Authors :
Presenting Author: Saeed Jahromi, M.Sc. – Cook Children's Health Care System
Margherita A.G. Matarrese, M.Sc. – Department of Engineering – Università Campus Bio-Medico di Roma; Lorenzo Fabbri, B.Sc. – Department of Bioengineering – The University of Texas at Arlington; Eleonora Tamilia, Ph.D. – Harvard Medical School; M Scott Perry, M.D. – Cook Children's Health Care System; Joseph Madsen, M.D. – Department of Neurosurgery – Harvard Medical School; Jeffrey Bolton, M.D. – Department of Neurology – Harvard Medical School; Scellig S.D. Stone, M.D., Ph.D. – Department of Neurosurgery – Harvard Medical School; Phillip Pearl, M.D. – Department of Neurology – Harvard Medical School; Christos Papadelis, Ph.D. – Cook Children's Health Care System
Rationale:
Interictal spikes, ripples, and fast ripples are recognized epilepsy biomarkers. Yet, they have limited specificity to delineate the epileptogenic zone (EZ). Interictal spikes and ripples are common but often occur in both the EZ and a large area whose resection is unnecessary for seizure freedom. Contrarily, fast ripples are more specific to the EZ but are difficult to record with macroelectrodes due to their low amplitude and high frequency and are less often present in intracranial EEG (iEEG) recordings. Previous studies have shown that spikes and ripples propagate across large brain areas; the onset of this propagating activity is a more specific biomarker of epilepsy than areas of spread. Here, we examined whether the spike-ripple onset overlap (SRO) serves as a highly specific biomarker of the EZ. We hypothesize that this biomarker can be seen in most children with drug resistant epilepsy (DRE) and its resection predicts surgical outcome.
Methods:
We retrospectively analyzed five minute iEEG recordings of 41 consecutive DRE children (24 males) who had resective surgery [median age: 13 years (6.5-16)] divided into good outcome (25 patients, Engel I) and poor outcome (16 patients, Engel 2-4) groups (follow-up >1 year). We identified spikes, ripples, and fast ripples from interictal iEEG data, and their spatiotemporal propagations, using an in-house algorithm (Figure 1A). We then scored iEEG electrodes based on their temporal occurrence in propagations. To determine onset and spread areas for each propagation type, we established optimal thresholds for the scores, using resection of good outcome patients as reference, and mapped SRO (Fig. 1B). We calculated the closest distance and overlap between these areas and resection utilizing Fisher's exact test to evaluate the significance of resecting these areas on outcome.
Results:
We found spikes and ripples in all patients, fast ripples in 16 patients, and mapped their propagations in 39, 40, and 10 patients respectively. We calculated propagation rates as the number of propagations per second for spikes [0.31 (0.03-0.71)], ripples [0.09 (0.04-0.30)], and fast ripples [0.11 (0.03-0.27)] and found SRO in 83% of the cohort. In good outcome patients, the distance of SRO from resection was 6.2 mm [4.8-9.3; p< 0.01], lower than onset zones [spikes: 8.5 mm (7.1-11.7); ripples: 9.7 mm (6.0-14.3)]. Resection percentage was higher for SRO [89% (66-100), p< 0.01] compared to onsets [spikes: 71% (51-81); ripples: 67% (53-85)] (Figure 2A). A 50% resection ratio of onset zones, fast ripple zone and SRO predicted outcome. By raising the ratio to 80%, only SRO predicted outcome (p=0.01) (Figure 2B).