Presurgical EEG Network Analysis Predicts Seizure Outcomes After Corpus Callosotomy in Children with Drug-resistant Epilepsy
Abstract number :
2.443
Submission category :
9. Surgery / 9B. Pediatrics
Year :
2024
Submission ID :
1076
Source :
www.aesnet.org
Presentation date :
12/8/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Georgios Ntolkeras, MD – Boston Childrens Hospital
Vitor Pimenta de Figueiredo, BS – Boston Children's Hospital
Navaneethakrishna Makaram, PhD, MS – Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
Scellig Stone, MD – Boston Children's Hospital
Phillip Pearl, MD – Boston Children’s Hospital
Alexander Rotenberg, MD PhD – Boston Children's Hospital - Harvard Medical School
Ellen Grant, MD – Boston Children's Hospital
Eleonora Tamilia, PhD – Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
Rationale: Corpus callosotomy (CC) is a palliative surgical procedure for drug-resistant epilepsy (DRE) aiming to reduce the severity/frequency of generalized seizures by disconnecting the two cerebral hemispheres. However, seizure reduction after CC is variable and difficult to predict. Not all patients with generalized seizures benefit in the same way from CC, and the literature indicates a variation of clinical outcome predictors.
In this study, we determined the presurgical ictal and interictal EEG networks of 34 children with CC and tested whether the strength of the network connections between and within hemispheres predicted post-surgical seizure reduction.
Methods: We studied scalp EEG from children with DRE who had CC with known outcome. We analyzed 5 min of interictal data and 3 s around each ictal onset (Fig 1A).
We estimated brain functional connectivity (FC) in five frequencies (delta, theta, beta, alpha, gamma) (Fig 1A) and computed: within-hemisphere FC (WH-FC), between-hemisphere FC (BH-FC), and interhemispheric asymmetry (IHA) (Fig 1A). Each patient was classified based on their seizure-reduction outcome (Excellent: >90%, Intermediate: 50-90%, Poor: < 50%) (Fig1C).
We tested whether the EEG network characteristics correlated with outcome (Spearman correlation, Bonferroni correction for multiple comparisons), and predicted the likelihood of excellent seizure-reduction (ROC-curve analysis; Excellent vs. Intermediate/Poor) separately for ictal and interictal data.
Results: We included 34 children (Fig 1C; median age:10.4 years, IQR: 6.1-12.7): seizure reduction was poor in 15, intermediate in 8, good in 11.
Interictal gamma and alpha-network characteristics correlated with post-CC seizure reduction: the greater the WH- and BH-FC, the greater the reduction post surgery (alpha: R=0.53; R=0.50; p< 0.05, gamma: R=0.51; p< 0.05) (Fig. 2A). BH-FC, WH-FC, and asymmetry of interictal gamma- and alpha-network predicted excellent seizure-reduction with an Area-Under-ROC-Curve (AUROCC) of 87-90% for gamma and 87-89% for alpha.
For the ictal data, in patients with spams or head-drops, high IHA of the alpha-network correlated with high post-CC seizure reduction (R=0.58; p< 0.05, Fig 2B) and predicted excellent outcome with AUROCC of 76%. Other seizure types (tonic or myoclonic) were less frequent in our cohort, thus their correlation with outcome could not be investigated. We did not study the ictal gamma networks due to the presence of muscular and movement artifacts at ictal onset in those frequencies.
Conclusions: We show that presurgical EEG network analysis, during ictal and interictal periods, helps predict post-CC seizure outcomes in children with DRE.
Our data suggest that children with overconnected brain networks (characterized by high FC in the interictal period and high asymmetry at ictal onset) are most likely to experience excellent post-CC seizure reduction. Future studies comparing post-CC connectivity changes may help support these findings.
Funding: Office of Faculty Development, Boston Children's Hospital; National Institutes of Health, Grant/Award Number: R03NS127044
Surgery