静息态功能连接特异性与机器学习的癫痫定侧

COMBINING RESTING-STATE FUNCTIONAL CONNECTIVITY SPECIFICITY AND MACHINE LEARNING TO LOCALIZE PAROXYSMAL SIDE OF EPILEPTIC PATIENTS

为探索癫痫发作侧的脑功能影像标记,提出静息态功能磁共振的功能连接特异性模型和有监督机器学习联合方案。选取20名结构影像提示发作侧的颞叶癫痫患者(均分左、右两组)和142名健康人;以健康人为参照,构建功能连接特异性模型,为每位患者每个脑区功能连接打分;统计分析左右组间打分值差异显著性,获得对发作侧敏感的标志性脑区;以其打分值为特征向量输入到概率神经网络实现定侧并使用交叉验证。结果显示,对发作侧敏感的功能影像学标记在杏仁核、中央旁小叶等6个脑区,分类准确率达90.0%,高于目前机器学习辅助癫痫研究水准。

To explore the functional brain imaging markers of epileptic seizure side,a joint scheme of functional connectivity specificity modeling and supervised machine learning with resting-state functional magnetic resonance is proposed.Twenty temporal lobe epilepsy patients with structural images suggestive of the seizure side(equally divided into left and right groups)and 142 healthy individuals were selected.We used healthy individuals as reference,and a functional connectivity specificity model was constructed to score the functional connectivity of each brain region for each patient.The significance of the difference in scoring values between the left and right groups was statistically analyzed to obtain the landmark brain regions that were sensitive to the seizure side.The scoring values were used as a feature vector inputted into a probabilistic neural network to achieve the fixation of the side and cross validation was used.The results show that:functional imaging markers sensitive to the ictal side are in six brain regions,including the amygdala and paracentral lobule,with a classification accuracy of 90.0%,which is higher than the current level of machine learning-assisted epilepsy research.