap and to identify which intracranial-volume (ICV)-normalized lobar volumes best predict bias-corrected brain-age gap (BAGc).
SETTING AND PARTICIPANTS: Eighty-five male (mean age 30.1 ± 4.5 years) Canadian Special Operations Forces (CANSOF) active breachers.
DESIGN: Cross-sectional.
MAIN MEASURES: T1-weighted MRI was processed using the Brain Structure Ages (BSA) pipeline to derive global brain age and lobar volumes. Raw brain-age gap was calculated as estimated age minus chronological age. BAGc was obtained by residualizing raw BAG on chronological age and sex. Linear models tested associations between BAGc and ICV-normalized lobar volumes, adjusting for age. Multiple comparisons were controlled using Benjamini-Hochberg false discovery rate (FDR). Outlier robustness was assessed with Tukey IQR trimming.
RESULTS: Participants showed a significant positive raw brain-age gap (mean 8.81 ± 3.98 years; P < .001). After age-bias correction, smaller temporal (β = -0.41, q = 0.002) and frontal (β = -0.43, q = 0.006) lobe volumes were significantly associated with higher BAGc. Associations involving the limbic, parietal, and occipital lobes were not statistically significant after correction for multiple comparison. Findings were stable across outlier-trimming strategies.
CONCLUSIONS: A positive raw brain-age gap was observed within this cohort of military breachers, with age-independent variation in brain-age deviation showing regionally patterned associations. After age-bias correction, higher BAGc was most strongly associated with relatively smaller frontal and temporal lobe volumes. These findings provide a foundation for future studies incorporating detailed exposure characterization, clinical measures, appropriate comparison groups, longitudinal follow-up, and multimodal imaging.
SETTING AND PARTICIPANTS: Eighty-five male (mean age 30.1 ± 4.5 years) Canadian Special Operations Forces (CANSOF) active breachers.
DESIGN: Cross-sectional.
MAIN MEASURES: T1-weighted MRI was processed using the Brain Structure Ages (BSA) pipeline to derive global brain age and lobar volumes. Raw brain-age gap was calculated as estimated age minus chronological age. BAGc was obtained by residualizing raw BAG on chronological age and sex. Linear models tested associations between BAGc and ICV-normalized lobar volumes, adjusting for age. Multiple comparisons were controlled using Benjamini-Hochberg false discovery rate (FDR). Outlier robustness was assessed with Tukey IQR trimming.
RESULTS: Participants showed a significant positive raw brain-age gap (mean 8.81 ± 3.98 years; P < .001). After age-bias correction, smaller temporal (β = -0.41, q = 0.002) and frontal (β = -0.43, q = 0.006) lobe volumes were significantly associated with higher BAGc. Associations involving the limbic, parietal, and occipital lobes were not statistically significant after correction for multiple comparison. Findings were stable across outlier-trimming strategies.
CONCLUSIONS: A positive raw brain-age gap was observed within this cohort of military breachers, with age-independent variation in brain-age deviation showing regionally patterned associations. After age-bias correction, higher BAGc was most strongly associated with relatively smaller frontal and temporal lobe volumes. These findings provide a foundation for future studies incorporating detailed exposure characterization, clinical measures, appropriate comparison groups, longitudinal follow-up, and multimodal imaging.