Zaburzenia mowy związane z patologiczną łącznością mózgu u dzieci z epilepsją

Language impairment is common in childhood epilepsy and may arise in part from disruption of the distributed brain networks that support language. In self-limited epilepsy with centrotemporal spikes (SeLECTS)-the most common focal epilepsy of childhood-hyperconnectivity has been linked to poor language outcomes, but the specific network patterns associated with language dysfunction remain unclear, limiting the ability to target neurostimulation rationally. We recorded high-density EEG from 27 children with SeLECTS and 29 age-matched controls during verb generation and rest, and quantified functional connectivity across multiple frequency bands and bilateral frontal, temporal, occipital, and motor regions. Using multivariate pattern analysis, we identified connectivity patterns that predicted language ability, distinguished patterns shared across groups from those specific to SeLECTS and tested whether spatially specific connectivity provided information beyond whole-brain or hemispheric averages and conventional clinical variables. Frontal and occipitotemporal connectivity, particularly within the left hemisphere, predicted language ability across groups, whereas motor-network connectivity emerged as the dominant SeLECTS-specific predictor, linking the epileptogenic network to language dysfunction. Connectivity between specific regions outperformed averaged connectivity measures and predicted language beyond epilepsy diagnosis and antiseizure medication use. Task-based connectivity also outperformed resting-state connectivity. These findings show that language ability is associated with distributed yet spatially specific patterns of brain connectivity, while epilepsy introduces distinct alterations centered on the epileptogenic network. Identifying these disease-specific network patterns provides mechanistic insight into language dysfunction and a rational basis for spatially targeted neuromodulation.