Electrophysiological brain network biomarkers of ictal cardiac autonomic dysfunction during temporal lobe seizures
Abstract number :
1.272
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2025
Submission ID :
1052
Source :
www.aesnet.org
Presentation date :
12/6/2025 12:00:00 AM
Published date :
Authors :
Presenting Author: Haatef Pourmotabbed, MS – Vanderbilt University
Alexander Douma, BS – Vanderbilt University
Derek Doss, PhD – Vanderbilt University
Graham Johnson, MD, PhD – Mayo Clinic
Ghassan Makhoul, BS – Vanderbilt University Medical Center
Tyler Ball, MD – Vanderbilt University Medical Center
Sarah Bick, MD – Vanderbilt University Medical Center
Victoria Morgan, PhD – Vanderbilt University Medical Center
Dario Englot, MD PhD – Vanderbilt University Medical Center
William Nobis, MD, PhD – Vanderbilt University Medical Center
Catie Chang, PhD – Vanderbilt University
Rationale: Temporal lobe seizures are frequently accompanied by adverse cardiac, respiratory, and autonomic events, such as tachycardia, ictal central apnea, and elevated blood pressure. Furthermore, severe cardiorespiratory dysfunction during seizures has been implicated as a major cause of sudden unexpected death in epilepsy (SUDEP). However, the neural circuit mechanisms underlying ictal cardiac autonomic dysfunction remain largely unknown. Therefore, the purpose of this work is to leverage multimodal electrophysiology and peripheral physiological data to identify brain network biomarkers of cardiac and autonomic abnormalities during temporal lobe seizures.
Methods: This study included simultaneous stereo-EEG (SEEG) and EKG data collected during 70 non-convulsive temporal lobe seizures from 10 epilepsy patients. SEEG contacts were grouped according to seven brain areas ipsilateral to the seizure onset zone: the hippocampus, amygdala, insula, superior temporal lobe/temporal pole (STL), middle temporal lobe (MTL), superior/middle frontal gyrus (S/MFG), and inferior frontal gyrus/orbitofrontal cortex (IFG/OFC). The heart rate (HR) and heart rate variability (HRV RMSSD) were derived from the EKG data in the preictal and ictal periods, and the power spectral density (PSD) and functional connectivity (FC) strength (imaginary coherence) were derived from the SEEG data for each brain area in six frequency bands. Mixed-effects models were used to compare the HR and HRV between the seizure periods and to relate the preictal-to-ictal change in PSD and FC to the change in HR and HRV, while covarying for the side of onset.
Results: The HR was increased and the HRV was reduced in the ictal relative to the preictal period (p < 0.05, false discovery rate [FDR]-corrected), and the preictal-to-ictal change in HR was negatively correlated with the change in HRV (Fig. 1). In addition, a greater elevation in HR was associated with a greater preictal-to-ictal increase in both the FC and PSD (Fig. 2). The relationship between HR and FC was significant (pFDR < 0.05) for the IFG/OFC, STL, MTL, and insula in all frequency bands, the S/MFG in beta and gamma, and the amygdala and hippocampus in theta and gamma. The relationship between HR and PSD was significant for the IFG/OFC, STL, and insula in all frequency bands, the MTL and S/MFG in theta, alpha, beta, and low gamma, and the amygdala and hippocampus in theta and gamma. The PSD and FC were not significantly related to the HRV.
Neurophysiology