Nicolás von Ellenrieder

The integration of electrophysiological recordings with multimodal neuroimaging data holds great promise for advancing our understanding of brain function and neurological disorders. To facilitate this endeavor, we present electro-MICA, an open-access Python toolbox designed to project electrophysiological features from scalp and intracranial electroencephalography (EEG) onto cortical and hippocampal surfaces generated by validated multimodal imaging ecosystems. The toolbox comprises two pipelines: one for intracranial EEG (iEEG) recorded with stereo-EEG depth electrodes and one for scalp EEG source localization. Both pipelines are grounded in numerical solutions to the electromagnetic equations governing electric activity in the brain, solved using the Boundary Element Method. A key methodological contribution is the use of a current density double layer model for neural generators, which avoids the mathematical singularities introduced by conventional dipole-based models when electrodes are near the cortical surface, a situation that can arise in iEEG. Electrode contacts are additionally modeled with non-zero length, improving physical realism. Scalp EEG source localization is performed using eLORETA on a subject-specific three-layer head model derived from the anatomical input. Validation against empirical gamma-band iEEG data from 32 subjects demonstrates that the distributed generator model outperforms both distance-based and dipole-based alternatives. An illustrative clinical example demonstrates the toolbox’s capacity to reveal associations between intracranial spike rates, cortical thickness, and anatomical connectivity in an epilepsy patient. Electro-MICA requires no parameter selection from the user, facilitating straightforward multimodal analyses. The toolbox is available at http://github.com/MICA-MNI/electromica with extensive online documentation at http://electromica.readthedocs.io.

The integration of electrophysiological recordings with multimodal neuroimaging data holds great promise for advancing our understanding of brain function and neurological disorders. To facilitate this endeavour, we present electro-MICA, an open-access Python toolbox designed to project electrophysiological features from scalp and intracranial electroencephalography (EEG) onto cortical and hippocampal surfaces generated by validated multimodal imaging ecosystems. The toolbox comprises two pipelines: one for intracranial EEG (iEEG) recorded with stereo-EEG depth electrodes, and one for scalp EEG source localization. Both pipelines are grounded in numerical solutions to the electromagnetic equations governing electric activity in the brain, solved using the Boundary Element Method. A key methodological contribution is the use of a current density double layer model for neural generators, which avoids the mathematical singularities introduced by conventional dipole-based models when electrodes are near the cortical surface, a situation that can arise in iEEG. Electrode contacts are additionally modeled with non-zero length, improving physical realism. Scalp EEG source localization is performed using eLORETA on a subject-specific three-layer head model derived from the anatomical input. Validation against empirical gamma-band iEEG data from 32 subjects demonstrates that the distributed generator model outperforms both distance-based and dipole-based alternatives. An illustrative clinical example demonstrates the toolbox's capacity to reveal associations between intracranial spike rates, cortical thickness, and anatomical connectivity in an epilepsy patient. Electro-MICA requires no parameter selection from the user, facilitating straightforward multimodal analyses in both research and clinical settings. The toolbox is available at github.com/MICA-MNI/electromica with extensive online documentation at electromica.readthedocs.io.

Epilepsy surgery outcomes after intracranial EEG remain suboptimal necessitating the discovery of additional biomarkers to define the epileptogenic zone. Fast ripples (FRs) are a promising, new interictal epilepsy biomarker. By analyzing a multicenter data set consisting of overnight stereo-EEG (SEEG) recordings, we aimed at validating FRs as an accurate marker of the epileptogenic zone. We hypothesized that removing ≥60% of total FR events would significantly increase the odds of good postsurgical outcome (Engel class I). In addition, we compared FRs with spikes, and spikes co-occurring with FRs (spike-FRs) as surgery outcome predictors. This retrospective cohort study included consecutive patients from 4 epilepsy surgery centers in Canada, Finland, and Denmark, who underwent SEEG followed by resective surgery or a preplanned ablation procedure separate from the SEEG and had at least 1 year of follow-up. We detected FRs and spikes automatically from overnight SEEG recordings edited for artifacts. To calculate resection ratios of the detected events, we determined resected SEEG contacts by superimposing the peri-implantation and postresection images. We evaluated postsurgical seizure outcomes from medical records. Of the 73 included patients (mean age 23 ± 12 years, 41% female), 46 had good and 27 had poor (Engel classes II-IV) outcome at the latest follow-up. Patients with FR resection ratio ≥0.6 were more likely to achieve good postsurgical outcome (p < 0.001, diagnostic odds ratio [DOR] 10, 95% CI 2.7-39). Of those with ≥0.6 FR resection ratio, 26 of 29 (90%, 95% CI 74%-96%) achieved good outcome, whereas of those with p = 0.007, DOR 4.1, 95% CI 1.4-12, accuracy 64%, 95% CI 52%-75%), whereas the spike resection ratio ≥0.6 was not. In accordance with our hypothesis, the FR resection ratio ≥0.6 significantly increased the odds of attaining good postsurgical seizure outcome. Although the FR resection ratio ≥0.6 accurately predicted good postsurgical outcome, resecting <0.6 of FRs did not necessarily mean poor outcome. As predictors of postsurgical outcome, spikes fared poorly, whereas spike-FRs were comparable with FRs.

Objective: Focal epilepsy is caused by focal brain pathologies sometimes with involvement of surrounding or distant tissue. However, within the epileptogenic zone (EZ), which can be resected to cure epilepsy, the contribution of histopathological cells to epileptogenicity remains unknown. We hypothesized that areas showing neurophysiological biomarkers of epileptogenicity would more often contain histopathological cells compared to areas without such biomarkers.Methods: Pediatric epilepsy patients with nonlesional magnetic resonance imaging (MRI), or with lesions with unclear borders, underwent stereoelectroencephalographic (SEEG) exploration followed by resective surgery of the suspected EZ. Tissue specimens were taken from locations where the SEEG contacts had been, using intraoperative MRI‐guided precise neuronavigation. Then, we explored the association between histopathology and rates of interictal epileptic discharges, ripples, and fast ripples (FRs), and the channels of the seizure onset zone (SOZ).Results: The association between histopathology and ictal/interictal activity was low and not statistically significant in 260 specimens from 20 surgeries. Rates of interictal events were slightly lower for pathological samples than in normal tissue (low effect size, Cliff |d| < .15, p > .1). The classification accuracy of tissue as normal or pathological based on interictal activity/SOZ was low (accuracy ≤ 54%). As a secondary outcome, SEEG events were excellent predictors of surgical outcome, FRs leading to perfect prediction (20/20).Significance: Histopathological tissue initiates epileptogenicity in focal epilepsy. However, our findings suggest that the EZ is not strictly a histopathological entity but a hybrid of abnormal cells and normal‐appearing cells. Thus, SEEG events are biomarkers of epileptogenicity and not histopathology. Resecting of electrophysiological biomarkers of epileptogenicity may be more important than resecting all histopathology.

Objective: Memory impairment is common in people with temporal lobe epilepsy (TLE). Recent studies in healthy subjects showed a positive correlation between sleep spindles coupled to slow waves (SWs) and memory performance. We aimed to determine differences in spindle–SW coupling in TLE patients compared to healthy controls using combined high‐density electroencephalography and polysomnography. Methods: The study population consisted of 20 patients (12 female, 36.5 ± 9.9 years old) with unilateral drug‐resistant TLE (10 left temporal) and 20 age‐ and sex‐matched controls (12 female, 31.2 ± 6.3 years old). Spindles (10–16 Hz, .5–3 s) and SWs (.5–4 Hz) were automatically detected during all N2 and N3 epochs using validated detectors. Coupling of spindles with SWs was defined as overlap between both detected events. Results: Coupled spindle–SW rates (per minute) were globally reduced in patients with TLE compared to healthy controls (median = .18 [interquartile range (IQR) = .08–.36] vs. .35 [IQR = .24–.46], p = .014, d = −.46). This reduction was also found for coupled fast spindle (12–16 Hz)–SW (.06 [IQR = .02–.13] vs. .18 [IQR = .07–.25], p = .013, d = −.46) and slow spindle (10–12 Hz)–SW rates (.11 [IQR = .04–.23] vs. .19 [IQR = .13–.27], p = .034, d = −.40). Within TLE patients, there was no local difference between the coupling rates in the lobe with the epileptic focus compared to the contralateral side (.09 [IQR = .02–.13] vs. .07 [IQR = .02–.13], p = .18). The effect size of the reduction was stronger in early than late sleep for both N2 and N3 sleep (early N2 d = −.50 vs. late N2 d = −.39; early N3 d = −.53 vs. late N3 d = −.47). Significance: Despite a focal epileptic generator, patients with unilateral TLE showed a widespread decoupling between sleep spindles and SWs that was most prominent in early sleep. As coupling was shown to be associated with neuropsychological performance in healthy people, this global decoupling may constitute one potential mechanism of poor memory performance in people with TLE.

Electro/Magneto‐EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth‐weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high‐density EEG (HD‐EEG) simulations of epileptic activity and actual MEG/HD‐EEG recordings from patients with focal epilepsy. We incorporated depth‐weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD‐EEG involving a wide range of spatial extents and signal‐to‐noise ratio (SNR) levels, before investigating EMSI on clinical HD‐EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth‐weighted cMEM and depth‐weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth‐weighted cMEM improved the localization when compared to cMEM and depth‐weighted MNE, whereas depth‐weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD‐EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth‐weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD‐EEG and clinical MEG and HD‐EEG for epilepsy patients.

Transitions between wake and sleep states show a progressive pattern underpinned by local sleep regulation. In contrast, little evidence is available on non–rapid eye movement (NREM) to rapid eye movement (REM) sleep boundaries, considered as mainly reflecting subcortical regulation. Using polysomnography (PSG) combined with stereoelectroencephalography (SEEG) in humans undergoing epilepsy presurgical evaluation, we explored the dynamics of NREM-to-REM transitions. PSG was used to visually score transitions and identify REM sleep features. SEEG-based local transitions were determined automatically with a machine learning algorithm using features validated for automatic intra-cranial sleep scoring (10.5281/zenodo.7410501). We analyzed 2988 channel-transitions from 29 patients. The average transition time from all intracerebral channels to the first visually marked REM sleep epoch was 8 s ± 1 min 58 s, with a great heterogeneity between brain areas. Transitions were observed first in the lateral occipital cortex, preceding scalp transition by 1 min 57 s ± 2 min 14 s (d = −0.83), and close to the first sawtooth wave marker. Regions with late transitions were the inferior frontal and orbital gyri (1 min 1 s ± 2 min 1 s, d = 0.43, and 1 min 1 s ± 2 min 5 s, d = 0.43, after scalp transition). Intracranial transitions were earlier than scalp transitions as the night advanced (last sleep cycle, d = −0.81). We show a reproducible gradual pattern of REM sleep initiation, suggesting the involvement of cortical mechanisms of regulation. This provides clues for understanding oneiric experiences occurring at the NREM/REM boundary.