ArticleWorld journal of pediatrics : WJP2024
Neural network mapping of gelastic behavior in children with hypothalamus hamartoma.
Article in World journal of pediatrics : WJP, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Trends in the studies of pharmacoresistant epilepsy- a review based on literature analysis (2015-2025).Acta epileptologica · 2026Review
- Ictal laughter induced by repetitive transcranial magnetic stimulation in stroke patients: a case series.Frontiers in psychiatry · 2026Article
- Association between maternal blood lipids during pregnancy and offspring growth trajectories in a predominantly macrosomic cohort: findings from the ROLO longitudinal birth cohort study.European journal of pediatrics · 2023Article
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11 authors.
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Abstract
backgroundHypothalamus hamartomas (HHs) are rare, congenital, tumor-like, and nonprogressive malformations resulting in drug-resistant epilepsy, mainly affecting children. Gelastic seizures (GS) are an early hallmark of epilepsy with HH. The aim of this study was to explore the disease progression and the underlying physiopathological mechanisms of pathological laughter in HH.
methodsWe obtained clinical information and metabolic images of 56 HH patients and utilized ictal semiology evaluation to stratify the specimens into GS-only, GS-plus, and no-GS subgroups and then applied contrasted trajectories inference (cTI) to calculate the pseudotime value and evaluate GS progression. Ordinal logistic regression was performed to identify neuroimaging-clinical predictors of GS, and then voxelwise lesion network-symptom mapping (LNSM) was applied to explore GS-associated brain regions.
resultscTI inferred the specific metabolism trajectories of GS progression and revealed increased complexity from GS to other seizure types. This was further validated via actual disease duration (Pearson R = 0.532, P = 0.028). Male sex [odds ratio (OR) = 2.611, P = 0.013], low age at seizure onset (OR = 0.361, P = 0.005), high normalized HH metabolism (OR = - 1.971, P = 0.037) and severe seizure burden (OR = - 0.006, P = 0.032) were significant neuroimaging clinical predictors. LNSM revealed that the dysfunctional cortico-subcortico-cerebellar network of GS and the somatosensory cortex (S1) represented a negative correlation.
conclusionsThis study sheds light on the clinical characteristics and progression of GS in children with HH. We identified distinct subtypes of GS and demonstrated the involvement of specific brain regions at the cortical-subcortical-cerebellar level. These valuable results contribute to our understanding of the neural correlates of GS.
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