Abnormal dynamic connectivity patterns in self-limited epilepsy with centrotemporal spikes

To characterize the dynamic functional network connectivity (dFNC) patterns in children with self-limited epilepsy with centrotemporal spikes (SeLECTS) and to uncover potential abnormalities in neural regulation and related functional impairments.

Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 61 children with SeLECTS and 69 healthy controls (HCs). Independent component analysis (ICA), the sliding window approach and hidden markov modeling (HMM) were employed to systematically investigate potential differences in dFNC properties between the two groups.

The dFNC analysis identified four dynamic states, with State 1 occurring most frequently. State 1 and State 3 represented two polarized connectivity patterns, with State 1 characterized by weak/negative connections and State 3 by widespread strong connections. In both states, children with SeLECTS showed significantly reduced connectivity within the dorsal attention network (DAN) compared with HCs (p < 0.001, FDR-corrected). In the connectivity-balanced State 2, children with SeLECTS showed significantly reduced fractional windows (p = 0.009) and mean dwell time (p = 0.018) compared with HCs, whereas no significant differences were observed in State 4. In addition, temporal variability of functional connectivity between the DAN and visual network (VIS) was significantly reduced in SeLECTS (p < 0.001, FDR-corrected), and this variability was positively correlated with full-scale intelligence quotient (FIQ) (p < 0.05). HMM results from another dynamic perspective further confirmed and echoed the above abnormalities.

This study revealed abnormal dynamic connectivity patterns of brain networks in children with SeLECTS from a multidimensional dynamic perspective. These macroscopic abnormalities may reflect an underlying excitation-inhibition imbalance in neural networks and provide new insights into brain functional reorganization and the potential neurobiological mechanisms of SeLECTS.