The neuronal network of laughing in young patients with untreated narcolepsy

To investigate the neuronal correlates of spontaneous laughter in drug-naive pediatric patients with narcolepsy type I (NT1) compared to healthy controls by means of blood oxygen level-dependent (BOLD) MRI.

Objective To investigate the neuronal correlates of spontaneous laughter in drug-naive pediatric patients with narcolepsy type I (NT1) compared to healthy controls by means of blood oxygen leveldependent (BOLD) MRI. Methods Twenty-one children/adolescents with recent onset of NT1 and 21 age- and sex-matched healthy controls were studied with fMRI while viewing funny videos using a naturalistic paradigm. Whole-brain hemodynamic correlates of spontaneous laughter were investigated in each group and compared by use of appropriate second-level general linear model analyses. If recorded, cataplexy events were treated as the effect of no interest at the single-participant level. Correlations analyses between these contrasts and behavioral findings were performed. Results Emotion-induced laughter occurred in 16 patients (294 events) and 21 controls (357 events). In controls, laughter-related BOLD increases involved a widespread cortical and subcortical network including the bilateral motor and premotor areas, cingulated cortex, insula, and amygdala. In NT1, laughter induced BOLD signal increments in the motor cortex, right thalamus, and left subthalamic nucleus/zona incerta (STN/ZI). STN/ZI and thalamic changes were significantly higher during fMRI sessions with laughter without cataplexy compared to sessions in which laughter was associated with cataplexy. Conclusion Laughter expression in individuals with NT1 involves different brain circuits compared to controls by means of overactivation of cortical and subcortical regions belonging to the volitional control of laughter. The activation of the STN/ZI region observed predominantly in patients with NT1 during laugh episodes without cataplexy suggests that the ZI could act to prevent cataplexy.