Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Abstract

High frequency oscillations (HFOs, 80–500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

Scalp ripples as prognostic biomarkers of epileptogenicity in pediatric surgery

“…All patients showed icEEG ripples. The icEEG-ripplezone included 33 (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41) contacts with mean rate of 9.8 (6.3-14.3) ripples/min. Of all the ripple sources within icEEG coverage, 79% for ESI and 83% for MSI were concordant to the icEEG-ripple-zone, without difference between lesional and nonlesional patients (ESI: P = 0.8; MSI: P = 0.5).…”

“…Previous studies showed that noninvasively localized ripple generators correlate with regions generating interictal spikes or with different approximations of the EZ defined by clinical information . However, most of these studies included patients who did not necessarily undergo surgery, with the exception of two MEG studies on patients with focal MRI lesions or insular epilepsy .…”

“…Increasing evidence showed that HFOs below 250 Hz (ripples) can be seen on scalp EEG [10][11][12][13][14][15][16][17] and magnetoencephalography [18][19][20][21][22][23][24][25][26][27] (MEG) recordings, despite the small extent of their cortical generators. 11,16,28 Yet, their translation to clinical practice is still limited.…”

“…3,[29][30][31] Previous studies showed that noninvasively localized ripple generators correlate with regions generating interictal spikes 22,27 or with different approximations of the EZ defined by clinical information. 14,19,22,23,[25][26][27] However, most of these studies included patients who did not necessarily undergo surgery, with the exception of two MEG studies on patients with focal MRI lesions 25 or insular epilepsy. 26 Given this paucity of noninvasive studies on surgical cohorts, the prognostic value of scalp-recorded ripples for epilepsy surgery, as yet, remains largely unknown, particularly in children.…”

Fast oscillations >40Hz localize the epileptogenic zone: an electrical source imaging study using high-density electroencephalography

“…MEG studies from different centers suggest that it is possible to detect isolated FO events from interictal MEG recordings with satisfactory data quality [von Ellenrieder et al, 2016; van Klink et al, 2016a; Papadelis et al, 2016]. Four studies validated FO localization against the postsurgical cavity in good outcome patients as gold standard for the EZ; they point towards usefulness of FOs for delineating the EZ [van Klink et al, 2017; Velmurugan et al, 2019; Yin et al, 2019; Tamilia et al, 2020].…”