Ripples on spikes show increased phase‐amplitude coupling in mesial temporal lobe epilepsy seizure‐onset zones

Abstract

Objective Ripples (80–150 Hz) recorded from clinical macroelectrodes have been shown to be an accurate biomarker of epileptogenic brain tissue. We investigated coupling between epileptiform spike phase and ripple amplitude to better understand the mechanisms that generate this type of pathological ripple (pRipple) event. Methods We quantified phase amplitude coupling (PAC) between epileptiform EEG spike phase and ripple amplitude recorded from intracranial depth macroelectrodes during episodes of sleep in 12 patients with mesial temporal lobe epilepsy. PAC was determined by 1) a phasor transform that corresponds to the strength and rate of ripples coupled with spikes, and a 2) ripple-triggered average to measure the strength, morphology, and spectral frequency of the modulating and modulated signals. Coupling strength was evaluated in relation to recording sites within and outside the seizure onset zone (SOZ). Results Both the phasor transform and ripple-triggered averaging methods showed ripple amplitude was often robustly coupled with epileptiform EEG spike phase. Coupling was more regularly found inside than outside the SOZ, and coupling strength correlated with the likelihood a macroelectrode’s location was within the SOZ (p<0.01). The ratio of the rate of ripples coupled with EEG spikes inside the SOZ to rates of coupled ripples in non-SOZ was greater than the ratio of rates of ripples on spikes detected irrespective of coupling (p<0.05). Coupling strength correlated with an increase in mean normalized ripple amplitude (p<0.01), and a decrease in mean ripple spectral frequency (p<0.05). Significance Generation of low-frequency (80–150 Hz) pRipples in the SOZ involves coupling between epileptiform spike phase and ripple amplitude. The changes in excitability reflected as epileptiform spikes may also cause clusters of pathologically interconnected bursting neurons to grow and synchronize into aberrantly large neuronal assemblies.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

“…A 2–5-fold increase in the rate of ripples in the seizure onset zone, relative to the NSOZ, was evident in patients with mesial temporal lobe epilepsy, mesial temporal lobe epilepsy plus (Barba et al, 2007, 2016), and neocortical epilepsy. The accuracy of ripple rates for classifying the SOZ is consistent with previously published studies (Jacobs et al, 2008, 2009; Worrell et al, 2008; Wang et al, 2013; Weiss et al, 2016). …”

“…Prior investigations have also demonstrated that ripples on spike rates are better than ripple on oscillation rates for delineating the seizure onset zone (Wang et al, 2013; Weiss et al, 2016). …”

A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings

“…One-second trials of ripples occurring on inter-ictal discharges were identified using a previously described algorithm (Weiss et al, 2016b; Shimamoto et al, 2018). In brief, (1) INFOMAX independent component analysis (Bell and Sejnowski, 1997) was applied to referential recordings to reduce muscle contamination, and demarcate artefactual ripple events produced by muscle contamination (2) ripples were detected using a Hilbert detector applied to the band-pass filtered and ICA processed signal, (3) for each ripple detected a one-second trial was generated with a ripple centered at 0.5 s, (4) To distinguish ripples that occur during epileptiform spikes from all other ripples, we utilized a validated method (Shimamoto et al, 2018).…”

“…In both manual and automated HFO detection, it is common practice to first apply a high pass filter to the continuous intracranial EEG (iEEG) or local field potential (LFP) recordings (Weiss et al, 2016a, 2016b). After high-pass filtering, HFOs can be observed visually or detected automatically as an increase in the signal amplitude above a threshold of 3–5 standard deviations of the mean for at least three oscillatory cycles (Csicsvari et al, 1999; Staba et al, 2002; Weiss et al, 2016b).…”

“…After high-pass filtering, HFOs can be observed visually or detected automatically as an increase in the signal amplitude above a threshold of 3–5 standard deviations of the mean for at least three oscillatory cycles (Csicsvari et al, 1999; Staba et al, 2002; Weiss et al, 2016b). …”