Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue

Abstract

The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localizing epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner. To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 participants (21 598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We proposed that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures postoperatively. We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions that were spared by surgery were more abnormal than those resected only in patients with persistent postoperative seizures (t = -3.6, P = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (area under curve 0.75 P = 0.0003). Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.

Keywords: EEG; cortical localization; epilepsy surgery; epileptogenic zone; intracranial electrodes.

Figure 1

Normative band power varies across regions. Mean relative band power in each region for each of the five frequency bands of interest. The colour axes scale differs for each frequency band with generally higher power in lower frequencies.

Figure 2

Normative band power as a reference to detect abnormalities in individual patients. (A) Visualization of the regions covered by the implanted electrodes in an example patient with epilepsy. 18 of the 128 regions were sampled by the electrode contacts in this patient (black circles). Time series from two example regions are shown that are without obvious epileptiform activity (inset). One example region (left lateral occipital gyrus 2) was the seizure onset zone in this patient. (B) Relative band power for each of the two regions, across each frequency band is plotted for the normative data (coloured violin plot; each point is a normative participant). Data are standardized (mean subtracted and divided by standard deviation). Relative band power z-score for Patient 1216 is plotted as a vertical dashed line on the same scale. The z-scores indicates that the left middle temporal gyrus is normal in all frequency bands (maximum absolute z = 1.04). The left lateral occipital gyrus is more abnormal in theta (maximum absolute z = 2.99) and gamma (absolute z = 1.59). (C) Maximum absolute z-score for each region plotted for the patient. Larger values indicate greater abnormality in any frequency band.

Figure 3

Interictal band power abnormality as a marker of epileptogenic tissue in two example individual patients. [A(i) and B(i)] Postoperative T₁-weighted MRI scans showing the location of the resection as indicated by the green arrow. [A(ii)] Replication of the patient in Fig. 2 with the regions that were later surgically resected circled in black. Non-resected regions are circled in white. A direct comparison and quantification in the lower panel shows resected regions to be more abnormal than spared. Each data-point is a separate region. This patient was seizure free after surgery (ILAE1). [B(ii)] Visualization of data from a second patient with a frontal lobe implantation. Multiple abnormal regions were present outside the resection and spared by surgery. This patient had had continued postoperative seizures (ILAE4). In both patients, the $D_{RS}$ metric quantified the difference between resected and spared regions in terms of their abnormality.

Figure 4

Interictal band power abnormality distribution in resected versus spared tissue explains postsurgical seizure freedom. (A) The $D_{RS}$ values, which indicate whether resected regions were more abnormal than spared regions, for each patient separated by outcome group. At a group level, the resected regions were more abnormal than spared regions in ILAE3+ patients, with substantially and significantly higher $D_{RS}$ values. Each point is an individual patient, black horizontal line indicates the mean, grey box indicates the standard deviation. (B) Using $D_{RS}$ as a binary classifier with a receiver operator characteristic curve (ROC) allows a calculation of the area under the curve (AUC) = 0.75 to predict ILAE outcome class. (C and D) Replication of the findings in (A) using other data segments at least 4 h away from the first data segment.

Figure 5

Resection of interictal spikes does not explain outcome. Data-points represent individual patients and indicate the dice overlap of regions containing contacts with interictal spikes and regions that were later resected. Although regions with spikes were more commonly resected (mean dice >0.5), the effect does not explain the outcome.