颞叶癫痫致痫侧静息态脑功能网络拓扑的特异性研究

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...

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

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.