Coetzee John P, Kang Xiaojian, Liou-Johnson Victoria, Luttenbacher Ines, Seenivasan Srija, Eshghi Elika, Grewal Daya, Shah Siddhi, Hillary Frank, Dennis Emily L, Adamson Maheen M
Rehabilitation Service, VA Palo Alto Health Care System, Palo Alto, CA, United States.
Department of Psychiatry and Behavioral Sciences, Stanford Medicine, Stanford, CA, United States.
Front Aging Neurosci. 2025 May 15;17:1472207. doi: 10.3389/fnagi.2025.1472207. eCollection 2025.
Traumatic brain injury (TBI) is associated with increased dementia risk. This may be driven by underlying biological changes resulting from the injury. Machine learning algorithms can use structural MRIs to give a predicted brain age (pBA). When the estimated age is greater than the chronological age (CA), this is called the brain age gap (BAg). We analyzed this outcome in men and women with and without TBI.
To determine whether factors that contribute to BAg, as estimated using the brainageR algorithm, differ between men and women who are US military Veterans with and without TBI.
In an exploratory, hypothesis-generating analysis, we analyzed data from 85 TBI patients and 22 healthy controls (HCs). High-resolution T1W images were processed using FreeSurfer 7.0. pBAs were calculated from T1s. Differences between the two groups were tested using the Mann-Whitney U. Associations between the BAg and other factors were tested using partial Pearson's within groups, controlling for CA, followed by construction of regression models.
After correcting for multiple comparisons, TBI patients and HCs differed on PCL score (higher for TBI patients) and cortical thickness (CT) in both hemispheres (higher for HCs). Among women TBI patients, BAg was correlated with pBA and hippocampal volume (HV), and among men TBI patients, BAg was correlated with pBA and CT. Among both men and women HCs, BAg was correlated only with CA. Four hierarchical regression models were constructed to predict BAg in each group, which controlled for CA and excluded pBA for multicollinearity. These models showed that HV predicted BAg among women with TBI, while CT predicted BAg among men with TBI, while only CA predicted BAg among HCs.
These results offer tentative support to the view the factors associated with BAg among individuals with TBI differ from factors associated with BAg among HCs, and between men and women. Specifically, BAg among individuals with TBI is predicted by neuroanatomical factors, while among HCs it is predicted only by CA. This may reflect features of the algorithm, an underlying biological process, or both.
创伤性脑损伤（TBI）与痴呆风险增加有关。这可能是由损伤导致的潜在生物学变化所驱动。机器学习算法可以使用结构磁共振成像（MRI）来给出预测脑龄（pBA）。当估计年龄大于实际年龄（CA）时，这被称为脑龄差距（BAg）。我们分析了有和没有TBI的男性和女性的这一结果。
确定使用brainageR算法估计的导致BAg的因素在有和没有TBI的美国退伍军人男性和女性之间是否存在差异。
在一项探索性的、产生假设的分析中，我们分析了85名TBI患者和22名健康对照（HCs）的数据。使用FreeSurfer 7.0处理高分辨率T1加权图像。从T1图像计算pBA。两组之间的差异使用曼-惠特尼U检验。在控制CA的情况下，使用组内偏皮尔逊检验BAg与其他因素之间的关联，随后构建回归模型。
在进行多重比较校正后，TBI患者和HCs在PCL评分（TBI患者更高）和两个半球的皮质厚度（CT）（HCs更高）方面存在差异。在女性TBI患者中，BAg与pBA和海马体积（HV）相关，在男性TBI患者中，BAg与pBA和CT相关。在男性和女性HCs中，BAg仅与CA相关。构建了四个层次回归模型来预测每组中的BAg，模型控制了CA并排除了因多重共线性而产生的pBA。这些模型表明，HV预测了有TBI的女性中的BAg，而CT预测了有TBI的男性中的BAg，而在HCs中只有CA预测BAg。
这些结果为以下观点提供了初步支持，即与有TBI的个体中的BAg相关的因素不同于与HCs中的BAg相关的因素，并且在男性和女性之间也存在差异。具体而言，有TBI的个体中的BAg由神经解剖学因素预测，而在HCs中仅由CA预测。这可能反映了算法的特征、潜在的生物学过程，或两者兼而有之。
