Evolution of Brain Glucose Metabolic Abnormalities in Children With Epilepsy and SCN1A Gene Variants

Ananyaa Kumar1, Csaba Juhász1,2,3,4, Aimee Luat2,3

  • 11 PET Center and Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA.

Journal of Child Neurology
|September 6, 2018
PubMed

Insights

Children with SCN1A gene variants and epilepsy show normal brain glucose metabolism early on. By age 4, they develop hypometabolism in key brain regions, potentially serving as a disease biomarker.

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Genetics

Background:

  • Epilepsy associated with SCN1A variants often presents with drug-refractory seizures.
  • Standard MRI may not reveal abnormalities in these cases.
  • Longitudinal metabolic changes in SCN1A-related epilepsy are not well-characterized.

Purpose of the Study:

  • To investigate the longitudinal changes in cerebral glucose metabolism in children with SCN1A variants and epilepsy.
  • To determine if FDG-PET can identify characteristic metabolic patterns.
  • To explore the potential of FDG-PET as a biomarker for disease progression.

Main Methods:

  • Three children with SCN1A variants and refractory epilepsy underwent FDG-PET scans at two time points: early childhood (6 months-1 year) and later childhood (3.5-5.5 years).
  • FDG uptake was quantified and compared to age- and gender-matched controls.
  • Regional metabolic patterns were analyzed across different cortical areas.

Main Results:

  • At baseline, children with SCN1A variants showed normal brain glucose metabolism, similar to controls.
  • At follow-up, significant bilateral cortical hypometabolism was observed, particularly in the frontal, parietal, and temporal lobes.
  • Milder hypometabolism was noted in the occipital cortex.

Conclusions:

  • Children with epilepsy and SCN1A variants exhibit normal glucose metabolism in infancy.
  • Progressive bilateral cortical hypometabolism develops by early childhood, with specific regional predilections.
  • This distinct metabolic pattern may serve as a characteristic biomarker for SCN1A-related epilepsy and aid in monitoring treatment response.

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