Graph Theory-Based Electroencephalographic Connectivity and Its Association with Ketogenic Diet Effectiveness in Epileptic Children

Nutrients. 2021 Jun 25;13(7):2186. doi: 10.3390/nu13072186.

Abstract

Ketogenic diet therapies (KDTs) are widely used treatments for epilepsy, but the factors influencing their responsiveness remain unknown. This study aimed to explore the predictors or associated factors for KDTs effectiveness by evaluating the subtle changes in brain functional connectivity (FC) before and after KDTs. Segments of interictal sleep electroencephalography (EEG) were acquired before and after six months of KDTs. Analyses of FC were based on network-based statistics and graph theory, with a focus on different frequency bands. Seventeen responders and 14 non-responders were enrolled. After six months of KDTs, the responders exhibited a significant functional connectivity strength decrease compared with the non-responders; reductions in global efficiency, clustering coefficient, and nodal strength in the beta frequency band for a consecutive range of weighted proportional thresholds were observed in the responders. The alteration of betweenness centrality was significantly and positively correlated with seizure reduction rate in alpha, beta, and theta frequency bands in weighted adjacency matrices with densities of 90%. We conclude that KDTs tended to modify minor-to-moderate-intensity brain connections; the reduction of global connectivity and the increment of betweenness centrality after six months of KDTs were associated with better KD effectiveness.

Keywords: drug-resistant epilepsy; functional connectivity; graph theory; interictal electroencephalography; ketogenic diet therapy.

Publication types

  • Clinical Trial

MeSH terms

  • Adolescent
  • Brain / physiopathology
  • Child
  • Child, Preschool
  • Diet, Ketogenic / methods
  • Diet, Ketogenic / psychology*
  • Electroencephalography*
  • Epilepsy / diet therapy*
  • Epilepsy / physiopathology*
  • Female
  • Humans
  • Infant
  • Male
  • Models, Theoretical*
  • Neural Networks, Computer
  • Young Adult