颞叶癫痫致痫侧静息态脑功能网络拓扑的特异性研究

Study on Topological Specificity of Resting-State Functional Brain Networks in Epileptogenic Hemisphere of Temporal Lobe Epilepsy

癫痫是世界常见的典型脑内异常神经放电导致中枢认知功能网络失调的神经疾病。作为当今先进技术的静息态功能磁共振成像(rfMRI),其功能连接(fMRI-FC)可为评估脑功能提供科学的检测指标。在此提出参照健康人的癫痫脑功能网络多节点指标融合的特异性模型,提取致痫侧脑功能网络拓扑属性,试图在高阶计算fMRI-FC,以提高其检测精准性,并在机器学习中验证特异性模型的分类性能。首先,取20例结构像海马阳性的内侧颞叶癫痫患者(左、右致痫侧各10例)和139例健康人的rfMRI数据;其次,以FC为边搭建脑功能网络,分别计算患者和健康人的4个局部节点指标;再次,以健康人为参照,构建脑功能网络的特异性模型。通过差值统计和ROC曲线分析,提取4组对致痫侧敏感性高的单一节点指标和1组其指标融合作为特征,分别采用留一法和随机验证分析其分类效果;又基于脑功能网络多节点指标融合,构建非特异性模型同上述操作并比较,以强调特异性模型的优势。结果表明,脑功能网络特异性模型在多节点指标融合的特征向量上表现出更好的分类性能,留一法和随机验证平均分类精度达100%和95.0%±8.7%。脑功能网络多节点指标融合的特异性模型能有效地提取致痫侧脑功能网络特征,为机器学习方法辅助fMRI-FC定位癫痫脑致痫侧提供一种新思路。

Epilepsy is a typical neurological disease worldwide with abnormal neural discharges in the brain leading to dysfunction in the central cognitive functional networks.As an advanced technology today,the functional connectivity(fMRI-FC)derived from the resting-state functional magnetic resonance imaging(rfMRI)provides a scientific detection index for assessing the brain functions.Here,a fMRI-FC specificity model was proposed with reference to healthy individuals,based on multiple nodes indexes fusion in the whole brain functional networks in epilepsy,aiming to improve fMRI-FC detection to a high-order level.To validate the effectiveness,the model was employed to build the functional network topological metrics,and then applied to classify the epileptogenic hemisphere by a machine learning method.Firstly,the rfMRI data of a total of 20 mesial temporal lobe epilepsy patients,whose epileptogenic hemispheres were indicated by the positive hippocampal formation on the structure MRI(10 patients on each epileptogenic hemisphere)and a total of 139 healthy individuals were collected.Secondly,with FC as the edge,the brain functional networks were constructed.A total of 4 local nodes metrics were calculated for patients and healthy individuals.Thirdly,the fMRI-FC specificity model was constructed,with reference to the healthy individuals.The groups including 4 nodal indexes and 1 group of these indexes fusion were statistically employed to extract the sensitive brain areas to the epileptogenic hemisphere by ROC curve analysis,and the indexes of these areas were considered as the features to classify the epileptogenic hemisphere of the patients.The classification performance was analyzed by the leave-one-out method and random cross-validation.A fMRI-FC non-specific model was constructed by the multiple nodes indexes fusion of brain functional networks and was compared with the specific model built by us.The fMRI-FC specificity model of multiple nodes indexes fusion could classify the epileptogenic hemisphere effectively at an average classification accuracy of 95.0%±8.7%,that was validated by random cross-validation,and even 100%by leave-one-out method.The fMRI-FC specificity model of multiple nodes indexes fusion could effectively improve the localizing accuracy of epileptogenic hemisphere.Therefore,it might provide a new way for machine learning-aided assessing the epileptic brain by fMRI-FC.