Multivariate analysis of structural and diffusion imaging in traumatic brain injury.
Multivariate analysis of structural and diffusion imaging in traumatic brain injury.
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DOI：
10.1016/j.acra.2008.07.007
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发表时间：
2008-11
影响因子：
4.8
通讯作者：
Whyte J
中科院分区：
文献类型：
--
作者：
Avants B;Duda JT;Kim J;Zhang H;Pluta J;Gee JC;Whyte J
扩散张量（DT）和T1结构磁共振图像提供了独特的和互补的工具，量化活的大脑。我们利用这两种方式在一个一致的和固有的多变量（MV）的统计框架，统一的临床数据集的分析的同构归一化方法。我们使用这种技术来研究创伤性脑损伤（TBI）的MV效应。我们对比了12名TBI幸存者和9名匹配对照的丘脑和海马中基于T1和DT图像的测量值，这些测量值标准化为DT和T1模板空间。归一化方法使用拓扑保持和无偏的映射。标准化是基于每个体素的完整信息张量，同时，从T1数据导出的高分辨率特征之间的相似性。该技术被称为多变量神经解剖学对称归一化（SyNMN）。局部体积和平均扩散的逐体素MV统计用具有多重比较校正的Hotelling T2检验评估。TBI显著（FDR p < 0.05）减少了体积，并增加了丘脑背内侧和海马前部一致位置的平均扩散。SyNMN揭示了创伤性脑损伤损害边缘系统的证据。该TBI形态测量研究和将SyNMN与其他方法进行对比的额外性能评估表明，DT组件可能有助于归一化质量。
Diffusion tensor (DT) and T1 structural magnetic resonance images provide unique and complementary tools for quantifying the living brain. We leverage both modalities in a diffeomorphic normalization method that unifies analysis of clinical datasets in a consistent and inherently multivariate (MV) statistical framework. We use this technique to study MV effects of traumatic brain injury (TBI). We contrast T1 and DT image-based measurements in the thalamus and hippocampus of twelve TBI survivors and nine matched controls normalized to a combined DT and T1 template space. The normalization method uses maps that are topology-preserving and unbiased. Normalization is based upon the full tensor of information at each voxel and, simultaneously, the similarity between high-resolution features derived from T1 data. The technique is termed symmetric normalization for multivariate neuroanatomy (SyNMN). Voxel-wise MV statistics on the local volume and mean diffusion are assessed with Hotelling’s T2 test with correction for multiple comparisons. TBI significantly (FDR p < 0.05) reduces volume and increases mean diffusion at coincident locations in mediodorsal thalamus and anterior hippocampus. SyNMN reveals evidence that traumatic brain injury compromises the limbic system. This TBI morphometry study and an additional performance evaluation contrasting SyNMN with other methods suggest that the DT component may aid normalization quality.
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影响因子：
10.6
作者：
Drevets, WC
通讯作者：
Drevets, WC
影响因子：
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作者：
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通讯作者：
Ptito, Alain
影响因子：
3.5
作者：
Hashimoto, Keiji;Okumura, Ayumi;Nakamura, Toshinori
通讯作者：
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影响因子：
4.2
作者：
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通讯作者：
Peterson, Derick
影响因子：
7.6
作者：
Duan, Ye;Li, Xiaoling;Xi, Yongjian
通讯作者：
Xi, Yongjian