Brain Metabolic Changes Associated with Post-stroke Pathological Laughing and Crying: An 18F-FDG-PET Study in Pontine Stroke

Abstract

Background: Pathological laughing and crying (PLC) is characterized by sudden, uncontrollable, and inappropriate episodes of laughter or crying. While previous studies have identified PLC-associated structural lesions, the underlying metabolic alterations in these patients remain unclear. Objective: We aimed to investigate cerebral metabolic alterations in patients with PLC following pontine stroke using 18F-fluorodeoxyglucose-positron emission tomography imaging. Methods: In this retrospective study, we included 49 patients with pontine stroke admitted to Severance Hospital between January 2011 and December 2021. Patients were classified into PLC (n = 20) and non-PLC (n = 29) groups. 18F-fluorodeoxyglucose-positron emission tomography images obtained within 14 days of admission were analyzed using the SPM 12 software. Voxel-wise two-sample t-tests were performed to compare brain metabolism between the two groups (Pfamily-wise error-corrected < 0.05). Multiple regression analysis was conducted to identify brain regions significantly associated with PLC severity, adjusting for age and stroke lesion volume. Results: Compared with that of the non-PLC group, the PLC group exhibited significant hypometabolism in the right superior frontal gyrus (Pfamily-wise error-corrected < 0.05). Multiple regression analysis revealed that decreased metabolism in the right inferior and middle temporal gyri was significantly correlated with higher Pathological Laughter and Crying Scale scores, indicating greater PLC severity. No brain regions showed positive correlations with the Pathological Laughter and Crying Scale scores. Conclusions: Our findings reveal that PLC following pontine stroke is associated with distinct patterns of hypometabolism, particularly in the right superior frontal gyrus and the right inferior and middle temporal gyri. These regions may contribute to the regulation of emotional expression and provide insights into the neural mechanisms underlying PLC.