Fast oscillations &gt;40 Hz localize the epileptogenic zone: An electrical source imaging study using high-density electroencephalography

Avigdor, Tamir; Abdallah, Chifaou; Ellenrieder, Nicolás von; Hedrich, Tanguy; Rubino, Annalisa; Russo, Giorgio Lo; Bernhardt, Boris C.; Nobili, Lino; Grova, Christophe; Frauscher, Birgit

doi:10.1016/j.clinph.2020.11.031

Cited by 26 publications

(15 citation statements)

References 63 publications

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“…HD-EEG data of healthy controls were recorded with a 256-channel EGI system (Electrical Geodesic Inc., EGI, now Magstim EGI, Eden Prairie, MN, USA) at a 1000 Hz sampling rate. The 10–10 system was approximated from the full montage as done in our previous work 17 . Electrodes were grouped into 11 regions: five regions per hemisphere (frontal, central, temporal, parietal, occipital) and midline (see workflow in Fig.…”

Section: Methodsmentioning

confidence: 99%

Focal epilepsy disrupts spindle structure and function

Schiller

Avigdor

Abdallah

et al. 2022

Sci Rep

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Sleep spindles are the hallmark of N2 sleep and are attributed a key role in cognition. Little is known about the impact of epilepsy on sleep oscillations underlying sleep-related functions. This study assessed changes in the global spindle rate in patients with epilepsy, analysed the distribution of spindles in relation to the epileptic focus, and performed correlations with neurocognitive function. Twenty-one patients with drug-resistant focal epilepsy (12 females; mean age 32.6 ± 10.7 years [mean ± SD]) and 12 healthy controls (3 females; 24.5 ± 3.3 years) underwent combined whole-night high-density electroencephalography and polysomnography. Global spindle rates during N2 were lower in epilepsy patients compared to controls (mean = 5.78/min ± 0.72 vs. 6.49/min ± 0.71, p = 0.02, d = − 0.70). Within epilepsy patients, spindle rates were lower in the region of the epileptic focus compared to the contralateral region (median = 4.77/min [range 2.53–6.18] vs. 5.26/min [2.53–6.56], p = 0.02, rank biserial correlation RC = − 0.57). This decrease was driven by fast spindles (12–16 Hz) (1.50/min [0.62–4.08] vs. 1.65/min [0.51–4.28], p = 0.002, RC = − 0.76). The focal reduction in spindles was negatively correlated with two scales of attention (r = − 0.54, p = 0.01; r = − 0.51, p = 0.025). Patients with focal epilepsy show a reduction in global and local spindle rates dependent on the region of the epileptic focus. This may play a role in impaired cognitive functioning. Future work will show if the local reduction in spindles can be used as potential marker of the epileptic focus.

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Section: Methodsmentioning

confidence: 99%

Focal epilepsy disrupts spindle structure and function

Schiller

Avigdor

Abdallah

et al. 2022

Sci Rep

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“…wMEM applies a discrete wavelet transformation (Daubechies wavelets) to characterize the oscillatory patterns in the data before applying the MEM solver (Lina et al, 2012). We validated wMEM for localizing oscillatory patterns at seizure onset (Pellegrino et al, 2016), interictal bursts of high frequency oscillations (Avigdor et al, 2021; von Ellenrieder et al, 2016) and MEG resting state fluctuations (Aydin et al, 2020). Both wMEM and cMEM implementations are available within the Brain Entropy in space and time (Best) plugin of Brainstorm software (https://neuroimage.usc.edu/brainstorm/Tutorials/TutBEst/).…”

Section: Methodsmentioning

confidence: 99%

“…To investigate this question, we applied wavelet-based Maximum Entropy on the Mean (wMEM) (Aydin et al, 2020; Lina et al, 2012; Pellegrino et al, 2016; von Ellenrieder et al, 2016). wMEM is an EEG/MEG source imaging technique we developed and adapted to localize resting state oscillatory patterns, which proved its unique ability to recover the location and the spatial extent of the underlying oscillatory generators (Avigdor et al, 2021; Aydin et al, 2020; Pellegrino et al, 2016; von Ellenrieder et al, 2016). An original method proposed by our group (Abdallah et al, 2022; Grova et al, 2016) to estimate IEEG signals from MEG sources was then applied to support a quantitative comparison between the MNI IEEG atlas (electrical potentials) and MEG sources (cortical current densities), at the location of each IEEG electrode contact of the atlas.…”

Section: Introductionmentioning

confidence: 99%

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

Afnan

Ellenrieder

Lina

et al. 2022

Preprint

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Background: Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. Method: We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (IEEG) atlas (https://mni-open-ieegatlas.research.mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space, by applying a forward model to the MEG reconstructed source maps and estimated virtual IEEG (VIEEG) potentials on each IEEG channel location and quantitatively compared those with actual IEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. Results: The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the VIEEG than in the IEEG were more accurately recovered. In the deep regions, MEG estimated amplitudes were largely underestimated and the spectra were poorly recovered. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of IEEG but detected by MEG. Importantly, we found that MEG estimated spectra were more comparable to spectra from the IEEG atlas after the aperiodic components were removed. Conclusion: This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies.

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“…We decided to use HD-EEG, because previous studies reported a higher correspondence of areas with high ripple rates between intracranial EEG and HD-EEG compared to 10-20 EEG (31). A study conducted by Avigdor et al (46) retrospectively analyzed EEG post-surgical recordings of patients with drug resistant epilepsy and found that the highest rates of HFOs can be found within the resected areas. Using both HD-EEG and the reduced 10-10 as well as the 10-20 EEG they showed that the detection of HFOs is even more accurate when conducted using HD-EEG and can be used for the identification of the epileptogenic zone (EZ.)…”

Section: Data Acquisitionmentioning

confidence: 99%

Are High Frequency Oscillations in Scalp EEG Related to Age?

et al. 2022

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BackgroundHigh-frequency oscillations (HFOs) have received much attention in recent years, particularly in the clinical context. In addition to their application as a marker for pathological changes in patients with epilepsy, HFOs have also been brought into context with several physiological mechanisms. Furthermore, recent studies reported a relation between an increase of HFO rate and age in invasive EEG recordings. The present study aimed to investigate whether this relation can be replicated in scalp-EEG.MethodsWe recorded high-density EEG from 11 epilepsy patients at rest as well as during motor performance. Manual detection of HFOs was performed by two independent raters following a standardized protocol. Patients were grouped by age into younger (<25 years) and older (>50 years) participants.ResultsNo significant difference of HFO-rates was found between groups [U = 10.5, p = 0.429, r = 0.3].ConclusionsLack of replicability of the age effect of HFOs may be due to the local propagation patterns of age-related HFOs occurring in deep structures. However, limitations such as small sample size, decreased signal-to-noise ratio as compared to invasive recordings, as well as HFO-mimicking artifacts must be considered.

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

Cited by 26 publications

References 63 publications

Focal epilepsy disrupts spindle structure and function

Focal epilepsy disrupts spindle structure and function

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

Are High Frequency Oscillations in Scalp EEG Related to Age?

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