Predictive Modeling Based on Functional Connectivity of Interictal Scalp EEG for Infantile Epileptic Spasms Syndrome
Abstract number :
3.263
Submission category :
3. Neurophysiology / 3C. Other Clinical EEG
Year :
2024
Submission ID :
75
Source :
www.aesnet.org
Presentation date :
12/9/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Sotaro Kanai, MD, PhD – Tottori University, Faculty of Medicine
Masayoshi Oguri, PhD – Kagawa Prefectural University of Health Sciences
Tohru Okanishi, MD, PhD – Tottori University
Yosuke Miyamoto, MD – Kyoto Prefectural University of Medicine
Masanori Maeda, MD, PhD – Wakayama Medical University
Kotaro Yazaki, MD – Osaka Metropolitan University Graduate School of Medicine
Ryuki Matsuura, MD, PhD – Saitama Children’s Medical Center
Takenori Tozawa, MD, PhD – Kyoto Prefectural University of Medicine
Satoru Sakuma, MD, PhD – Osaka Metropolitan University Graduate School of Medicine
Tomohiro Chiyonobu, MD, PhD – Kyoto Prefectural University of Medicine
Shin-ichiro Hamano, MD, PhD – Saitama Children’s Medical Center
Yoshihiro Maegaki, MD, PhD – Tottori University, Faculty of Medicine
Rationale: Predicting long-term outcomes for infantile epileptic spasms syndrome (IESS) is challenging. Few factors, such as symptomatic etiology, early age at onset, and delayed or inappropriate treatment, can predict unfavorable outcomes at the onset of IESS. We postulated that even within a cohort with IESS of various etiologies, distinct pathological differences might influence seizure outcomes from the onset. This study aims to delineate the electrophysiological variances between infants with IESS and healthy controls and to devise a predictive model for long-term seizure outcomes.
Methods: We conducted a retrospective multi-center study in Japan, including 30 individuals in the seizure-free group, 23 in the seizure-residual group (based on the most recent follow-up assessment), and 20 in the control group. We performed a comprehensive quantitative analysis of pretreatment electroencephalography (EEG), including the relative power spectrum (rPS), weighted phase-lag index (wPLI), and network metrics (Figure 1). K-fold cross-validation and receiver operating characteristic curves were used to determine the predictive capabilities of our analyses. Follow-up EEGs at 2 years of age were also analyzed to elucidate physiological changes among groups.
Results: Infants in the seizure-residual group exhibited increased rPS in theta and alpha bands at IESS onset compared to the other groups (all p < 0.0001) (Figure 2A). The control group showed higher rPS in fast frequency bands, indicating potentially enhanced cognitive function. The seizure-free group presented increased wPLI across all frequency bands (all p < 0.0001). Global network analyses did not distinctly separate IESS groups from the control group. In the seizure-free group, the wPLI values in follow-up EEG were significantly lower than those at onset in all but the alpha frequency band (all p < 0.0001), with increasing rPS in high-frequency bands (all p < 0.0001) (Figure 2B). Our predictive model utilizing wPLI anticipated long-term outcomes at IESS onset with an area under the curve of 0.75 and an evaluation accuracy of 69.8%.
Neurophysiology