Maurer J Michael, Harenski Keith A, Paul Subhadip, Vergara Victor M, Stephenson David D, Gullapalli Aparna R, Anderson Nathaniel E, Clarke Gerard J B, Nyalakanti Prashanth K, Harenski Carla L, Decety Jean, Mayer Andrew R, Arciniegas David B, Calhoun Vince D, Parrish Todd B, Kiehl Kent A
The Mind Research Network, Albuquerque, NM, USA.
Department of Biomedical Science and Technology and Department of Sports Science, Ramakrishna Mission Vivekananda Educational and Research Institute (RKMVERI), West Bengal, India.
Neuroimage Rep. 2023 Mar;3(1). doi: 10.1016/j.ynirp.2023.100157. Epub 2023 Jan 30.
Individuals with acute and chronic traumatic brain injury (TBI) are associated with unique white matter (WM) structural abnormalities, including fractional anisotropy (FA) differences. Our research group previously used FA as a feature in a linear support vector machine (SVM) pattern classifier, observing high classification between individuals with and without acute TBI (i.e., an area under the curve [AUC] value of 75.50%). However, it is not known whether FA could similarly classify between individuals with and without history of chronic TBI. Here, we attempted to replicate our previous work with a new sample, investigating whether FA could similarly classify between incarcerated men with ( = 80) and without ( = 80) self-reported history of chronic TBI. Additionally, given limitations associated with FA, including underestimation of FA values in WM tracts containing crossing fibers, we extended upon our previous study by incorporating neurite orientation dispersion and density imaging (NODDI) metrics, including orientation dispersion (ODI) and isotropic volume (Viso). A linear SVM based classification approach, similar to our previous study, was incorporated here to classify between individuals with and without self-reported chronic TBI using FA and NODDI metrics as separate features. Overall classification rates were similar when incorporating FA and NODDI ODI metrics as features (AUC: 82.50%). Additionally, NODDI-based metrics provided the highest sensitivity (ODI: 85.00%) and specificity (Viso: 82.50%) rates. The current study serves as a replication and extension of our previous study, observing that multiple diffusion MRI metrics can reliably classify between individuals with and without self-reported history of chronic TBI.
急性和慢性创伤性脑损伤（TBI）患者存在独特的白质（WM）结构异常，包括分数各向异性（FA）差异。我们的研究小组之前将FA作为线性支持向量机（SVM）模式分类器的一个特征，观察到急性TBI患者和非急性TBI患者之间有较高的分类准确率（即曲线下面积[AUC]值为75.50%）。然而，尚不清楚FA是否能同样区分有和没有慢性TBI病史的个体。在此，我们试图用一个新样本重复我们之前的工作，研究FA是否能同样区分有（n = 80）和没有（n = 80）自我报告慢性TBI病史的被监禁男性。此外，鉴于与FA相关的局限性，包括对含有交叉纤维的WM束中FA值的低估，我们在之前研究的基础上进行了扩展，纳入了神经突方向离散度和密度成像（NODDI）指标，包括方向离散度（ODI）和各向同性体积（Viso）。这里采用了一种类似于我们之前研究的基于线性SVM的分类方法，以FA和NODDI指标作为单独特征，对有和没有自我报告慢性TBI的个体进行分类。将FA和NODDI ODI指标作为特征纳入时，总体分类率相似（AUC：82.50%）。此外，基于NODDI的指标提供了最高的敏感性（ODI：85.00%）和特异性（Viso：82.50%）率。本研究是我们之前研究的重复和扩展，观察到多种扩散MRI指标可以可靠地区分有和没有自我报告慢性TBI病史的个体。
