Altered resting-state neural networks in children and adolescents with functional neurological disorder

OBJECTIVES: Previous studies with adults suggest that aberrant communication between neural networks underpins functional neurological disorder (FND). The current study adopts a data-driven approach to investigate the extent that functional resting-state networks are disrupted in a pediatric mixed-FND cohort. METHODS: 31 children with mixed FND and 33 age- and sex-matched healthy controls completed resting-state fMRI scans. Whole-brain independent component analysis (pFWE < 0.05) was then used to identify group differences in resting-state connectivity. Self-report measures included the Depression, Anxiety and Stress Scale (DASS-21) and Early Life Stress Questionnaire (ELSQ). Resting-state heart rate (HR) and cortisol-awakening response (CAR) were available in a subset. RESULTS: Children with FND showed wide-ranging connectivity changes in eight independent components corresponding to eight resting-state neural networks: language networks (IC6 and IC1), visual network, frontoparietal network, salience network, dorsal attention network, cerebellar network, and sensorimotor network. Children whose clinical presentation included functional seizures (vs children with other FND symptoms) showed greater connectivity decreases in the frontoparietal and dorsal attentional networks. Subjective distress (total DASS score), autonomic arousal (indexed by HR), and HPA dysregulation (attenuated/reversed CAR) contributed to changes in neural network connectivity. Children with FND (vs controls) reported more subjective distress (total DASS score) and more adverse childhood experiences (ACEs) across their lifespan. CONCLUSIONS: Children with FND demonstrate changes in resting-state connectivity. Identified network alterations underpin a broad range of functions typically disrupted in children with FND. This study complements the adult literature by suggesting that FND in children and adolescents emerges in the context of their lived experience and that it reflects aberrant communication across neural networks.