Jerome Engel

This practice guideline from the American Epilepsy Society (AES) and the Congress of Neurological Surgeons (CNS) provides evidence-based recommendations for surgical treatment of epilepsy for adults and children. It is an update to previous practice parameters published by the Academy of Neurology in 2003. The multidisciplinary panel addressed resective procedures for mesial temporal epilepsy (MTLE), neocortical epilepsy, and resection/disconnection of hypothalamic hamartoma. It also examined evidence for callosotomy in developmental and epileptic encephalopathies. It did not intentionally address neurostimulation treatment. A comprehensive literature review was conducted using the Grading of Recommendations Assessment, Development and Education (GRADE) methodology, and GRADE was also used for decision-making. This guideline considers not only seizure outcomes but also other critical and important outcomes. Seizures, quality of life, mortality, adverse events, and verbal memory loss were considered critical outcomes, while other neuropsychological deficits besides verbal memory loss (ie, naming, fluency), neuropsychiatric, and social outcomes were considered important outcomes. Following the GRADE process, the following strong recommendation was made based on a moderate level of evidence: (1) anterior temporal lobectomy (ATL) is recommended for people with MTLE at low risk for cognitive decline. Due to very low to low certainty of evidence the panel suggested the following conditional recommendations: (1) ATL for people with MTLE at high risk for cognitive decline, (2) selective amygdalo-hippocampectomy (SAH) in MTLE, (3) laser interstitial thermal therapy (LITT) in MTLE, (4) neocortical resection in neocortical epilepsy, and (5) callosotomy in people with drop attacks, Lennox-Gastaut Syndrome (LGS) or developmental and epileptic encephalopathies. The panel recognized that conditional recommendations need to account for individual patient circumstances and mandate shared decision-making to ensure that decisions align with each person's values and preferences. For hypothalamic hamartoma, the panel strongly recommended LITT despite low levels of evidence due to a paradigmatic situation where open surgery has significant risks that outweigh the benefits. If LITT is not available, the panel suggested radiosurgery for hypothalamic hamartoma (conditional recommendation). The panel highlighted that every person with drug-resistant epilepsy should have access to a tertiary epilepsy care center that performs surgical procedures and advanced epilepsy diagnostics (good practice statement).

Objective: The development of posttraumatic epilepsy after traumatic brain injury (TBI) is potentially identifiable by measuring biomarkers of epileptogenesis, namely pathological high‐frequency oscillations (pHFOs). pHFOs are promising candidates, but it remains uncertain whether they can be detected early after TBI in clinical settings. This study was undertaken to determine the incidence and location of pHFOs as recorded from scalp and intracranial electroencephalography (EEG) during the first week after acute TBI and to determine the association of pHFOs with late posttraumatic seizures (PTS). Methods: We analyzed continuous EEG from 35 TBI patients with acute hemorrhagic TBI (Glasgow Coma Scale = 3–13) enrolled in the multicenter EpiBioS4Rx cohort. Automated pHFO detection was followed by independent experts' verification. The rate of two types of pHFO ripples (70–250 Hz) and fast ripples (250–500 Hz) were computed using scalp and intracranial EEG. Firth logistic regression models estimated associations between pHFOs rates and late PTS occurrence. Results: 16 of 35 patients (45.7%) developed late PTS. Verified scalp ripples were observed in 17 patients (48.6%), whereas no fast ripples were confirmed on expert review. Ripple activity was most frequent over frontal regions (13/17, 76.5%, p = .049, 95% confidence interval [CI] = .50–.93) and similarly frequent in perihemorrhagic locations (13/17, 76.5%, p = .049, 95% CI = .50–.93). Among 10 patients with intracranial EEG, verified ripples occurred in five (50%), including two of two with pericontusional strip electrodes. Significance: In critically ill patients with acute hemorrhagic TBI monitored in the intensive care unit, both scalp and intracranial EEG can detect pHFOs within the first week after injury, demonstrating the technical feasibility of capturing these signals in a real‐world clinical setting. Verified pHFOs were most frequently observed over frontal and perihemorrhagic regions, consistent with early perilesional hyperexcitability. pHFOs appear to be mechanistically grounded markers of perilesional hyperexcitability. Standardized, high‐sampling EEG studies with targeted pericontusional coverage are needed to establish prognostic performance for late PTS.

Thalamic neuromodulation is an emerging therapeutic strategy for patients in whom resection of the epileptogenic zone is not feasible. Yet the mechanisms by which thalamic stimulation modulates epileptogenic network dynamics remain unclear. We designed this study to test whether brief thalamic stimulation transiently modulates cortico-cortical activity and coupling within patient-specific epileptic networks. We studied 10 patients undergoing stereo-EEG with sampling of the anterior or centromedian thalamic nuclei. During wakefulness, we delivered low-frequency single pulse stimulations at 1 Hz and compared post-stimulation epochs (15–900 ms) with a pre-stimulation baseline. We quantified band-limited spectral power and Granger causality between bipolar cortical contacts, focusing on slow (3.0–13.9 Hz) and fast (14.0–79.9 Hz) activity. Contacts were grouped as seizure onset zone, early-propagating regions, or other regions, emphasizing the hemisphere ipsilateral to the stimulated nucleus. Thalamic stimulation produced a rapid, distributed modulation of cortical dynamics. Across patients, power in early-propagating regions decreased consistently in both slow and fast bands, whereas seizure onset zone and other regions showed smaller and less consistent changes, indicating reduced recruitment of cortical regions that often participate in seizure propagation. Directed interactions measured by Granger causality increased modestly at the network level, with the most consistent change between the seizure onset zone and other regions, and a significant increase in influence directed toward the seizure onset zone. These effects were observed with both nuclei despite heterogeneous implant strategies, suggesting a conserved, thalamus-initiated rebalancing of cortico-cortical coupling. Notably, when the anterior nucleus was stimulated in patients with limbic seizure onset zone, the power decrease in early-propagating regions and the increase in directed influence between the seizure onset zone and other regions were prominent, consistent with anterior thalamic projections to the limbic network. Taken together, these findings point to a rapid, nucleus-specific modulation that engages thalamic nodes while recruiting broader cortico-cortical influence toward the seizure onset zone. The sub-second markers—characterized by reduced power in early-propagating regions and selective increases in directed influence between seizure onset zone and other regions— support a model in which thalamic input modulates cortico-cortical coupling to limit propagation of epileptic activity from seizure onset zone to early-propagating regions. By characterizing network effects on the timescale of therapeutic stimulations, this work identifies mechanistic markers for network-informed neuromodulation, where transient EEG responses can guide nucleus selection and parameter titration. Prospective studies that link these markers to long-term seizure outcomes and stratify by thalamocortical anatomy are warranted.

Objective: Interictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinical application. We investigated whether the distinct mechanisms underlying pathological and physiological HFOs are encapsulated in their signal morphology in intracranial EEG (iEEG) recordings and whether this mechanism-driven distinction could be simulated by a deep generative model. Methods: In a retrospective cohort of 185 epilepsy patients who underwent iEEG monitoring, we analyzed 686,410 HFOs across 18,265 brain contacts. To learn morphological characteristics, each event was transformed into a time-frequency plot and input into a variational autoencoder. We characterized latent space clusters containing morphologically defined putative pathological HFOs (mpHFOs) using interpretability analysis, including latent space disentanglement and time-domain perturbation. Results: mpHFOs showed strong associations with expert-defined spikes and were predominantly located within the seizure onset zone (SOZ). Discovered novel pathological features included high power in the gamma (30–80 Hz) and ripple (>80 Hz) bands centered on the event. These characteristics were consistent across multiple variables, including institution, electrode type, and patient demographics. Predicting 12-month postoperative seizure outcomes using the resection ratio of mpHFOs outperformed unclassified HFOs (F1=0.72 vs. 0.68) and matched current clinical standards using SOZ resection (F1=0.74). Combining mpHFO data with demographic and SOZ resection status further improved prediction accuracy (F1=0.83). Interpretation: Our data-driven approach yielded a novel, explainable definition of pathological HFOs, which has the potential to further enhance the clinical use of HFOs for EZ delineation.

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings. Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware. Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training. Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

In the context of epilepsy studies, intracranially-recorded interictal high-frequency oscillations (HFOs) in EEG signals are emerging as promising spatial neurophysiological biomarkers for epileptogenic zones. While significant efforts have been made in identifying and understanding these biomarkers, deep learning is carving novel avenues for biomarker detection and analysis. Yet, transitioning such methodologies to clinical environments is difficult due to the rigorous computational needs of processing EEG data via deep learning. This paper presents our development of an advanced end to end software platform, PyHFO, aimed at bridging this gap. PyHFO provides an integrated and user-friendly platform that includes time-efficient HFO detection algorithms such as short-term energy (STE) and Montreal Neurological Institute and Hospital (MNI) detectors and deep learning models for artifact and HFO with spike classification. This application functions seamlessly on conventional computer hardware. Our platform has been validated to adeptly handle datasets from 10-minute EEG recordings captured via grid/strip electrodes in 19 patients. Through implementation optimization, PyHFO achieves speeds up to 50 times faster than the standard HFO detection method. Users can either employ our pre-trained deep learning model for their analyses or use their EEG data to train their model. As such, PyHFO holds great promise for facilitating the use of advanced EEG data analysis tools in clinical practice and large-scale research collaborations.

The neuronal circuit disturbances that drive interictal and ictal epileptiform discharges remains elusive. Using a combination of extraoperative macro- and micro-electrode interictal recordings in six presurgical patients during non-rapid eye movement (REM) sleep we found that, exclusively in the seizure onset zone, fast ripples (FR; 200-600Hz), but not ripples (80-200 Hz), frequently occur <300 msec before an interictal intracranial EEG (iEEG) spike with a probability exceeding chance (bootstrapping, p<1e-5). Such FR events are associated with higher spectral power (p<1e-10) and correlated with more vigorous neuronal firing than solitary FR (generalized linear mixed-effects model, GLMM, p<1e-3) irrespective of FR power. During the iEEG spike that follows a FR, action potential firing is lower than during a iEEG spike alone (GLMM, p<1e-10), reflecting an inhibitory restraint of iEEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike FR in a separate cohort of 23 patients implanted with stereo EEG electrodes who underwent resections. In non-REM sleep recordings, sites containing a high proportion of FR preceding iEEG spikes correlate with brain areas where seizures begin more than solitary FR (p<1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that FR preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating interictal epileptiform discharges.

Objective: How responsive neurostimulation (RNS) decreases seizure frequency is unclear. Stimulation may alter epileptic networks during inter-ictal epochs. Since fast ripples (FR) may be a substrate of the epileptic network, we examined whether stimulation of FR generating networks differed in RNS super- and intermediate-responders. Methods: In 10 patients, we detected FR during sleep from stereo EEG (SEEG) contacts during the pre-surgical evaluation in patients with subsequent RNS placement. The normalized coordinates of the SEEG contacts were compared with that of the eight RNS contacts, and RNS stimulated SEEG contacts were defined as within 1.5 cm ³ of the RNS contacts. We compared the post-RNS placement seizure outcome to 1) the ratio of stimulated SEEG contacts in the seizure-onset zone (SOZ SR); 2) the ratio of FR events on stimulated contacts (FR SR); and 3) the global efficiency of the FR temporal correlational network on stimulated contacts (FR SGe). Results: We found that the SOZ SR (p=0.18) and FR SR (p=0.06) did not differ in the RNS super- and intermediate-responders, but the FR SGe did (p=0.02). In super-responders highly active desynchronous sites of the FR network were stimulated. Significance: RNS that better targets FR networks, as compared to the SOZ, may reduce epileptogenicity more. Short summary: Responsive neurostimulation (RNS) may reduce seizures by altering the epileptic network. Fast ripples (FR) may be a substrate of the epileptic network. We investigated, in 10 patients, if RNS stimulation of FR generating brain differed in RNS super-responders and intermediate-responders. The RNS stimulated brain sites were defined as contacts in the pre-surgical stereo EEG implant within 1.5 cm ³ of the subsequently placed RNS stimulation contacts. FR events were more highly active and temporally desynchronous (p<0.05) on the stimulated contacts in the RNS super-responders. We show RNS that better targets FR networks, as compared to the seizure onset zone, may reduce epileptogenicity more.

Nodding syndrome (NS) is a poorly understood form of childhood-onset epilepsy that is characterized by the pathognomonic ictal phenomenon of repetitive vertical head drops. To evaluate the underlying ictal neurophysiology, ictal EEG features were evaluated in nine participants with confirmed NS from South Sudan, Tanzania, and Uganda and ictal presence of high frequency gamma oscillations on scalp EEG were assessed. Ictal EEG during the head nodding episode predominantly showed generalized slow waves or sharp-and-slow wave complexes followed by electrodecrement. Augmentation of gamma activity (30-70 Hz) was seen during the head nodding episode in all the participants. We confirm that head nodding episodes in persons with NS from the three geographically distinct regions in sub-Saharan Africa share the common features of slow waves with electrodecrement and superimposed gamma activity. ANN NEUROL 2022