计算机应用与软件2024，Vol.41Issue(8)：67-73,7.DOI:10.3969/j.issn.1000-386x.2024.08.010
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
摘要
Abstract
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.关键词
静息态功能磁共振/功能连接特异性/概率神经网络/颞叶癫痫/发作侧Key words
Resting-state functional magnetic resonance/Functional connectivity specificity/Probabilistic neural network/Temporal lobe epilepsy/Seizure lateralization分类
信息技术与安全科学引用本文复制引用
宋子博,葛曼玲,付晓璇,陈盛华,郭志彤,张其锐,张志强..静息态功能连接特异性与机器学习的癫痫定侧[J].计算机应用与软件,2024,41(8):67-73,7.基金项目
国家自然科学基金项目(81871345) （81871345）
河北省自然科学基金项目(E2019202019). （E2019202019）
