Generalizability of electroencephalographic interpretation using artificial intelligence: An external validation study.

OBJECTIVE: The automated interpretation of clinical electroencephalograms (EEGs) using artificial intelligence (AI) holds the potential to bridge the treatment gap in resource-limited settings and reduce the workload at specialized centers. However, to facilitate broad clinical implementation, it is essential to establish generalizability across diverse patient populations and equipment. We assessed whether SCORE-AI demonstrates diagnostic accuracy comparable to that of experts when applied to a geographically different patient population, recorded with distinct EEG equipment and technical settings. METHODS: We assessed the diagnostic accuracy of a "fixed-and-frozen" AI model, using an independent dataset and external gold standard, and benchmarked it against three experts blinded to all other data. The dataset comprised 50% normal and 50% abnormal routine EEGs, equally distributed among the four major classes of EEG abnormalities (focal epileptiform, generalized epileptiform, focal nonepileptiform, and diffuse nonepileptiform). To assess diagnostic accuracy, we computed sensitivity, specificity, and accuracy of the AI model and the experts against the external gold standard. RESULTS: We analyzed EEGs from 104 patients (64 females, median age = 38.6 [range = 16-91] years). SCORE-AI performed equally well compared to the experts, with an overall accuracy of 92% (95% confidence interval [CI] = 90%-94%) versus 94% (95% CI = 92%-96%). There was no significant difference between SCORE-AI and the experts for any metric or category. SCORE-AI performed well independently of the vigilance state (false classification during awake: 5/41 [12.2%], false classification during sleep: 2/11 [18.2%]; p = .63) and normal variants (false classification in presence of normal variants: 4/14 [28.6%], false classification in absence of normal variants: 3/38 [7.9%]; p = .07). SIGNIFICANCE: SCORE-AI achieved diagnostic performance equal to human experts in an EEG dataset independent of the development dataset, in a geographically distinct patient population, recorded with different equipment and technical settings than the development dataset.

Investigating current clinical opinions in stereoelectroencephalography-informed epilepsy surgery.

OBJECTIVE: Stereoelectroencephalography (SEEG) is increasingly utilized worldwide in epilepsy surgery planning. International guidelines for SEEG terminology and interpretation are yet to be proposed. There are worldwide differences in SEEG definitions, application of features in epilepsy surgery planning, and interpretation of surgical outcomes. This hinders the clinical interpretation of SEEG findings and collaborative research. We aimed to assess the global perspectives on SEEG terminology, differences in the application of presurgical features, and variability in the interpretation of surgery outcome scores, and analyze how clinical expert demographics influenced these opinions. METHODS: We assessed the practices and opinions of epileptologists with specialized training in SEEG using a survey. Data were qualitatively analyzed, and subgroups were examined based on geographical regions and years of experience. Primary outcomes included opinions on SEEG terminology, features used for epilepsy surgery, and interpretation of outcome scores. Additionally, we conducted a multilevel regression and poststratification analysis to characterize the nonresponders. RESULTS: A total of 321 expert responses from 39 countries were analyzed. We observed substantial differences in terminology, practices, and use of presurgical features across geographical regions and SEEG expertise levels. The majority of experts (220, 68.5%) favored the Lüders epileptogenic zone definition. Experts were divided regarding the seizure onset zone definition, with 179 (55.8%) favoring onset alone and 135 (42.1%) supporting onset and early propagation. In terms of presurgical SEEG features, a clear preference was found for ictal features over interictal features. Seizure onset patterns were identified as the most important features by 265 experts (82.5%). We found similar trends after correcting for nonresponders using regression analysis. SIGNIFICANCE: This study underscores the need for standardized terminology, interpretation, and outcome assessment in SEEG-informed epilepsy surgery. By highlighting the diverse perspectives and practices in SEEG, this research lays a solid foundation for developing globally accepted terminology and guidelines, advancing the field toward improved communication and standardization in epilepsy surgery.

Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery.

OBJECTIVE: The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity. Approach: We utilized 320 stereo-electroencephalography seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement and features for classifying seizure similarity. Main results: The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho=0.75, p<0.001). Additionally, the moderate inter-rater agreement confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers. Significance: We demonstrated the feasibility and validity of a stereo-electroencephalography seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome. .

EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy.

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.

NREM sleep brain networks modulate cognitive recovery from sleep deprivation.

Decrease in cognitive performance after sleep deprivation followed by recovery after sleep suggests its key role, and especially non-rapid eye movement (NREM) sleep, in the maintenance of cognition. It remains unknown whether brain network reorganization in NREM sleep stages N2 and N3 can uniquely be mapped onto individual differences in cognitive performance after a recovery nap following sleep deprivation. Using resting state functional magnetic resonance imaging (fMRI), we quantified the integration and segregation of brain networks during NREM sleep stages N2 and N3 while participants took a 1-hour nap following 24-hour sleep deprivation, compared to well-rested wakefulness. Here, we advance a new analytic framework called the hierarchical segregation index (HSI) to quantify network segregation across spatial scales, from whole-brain to the voxel level, by identifying spatio-temporally overlapping large-scale networks and the corresponding voxel-to-region hierarchy. Our results show that network segregation increased in the default mode, dorsal attention and somatomotor networks during NREM sleep compared to wakefulness. Segregation within the visual, limbic, and executive control networks exhibited N2 versus N3 sleep-specific voxel-level patterns. More segregation during N3 was associated with worse recovery of working memory, executive attention, and psychomotor vigilance after the nap. The level of spatial resolution of network segregation varied among brain regions and was associated with the recovery of performance in distinct cognitive tasks. We demonstrated the sensitivity and reliability of voxel-level HSI to provide key insights into within-region variation, suggesting a mechanistic understanding of how NREM sleep replenishes cognition after sleep deprivation.

A spatial perturbation framework to validate implantation of the epileptogenic zone.

Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.

Explaining slow seizure propagation with white matter tractography.

Epileptic seizures recorded with stereoelectroencephalography (SEEG) can take a fraction of a second or several seconds to propagate from one region to another. What explains such propagation patterns? We combine tractography and SEEG to determine the relationship between seizure propagation and the white matter architecture and to describe seizure propagation mechanisms. Patient-specific spatiotemporal seizure propagation maps were combined with tractography from diffusion imaging of matched subjects from the Human Connectome Project. The onset of seizure activity was marked on a channel-by-channel basis by two board-certified neurologists for all channels involved in the seizure. We measured the tract connectivity (number of tracts) between regions-of-interest pairs among the seizure onset zone, regions of seizure spread, and non-involved regions. We also investigated how tract-connected the seizure onset zone is to regions of early seizure spread compared to regions of late spread. Comparisons were made after correcting for differences in distance. Sixty-nine seizures were marked across 26 patients with drug-resistant epilepsy; 11 were seizure free after surgery (Engel IA) and 15 were not (Engel IB-IV). The seizure onset zone was more tract connected to regions of seizure spread than to non-involved regions (p<0.0001); however, regions of seizure spread were not differentially tract-connected to other regions of seizure spread compared to non-involved regions. In seizure free patients only, regions of seizure spread were more tract connected to the seizure onset zone than to other regions of spread (p<0.0001). Over the temporal evolution of a seizure, the seizure onset zone was significantly more tract connected to regions of early spread compared to regions of late spread in seizure free patients only (p<0.0001). By integrating information on structure, we demonstrate that seizure propagation is likely mediated by white matter tracts. The pattern of connectivity between seizure onset zone, regions of spread and non-involved regions demonstrates that the onset zone may be largely responsible for seizures propagating throughout the brain, rather than seizures propagating to intermediate points, from which further propagation takes place. Our findings also suggest that seizure propagation over seconds may be the result of a continuous bombardment of action potentials from the seizure onset zone to regions of spread. In non-seizure free patients, the paucity of tracts from the presumed seizure onset zone to regions of spread suggests that the onset zone was missed. Fully understanding the structure-propagation relationship may eventually provide insight into selecting the correct targets for epilepsy surgery.

Systematic review of seizure-onset patterns in stereo-electroencephalography: Current state and future directions.

OBJECTIVE: Increasing evidence suggests that the seizure-onset pattern (SOP) in stereo-electroencephalography (SEEG) is important for localizing the "true" seizure onset. Specifically, SOPs with low-voltage fast activity (LVFA) are associated with seizure-free outcome (Engel I). However, several classifications and various terms corresponding to the same pattern have been reported, challenging its use in clinical practice. METHOD: Following the Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guideline, we performed a systematic review of studies describing SOPs along with accompanying figures depicting the reported SOP in SEEG. RESULTS: Of 1799 studies, 22 met the selection criteria. Among the various SOPs, we observed that the terminology for low frequency periodic spikes exhibited the most variability, whereas LVFA is the most frequently used term of this pattern. Some SOP terms were inconsistent with standard EEG terminology. Finally, there was a significant but weak association between presence of LVFA and seizure-free outcome. CONCLUSION: Divergent terms were used to describe the same SOPs and some of these terms showed inconsistencies with the standard EEG terminology. Additionally, our results confirmed the link between patterns with LVFA and seizure-free outcomes. However, this association was not strong. SIGNIFICANCE: These results underline the need for standardization of SEEG terminology.

Atypical connectome topography and signal flow in temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

Intracerebral Dynamics of Sleep Arousals: A Combined Scalp-Intracranial EEG Study.

As an intrinsic component of sleep architecture, sleep arousals represent an intermediate state between sleep and wakefulness and are important for sleep-wake regulation. They are defined in an all-or-none manner, whereas they actually present a wide range of scalp-electroencephalography (EEG) activity patterns. It is poorly understood how these arousals differ in their mechanisms. Stereo-EEG (SEEG) provides the unique opportunity to record intracranial activities in superficial and deep structures in humans. Using combined polysomnography and SEEG, we quantitatively categorized arousals during nonrapid eye movement sleep into slow wave (SW) and non-SW arousals based on whether they co-occurred with a scalp-EEG SW event. We then investigated their intracranial correlates in up to 26 brain regions from 26 patients (12 females). Across both arousal types, intracranial theta, alpha, sigma, and beta activities increased in up to 25 regions (p < 0.05; d = 0.06-0.63), while gamma and high-frequency (HF) activities decreased in up to 18 regions across the five brain lobes (p < 0.05; d = 0.06-0.44). Intracranial delta power widely increased across five lobes during SW arousals (p < 0.05 in 22 regions; d = 0.10-0.39), while it widely decreased during non-SW arousals (p < 0.05 in 19 regions; d = 0.10-0.30). Despite these main patterns, unique activities were observed locally in some regions such as the hippocampus and middle cingulate cortex, indicating spatial heterogeneity of arousal responses. Our results suggest that non-SW arousals correspond to a higher level of brain activation than SW arousals. The decrease in HF activities could potentially explain the absence of awareness and recollection during arousals.

Consistency of electrical source imaging in presurgical evaluation of epilepsy across different vigilance states.

OBJECTIVE: The use of electrical source imaging (ESI) in assessing the source of interictal epileptic discharges (IEDs) is gaining increasing popularity in presurgical work-up of patients with drug-resistant focal epilepsy. While vigilance affects the ability to locate IEDs and identify the epileptogenic zone, we know little about its impact on ESI. METHODS: We studied overnight high-density electroencephalography recordings in focal drug-resistant epilepsy. IEDs were marked visually in each vigilance state, and examined in the sensor and source space. ESIs were calculated and compared between all vigilance states and the clinical ground truth. Two conditions were considered within each vigilance state, an unequalized and an equalized number of IEDs. RESULTS: The number, amplitude, and duration of IEDs were affected by the vigilance state, with N3 sleep presenting the highest number, amplitude, and duration for both conditions (P < 0.001), while signal-to-noise ratio only differed in the unequalized condition (P < 0.001). The vigilance state did not affect channel involvement (P > 0.05). ESI maps showed no differences in distance, quality, extent, or maxima distances compared to the clinical ground truth for both conditions (P > 0.05). Only when an absolute reference (wakefulness) was used, the channel involvement (P < 0.05) and ESI source extent (P < 0.01) were impacted during rapid-eye-movement (REM) sleep. Clustering of amplitude-sensitive and -insensitive ESI maps pointed to amplitude rather than the spatial profile as the driver (P < 0.05). INTERPRETATION: IED ESI results are stable across vigilance states, including REM sleep, if controlled for amplitude and IED number. ESI is thus stable and invariant to the vigilance state.

Rapid eye movement sleep affects interictal epileptic activity differently in mesiotemporal and neocortical areas.

OBJECTIVE: Rapid eye movement (REM) sleep reduces the rate and extent of interictal epileptiform discharges (IEDs). Breakthrough epileptic activity during REM sleep is therefore thought to best localize the seizure onset zone (SOZ). We utilized polysomnography combined with direct cortical recordings to investigate the influences of anatomical locations and the time of night on the suppressive effect of REM sleep on IEDs. METHODS: Forty consecutive patients with drug-resistant focal epilepsy underwent combined polysomnography and stereo-electroencephalography during presurgical evaluation. Ten-minute interictal epochs were selected 2 h prior to sleep onset (wakefulness), and from the first and second half of the night during non-REM (NREM) sleep and REM sleep. IEDs were detected automatically across all channels. Anatomic localization, time of night, and channel type (within or outside the SOZ) were tested as modulating factors. RESULTS: Relative to wakefulness, there was a suppression of IEDs by REM sleep in neocortical regions (median = -27.6%), whereas mesiotemporal regions showed an increase in IEDs (19.1%, p = .01, d = .39). This effect was reversed when comparing the regional suppression of IEDs by REM sleep relative to NREM sleep (-35.1% in neocortical, -58.7% in mesiotemporal, p < .001, d = .39). Across all patients, no clinically relevant novel IED regions were observed in REM sleep versus NREM or wakefulness based on our predetermined thresholds (4 IEDs/min in REM, 0 IEDs/min in NREM and wakefulness). Finally, there was a reduction in IEDs in late (NREM: 1.08/min, REM: .61/min) compared to early sleep (NREM: 1.22/min, REM: .69/min) for both NREM (p < .001, d = .21) and REM (p = .04, d = .14). SIGNIFICANCE: Our results demonstrate a spatiotemporal effect of IED suppression by REM sleep relative to wakefulness in neocortical but not mesiotemporal regions, and in late versus early sleep. This suggests the importance of considering sleep stage interactions and the potential influences of anatomical locations when using IEDs to define the epileptic focus.

The Differing Effects of Sleep on Ictal and Interictal Network Dynamics in Drug-Resistant Epilepsy.

OBJECTIVE: Sleep has important influences on focal interictal epileptiform discharges (IEDs), and the rates and spatial extent of IEDs are increased in non-rapid eye movement (NREM) sleep. In contrast, the influence of sleep on seizures is less clear, and its effects on seizure topography are poorly documented. We evaluated the influences of NREM sleep on ictal spatiotemporal dynamics and contrasted these with interictal network dynamics. METHODS: We included patients with drug-resistant focal epilepsy who underwent continuous intracranial electroencephalography (iEEG) with depth electrodes. Patients were selected if they had 1 to 3 seizures from each vigilance state, wakefulness and NREM sleep, within a 48-hour window, and under the same antiseizure medication. A 10-minute epoch of the interictal iEEG was selected per state, and IEDs were detected automatically. A total of 25 patients (13 women; aged 32.5 ± 7.1 years) were included. RESULTS: The seizure onset pattern, duration, spatiotemporal propagation, and latency of ictal high-frequency activity did not differ significantly between wakefulness and NREM sleep (all p > 0.05). In contrast, IED rates and spatial distribution were increased in NREM compared with wakefulness (p < 0.001, Cliff's d = 0.48 and 0.49). The spatial overlap between vigilance states was higher for seizures (57.1 ± 40.1%) than IEDs (41.7 ± 46.2%; p = 0.001, Cliff's d = 0.51). INTERPRETATION: In contrast to its effects on IEDs, NREM sleep does not affect ictal spatiotemporal dynamics. This suggests that once the brain surpasses the seizure threshold, it will follow the underlying epileptic network irrespective of the vigilance state. These findings offer valuable insights into neural network dynamics in epilepsy and have important clinical implications for localizing seizure foci. ANN NEUROL 2023.

Estimation of fMRI responses related to epileptic discharges using Bayesian hierarchical modeling.

Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a unique and noninvasive method for epilepsy presurgical evaluation. When selecting voxels by null-hypothesis tests, the conventional analysis may overestimate fMRI response amplitudes related to interictal epileptic discharges (IEDs), especially when IEDs are rare. We aimed to estimate fMRI response amplitudes represented by blood oxygen level dependent (BOLD) percentage changes related to IEDs using a hierarchical model. It involves the local and distributed hemodynamic response homogeneity to regularize estimations. Bayesian inference was applied to fit the model. Eighty-two epilepsy patients who underwent EEG-fMRI and subsequent surgery were included in this study. A conventional voxel-wise general linear model was compared to the hierarchical model on estimated fMRI response amplitudes and on the concordance between the highest response cluster and the surgical cavity. The voxel-wise model overestimated fMRI responses compared to the hierarchical model, evidenced by a practically and statistically significant difference between the estimated BOLD percentage changes. Only the hierarchical model differentiated brief and long-lasting IEDs with significantly different BOLD percentage changes. Overall, the hierarchical model outperformed the voxel-wise model on presurgical evaluation, measured by higher prediction performance. When compared with a previous study, the hierarchical model showed higher performance metric values, but the same or lower sensitivity. Our results demonstrated the capability of the hierarchical model of providing more physiologically reasonable and more accurate estimations of fMRI response amplitudes induced by IEDs. To enhance the sensitivity of EEG-fMRI for presurgical evaluation, it may be necessary to incorporate more appropriate spatial priors and bespoke decision strategies.

Atypical connectome topography and signal flow in temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) is one of the most common pharmaco-resistant epilepsies in adults. While hippocampal pathology is the hallmark of this condition, emerging evidence indicates that brain alterations extend beyond the mesiotemporal epicenter and affect macroscale brain function and cognition. We studied macroscale functional reorganization in TLE, explored structural substrates, and examined cognitive associations. We investigated a multisite cohort of 95 patients with pharmaco-resistant TLE and 95 healthy controls using state-of-the-art multimodal 3T magnetic resonance imaging (MRI). We quantified macroscale functional topographic organization using connectome dimensionality reduction techniques and estimated directional functional flow using generative models of effective connectivity. We observed atypical functional topographies in patients with TLE relative to controls, manifesting as reduced functional differentiation between sensory/motor networks and transmodal systems such as the default mode network, with peak alterations in bilateral temporal and ventromedial prefrontal cortices. TLE-related topographic changes were consistent in all three included sites and reflected reductions in hierarchical flow patterns between cortical systems. Integration of parallel multimodal MRI data indicated that these findings were independent of TLE-related cortical grey matter atrophy, but mediated by microstructural alterations in the superficial white matter immediately beneath the cortex. The magnitude of functional perturbations was robustly associated with behavioral markers of memory function. Overall, this work provides converging evidence for macroscale functional imbalances, contributing microstructural alterations, and their associations with cognitive dysfunction in TLE.

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

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; we finally 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. Overall, our wMEM results were similar to those obtained with minimum norm or beamformer source localization. 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.

Relative entropy is an easy-to-use invasive electroencephalographic biomarker of the epileptogenic zone.

OBJECTIVE: High-frequency oscillations are considered among the most promising interictal biomarkers of the epileptogenic zone in patients suffering from pharmacoresistant focal epilepsy. However, there is no clear definition of pathological high-frequency oscillations, and the existing detectors vary in methodology, performance, and computational costs. This study proposes relative entropy as an easy-to-use novel interictal biomarker of the epileptic tissue. METHODS: We evaluated relative entropy and high-frequency oscillation biomarkers on intracranial electroencephalographic data from 39 patients with seizure-free postoperative outcome (Engel Ia) from three institutions. We tested their capability to localize the epileptogenic zone, defined as resected contacts located in the seizure onset zone. The performance was compared using areas under the receiver operating curves (AUROCs) and precision-recall curves. Then we tested whether a universal threshold can be used to delineate the epileptogenic zone across patients from different institutions. RESULTS: Relative entropy in the ripple band (80-250 Hz) achieved an average AUROC of .85. The normalized high-frequency oscillation rate in the ripple band showed an identical AUROC of .85. In contrast to high-frequency oscillations, relative entropy did not require any patient-level normalization and was easy and fast to calculate due to its clear and straightforward definition. One threshold could be set across different patients and institutions, because relative entropy is independent of signal amplitude and sampling frequency. SIGNIFICANCE: Although both relative entropy and high-frequency oscillations have a similar performance, relative entropy has significant advantages such as straightforward definition, computational speed, and universal interpatient threshold, making it an easy-to-use promising biomarker of the epileptogenic zone.

Atypical intrinsic neural timescales in temporal lobe epilepsy.

OBJECTIVE: Temporal lobe epilepsy (TLE) is the most common pharmacoresistant epilepsy in adults. Here we profiled local neural function in TLE in vivo, building on prior evidence that has identified widespread structural alterations. Using resting-state functional magnetic resonance imaging (rs-fMRI), we mapped the whole-brain intrinsic neural timescales (INT), which reflect temporal hierarchies of neural processing. Parallel analysis of structural and diffusion MRI data examined associations with TLE-related structural compromise. Finally, we evaluated the clinical utility of INT. METHODS: We studied 46 patients with TLE and 44 healthy controls from two independent sites, and mapped INT changes in patients relative to controls across hippocampal, subcortical, and neocortical regions. We examined region-specific associations to structural alterations and explored the effects of age and epilepsy duration. Supervised machine learning assessed the utility of INT for identifying patients with TLE vs controls and left- vs right-sided seizure onset. RESULTS: Relative to controls, TLE showed marked INT reductions across multiple regions bilaterally, indexing faster changing resting activity, with strongest effects in the ipsilateral medial and lateral temporal regions, and bilateral sensorimotor cortices as well as thalamus and hippocampus. Findings were similar, albeit with reduced effect sizes, when correcting for structural alterations. INT reductions in TLE increased with advancing disease duration, yet findings differed from the aging effects seen in controls. INT-derived classifiers discriminated patients vs controls (balanced accuracy, 5-fold: 76% ± 2.65%; cross-site, 72%-83%) and lateralized the focus in TLE (balanced accuracy, 5-fold: 96% ± 2.10%; cross-site, 95%-97%), with high accuracy and cross-site generalizability. Findings were consistent across both acquisition sites and robust when controlling for motion and several methodological confounds. SIGNIFICANCE: Our findings demonstrate atypical macroscale function in TLE in a topography that extends beyond mesiotemporal epicenters. INT measurements can assist in TLE diagnosis, seizure focus lateralization, and monitoring of disease progression, which emphasizes promising clinical utility.

A Subpopulation of Spikes Predicts Successful Epilepsy Surgery Outcome.

OBJECTIVE: Epileptic spikes are the traditional interictal electroencephalographic (EEG) biomarker for epilepsy. Given their low specificity for identifying the epileptogenic zone (EZ), they are given only moderate attention in presurgical evaluation. This study aims to demonstrate that it is possible to identify specific spike features in intracranial EEG that optimally define the EZ and predict surgical outcome. METHODS: We analyzed spike features on stereo-EEG segments from 83 operated patients from 2 epilepsy centers (37 Engel IA) in wakefulness, non-rapid eye movement sleep, and rapid eye movement sleep. After automated spike detection, we investigated 135 spike features based on rate, morphology, propagation, and energy to determine the best feature or feature combination to discriminate the EZ in seizure-free and non-seizure-free patients by applying 4-fold cross-validation. RESULTS: The rate of spikes with preceding gamma activity in wakefulness performed better for surgical outcome classification (4-fold area under receiver operating characteristics curve [AUC] = 0.755 ± 0.07) than the seizure onset zone, the current gold standard (AUC = 0.563 ± 0.05, p = 0.015) and the ripple rate, an emerging seizure-independent biomarker (AUC = 0.537 ± 0.07, p = 0.006). Channels with a spike-gamma rate exceeding 1.9/min had an 80% probability of being in the EZ. Combining features did not improve the results. INTERPRETATION: Resection of brain regions with high spike-gamma rates in wakefulness is associated with a high probability of achieving seizure freedom. This rate could be applied to determine the minimal number of spiking channels requiring resection. In addition to quantitative analysis, this feature is easily accessible to visual analysis, which could aid clinicians during presurgical evaluation. ANN NEUROL 2023;93:522-535.

Focal epilepsy impacts rapid eye movement sleep microstructure.

STUDY OBJECTIVES: Whereas there is plenty of evidence on the influence of epileptic activity on non-rapid eye movement (NREM) sleep macro- and micro-structure, data on the impact of epilepsy on rapid eye movement (REM) sleep remains sparse. Using high-density electroencephalography (HD-EEG), we assessed global and focal disturbances of sawtooth waves (STW) as cortically generated sleep oscillations of REM sleep in patients with focal epilepsy. METHODS: Twenty-two patients with drug-resistant focal epilepsy (13 females; mean age, 32.6 ± 10.7 years; 12 temporal lobe epilepsy) and 12 healthy controls (3 females; 24.0 ± 3.2 years) underwent combined overnight HD-EEG and polysomnography. STW rate, duration, frequency, power, spatial extent, IED rates and sleep homeostatic properties were analyzed. RESULTS: STW rate and duration were reduced in patients with focal epilepsy compared to healthy controls (rate: 0.64/min ± 0.46 vs. 1.12/min ± 0.41, p = .005, d = -0.98; duration: 3.60 s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01). Not surprisingly given the fronto-central maximum of STW, the reductions were driven by extratemporal lobe epilepsy patients (rate: 0.45/min ± 0.31 vs. 1.12/min ± 0.41, p = .0004, d = -1.35; duration: 3.49 s ± 0.92 vs. 4.57 ± 1.00, p = .017, d = -0.99) and were more pronounced in the first vs. the last sleep cycle (rate first cycle patients vs. controls: 0.60/min ± 0.49 vs. 1.10/min ± 0.55, p = .016, d = -0.90, rate last cycle patients vs. controls: 0.67/min ± 0.51 vs. 0.99/min ± 0.49, p = .11, d = -0.62; duration first cycle patients vs. controls: 3.60s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01, duration last cycle patients vs. controls: 3.66s ± 0.84 vs. 4.51 ± 1.26, p = .039, d = -0.80). There was no regional decrease of STWs in the region with the epileptic focus vs. the contralateral side (all p > .05). CONCLUSION: Patients with focal epilepsy and in particular extratemporal lobe epilepsy show a global reduction of STW activity in REM sleep. This may suggest that epilepsy impacts cortically generated sleep oscillations even in REM sleep when epileptic activity is low.