Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA.
Mid-Atlantic Mental Illness Research Education and Clinical Center, Durham VA Medical Center, Durham, North Carolina, USA.
Hum Brain Mapp. 2022 Jun 1;43(8):2653-2667. doi: 10.1002/hbm.25811. Epub 2022 Mar 15.
Mild Traumatic brain injury (mTBI) is a signature wound in military personnel, and repetitive mTBI has been linked to age-related neurogenerative disorders that affect white matter (WM) in the brain. However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non-negative matrix factorization (NMF) is a data-driven approach that detects covarying patterns (components) within high-dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self-reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (n = 1,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) Military Brain Injury working group. Regressions were used to examine TBI- and mTBI-related associations in NMF-derived components while adjusting for age, sex, post-traumatic stress disorder, depression, and data acquisition site/scanner. We found significantly stronger age-dependent effects of lower FA in Veterans with TBI than Veterans without in four components (q < 0.05), which are spatially unconstrained by traditionally defined WM tracts. One component, occupying the most peripheral location, exhibited significantly stronger age-dependent differences in Veterans with mTBI. We found NMF to be powerful and effective in detecting covarying patterns of FA associated with mTBI by applying standard parametric regression modeling. Our results highlight patterns of WM alteration that are differentially affected by TBI and mTBI in younger compared to older military Veterans.
轻度创伤性脑损伤(mTBI)是军事人员的标志性创伤,重复性 mTBI 与影响大脑白质(WM)的与年龄相关的神经退行性疾病有关。然而,特定 WM 束损伤的发现一直存在差异且不一致。这可能是由于 mTBI 相关的机制、病因和合并症的异质性。非负矩阵分解(NMF)是一种数据驱动的方法,可检测高维数据中的共变模式(成分)。我们将 NMF 应用于有和没有自我报告 TBI 史的退伍军人的扩散成像数据。NMF 从通过 ENIGMA(通过荟萃分析增强神经影像学遗传学)军事脑损伤工作组收集的大型数据集(n = 1,475)中的分数各向异性(FA)中识别出 12 个独立成分。回归用于在调整年龄、性别、创伤后应激障碍、抑郁和数据采集地点/扫描仪后,检查 NMF 衍生成分中的 TBI 和 mTBI 相关关联。我们发现,在四个成分中,与没有 TBI 的退伍军人相比,有 TBI 的退伍军人的 FA 值较低,年龄依赖性更强(q < 0.05),这四个成分不受传统定义的 WM 束的空间限制。一个成分占据最外围的位置,在 mTBI 退伍军人中,FA 随年龄的依赖性差异明显更强。通过应用标准参数回归模型,我们发现 NMF 在检测与 mTBI 相关的 FA 共变模式方面非常强大和有效。我们的研究结果突出了 WM 改变的模式,这些模式在年轻的军事退伍军人中受到 TBI 和 mTBI 的不同影响。