arXiv:2511.03767q-bio.QMeess.IV2025-11中稿 · SPIE Medical Imagi…被引 2

从多中心脑损伤影像中发现3类关键脑区异常,揭示潜在病理路径。

Phenotype discovery of traumatic brain injury segmentations from heterogeneous multi-site data

  • 通过多中心数据标准化与132个脑区分割,构建可比性结构指标
  • 37个脑区体积差异显著,集中于脑干、皮层下灰质及白质区域
  • 结合独立成分分析揭示三类损伤模式,适用于神经康复研究者

创伤性脑损伤(TBI)具有内在异质性,传统临床评估工具如格拉斯哥昏迷量表难以反映其复杂性。来自联邦跨机构脑损伤研究(FITBIR)数据库的多中心磁共振影像数据包含25项研究、7,693次扫描,涵盖年龄、性别和TBI状态信息(5,811例TBI患者,1,882例对照)。研究首先将图像归一化至本地数据集,对132个感兴趣脑区进行分割。经质量控制、体积计算并剔除异常值后,计算每位参与者脑区体积的z分数(以对照组均值与标准差为基准)。通过多元线性回归校正性别、年龄和全脑体积后,发现TBI组与对照组在37个脑区存在显著差异(p < 0.05,经错误发现率校正)。进一步采用独立成分分析与聚类分析发现,异常主要集中在三类区域:1)脑干、枕极及眶后结构;2)皮层下灰质与岛叶皮层;3)大脑与小脑白质。该结果揭示了潜在的共通损伤路径。

原文摘要 · Abstract (English)

Traumatic brain injury (TBI) is intrinsically heterogeneous, and typical clinical outcome measures like the Glasgow Coma Scale complicate this diversity. The large variability in severity and patient outcomes render it difficult to link structural damage to functional deficits. The Federal Interagency Traumatic Brain Injury Research (FITBIR) repository contains large-scale multi-site magnetic resonance imaging data of varying resolutions and acquisition parameters (25 shared studies with 7,693 sessions that have age, sex and TBI status defined - 5,811 TBI and 1,882 controls). To reveal shared pathways of injury of TBI through imaging, we analyzed T1-weighted images from these sessions by first harmonizing to a local dataset and segmenting 132 regions of interest (ROIs) in the brain. After running quality assurance, calculating the volumes of the ROIs, and removing outliers, we calculated the z-scores of volumes for all participants relative to the mean and standard deviation of the controls. We regressed out sex, age, and total brain volume with a multivariate linear regression, and we found significant differences in 37 ROIs between subjects with TBI and controls (p < 0.05 with independent t-tests with false discovery rate correction). We found that differences originated in 1) the brainstem, occipital pole and structures posterior to the orbit, 2) subcortical gray matter and insular cortex, and 3) cerebral and cerebellar white matter using independent component analysis and clustering the component loadings of those with TBI.

脑损伤多中心研究影像分析脑区分割

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