Normative intracranial EEG highlights epileptic abnormalities across wakefulness and sleep

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Abstract
There is great interest in using quantitative methods to localize epileptic networks from intracranial EEG, a vital part of care for patients with drug resistant epilepsy (DRE). In particular, there is evidence that using interictal data for this purpose, which could eliminate the need to record seizures, has great potential to reduce morbidity from precipitated seizures and to decrease length of stay. How much of this data is required for this purpose, and from what state(s) of consciousness, is not known. In this study we analyzed interictal intracranial EEG (iEEG) data from 30 subjects and compared it against normative reference iEEG derived from 106 additional patients. We summarized brain activity and connectivity by computing spectral power and coherence in 6 frequency bands and computed z -scores relative to normal features within the same anatomical region. We used a validated algorithm to estimate the sleep or wake state. We applied a cross-validated random forest model to assign predicted abnormality value to each channel for each state of wakefulness. To determine the effectiveness of this approach for each unseen patient, we computed the area under the precision recall curve (AUPRC) between predicted abnormality within and outside of the resection zone. We further identified associations between predicted abnormalities and neuropsychological testing performance, highlighting applications of quantitative biomarkers to epilepsy comorbidities. We found that subjects with good seizure outcome (Engel 1) at 2 years had higher AUPRC than subjects with poor seizure outcome for predicted abnormalities in N2 sleep (Mann-Whitney test; p Holm=Bonferroni < 0.05). Combining features from wakefulness and NREM sleep best separated good and poor seizure outcome subgroups (Mann-Whitney test; p Holm=Bonferroni < 0.05, Cohen’s d = 1.62). Combinations of wake and sleep abnormalities and interictal spikes explained the variance in pre-surgical neuropsychological testing ( R 2 = 0.57-0.58).

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