Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis.

IF 4.1 2区医学 Q2 GERIATRICS & GERONTOLOGY

Frontiers in Aging Neuroscience Pub Date : 2025-05-15 eCollection Date: 2025-01-01 DOI:10.3389/fnagi.2025.1472207

John P Coetzee, Xiaojian Kang, Victoria Liou-Johnson, Ines Luttenbacher, Srija Seenivasan, Elika Eshghi, Daya Grewal, Siddhi Shah, Frank Hillary, Emily L Dennis, Maheen M Adamson

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引用次数: 0

Abstract

Background: 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.

Objective: 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.

Methods: 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 r within groups, controlling for CA, followed by construction of regression models.

Results: 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.

Interpretation: 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.

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预测创伤性脑损伤退伍军人和健康对照者的脑年龄：一项探索性分析。

背景：创伤性脑损伤（TBI）与痴呆风险增加相关。这可能是由损伤引起的潜在生物学变化引起的。机器学习算法可以使用结构核磁共振成像来预测大脑年龄（pBA）。当估计年龄大于实际年龄（CA）时，这被称为脑年龄差距（BAg）。我们分析了有和没有创伤性脑损伤的男性和女性的这一结果。目的：根据brainageR算法的估计，确定导致BAg的因素在有和没有创伤性脑损伤的美国退伍军人男女之间是否存在差异。方法：在一项探索性的假设生成分析中，我们分析了85名TBI患者和22名健康对照（hc）的数据。使用FreeSurfer 7.0处理高分辨率T1W图像。pBAs由t1计算。使用Mann-Whitney u检验两组之间的差异，使用组内部分Pearson’s r检验BAg和其他因素之间的关联，控制CA，然后构建回归模型。结果：经过多次比较校正后，TBI患者和hc患者在PCL评分（TBI患者较高）和两个半球皮质厚度（CT）（hc患者较高）上存在差异。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hcc中，BAg仅与CA相关。我们构建了四个层次回归模型来预测每组的BAg，这些模型控制了CA并排除了多重共线性的pBA。这些模型显示，HV预测TBI女性患者的BAg，而CT预测TBI男性患者的BAg，而只有CA预测hc患者的BAg。解释：这些结果为TBI患者中与BAg相关的因素不同于hcc患者中与BAg相关的因素以及男性和女性之间的观点提供了初步支持。具体来说，TBI患者的BAg可以通过神经解剖学因素来预测，而hc患者的BAg只能通过CA来预测。这可能反映了算法的特点，或潜在的生物学过程，或两者兼而有之。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Aging Neuroscience GERIATRICS & GERONTOLOGY-NEUROSCIENCES

CiteScore

6.30

自引率

8.30%

发文量

1426

期刊介绍： Frontiers in Aging Neuroscience is a leading journal in its field, publishing rigorously peer-reviewed research that advances our understanding of the mechanisms of Central Nervous System aging and age-related neural diseases. Specialty Chief Editor Thomas Wisniewski at the New York University School of Medicine is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.