Authors :
Presenting Author: Ruxue Gong, PhD – Emory University
Rebecca Roth, BS – Neurology – Emory University; Allen Chang, PhD – Medical University of South Carolina; Nishant Sinha, PhD – University of Pennsylvania; Alexandra Parashos, MD – Medical University of South Carolina; Kathryn Davis, MD – University of Pennsylvania; Ezequiel Gleichgerrcht, MD, PhD – Emory University; Leonardo Bonilha, MD,PhD – Emory University
Rationale:
Understanding spatiotemporal seizure dynamics has the potential to improve surgical outcomes in drug-resistant temporal lobe epilepsy (TLE). This study investigates ictal dynamics using a power mapping method applied to intracranial electroencephalography (iEEG) recordings.
Methods:
We collected iEEG recordings from 28 patients with TLE: 11 patients (58 seizures) with unfavorable surgical outcomes (NSF = non-seizure free, Engel/ILAE >= 2), and 17 patients (104 seizures) with post-operative seizure control (SF = seizure-free, Engel/ILAE = 1). The ictal iEEG signals were transformed into time-frequency power representations in six frequency bands: theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), gamma1 (35-55 Hz), gamma2 (65-115 Hz), and gamma3 (125-175 Hz).
To gain seizure-activated power values, we normalized the ictal power spectrums against a baseline taken at least six hours away from ictal activity. Electrodes' time-frequency power spectrums were then mapped onto the anatomical space by coregistering the electrode coordinates with individual T1-weighted MRI images. Using the AICHA atlas, we regionally grouped the anatomical power representation, enabling identification of activated brain regions during seizures in each frequency band. At each time point per seizure clip, power activation size across regions was computed as a ratio of activated regions to the total number of regions where iEEG electrodes landed. We compared spatiotemporal dynamics for seizures derived from SF versus NSF patients, including maximum and minimum proportion of activation regions and spreading rate (computed as the difference between maximum and minimum activation size divided by the corresponding time intervals). Additionally, we investigated the functional-structural relationship by correlating iEEG power values with fractional anisotropy (FA) from diffusion tensor imaging (DTI) across regions over time. Group comparisons employed the Wilcoxon rank-sum test with FDR multiple corrections (corrected p < 0.05).