Abnormal Static and Dynamic Functional Connectivity in Left and Right Temporal Lobe Epilepsy

ObjectiveTemporal lobe epilepsy (TLE) can be conceptualized as a network disease. However, the network characteristics in lateralization remain controversial. MethodsIn this study, resting-state functional MRI scans were acquired from 53 TLE patients [22 with left-side TLE (LTLE) and 31 with right-side TLE (RTLE)] and 37 matched healthy controls. We focused on the characteristics of static and dynamic functional connectivity, including static connectivity patterns and topological properties, as well as temporal properties of the dynamic connectivity state and the variability of the dynamic connectivity and network topological organization. Correlation analyses were conducted between abnormal static and dynamic properties and cognitive performances. ResultsThe static functional connectivity analysis presented a significantly decreased cortical-cortical connectivity pattern and increased subcortical-cortical connectivity pattern in RTLE. The global-level network in RTLE showed a significant decrease in global efficiency. The dynamic functional connectivity analysis revealed that RTLE patients exhibited aberrant connectivity states, as well as increased variability in the subcortical-cortical connectivity. The global-level network in RTLE revealed increased variance in global efficiency and local efficiency. The static or dynamic functional connectivity in LTLE did not show any significant abnormalities. The altered dynamic properties were associated with worsening cognitive performance in language and conceptual thinking by the TLE patients. ConclusionOur findings demonstrated the presence of abnormalities in the static and dynamic functional connectivity of TLE patients. RTLE patients exhibited more pronounced aberrant connectivity patterns and topological properties, which might represent a mechanism for reconfiguration of brain networks in RTLE patients. These observations extended our understanding of the pathophysiological network mechanisms of TLE.