Chappell Michael H, Brown Jennifer A, Dalrymple-Alford John C, Uluğ Aziz M, Watts Richard
Department of Physics and Astronomy, University of Canterbury, Christchurch, New Zealand.
Magn Reson Imaging. 2008 Dec;26(10):1398-405. doi: 10.1016/j.mri.2008.04.004. Epub 2008 May 27.
In this study, we present two different methods of multivariate analysis of voxel-based diffusion tensor imaging (DTI) data, using as an example data derived from 59 professional boxers and 12 age-matched controls. Conventional univariate analysis ignores much of the diffusion information contained in the tensor. Our first multivariate method uses the Hotelling's T(2) statistic and the second uses linear discriminant analysis to generate the linear discriminant function at each voxel to form a separability metric. Both multivariate methods confirm the findings from the individual metrics of large-scale changes in the bilateral inferior temporal gyri of the boxers, but they also reveal greater sensitivity as well as identifying major subcortical changes that had not been evident in the univariate analyses. Linear discriminant analysis has the added strength of providing a quantitative measure of the relative contribution of each metric to any differences between the two subject groups. This novel adaptation of statistical and mathematical techniques to neuroimaging analysis is important for two reasons. Clinically, it develops the findings of a previous mild head injury study, and, methodologically, it could equally well be applied to multivariate studies of other pathologies.
在本研究中，我们展示了两种基于体素的扩散张量成像（DTI）数据的多变量分析方法，并以59名职业拳击手和12名年龄匹配的对照者的数据为例。传统的单变量分析忽略了张量中包含的许多扩散信息。我们的第一种多变量方法使用霍特林T²统计量，第二种方法使用线性判别分析在每个体素处生成线性判别函数，以形成可分离性度量。两种多变量方法均证实了拳击手双侧颞下回大规模变化的个体指标所得到的结果，但它们也显示出更高的敏感性，同时识别出单变量分析中未明显显现的主要皮质下变化。线性判别分析的额外优势在于，它能提供每个指标对两个受试者组之间任何差异的相对贡献的定量测量。这种将统计和数学技术新颖地应用于神经影像学分析有两个重要原因。在临床上，它拓展了先前轻度头部损伤研究的结果；在方法学上，它同样可以很好地应用于其他病理学的多变量研究。
