颞叶癫痫患者脑白质纤维束追踪空间统计分析与自动识别

Tract-based spatial statistics analysis on the white matter of patients with temporal lobe epilepsy and automatic recognition

为了定位颞叶癫痫(TLE)患者脑白质微结构发生异常的重要脑区,本文设立了正常对照组(NC)与TLE组两组人群,采集了50位受试者(其中NC组28人,TLE组22人)的脑部弥散张量成像(DTI)影像,分别计算其部分各向异性(FA)、平均扩散率(MD)、扩散系数(AD)、径向扩散系数(RD)等参数,并采用纤维束追踪空间统计方法(TBSS),获取组间差异的脑区,然后利用支持向量机(SVM),对NC组与TLE组进行分类,并与支持向量机-递归特征消除法(SVM-RFE)进行比较,最后对重要脑区及其分布进行分析与讨论。实验结果表明,TLE患者的FA值存在明显降低的脑区主要有胼胝体、上纵束、放射冠、外囊、内囊、下额枕束、钩束、矢状层等,基本呈双侧分布,其中大部分脑区的MD、RD值明显增高,AD值虽有增高,但差异无统计学意义。支持向量机-纤维束追踪空间统计法(SVM-TBSS)利用FA、MD、RD进行分类的准确率分别为82%、76%、76%,特征融合后分类准确率为80%;SVM-RFE利用FA、MD、RD进行分类准确率分别为90%、90%和92%,特征融合后分类准确率达到100%,SVM-RFE分类性能明显优于SVM-TBSS,对分类有重要影响的特征主要分布于联络纤维和连合纤维脑区。研究结果表明,DTI参数能有效地反映TLE患者的脑白质纤维异常改变,可用于阐明其病理机制、定位病灶及实现自动诊断。

This study aims to determine the salient brain regions with abnormal changes in white matter struc- tures from diffusion tensor imaging （DTI） images of the patients with temporal lobe epilepsy （TLE）, and to discriminate the patients with TLE from normal controls （NCs）. Firstly, the DTI images from 50 subjects （28 NCs and 22 TLE） were acquired. Secondly, the four measures including the fractional anisotropy （FA）, the mean diffusivity （MD）, the axial diffusivity （AD） and the radial diffusivity （RD） were calculated. Thirdly, the tract-based spatial statistics （TBSS） was adopted to extract the measures in brain regions with significant differences between the two compared groups. Fourthly, the obtained measures were used as input features of the support vector machine （SVM） for classification, and the support vector machine-recursive feature elimination （SVM-RFE） was compared with the support vector machine-tract-based spatial statistics （SVM-TBSS） method. Finally, the essential brain regions and their spatial distribution were analyzed and discussed. The experimental results showed that the FA measures of the TLE group decreased significantly in the corpus callosum, superior longitudinal fasciculus, corona radiata, external capsule, internal capsule, inferior fronto-occipital fasciculus, fasciculus uncinatus and sagittal stratum, which were nearly bilaterally distributed, while the MDand RD increased significantly in most of these brain regions of the TLE group. Although the AD also increased, the differences were not statistically significant. The SVM-TBSS classifier obtained accuracies of 82%, 76% and 76% using the FA, MD and RD for classification, respectively, and 80% using combined measures. The SVM-RFE classifier obtained accuracies of 90%, 90% and 92% using the FA, MD and RD respectively, while the highest accuracy was 100% using combined measures. These results demonstrated that the SVM-RFE outperformed the SVM-TBSS, and the dominant characteristic influencing classification in brain regions were in associative and commissural fibers. These results illustrated that the measures of DTI images could reveal the abnormal changes in white matter structure of patients with TLE, providing effective information to clarify its pathological mechanism, localize the focus and diagnose automatically.