Ghodadra A, Alhilali L, Fakhran S
From the Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
AJNR Am J Neuroradiol. 2016 Feb;37(2):274-8. doi: 10.3174/ajnr.A4505. Epub 2015 Sep 24.
Principal component analysis, a data-reduction algorithm, generates a set of principal components that are independent, linear combinations of the original dataset. Our study sought to use principal component analysis of fractional anisotropy maps to identify white matter injury patterns that correlate with posttraumatic headache after mild traumatic brain injury.
Diffusion tensor imaging and neurocognitive testing with the Immediate Post-Concussion Assessment and Cognitive Test were performed in 40 patients with mild traumatic brain injury and 24 without posttraumatic headache. Principal component analysis of coregistered fractional anisotropy maps was performed. Regression analysis of the major principal components was used to identify those correlated with posttraumatic headache. Finally, each principal component that correlated with posttraumatic headache was screened against other postconcussive symptoms and demographic factors.
Principal component 4 (mean, 7.1 ± 10.3) correlated with the presence of posttraumatic headache in mild traumatic brain injury (odds ratio per SD, 2.32; 95% CI, 1.29-4.67; P = .01). Decreasing principal component 4 corresponded with decreased fractional anisotropy in the midsplenium and increased fractional anisotropy in the genu of the corpus callosum. Principal component 4 identified patients with posttraumatic headache with an area under the receiver operating characteristic curve of 0.73 and uniquely correlated with posttraumatic headache and no other postconcussive symptom or demographic factors.
Principal component analysis can be an effective data-mining method to identify white matter injury patterns on DTI that correlate with clinically relevant symptoms in mild traumatic brain injury. A pattern of reduced fractional anisotropy in the splenium and increased fractional anisotropy in the genu of the corpus callosum identified by principal component analysis can help identify patients at risk for posttraumatic headache after mild traumatic brain injury.
主成分分析是一种数据降维算法,可生成一组主成分,这些主成分是原始数据集的独立线性组合。我们的研究旨在使用分数各向异性图的主成分分析来识别与轻度创伤性脑损伤后创伤后头痛相关的白质损伤模式。
对40例轻度创伤性脑损伤患者和24例无创伤后头痛的患者进行了扩散张量成像和使用即刻脑震荡后评估和认知测试的神经认知测试。对配准后的分数各向异性图进行主成分分析。使用主要主成分的回归分析来识别与创伤后头痛相关的主成分。最后,针对其他脑震荡后症状和人口统计学因素对与创伤后头痛相关的每个主成分进行筛选。
主成分4(平均值为7.1±10.3)与轻度创伤性脑损伤中创伤后头痛的存在相关(每标准差的优势比为2.32;95%可信区间为1.29 - 4.67;P = 0.01)。主成分4降低与胼胝体中部的分数各向异性降低以及胼胝体膝部的分数各向异性增加相对应。主成分4识别创伤后头痛患者的受试者工作特征曲线下面积为0.73,且仅与创伤后头痛相关,与其他脑震荡后症状或人口统计学因素无关。
主成分分析可以是一种有效的数据挖掘方法,用于识别弥散张量成像上与轻度创伤性脑损伤中临床相关症状相关的白质损伤模式。通过主成分分析确定的胼胝体中部分数各向异性降低和胼胝体膝部分数各向异性增加的模式有助于识别轻度创伤性脑损伤后有创伤后头痛风险的患者。