Faculty of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
Beijing Universal Medical Imaging Diagnostic Center, Beijing, China.
J Neuroimaging. 2022 Sep;32(5):977-990. doi: 10.1111/jon.13012. Epub 2022 Jun 7.
Understanding the pathogenesis of temporal lobe epilepsy (TLE) is essential for its diagnosis and treatment. The study aimed to explore regional homogeneity (ReHo) and changes in effective connectivity (EC) between brain regions in TLE patients, hoping to discover potential abnormalities in certain brain regions in TLE patients.
Resting-state functional magnetic resonance data were collected from 23 TLE patients and 32 normal controls (NC). ReHo was used as a feature of multivariate pattern analysis (MVPA) to explore the ability of its alterations in identifying TLE. Based on the results of the MVPA, certain brain regions were selected as seed points to further explore alterations in EC between brain regions using Granger causality analysis.
MVPA results showed that the classification accuracy for the TLE and NC groups was 87.27%, and the right posterior cerebellum lobe, right lingual gyrus (LING_R), right cuneus (CUN_R), and left superior temporal gyrus (STG_L) provided significant contributions. Moreover, the EC from STG_L to right fusiform gyrus (FFG_R) and LING_R and the EC from CUN_R to the right occipital superior gyrus (SOG_R) and right occipital middle gyrus (MOG_R) were altered compared to the NC group.
The MVPA results indicated that ReHo abnormalities in brain regions may be an important feature in the identification of TLE. The enhanced EC from STG_L to FFG_R and LING_R indicates a shift in language processing to the right hemisphere, and the weakened EC from SOG_R and MOG_R to CUN_R may reveal an underlying mechanism of TLE.
了解颞叶癫痫（TLE）的发病机制对于其诊断和治疗至关重要。本研究旨在探讨 TLE 患者脑区局部一致性（ReHo）和脑区间有效连接（EC）的变化，以期发现 TLE 患者某些脑区的潜在异常。
对 23 例 TLE 患者和 32 例正常对照者（NC）进行静息态功能磁共振数据采集。采用 ReHo 作为多元模式分析（MVPA）的特征，以探索其改变识别 TLE 的能力。基于 MVPA 的结果，选择某些脑区作为种子点，进一步采用格兰杰因果分析探讨脑区间 EC 的变化。
MVPA 结果显示，TLE 组和 NC 组的分类准确率为 87.27%，右侧小脑后叶、右侧舌回（LING_R）、右侧楔叶（CUN_R）和左侧颞上回（STG_L）提供了显著贡献。此外，与 NC 组相比，STG_L 到右侧梭状回（FFG_R）和 LING_R 的 EC 以及 CUN_R 到右侧枕上回（SOG_R）和右侧枕中回（MOG_R）的 EC 发生了改变。
MVPA 结果表明，脑区 ReHo 异常可能是识别 TLE 的重要特征。STG_L 到 FFG_R 和 LING_R 的 EC 增强表明语言处理向右侧半球转移，而 SOG_R 和 MOG_R 到 CUN_R 的 EC 减弱可能揭示了 TLE 的潜在机制。
