Ke Ming, Yao Xinyi, Cao Peihui, Liu Guangyao
School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050 China.
Department of Nuclear Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730030 China.
Cogn Neurodyn. 2025 Dec;19(1):7. doi: 10.1007/s11571-024-10191-0. Epub 2025 Jan 6.
Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.
The online version contains supplementary material available at 10.1007/s11571-024-10191-0.
青少年肌阵挛性癫痫（JME）在多个频率下表现出脑网络功能连接异常。我们使用多层网络模型来处理不同频率下的异质性特征，并评估JME患者脑网络功能整合和分离的机制。为了解决网络构建过程中出现虚假边或缺失边的可能性，我们将多层网络与链路预测技术相结合。从40例JME患者和40名健康对照者获取静息态功能磁共振成像（rs-fMRI）数据。利用多层网络框架整合来自不同频段的信息，并融合相似性度量进行链路预测。最后，计算重新配置的多层频率网络的多重度熵和多层聚类系数。结果表明，JME患者的多层脑网络整合和分离信息的能力显著降低，且与JME症状严重程度显著相关。这种差异在默认模式网络（DMN）、运动和躯体感觉网络（SMN）以及听觉网络（AN）中尤为明显。此外，在楔前叶、枕下回、颞中回、丘脑和岛叶中发现了显著差异。结果表明，JME患者存在脑功能异常且跨频率交互减少。这可能是由于DMN、SMN和AN内及之间在多个频段的连接分布变化，导致连接模式不稳定。这些变化的产生与JME的病理机制有关，可能会加重患者的认知和行为问题。
在线版本包含可在10.1007/s11571-024-10191-0获取的补充材料。
