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

1. Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada. 2. Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University 3. Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada. 4. Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada. 5. Claudio Munari Epilepsy Center, Niguarda Hospital, Milan, Italy. 6. Department of Neuroscience (DINOGMI), University of Genoa, Italy 7. IRCCS, Child Neuropsychiatry Unit, Instituto Giannina Gaslini, Genoa, Italy 8. Department of Medicine, Queens University, Kingston, ON, Canada

P a g e | 6 Avigdor et al. | Fast oscillations localize the epileptogenic zone availability of electrode positions and MRI co-registration, a minimum sampling rate of 500 Hz, as well as satisfactory data quality. Patient demographic and clinical information is provided in Table 1. The study conforms to the Declaration of Helsinki and was approved by Niguarda Hospital in Milan, Italy. A written informed consent was signed by all patients prior to study participation.

HD-EEG data acquisition and preprocessing
HD-EEG was recorded using a 256-electrode EGI system (Electrical Geodesic Incs, EGI, now Philips Neuro, Eugene, OR, USA) with a sampling rate of 500 Hz and hardware filter settings of 0.3 Hz for the high pass and 200 Hz for the low pass filter. The recording duration was approximately 1.5 h; this duration was chosen to enable the patient to fall asleep. The impedance was kept under 70 kOhm.
HD-EEG processing was performed with the Brainstorm software package [Tadel et al., 2011].
For interictal spike detection and analysis, preprocessing included 0.3-70 Hz band-pass filtering and DC correction (baseline window from −1000 ms to−500 ms before the marked spikes). No filter was applied for FO events.
The EEG sensor positions were estimated using digitalization with a SofTaxicOptic system (EMS s.r.l., Bologna, Italy). A linear coregistration with a pre-implant MRI (Achieva 1.5 T, Philips Healthcare) was performed. The digitized positions of the electrodes were then coregistered to the scalp surface segmented from the anatomical MRI of each patient, using a surface matching algorithm within the Brainstorm software. The HD-EEG electrodes located on the face and on the neck (approximately 40 channels) were excluded for further analysis in order to avoid artifacts caused by muscle or poor-contact electrodes [Hedrich et al., 2017].

Interictal event marking
Spikes were marked at their peak by an epileptologist (CA). All spike events occurring during N1, N2 or N3 sleep were screened for FO candidate events by a second epileptologist (BF). FO were defined as at least four oscillations clearly standing out of the background EEG in the gamma (40-80 Hz) and ripple (80-160 Hz) frequency band in the same electrodes as the spike that was marked at that time. FO candidate events were verified using a time-frequency plot screened for an isolated peak within the FO band (gamma or ripple) with no other visible peaks in the TF-plot in the same threshold within 0.5 s before and 0.5 s after (see Figure 1). TF plot (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ;https://doi.org/10.1101https://doi.org/10. /2020 which is a projection method that estimates a coefficient, which characterizes the possible contribution of each dipolar source to the data. We make use of it in the MEM reference model, in which a hidden variable is associated to each parcel in order to model the probability of the parcel to be active or to be switched off [Chowdhury et al., 2013;Chowdhury et al., 2016;Hedrich et al., 2017;Pellegrino et al., 2018]. The wMEM extension of the MEM framework [Lina et al., 2014] decomposes the signal in a discrete wavelet basis before performing MEM source localization on each time-frequency box. Thus, wMEM is particularly suited to localize oscillatory patterns, as evaluated with realistic simulations, but also when localizing FOs [Kybic et al., 2006;von Ellenrieder et al., 2016] and oscillatory patterns at seizure onset [Pellegrino et al., 2016]. Two different variations of MEM were used in this study: wavelet MEM (wMEM) was adopted to localize FOs, whereas coherent MEM (cMEM) was adopted to localize spikes. We used the standard settings of the wMEM and cMEM as provided in Brainstorm [Chowdhury et al., 2013;Lina et al., 2014], except that for wMEM we set the amount of vanishing moments for (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 8 Avigdor et al. | Fast oscillations localize the epileptogenic zone the Daubechies wavelet to 8. The use of 8 vanishing moments instead of the default 4 was done in order to capture the complexity of FOs. The baseline to model the diagonal noise covariance matrix for both wMEM and cMEM was chosen for each patient visually during an artifact-and spike-free 2-second period. wMEM was performed on the marked duration of the FO by selecting the only time-frequency box exhibiting the largest amount of energy as recommended [Kybic et al., 2006;von Ellenrieder et al., 2016]. The spike map was computed for -50 to +50 ms from the spike peak using cMEM.

IEDs and FO Consensus maps
In order to take into account reliability and reproducibility between epileptic discharges to be localized (IEDs or FOs), we applied the concept of consensus maps of source localization [Chowdhury et al., 2018]. In order to estimate these consensus maps for every patient, we first applied cMEM or wMEM to generate source maps of all single events, FO or spike events. In a second step, we estimated a similarity index between all single event source maps (FOs or spikes), based on spatio-temporal correlation around the peak of the event The source maps were then clustered using a hierarchical clustering approach (Ward's hierarchical clustering) followed by thresholding of the dendrogram to obtain the optimal number of clusters. This consensus approach was considered instead of simple averaging of the event followed by one source localization, in order to enhance reliability between source localization results and reducing the influence of more noisy maps. We previously carefully demonstrated the robustness of this approach when considering either EEG source imaging, MEG source imaging or EEG-MEG fusion source imaging [Chowdhury et al., 2018].

Generator depth
The depth of the generator was assessed on the basis of all available clinical information. The generator depth was divided into 3 categories: superficial, intermediate and deep as done in previous work [Cuello-Oderiz et al., 2017]. Superficial was considered as involving the neocortex adjacent to the skull including the bottom of the sulcus; deep, was considered as involving the medial aspects of the frontal, parietal, occipital, and temporal lobes and temporooccipital basal regions; and intermediate, generators found in regions not fitting the above two categories.

Metrics & Statistics
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 9 Avigdor et al. | Fast oscillations localize the epileptogenic zone Final maps were thresholded at 50% of the maximum reconstructed intensity for both visualization and statistical purposes. We previously demonstrated that MEM source localization results provide maps with high contrast, therefore results on the spatial extent are relatively stable within a large range of detection threshold, as opposed to other standard source localization techniques [Chowdhury et al., 2018;Pellegrino et al., 2018;Pellegrino et al., 2019]. In order to assess the success or failure of the localization, we considered the surgical cavity as a reference. Since all selected patients are > 2 year postsurgically seizure-free (Engel 1a), we are sure that the presumed epileptogenic zone was indeed localized within the cavity.
The surgical cavity was fitted as a surface-based region of interest (scout) on the brain source model (see Supplementary Figure 1). This was done visually using the post-surgical MRI coregistered to the intact cortical surface of the pre-surgical MRI, using the Brainstorm software.
The coregistration between presurgical and postsurgical MRI was obtained with a 6 parameter rigid body coregistration using the MINC toolkit (https://bic-mni.github.io/). All evaluation metrics were then estimated using this scout of the surgical cavity as our clinical reference. We assessed the following validation metrics: the minimum Distance Localization Error (Dmin), Spatial Dispersion (SD), the spatial Map Overlap (MO), and Signal to Noise Ratio (SNR).
-Dmin: the minimum distance localization error was computed as the distance in mm from the maximum of the map to the closest vertex belonging to the cavity. Whenever this maximum was located inside the cavity, Dmin was set to 0 mm. prior to the event, the calculation was done on the filtered signal for each type of event (0.3-70Hz for spikes and 40-80Hz for FOs). For spikes the calculation was made on the average spike and for FOs each event was calculated then the average for each patient then between patients to reach the final SNR (SNRs are shown in normalized units, not in dB).
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 10 Avigdor et al. | Fast oscillations localize the epileptogenic zone For each of the validation metrics described above, we considered only the best as defined by the cluster with the highest number of events cluster for both spikes and FOs i.e after clustering all the events for each type event (spike or FO) only the one with highest event numbers was selected for further analysis. We used the Welch's t-test as not every patient had FOs thus creating unequal groups. The Fisher exact test was used to assess the distribution of generator depth categories between patients with and without FOs.

Detection of FOs
FOs in the gamma band (40-80 Hz) were found in five of the ten patients ( Table 2). The mean number of FOs was 12.2±3.9 (range, 10-20) with a mean SNR of 3.4±1.4. Figure 1 shows representative examples of FOs of the 5 patients in the unfiltered signal, the filtered signal, as well as the time frequency plot. Spikes were present in all ten patients. The mean number of spikes was 99.6±99.1 (range, 12-303) with a mean SNR of 9.3±2.7. As expected, the SNR was significantly lower in FOs than in spikes (p<0.05).  Figure 2) with a Dmin of 6.45±20.41 mm. Source maps were localized within the surgical cavity with an SD of 6.2±10.9 mm and an MO of 76.0±30.0%. The spread outside the cavity as well as the activation overlap with the surgical cavity did not differ significantly between FOs and spikes (SD: p=0.17; MO: p=0.09). For further details see Figure 3.

Usefulness of the consensus map approach for FO source localization
The consensus map approach for FOs sources resulted in less disperse maps compared to the one obtained by simply averaging of all FO maps (SD difference between cluster versus averaged map: 10.3±4.8mm versus 9.4±3.2mm; p=0.4). This difference in SD became significant when no thresholding was considered prior to the estimation of SD, resulting in a SD (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 12 Avigdor et al. | Fast oscillations localize the epileptogenic zone We found that the maxima of the electrical source localization maps of FOs correctly localized the EZ as approximated by the surgical cavity in all patients. The ability of FOs to non-invasively localize the EZ was recently demonstrated using MEG [von Ellenrieder et al., 2016;Nissen et al., 2016;Velmurugan et al., 2019;Yin et al., 2019;Tamilia et al., 2020]. Velmurugan and colleagues [2019] have recently shown the utility of high-frequency oscillations in correctly localizing the EZ in MEG in 52 patients thus demonstrating its potential as a scalp biomarker.
Yet, while MEG has advantages such as better SNR and less complicated head modeling impacting spatial resolution when compared to EEG [Ilmoniemi and Sarvas, 2019], the lack of availability in most centers and its high maintenance cost makes it less ideal as a tool for everyday use in presurgical evaluation. In addition, it is worth noting that while EEG has been shown to detect higher numbers of FOs [van Klink et al., 2019, Tamilia et al., 2020, spatial coverage is key due to the low SNR and a high-density array is preferable. Thus, this proof of principle study using HD-EEG with 256 electrodes together with the already existing evidence in MEG and EEG [Thomschewsky et al., 2019;van Klink et al., 2019, Tamilia et al., 2020 points to HD-EEG being a feasible candidate for FO localization of the EZ during pre-surgical evaluation.

Comparison of FO to spike sources
When comparing FO to spike source localization, we found that FO source localization and spike source localization were concordant with the latter resulting in localizations in all ten study subjects. Note is made that one of the spike localizations was outside and far from the epileptic generator. The patient was identified as having a deep generator in the posterior cingulate gyrus determined by a focal MRI lesion and PET hypometabolism, and the spikes we localized along the cortical surface corresponded hence to propagated activity. In this patient, FOs were absent suggesting that source localization results in presence of FOs are very likely pointing to the "true" onset generator [Cuello-Oderiz et al., 2017], whereas absence of FOs might constitute a red flag for propagated activity [Plummer et al., 2019]. Our finding might hence be able to address a longstanding problem inherent to non-invasive source localization. However, careful evaluation of propagation of source localization along the peak of the spike vs. FOs localization was out of the scope of this study and will be considered in future investigations.

Different measures for source localization quality
In clinical practice most studies assessed the concordance of the source with the assumed EZ at the lobar level [Duez et al., 2019;Rampp et al., 2019], or at best at the sub-lobar level [Heers (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. [2018] in order to assess the quality of our results as objectively as possible regarding the source map maxima, its spatial extent, and the spatial overlap with the resection cavity. The originality of our proposed approach was also to consider an accurate delineation of the resection cavity as our reference for the evaluation of source localization results, whereas such a comparison was more qualitative in other studies [Abdallah et al., 2017]. The accuracy of FOs localization was high with the maxima localized to the surgical cavity in all patients. Albeit being not significant, the extent of the source seemed to be more widespread in FOs compared to spikes. However, whereas the estimation of the spatial extent of spike maps with cMEM has been carefully evaluated by our group [Chowdhury et al., 2016;Pellegrino et al., 2016;Pellegrino et al., 2018], the evaluation of the spatial extent of wMEM for FOs would require further careful investigation. Moreover, it is difficult to disentangle whether the spatial extent of FOs maps was true extent or only resulted from the localization of low SNR events. This might be potentially explained by the small number of FOs given the short recording time of 90 minutes, as well as by the lack of consolidated sleep in the present study; longer recording durations preferably overnight are likely to be more favorable regarding FO quantity and SNR.
Future research performing prolonged overnight sleep recordings is awaited for confirmation.

Importance of consensus map approach for FO source maps
In this study we applied for the first time for spike and FOs in HD-EEG the consensus map approach we recently proposed as a more robust approach than events averaging to provide reliable source localization, while taking into account the reproducibility of single discharges source maps [Chowdhury et al., 2018]. In this study, this was our first attempt to consider consensus maps from wMEM results for FO localization. We therefore first performed a single event FO source localization, using only the time-frequency box exhibiting the largest amount of energy along the FOs, following the exact same methodology proposed in MEG by our group [von Ellenrieder et al., 2016;Chowdhury et al., 2018]. We then compared every single FO source map using a hierarchical clustering in order to separate the data from events that were not in agreement with the majority of events and which would therefore add noise to the maps, as done in our previous work for spikes [Chowdhury et al., 2018]. This consensus map approach seems to be particularly useful for FOs, which in case of a discordant FO event tend to create a noisier map. Figure 4 shows a significant improvement of source localization of FOs using this consensus map approach, when compared to standard averaging of all FO maps, (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Limitations
First, we would like to acknowledge that our sample size is small. However, all 10 patients selected for this study were postsurgically seizure-free with an at least 1 year postsurgical follow-up, which allowed us to have the best estimate possible for approximation of the EZ in this proof-of-principle study. An alternative would have been to validate against the intracranially identified SOZ [Papadelis et al., 2016;Kuhnke et al., 2018;Dirodi et al., 2019]. Given however, that up to ~50 % of patients in whom the SOZ has been surgically removed, do not become seizure-free after surgery [Krucoff et al., 2017] , this seemed to us to not be the best option for the validation of our approach, even though a large resection diminishes the sensitivity to the real EZ (see patient 1 for an example). Second, we only had short recordings of approximately This study demonstrated the ability of 256-channel HD-EEG to correctly identify the EZ using source localization of FOs. Presence or absence of FOs was shown to be dependent on the presence of a surface close generator. This points to an added value of FO source localization, as presence of concordant spike and FO sources could confirm correct localization of the EZ, whereas lack of FO sources might point to the fact that the identified spike source could be the correlate of rather propagated activity and not the primary source, a problem inherent to noninvasive source imaging. Future research is needed with a larger cohort of patients and longer (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 16 Avigdor et al. | Fast oscillations localize the epileptogenic zone recording durations with consolidated sleep for improved data quality in order to further validate the viability of the use of scalp FOs for clinical decision making.

DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020.  (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. For each patient the number of marked and localizable epileptic spikes and FOs are presented.
The depth of the generator is given. It is based on the available clinical information by an epileptologist. FO, fast oscillation.
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 28 Avigdor et al. | Fast oscillations localize the epileptogenic zone (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 29 Avigdor et al. | Fast oscillations localize the epileptogenic zone and C) are presented following the same orientation as the corresponding source maps. All source localization results are presented using a color map scaled to the maximum reconstructed intensity of the corresponding map and thresholded at 50% of their maximum (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 30 Avigdor et al. | Fast oscillations localize the epileptogenic zone value. Note that the current amplitude of sources of FOs was as expected several orders of magnitude lower than that of spike sources as well as that patient 1's large cavity is due to a disconnecting surgery. The topographical maps are oriented to the same direction as the source localization map results.
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 31 Avigdor et al. | Fast oscillations localize the epileptogenic zone (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint this version posted March 4, 2020. ; https://doi.org/10.1101/2020.03.02.973602 doi: bioRxiv preprint P a g e | 32 Avigdor et al. | Fast oscillations localize the epileptogenic zone the most events was chosen as consensus map. The cavity is marked in gray and the spurious activity outside the cavity is then depicted. Note that the maximum is inside the obscured cavity (as shown in Fig. 2) and only the spread is shown.
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.