EEG Connectivity Features During Preictal Phases as a Seizure Prediction Biomarker
Abstract number :
2.179
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2024
Submission ID :
905
Source :
www.aesnet.org
Presentation date :
12/8/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Michele Jackson, BA – Boston Childrens Hospital
Navaneethakrishna Makaram, PhD, MS – Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
Tanuj Hasija, PhD, Msc. – Paderborn University
Doroteja Dragovic, MS – Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
Edeline Jean Baptiste, BS – Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
Saeid Sadeghian, MD – Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
Stephanie Dailey, BA – Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
Fatemeh Mohammad Alizadeh Chafjiri, MD – Boston Children’s Hospital, 300 Longwood Ave, Boston, MA 02115, USA
William Bosl, PhD – Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA, Clinical Neuroinformatics & AI Laboratory, The Data Institute, University of San Francisco, San Francisco, CA, USA
Eleonora Tamilia, PhD – Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
Tobias Loddenkemper, MD – Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
Solveig Vieluf, PhD – LMU University Hospital, LMU Munich
Rationale: Seizure forecasting may enable improved seizure preventive care. We aim to advance prediction biomarker development by evaluating brain connectivity strength across electroencephalogram (EEG) frequency bands from patients with generalized tonic-clonic (GTC) and focal to bilateral tonic-clonic (FBTC) seizures during preictal periods.
Methods:
We included patients with epilepsy, aged 1 month to 21 years who were enrolled (2015-2021) in the long-term monitoring unit at Boston Children’s Hospital and presented with GTC or FBTC seizures. Seizure onset, offset and semiology were determined through video-EEG review.
We analyzed standard 19-channel EEG (10-20 montage) and evaluated three 5-min periods prior to seizure onset: 1) distant preictal (120-115min before onset), 2) intermediate preictal (62.5-57.5min), 3) immediate preictal (5.5min-30s before onset). We filtered the signal (Notch:60Hz and bandpass:1-100Hz), detected and excluded artifacts with the cleanEEG1 pipeline, and removed bad channels. The signal was average referenced and segmented into 10 nonoverlapping 30s epochs. Functional connectivity (FC) matrices were obtained by computing the orthogonalized amplitude envelope correlation between each pair of EEG channels/epoch in 3 frequency bands: delta δ [1–4Hz]; theta θ (4–8Hz]; alpha α (8–13Hz]. FC matrix was averaged across epochs to obtain interval FC for each frequency band. To assess overall connectivity strength, we computed averages of the network degree distribution to obtain Mean FC (MFC). We performed a two-way mixed analysis of variance with preictal period as within and seizure type as between patient factors to compare frequency-specific MFC across preictal periods for GTC and FBTC seizure groups followed by post-hoc Bonferroni adjustments.
Results:
From 450 patients enrolled with 900 seizures, we included 27 patients (40 seizures) (51.9% male, median age:13.8 yrs) with 9 GTC and 31 FBTC seizures. Results revealed a main effect of the preictal period on delta, theta and alpha MFC (F(2,76)=3.67, p=0.03, ƞ2=0.09; F(1.59,60.10)=4.59, p=0.02, ƞ2=0.11; F(1.50,56.87)=4.57, p=0.02, ƞ2=0.11, Figure 1). MFC at intermediate preictal period for delta (0.14±0.01), theta (0.12±0.01) and alpha (0.11±0.01) were lower than immediately prior to seizure onset (immediate preictal) for delta (0.18±0.01), theta (0.16±0.02) and alpha (0.16±0.02) (p=0.03, p=0.02, p=0.04 respectively). MFC for theta showed a trend for a decrease from distant preictal period (0.14±0.02) to intermediate preictal period (p=0.07). Seizure group effect revealed a trend for theta MFC (F(1,38)=3.89, p=0.06, ƞ2=0.09). Interactions between seizure group and preictal period were not significant.
Conclusions:
EEG connectivity strength in delta, alpha and theta frequencies may first decrease during the intermediate preictal period followed by an increase immediately prior to the onset of GTC and FBTC seizures, and warrants further research into EEG-based biomarkers for seizure prediction.
1Delorme A et al. EEGLAB: an open source toolbox. J Neurosci Methods. 2004 Mar 15;134(1):9-21.
Funding: The Epilepsy Research Fund supported this study.
Neurophysiology