Lateralization of Temporal Lobe Epilepsy Based on Resting-State Functional Magnetic Resonance Imaging and Machine Learning.
Lateralization of Temporal Lobe Epilepsy Based on Resting-State Functional Magnetic Resonance Imaging and Machine Learning.
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DOI：
10.3389/fneur.2015.00184
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发表时间：
2015
影响因子：
3.4
通讯作者：
Hocking J
中科院分区：
文献类型：
--
作者：
Yang Z;Choupan J;Reutens D;Hocking J
颞叶癫痫（TLE）的偏侧化是手术成功缓解癫痫发作的关键。根据功能性磁共振成像的证据，TLE影响颞叶以外的大脑区域，并与异常的大脑网络有关。我们在这里提出了一个基于机器学习的方法来确定TLE的偏侧性，使用从大脑的静息状态功能连接中提取的特征。构建了一个全面的特征空间，以包括局部脑区域内、脑区域之间以及整个网络的网络属性。基于随机森林进行特征选择，并采用支持向量机训练线性模型来预测未见过患者的TLE偏侧性。对12例患者进行了留一交叉验证，预测准确率为83%。所选功能的重要性进行了分析，以证明在体素，区域和网络水平TLE侧化的静息状态连接属性的贡献。
Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.