Study 3
Brain activity with or without epileptic spikes: a simultaneous scalp and intracranial EEG study
M. Carboni, J. Heyse, P. De Stefano, R. Marquis, L. Spinelli, S. Momjian, K. Schaller, M. Seeck, P. Van Mierlo, S. Vulliémoz
In preparation: we present here an extended abstract.
INTRODUCTION
In focal epilepsy, the activity of physiological brain networks is disrupted also during periods without any epileptic discharges visible in scalp EEG (Centeno and Carmichael 2014). In presurgical evaluation, intracranial EEG is sometimes needed, especially when the identification of the epileptogenic zone is not clear. Simultaneous EEG recordings from scalp electrodes and intracranial electrodes provide the unique opportunity to investigate the generation of epileptic spikes and it has been shown that only a minority of these detected by intracranial EEG (icEEG) also appear on the scalp (Koessler et al. 2014; Tao et al. 2005) and only 14% of them show scalp EEG correlates visible on routine recordings (Ramantani et al. 2014).
Here we aimed at investigating the scalp related changes of epileptic activity during different periods as highlighted by subdural EEG recordings: frequency analysis, estimated source imaging and network features show different behaviors.
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METHODS
Patients: We recorded 9 patients (median age 40 years old, range 21-53 years, 3 female) from a total of 10 simultaneous intracranial-high-density-EEG (ic-hd-EEG) recordings acquired at the EEG and epilepsy unit of the Geneva University Hospital. This study was approved by the local ethics board.
EEG acquisition and pre-processing: Simultaneous high-density scalp and intracranial EEG (hd-ic-EEG) recordings were acquired in the context of pre-surgical evaluation at the University Hospital of Geneva (256 scalp electrodes, Electrical Geodesic Inc. system, sampling rate = 1000 Hz; Intracranial electrodes: SystemPlus Evolution MicromedTM, sampling rate = 2048Hz - 1cm spacing depth electrodes from Ad-TechTM implanted stereotaxically). icEEG data was resampled (linear interpolation from 2048 Hz to 1000 Hz) and the common reference start time between the two modalities was considered as started point. In only one patient the icEEG was recorded on auxiliary channels of EGI amplifier, therefore data did not need to be realigned.
A board-certified EEG expert (PDS or SV) first visually identified and marked epileptic activity (mainly IED in the hippocampal contacts (ic-IED) on the ic-EEG. Second, they visually identified and marked scalp EEG epochs with and without epileptic activity visible on the scalp. Epochs with visible epileptic activity on the scalp were discarded. Epochs without visible epileptic activity on the scalp were further divided based on ic-EEG as a) with ic-IED and b) without ic-IED. Scalp EEG data were further downsampled to 200 Hz for further analysis and epochs of 2-s around the marker were filtered in the interval [1-35] Hz with a 4th-order Butterworth filter avoiding phase-distortion. EEG epochs containing artefacts were discarded after visual inspection.
Figure 1: Summary of the analysis strategy: Simultaneous ic-hd-EEG and structural MRI (T1 or MPRAGE) were acquired.
Frequency analysis on the scalp EEG on different epochs was calculated. Around 5000 source-waveforms distributed
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equally in the grey matter were estimated by a distributed source localization algorithm (LAURA) from hd-EEG. Electric source imaging was computed on the average signal of the underlying epileptic activity epochs: localization accuracy based on Euclidean distance between the intracranial contact and ESI and based on sublobar classification was assessed.
The head model was based on the individual MRI and parceled into 82 regions of interest (ROI). The activity in each ROI was summarized with a unique time-series through SVD. Connectivity matrices were estimated through iPDC. Global Efficiency was calculated.
Spectral power analysis: In order to get an accurate representation of the spectral power distribution, we computed Welch’s power spectral density (PSD) for each scalp electrode. We used a down-sampled version of the hd-EEG set-up to the standardized EEG clinical montage with 25 channels (Seeck et al. 2017). For each subject, the PSD was computed on all epochs and compared between periods with and without ic-IED.
MRI acquisition and pre-processing: For each patient and control, we created a realistic head model based on individual structural MRI image, either T1 or MPRAGE, recorded on a 3T scanner (Siemens Prisma). Using Freesurfer v6.0.1 and the Connectome Mapper 3 open-source pre-processing software (Tourbier et al. 2019), we resampled each image to 1 mm3 isotropic resolution using cubic interpolation and we performed cortical and subcortical brain parcellation based on Desikan-Killiany (Desikan et al. 2006; Hagmann et al. 2008) anatomical atlas. This results in 82 parcels accounting for all grey matter structures, excluding brainstem and cerebellum. The 82 regions of interest (ROIs) were further regrouped in sublobes by a board-certified neurologist (SV).
Inverse Space Solution: The computation of the individual head model, the linear distributed inverse solution, as well as the parcellation of the brain into 82 ROIs were performed as described in our previous work (Carboni et al. 2019). We computed Electric Source Imaging (ESI) at the peak of the average signal of the epochs with ic-IED.
ESI localization accuracy: For the average signal of the epochs with ic-IED we computed the localization accuracy as the sub lobar precision: we check whether the intracranial contacts and the maxima of the ESI activation were in the same sublobe similarly to previous studies (Koessler et al.
2010).
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Connectivity and Efficiency estimation: The connectivity matrices were estimated using the Partial Directed Coherence (PDC) applied to ROI source activity time courses. To correct for the presence of spurious connections, each interaction was tested for statistical significance. PDC values were re-computed on surrogate data obtained with the iterative Amplitude Adjusted Fourier Transform (iAAFT) approach (Schreiber and Schmitz 1996). Only connections within the tail of the null distribution (i.e. above the 95th percentile) were retained. This was done for each single epoch with ic-IED and then averaged.
As a result, we obtained, for each patient, a 2-dimensional matrix ([#ROIs x #ROIs]) representing the directed information flow from one ROI to another. Finally, we computed the global efficiency as described in our previous work (Carboni et al. 2019) and we compared epochs with ic-IED and without ic-IED.
RESULTS
Frequency Analysis:
The frequency analysis at the group level shown stronger power activity in the slow frequency bands (delta and theta bands) at the group level in epochs with IED as compare to epochs without ic-IED (Figure 2).
Figure 2: Power spectra density of all patients: in red epochs with ic-IED, in green epochs without ic-IED. Shaded area represents the standard deviation across patients. * stands for p<0.05.
ESI localization accuracy:
In all patients, the ESI maxima of the averaged signal of all epochs with ic-IED was concordant at the sublobar level with the contacts marked with IED. In all patients the contacts marked with ic-IED as well as the ESI maxima were found in the medial anterior temporal sublobe, with a lateralization concordant with the epileptic focus lateralization.
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Figure 3: Example of concordant ESI activation in one patient. On the left the EEG signal averaged across all the epochs with ic-IED. On the right the ESI activation in green and red, in blu the ic-contacts implanted, in pink the ic-contacts with ic-IED.
Connectivity and Efficiency estimation:
The global efficiency shown an increase during epochs with ic-IED as compared to epochs without ic-IED.
Figure 4: Global efficiency in different condition, without and with ic-IED. * stands for p<0.05.
DISCUSSION AND FUTURE WORK
Our analysis aimed at identifying the changes in the scalp EEG and the related source activity and source connectivity to better define the epileptic network.
Here we showed promising results in which the source reconstruction from high-density EEG was able to properly localize the epileptic activity as identified by intracranial EEG recording. The frequency analysis showed a correlation between the increase in the slow activity, predominately in delta and theta bands, and the presence of underlying epileptic activity.
The averaging of the EEG signal allowed an increase in the signal-to-noise ratio, by attenuating the background activity and thus the contribution of nonepileptic cortical sources (Abraham and Ajmone Marsan 1958).
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This helped in revealing, even in absence of epileptic activity on the scalp, cortical sources that were consistent with the location of the ic-contacts marked with epileptic activity, in line with previous study done on spikes visible and invisible on the scalp (Koessler et al. 2010, 2014). It is important to consider that the ic-contact should only be considered as a marker for an epileptiform event in the vicinity, not necessarily showing the activation center of this event (Lantz et al. 1996). It is also important to consider the displacement errors of the intracranial electrode locations caused by the projection of the actual icEEG contact locations onto the MRI models.
These results points to the reliability of the ESI and connectivity analysis for the localization of important sources and drivers of the epileptic activity.
Epilepsy is a network disease and the study of network features are of predominant importance:
here we have shown an increase in the global efficiency during epochs with ic-IED as compare to epochs without ic-IED. This finding is consistent with our previous studies: the global efficiency has been found to be significantly increased (a) during scalp spikes in patients that will not recover as compare to these that will recover after surgery (Study 1), (b) during resting state in some canonical resting state networks in epileptic patients as compare to healthy controls (Study 2).
The increase in the global efficiency can be interpret as a marker for epilepsy.
Further investigations with different inverse solutions, different connectivity methods, different network features and frequency analysis on a single subject level are needed and further discussed in the next chapter of this thesis work.