Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression

Abstract

Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21-41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5-4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5-1.5 Hz signal and amplitude of 30-50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier-a hidden Markov model-was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.

Keywords: PGES; SUDEP; epilepsy; phase-amplitude coupling; seizure.

Graphical Abstract

Figure 1

Global PAC of PGES patient. (A) Scalp EEG traces of a baseline, seizure onset, seizure offset and a following PGES state in patient 10 seizure 2—seizure duration of 88 s, baseline 100 s before onset, PGES state begins 15 s after the seizure. (B) Global PAC comparison between baseline and PGES states in both intracranial and scalp EEGs, as well as the area of increased (>3 dB) PAC. (C) Median area of increased coupling—in number of phase-amplitude combinations—during baseline and PGES states in both intracranial and scalp EEG recordings for patient 10 (*Wilcoxon rank sum test, P < 0.0005). Red lines show 25% and 75% quartiles. Reference electrode—FCz.

Figure 2

Global PAC of PES patient. (A) Scalp EEG traces of a baseline, seizure onset, seizure offset and a following PES state in Patient 7 Seizure 2—seizure duration of 65 s, baseline 900 s before onset. (B) Zoomed in traces of the scalp EEG showing the actual PES state. Note lack of generalization of the state across all of the electrodes. (C) Global PAC comparison between baseline and PES states in both intracranial and scalp EEGs, as well as the area of increased (>3 dB) PAC. (D) Median area of increased coupling—in number of phase-amplitude combinations—during baseline and PES states in both intracranial and scalp EEG recordings for Patient 7 (*Wilcoxon rank sum test, P < 0.005). Red lines show 25% and 75% quartiles. Reference electrode—FCz.

Figure 3

Comparison of global PAC between baseline and P(G)ES states across patients. (A) Median CFC across all of the patients with PGES (n = 3) within iEEG and scalp EEG, highlighting the increased coupling in the PGES state using binned frequency ranges. (B) Median area of increased coupling—in number of phase-amplitude combinations—during baseline and PGES states in both intracranial and scalp EEG recordings for patients with PGES. Red lines show 25% and 75% quartiles. (C) Similar to part (A), median CFC across all of the patients with PES (n = 8) in iEEG and scalp EEG. (D) Median area of increased coupling during baseline and PES states in both intracranial and scalp EEG across patients with PES. Red lines show 25% and 75% quartiles. (E) Median CFC of both (A) and (C) pools joined together (n = 11). (F) Median area of increased coupling during baseline and postictal states across all patients. In parts (B), (D), (F), postictal states have significant increase in mean area—Wilcoxon rank sum test, P < 0.005.

Figure 4

CC of iEEG networks is elevated for P(G)ES state versus baseline. (A) Example network shown for baseline versus PGES state for Patient 10, Seizure 2. Network consists of outward shortest-path connections for the PES median channel (3), normalized to the PGES state. Edge length is scaled by physical distance in the iEEG bipolar montage, and colour and line width is scaled by the connection strength. (B) Summary of networks for all channel pairings for P10, Seizure 2, using cross-channel mean of CC. Here, it is shown as a function of PAC frequency ranges and normalized to the median of the baseline. (C, D) Same as (A) and (B) but for Patient 7, Seizure 2. (E) CC is generally elevated for the PES state when considering the lumped normalized data from all subjects (N_subjects = 9, N_events = 16). (*Wilcoxon rank sum test, P < 0.0005).

Figure 5

State classification of iEEG recordings using a four-state HMM. (A) State classification of an example iEEG trace from Patient 2 (reference electrode—FCz), where S2 is a seizure-like state, and S3 is PGES-like state. (B) Comparison of HMM-driven S3 state classification to the visually estimated PGES durations, with the overall correlation of 0.77 (P = 0.009). (C) Histogram of S2 durations, with a gamma function fitted to the data. The shape parameter of the gamma function—alpha—was calculated to be 3.54 (95% CI 1.59–7.89), which is consistent with the average alpha for seizure duration distributions from Suffczynski et al. (2006) of 3.03 and from Bauer et al. (2017) of 2.66. (D) Histogram of S3 durations, with gamma function fitted to the data with alpha parameter calculated to be 2.21 (95% CI 1.01–4.82) which is consistent with alpha for PGES duration distributions from Bauer et al. (2017) of 1.54. This can also be compared to alpha value of 1.83 (95% CI 1.04–3.20) obtained from distribution of visually estimated PGES durations (not shown). These shape parameters suggest that the transitions (to and from seizure states) occur not according to Poisson process, but rather that the probability of transition varies with time. For both sections (C) and (D), n = 11.

Figure 6

Ictal phase-amplitude coupling dynamics in the onset electrode of P(G)ES patients. (A) iEEG trace from PGES patient 10 (electrode LAT 2) with seizure occurring at 9845–10 033 s. (B) Four second samples of EEG trace with concomitant CFC co-modulograms of different points in the ictal state, baseline state and PGES state, plotted on the same scale. Clear ictal PAC dynamics appear, showing a phase slowing from theta to low delta frequency ranges throughout the seizure. (C) iEEG trace from PES Patient 7 (electrode RPF 6) with seizure occurring at 1038–1103. (D) Similar analysis to part (B) showing the same theta to low delta shift in frequencies. Reference electrode—FCz.

Figure 7

Correlation between frequency of the phase signal of PAC during seizure and postictal state duration. (A) An example patient showing the evolution of normalized phase frequency of the dominant PAC during and right after the seizure, with the red bar showing seizure offset and the blue line showing the fitted beta function. (B) Correlation between the inverse of the beta value and HMM-based postictal state durations, with the dotted line showing trendline.

Figure 8

EEG recording analysis from a SUDEP patient. (A) iEEG recordings from the SUDEP patient with seizure onset identified by a red line and seizure offset by a blue line. Below are the corresponding global PAC throughout the recording. These results highlight the relatively low variability in the PAC, compared to coupling dynamics shown in Figs 1 and 2 and the presence of P(G)ES-like PAC features throughout the recording. (B) Scalp EEG recordings (simultaneous with iEEG in (A). Below are the corresponding global PAC throughout the recording. Note the predominance of the same PAC marker throughout the recording as in (A). (C) Single iEEG trace (from LOF1 electrode) centred around the seizure. This iEEG trace is classified using an HMM. Reference electrode—FCz.