Advanced Characterization of Effective Connectivity in Temporal Lobe Epilepsy: A Granger Causality Approach Leveraging EEG Phase-Space Dynamics

Lashkari, Saleh; Pedram, Seyyed Soroosh; Ravari, Mohammad; Omouri Sarabi, Emad; Zendehbad, Seyyed ali; mazrooei rad, elias

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Advanced Characterization of Effective Connectivity in Temporal Lobe Epilepsy: A Granger Causality Approach Leveraging EEG Phase-Space Dynamics

Saleh Lashkari

, Seyyed Soroosh Pedram

, Mohammad Ravari

, Emad Omouri Sarabi

, Seyyed ali Zendehbad

, Elias Mazrooei rad ^*

Department of Biomedical Engineering, Khavaran Institute of Higher Education, Mashhad, Iran , elias_mazrooei@yahoo.com

Abstract: (18 Views)

Introduction: Temporal Lobe Epilepsy, especially when resistant to drugs, severely restricts individuals affected with the disease. The seizures can manifest as focal aware, focal impaired-awareness, secondarily generalized, or mixed. This variability allows for the estimation of effective connectivity within EEG signals, hence showing the activity patterns of brain regions. Mapping EEG signals into phase space provides meaningful information about brain dynamics and behavior. Thus, the present study aimed at quantifying effective connectivity in EEG signals by phase-space vector analysis and Poincaré sectioning. Materials and Methods: EEG signals from patients with drug-resistant mesial temporal lobe epilepsy were recorded in both pre-ictal and ictal states. Time delay was estimated with the Average Mutual Information (AMI) method, and phase-space reconstruction was carried out in two and three dimensions. A Poincaré section was then applied in 2-D phase space. The P8–O2 derivation was selected as the reference channel and its effective connectivity with five temporal channels (T7–FT9, T8–P8, F8–T8, T7–P7, and F7–T7) was investigated using Granger Causality (GC) indices computed through four different analytical approaches: frequency-domain GC, 2-D phase-space GC, 3-D phase-space GC, and Poincaré-section GC. Results: The GC index significantly increased from pre-ictal to ictal states, as expected from previous studies. Comparison of frequency-domain results with the new phase-space methods showed that the phase-space–based analyses provided more accurate estimation of GC increments. Among the methods compared, the 3-D phase-space approach had the largest increase in mean GC and showed the best performance. The T8–P8 channel pair seemed to bear the most salient changes between the two states and thus might represent the core biomarker site for seizure onset. Conclusion: Nonlinear methodologies of analysis, especially the combination of phase-space reconstruction and GC, represent a very powerful approach to effective connectivity study in EEG signals. These techniques can grasp complex dynamic couplings and directional information flows between different brain regions with good precisions of localizing epileptogenic networks. The proposed nonlinear methods present greater sensitivity and specificity in identifying mesial temporal lobe epilepsy and other seizure disorders compared to standard linear methods.

Keywords: Nervous System Diseases, Seizures, Methods

Type of Study: Research --- Open Access, CC-BY-NC | Subject: Basic research in Neuroscience

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