Normative intracranial EEG maps epileptogenic tissues in focal epilepsy

Abstract

Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.

Keywords: brain network model; epilepsy; epilepsy surgery; functional connectivity; intracranial EEG.

Figure 1

Construction of our normative iEEG atlas. (A) We aggregated 1772 normal channels across 106 patients from the MNI open iEEG atlas with (B) 532 clinically normal channels from 38 HUP patients. (C) We localized each channel to a region in a predefined atlas. (D) We did not observe significant differences in relative band power between HUP and MNI atlases. (E) Combining HUP and MNI data yielded a total composite, normative iEEG atlas of 2304 channels across 144 patients. ROI = region of interest.

Figure 2

Mapping abnormalities of iEEG spectral activity. (A) We aggregate all normative channels within a given region. (B) In an example test patient, we select each channel. (C) We calculate the power in each frequency band for each normative electrode and estimate the normal distribution across channels. (D) Comparison between the calculated relative band-power of the test channel shown in E with normative distribution in C. (F) This process yields a |z| score of spectral activity for each frequency band at each electrode contact in the test patient.

Figure 3

Mapping abnormalities of iEEG functional connectivity. (A) We aggregate normative connectivity between pairs of regions and (B) compare it to connections between the same pair of regions in a test subject. (C) Across all inter-regional pairs we calculate connectivity in each frequency band (D). (E) We also calculate connectivity in the test patient in each frequency band comprising an adjacency matrix in the test patient (F). In order to calculate abnormality scores for each node (G), we calculate the 75th percentile |z| across all edges for each node (H), yielding a single connectivity abnormality feature for each channel (I).

Figure 4

Univariate features are clinically useful. Within the eight most frequently resected regions in our dataset, spectral density is often different between the irritative zone, seizure onset zone, and the normative atlas. In each region and frequency band, we independently tested whether the median normalized band-power was different in the seizure onset zone versus the normative atlas, and the irritative zone versus the normative atlas. We completed this process for each of the following five frequency bands (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, beta: 13–30 Hz, gamma: 30–80 Hz) and across all 20 regions. We thus Bonferroni corrected each test for 100 multiple comparisons. The asterisk above the Greek letters means the seizure onset zone is significantly different from normal, whereas the plus symbol below the letters means the irritative zone is significantly different from normal, Bonferroni corrected for 100 comparisons to α = 0.0005. The shaded area represents the interquartile interval.

Figure 5

Bivariate features are clinically meaningful. (A) Mean coherence |z| is higher in the irritative zone (mean = 1.72 ± 0.43) and seizure onset zone (SOZ) (mean = 1.87 ± 0.44) compared with uninvolved brain (mean = 1.24 ± 0.39, rank-sum test one-tailed P < 1 × 10⁻⁵, Cohen’s d = 1.16 and rank-sum test one-tailed P < 1 × 10⁻⁵, Cohen’s d = 1.51 respectively). The seizure onset zone also has a higher |z| score versus the irritative zone (rank-sum test one-tailed P = 0.045, Cohen’s d = 0.34) (B) Mean coherence absolute z-score is positively correlated with the length of epilepsy diagnosis (Pearson r = 0.25, P = 0.030). (C) The absolute z-score of power is more positively correlated with the absolute z-score of coherence within each good outcome patient compared with within each poor outcome patient (rank-sum test one-tailed P = 0.037, Cohen’s d = 0.56).

Figure 6

Multivariate identification of epileptogenic regions. (A) Classification for resected seizure onset zone (SOZ) versus normal brain. Using a combination of bivariate and univariate features yields a better AUC (0.77, blue curve) compared with only bivariate (0.68, yellow curve) or only univariate features (0.65, purple curve). (B) Curvature test of feature importance reveals delta band coherence is the single best feature, followed by delta band power and gamma band coherence. (C) The irritative zone (IZ) has an intermediate level of predicted epileptogenicity compared with the seizure onset zone (rank-sum P = 0.012, Cohen’s d = 0.59) and uninvolved brain (rank-sum P = 0.0093, Cohen’s d = 0.27). (D) Quantifying the patient-specific area under the precision recall curve for the seizure onset zone reveals a better performance for Engel 1 subjects versus Engel 2+ subjects (AUPRC 0.25 ± 0.17 versus 0.14 ± 0.11, rank-sum P = 0.013, Cohen’s d = 0.78).

Figure 7

Mapping predicted epileptogenicity from abnormality scores.  Left: Distribution of electrode contacts on surface of brain (red: seizure onset zone, green: irritative zone, blue: uninvolved zone, pink dotted outline: resection zone). Middle: Recordings from a channel in the seizure onset, irritative and uninvolved zones (grey) and their |z| scores (blue to red) for each of the 10 features. Right: Predicted epileptogenicity from the random forest model at each electrode location (blue to red). (A) Good outcome (Engel 1A) patient with parietal lobe epilepsy, in which high levels of predicted epileptogenicity cluster in and near the resection zone. (B) Good outcome (Engel 1B) patient with temporal lobe epilepsy in which high levels of predicted epileptogenicity cluster in and near the resection zone. (C) Poor outcome (Engel 2A) patient underwent a right temporal lobe laser ablation with minimal improvement. The ablated nodes had low levels of abnormality; however, the contralateral mesial temporal lobe from which spikes were observed to originate had a higher level of predicted epileptogenicity and could have been a better surgical target.