Center for Imaging of Neurodegenerative Diseases, VA Medical Center, San Francisco, USA.
Neuroimage. 2013 May 1;71:224-32. doi: 10.1016/j.neuroimage.2013.01.014. Epub 2013 Jan 24.
Quantitative neuroimaging analyses have demonstrated gray and white matter abnormalities in group comparisons of different types of non-lesional partial epilepsy. It is unknown to what degree these type-specific patterns exist in individual patients and if they could be exploited for diagnostic purposes. In this study, a two-level multi-modality imaging Bayesian network approach is proposed that uses information about individual gray matter volume loss and white matter integrity to classify non-lesional temporal lobe epilepsy with (TLE-MTS) and without (TLE-no) mesial-temporal sclerosis and frontal lobe epilepsy (FLE).
25 controls, 19 TLE-MTS, 22 TLE-no and 14 FLE were studied on a 4T MRI and T1 weighted structural and DTI images acquired. Spatially normalized gray matter (GM) and fractional anisotropy (FA) abnormality maps (binary maps with voxels 1 SD below control mean) were calculated for each subject. At the first level, each group's abnormality maps were compared with those from all the other groups using Graphical-Model-based Morphometric Analysis (GAMMA). GAMMA uses a Bayesian network and a Markov random field based contextual clustering method to produce maps of voxels that provide the maximal distinction between two groups and calculates a probability distribution and a group assignment based on this information. The information was then combined in a second level Bayesian network and the probability of each subject to belong to one of the three epilepsy types calculated.
The specificities of the two level Bayesian network to distinguish between the three patient groups were 0.87 for TLE-MTS and TLE-no and 0.86 for FLE, the corresponding sensitivities were 0.84 for TLE-MTS, 0.72 for TLE-no and 0.64 for FLE.
The two-level multi-modality Bayesian network approach was able to distinguish between the three epilepsy types with a reasonably high accuracy even though the majority of the images were completely normal on visual inspection.
定量神经影像学分析已经证明，在不同类型的非病变性部分癫痫的组间比较中存在灰质和白质异常。目前尚不清楚这些特定类型的异常在个体患者中存在到何种程度，以及它们是否可以用于诊断目的。在这项研究中，提出了一种两级多模态成像贝叶斯网络方法，该方法利用个体灰质体积损失和白质完整性的信息来对伴有（TLE-MTS）和不伴有（TLE-no）内侧颞叶硬化的颞叶癫痫和额叶癫痫（FLE）进行分类。
对 25 名对照者、19 名 TLE-MTS 患者、22 名 TLE-no 患者和 14 名 FLE 患者进行了 4T MRI 和 T1 加权结构和 DTI 图像采集。为每个受试者计算了空间归一化灰质（GM）和各向异性分数（FA）异常图（体素值低于对照组平均值 1 个标准差的二进制图）。在第一级，使用基于图形模型的形态计量分析（GAMMA）比较每组的异常图与其他所有组的异常图。GAMMA 使用贝叶斯网络和基于马尔可夫随机场的上下文聚类方法生成能够最大程度区分两组的体素图，并基于此信息计算概率分布和组分配。然后将信息组合到二级贝叶斯网络中，并计算每个受试者属于三种癫痫类型之一的概率。
二级贝叶斯网络区分三组患者的特异性分别为 TLE-MTS 和 TLE-no 为 0.87，FLE 为 0.86，相应的敏感性分别为 TLE-MTS 为 0.84，TLE-no 为 0.72，FLE 为 0.64。
即使大多数图像在视觉检查下完全正常，两级多模态贝叶斯网络方法仍能够以相当高的准确率区分三种癫痫类型。
