Context: Anterior temporal lobectomy is an effective treatment for medically intractable temporal lobe seizures. Identification of seizure focus is essential to surgical success.
Objective: To examine the usefulness of presurgical electroencephalography (EEG), magnetic resonance imaging (MRI), and neuropsychological data in the lateralization of seizure focus.
Design: Presurgical EEG, MRI, and neuropsychological data were entered, independently and in combination, as indicators of seizure focus lateralization in discriminant function analyses, yielding correct seizure lateralization rates for each set of indicators.
Setting: Comprehensive Epilepsy Program, Shands Teaching Hospital, University of Florida, Gainesville.
Patients: Forty-four right-handed adult patients who ultimately underwent successful anterior temporal lobectomy. Left-handed patients, those with less-than-optimal surgical outcome, and any patients with a history of neurological insult unrelated to seizure disorder were excluded from this study.
Main outcome measures: For each patient presurgical EEG was represented as a seizure lateralization index reflecting the numbers of seizures originating in the left hemisphere, right hemisphere, and those unable to be lateralized. Magnetic resonance imaging data were represented as left-right difference in hippocampal volume. Neuropsychological data consisted of mean scores in each of 5 cognitive domains.
Results: The EEG was a better indicator of lateralization (89% correct) than MRI (86%), although not significantly. The EEG and MRI were significantly superior to neuropsychological data (66%) (P=.02 and .04, respectively). Combining EEG and MRI yielded a significantly higher lateralization rate (93%) than EEG alone (P<.01). Adding neuropsychological data improved this slightly (95%).
Conclusions: The EEG and MRI were of high lateralization value, while neuropsychological data were of limited use in this regard. Combining EEG, MRI, and neuropsychological improved focus lateralization relative to using these data independently.