Abstracts

Rationale:
Routine scalp EEG is important in the clinical diagnosis of epilepsy. However, a ‘normal’ scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in clinical care delivery [1]. Here we hypothesized that even ‘normal’ EEGs might contain subtle electrophysiological clues of epilepsy. Subtle pathologic changes such as neuronal loss and gliosis are common in chronic epilepsy, and individuals with seizures originating in their dominant hemispheres can experience significant disruptions in normal brain functions [2]. Hence, we investigated a) whether there are indicators of disrupted brain functions in ‘normal’ EEGs of epilepsy patients compared to healthy controls, and b) whether such disruptions are modulated by the side of brain generating seizures in focal epilepsy.
Method:
We analyzed scalp EEG recordings of age matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy (DRFE) who presented with ‘normal’ scalp EEGs at screening. We used the alpha rhythm during eyes-closed wakefulness as a surrogate for normal brain function [3]. After preprocessing, using a bipolar montage of eight channels, we extracted spectral power of the alpha rhythm (8-13 Hz) within 10-second windows. In healthy controls, we modeled the alpha power within each channel using a log-normal distribution, and analyzed a) the extent to which the same features of DRFE patients deviated from healthy, and b) whether there are differences within the DRFE patients based on the hemisphere generating seizures. Furthermore, we used those differences to classify whether an EEG is likely to have been recorded from a DRFE patient, and if so, their epileptic hemisphere using 5-fold cross-validation (Fig 1).
Results:
A comparison of the distribution of alpha power in DRFE against that of healthy is illustrated in Fig 2. We find that a) alpha power is significantly reduced in DRFE compared to healthy, and b) alpha power of right-handed DRFE patients with left hemispheric seizures is significantly lower compared to those with right hemispheric seizures (p< 0.05, KS test). We achieved an AUC of 0.88 in distinguishing DRFE patients and an AUC of 0.71 in identifying the epileptic hemisphere.
Conclusion:
Our results support that EEG-based measures of normal brain functions, such as the alpha rhythm, may help in identification of patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures, or other non-specific abnormalities. Although the alpha rhythm abnormalities are not specific to any neurological disease [4], we propose that such abnormalities can suggest a higher pre-test potential for epilepsy when an individual is screened for epilepsy for the first time. Going forward, we will investigate the contribution of medication to EEG abnormalities and the ability to differentiate subtypes of epilepsy. References •Ebersole, J.S. and R.F. Leroy, Evaluation of ambulatory cassette EEG monitoring: III. Diagnostic accuracy compared to intensive inpatient EEG monitoring. Neurology, 1983. 33(7): p. 853-60. •Engel, J., Jr., et al., Pathological findings underlying focal temporal lobe hypometabolism in partial epilepsy. Ann Neurol, 1982. 12(6): p. 518-28. •Buzsaki, G., Rhythms of the Brain. 2006(Oxford University Press). •Smith, S.J., EEG in the diagnosis, classification, and management of patients with epilepsy. J Neurol Neurosurg Psychiatry, 2005. 76 Suppl 2: p. ii2-7.
Funding:
:NIH grants NINDS-R01-NS92882, NINDS-UH3-NS095495, R01-NS063039, and R01-NS078136.