Functional Brain Network Analysis Using Electroencephalography in Late-onset Lennox–Gastaut Syndrome
Abstract number :
1.484
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2023
Submission ID :
1286
Source :
www.aesnet.org
Presentation date :
12/2/2023 12:00:00 AM
Published date :
Authors :
Presenting Author: Soyoung Park, MD – Soonchunhyang University Bucheon Hospital
Zhi Ji Wang, Researcher – Epilepsy Research Institute – Severance Children's Hospital, Yonsei University College of Medicine; Heung Dong Kim, Professor – Pediatrics – Severance Children's Hospital, Yonsei University College of Medicine; Hoon-Chul Kang, Professor – Severance Children's Hospital, Yonsei University College of Medicine; Nam Young Kim, Professor – Radio Frequency Integrated Circuit (RFIC), Kwangwoon University; Yun Jung Hur, Professor – Pediatrics – Inje University Haeundae Paik Hospital
Rationale:
Lennox–Gastaut syndrome (LGS) ) is a severe developmental epileptic encephalopathy that is mostly diagnosed in childhood on the basis of typical characteristics in electroencephalography (EEG), multiple types of seizures, and a certain degree of cognitive impairment after seizure onset. LGS primarily develops before eight years of age, showing a peak incidence between three and five years of age. However, some adolescent or adult onset cases have been reported (Chourasia et al., 2020; Shyu et al., 2011; Smith et al., 2018). Network analysis is one of the approaches used to explain the underlying mechanism of cognitive decline in epileptic encephalopathy. The clustering coefficient (CC) and characteristic path length (CPL) are common brain parameters of functional networks based on graph theory. The aim of this study was to explore the clinical and network characteristics of patients with late-onset LGS using EEG analysis based on graph theory. We hypothesized that the characteristics of late-onset LGS may be attributable to the different functional network from that in typical age-onset LGS.
Methods:
Late-onset LGS was defined by the appearance of LGS features after eight years of age. We reviewed the medical charts of nine patients with late-onset LGS, and performed electroencephalography connectivity analysis using graph theory. We assessed the clustering coefficient (CC) and characteristic path length (CPL), which are common basic measures of functional networks that represent local segregation and global integration. The characteristics and brain parameters of late-onset LGS were compared with a typical age-onset LGS group.
Results:
In the late-onset group, generalized paroxysmal fast activities (GPFAs) were found in six patients (66.7%), while the typical-onset group showed more predominant GPFAs (10 patients; 90.9%, p = 0.384). Normal Brain MRI findings were more frequently shown (6, 66.6%, p = 0.271) and functional outcome quotients were higher in the late-onset group (64.6, 48.3-87.2, p = 0.497) (Table 1). The late-onset group also showed higher median CC values for the cognitive components in all frequency bands except the delta band, with significant differences in the alpha (p = 0.045) and beta (p < 0.001) bands. The median CPL value was lower in the late-onset group only in the beta band, with no significance (Table 2).
Neurophysiology