Understanding how global brain networks are affected in epilepsy may elucidate the pathogenesis of seizures and its accompanying neurobehavioral comorbidities. We investigated functional changes within neural networks in temporal lobe epilepsy (TLE) using graph theory analysis of resting-state connectivity. Twenty-seven TLE presurgical patients (age 41.0 ± 12.3 years) and 85 age, gender, and handedness equivalent healthy controls (HCs; age 39.7 ± 16.9 years) were enrolled. Eyes-closed resting-state functional magnetic resonance image scans were analyzed to compare network properties and functional connectivity (FC) changes. TLE subjects showed significantly higher global efficiency, lower clustering coefficient ratio, and lower shortest path lengths ratio than HCs, as an indication of a more synchronized, yet less segregated network. A trend of functional reorganization with a shift of network hubs to the contralateral hemisphere was noted in TLE subjects. Support vector machine (SVM) with linear kernel was trained to separate between neural networks in TLE and HC subjects based on graph measurements. SVM analysis allowed separation between TLE and HC networks with 80.66% accuracy using eight features of graph measurements. Support vector regression (SVR) was used to predict neurocognitive performance from graph metrics. An SVR linear predictor showed discriminative prediction accuracy for four key neurocognitive variables in TLE (absolute R value range: 0.61-0.75). Despite TLE, our results showed both local and global network topology differences that reflect widespread alterations in FC in TLE. Network differences are discriminative between TLE and HCs using data-driven analysis and predicted severity of neurocognitive sequelae in our cohort.

中文翻译：

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功能连接性的图论分析与机器学习方法的结合展示了广泛的网络差异，并预测了颞叶癫痫的临床变量。

了解癫痫发作中全球大脑网络的影响方式可以阐明癫痫发作的发病机理及其伴随的神经行为合并症。我们使用静息状态连通性的图论分析研究了颞叶癫痫（TLE）的神经网络内的功能变化。研究入选了27例TLE术前患者（年龄41.0±12.3岁）和85位年龄，性别和手性相当的健康对照组（HCs；年龄39.7±16.9岁）。闭眼休息功能磁共振成像扫描进行了分析，以比较网络属性和功能连接（FC）的变化。与HC相比，TLE主题显示出明显更高的整体效率，更低的聚类系数比率和更低的最短路径长度比率，这表明网络更加同步，但隔离程度较低。在TLE受试者中注意到了功能重组的趋势，其中网络中心转移到对侧半球。训练具有线性核的支持向量机（SVM），以基于图测量将TLE和HC受试者的神经网络分开。支持向量机（SVM）分析允许使用八种图形测量功能在TLE网络与HC网络之间以80.66％的精度进行分离。支持向量回归（SVR）用于根据图形指标预测神经认知表现。SVR线性预测变量对TLE中的四个关键神经认知变量（绝对R值范围：0.61-0.75）具有判别预测准确性。尽管有TLE，我们的结果显示本地和全局网络拓扑结构的差异反映了TLE中FC的广泛变化。