Faculty Bibliography

The running-down phenomenon refers to 2 analogous but distinct entities that may be seen after epilepsy surgery. The first is clinical, and denotes a progressive diminution in seizures after epilepsy surgery in which the epileptogenic zone could not be completely removed (Modern Problems of Psychopharmacology 1970;4:306, Brain 1996:989). The second is electrographic, and refers to a progressive deactivation of a secondary seizure focus after removal of the primary epileptogenic zone. This progressive decrease in epileptiform activity may represent a reversal of secondary epileptogenesis, where a primary epileptogenic zone is postulated to activate epileptiform discharges at a second site and may become independent.³ The electrographic running-down phenomenon has been reported in only limited numbers of patients, using serial postoperative routine scalp electroencephalography (EEG) (Arch Neurol 1985;42:318). We present what is, to our knowledge, the most detailed demonstration of the electrographic running-down phenomenon in humans, made possible by chronic electrocorticography (ECoG). Our patient's left temporal seizure focus overlapped with language areas, limiting the resection to a portion of the epileptogenic zone, followed by implantation of a direct brain-responsive neurostimulator (RNS System, NeuroPace Inc.) to treat residual epileptogenic tissue. Despite the limited extent of the resection, the patient remains seizure-free more than 2 years after surgery, with the RNS System recording ECoG without delivering stimulation. We reviewed the chronic recordings with automated spike detection and inspection of electrographic episodes marked by the neurostimulator. These recordings demonstrate progressive diminution in spiking and rhythmic discharges, consistent with an electrographic running-down phenomenon.

In this study, we employed a kernel support vector machine to predict epilepsy localization and lateralization for patients with a diagnosis of epilepsy (n = 228). We assessed the accuracy to which indices of verbal memory, visual memory, verbal fluency, and naming would localize and lateralize seizure focus in comparison to standard electroencephalogram (EEG). Classification accuracy was defined as models that produced the least cross-validated error (CVϵ). In addition, we assessed whether the inclusion of norm-based standard scores, demographics, and emotional functioning data would reduce CVϵ. Finally, we obtained class probabilities (i.e., the probability of a particular classification for each case) and produced receiver operating characteristic (ROC) curves for the primary analyses. We obtained the least error assessing localization data with the Gaussian radial basis kernel function (RBF; support vectors = 157, CVϵ = 0.22). There was no overlap between the localization and lateralization models, such that the poorest localization model (the hyperbolic tangent kernel function; support vectors = 91, CVϵ = 0.36) outperformed the strongest lateralization model (RBF; support vectors = 201, CVϵ = 0.39). Contrary to our hypothesis, the addition of norm, demographics, and emotional functioning data did not improve the accuracy of the models. Receiver operating characteristic curves suggested clinical utility in classifying epilepsy lateralization and localization using neuropsychological indicators, albeit with better discrimination for localizing determinations. This study adds to the existing literature by employing an analytic technique with inherent advantages in generalizability when compared to traditional single-sample, not cross-validated models. In the future, class probabilities extracted from these and similar analyses could supplement neuropsychological practice by offering a quantitative guide to clinical judgements.

OBJECTIVE:We sought to determine whether the presence or surgical removal of certain nodes in a connectivity network constructed from intracranial electroencephalography recordings determines postoperative seizure freedom in surgical epilepsy patients. METHODS:We analyzed connectivity networks constructed from peri-ictal intracranial electroencephalography of surgical epilepsy patients before a tailored resection. Thirty-six patients and 123 seizures were analyzed. Their Engel class postsurgical seizure outcome was determined at least one year after surgery. Betweenness centrality, a measure of a node's importance as a hub in the network, was used to compare nodes. RESULTS:The presence of larger quantities of high-betweenness nodes in interictal and postictal networks was associated with failure to achieve seizure freedom from the surgery (p < 0.001), as was resection of high-betweenness nodes in three successive frequency groups in mid-seizure networks (p < 0.001). CONCLUSIONS:Betweenness centrality is a biomarker for postsurgical seizure outcomes. The presence of high-betweenness nodes in interictal and postictal networks can predict patient outcome independent of resection. Additionally, since their resection is associated with worse seizure outcomes, the mid-seizure network high-betweenness centrality nodes may represent hubs in self-regulatory networks that inhibit or help terminate seizures. SIGNIFICANCE/CONCLUSIONS:This is the first study to identify network nodes that are possibly protective in epilepsy.

OBJECTIVE:There is little detailed phenotypic characterization of bilateral hippocampal sclerosis (HS). We therefore conducted a multicenter review of people with pharmacoresistant epilepsy and bilateral HS to better determine their clinical characteristics. METHODS:Databases from 11 EPIGEN centers were searched. For identified cases, clinicians reviewed the medical notes, imaging, and electroencephalographic (EEG), video-EEG, and neuropsychometric data. Data were irretrievably anonymized, and a single database was populated to capture all phenotypic information. These data were compared with phenotyped cases of unilateral HS from the same centers. RESULTS:In total, 96 patients with pharmacoresistant epilepsy and bilateral HS were identified (43 female, 53 male; age range = 8-80 years). Twenty-five percent had experienced febrile convulsions, and 27% of patients had experienced status epilepticus. The mean number of previously tried antiepileptic drugs was 5.32, and the average number of currently prescribed medications was 2.99; 44.8% of patients had cognitive difficulties, and 47.9% had psychiatric comorbidity; 35.4% (34/96) of patients continued with long-term medical therapy alone, another 4 being seizure-free on medication. Sixteen patients proceeded to, or were awaiting, neurostimulation, and 11 underwent surgical resection. One patient was rendered seizure-free postresection, with an improvement in seizures for 3 other cases. By comparison, of 201 patients with unilateral HS, a significantly higher number (44.3%) had febrile convulsions and only 11.4% had experienced status epilepticus. Importantly, 41.8% (84/201) of patients with unilateral HS had focal aware seizures, whereas such seizures were less frequently observed in people with bilateral HS, and were never observed exclusively (P = .002; Fisher's exact test). SIGNIFICANCE/CONCLUSIONS:The current work describes the phenotypic spectrum of people with pharmacoresistant epilepsy and bilateral HS, highlights salient clinical differences from patients with unilateral HS, and provides a large platform from which to develop further studies, both epidemiological and genomic, to better understand etiopathogenesis and optimal treatment regimes in this condition.

OBJECTIVE Somatic variants are a recognized cause of epilepsy-associated focal malformations of cortical development (MCD). We hypothesized that somatic variants may underlie a wider range of focal epilepsy, including non-lesional focal epilepsy (NLFE). Through genetic analysis of brain tissue, we evaluated the role of somatic variation in focal epilepsy with and without MCD. METHODS We identified somatic variants through high-depth exome and ultra-high-depth candidate gene sequencing of DNA from epilepsy surgery specimens and leukocytes from 18 individuals with NLFE and 38 with focal MCD. RESULTS We observed somatic variants in five cases in SLC35A2, a gene associated with glycosylation defects and rare X-linked epileptic encephalopathies. Nonsynonymous variants in SLC35A2 were detected in resected brain, and absent from leukocytes, in 3/18 individuals (17%) with NLFE, one female and two males, with variant allele frequencies (VAFs) in brain-derived DNA of 2-14%. Pathologic evaluation revealed focal cortical dysplasia type Ia (FCD1a) in two of the three NLFE cases. In the MCD cohort, nonsynonymous variants in SCL35A2 were detected in the brains of two males with intractable epilepsy, developmental delay, and MRI suggesting FCD, with VAFs of 19-53%; FCD1a was not observed in either brain tissue specimen. INTERPRETATION We report somatic variants in SLC35A2 as an explanation for a substantial fraction of NLFE, a largely unexplained condition, as well as focal MCD, previously shown to result from somatic mutation but until now only in PI3K-AKT-mTOR pathway genes. Collectively, our findings suggest a larger role than previously recognized for glycosylation defects in the intractable epilepsies.

Purpose: As part of the Epilepsy Genetics Initiative, we re-evaluated clinically generated exome sequence data from 54 epilepsy patients and their unaffected parents to identify molecular diagnoses not provided in the initial diagnostic interpretation.

Reliable posture labels in hospital environments can augment research studies on neural correlates to natural behaviors and clinical applications that monitor patient activity. However, many existing pose estimation frameworks are not calibrated for these unpredictable settings. In this paper, we propose a semi-automated approach for improving upper-body pose estimation in noisy clinical environments, whereby we adapt and build around an existing joint tracking framework to improve its robustness to environmental uncertainties. The proposed framework uses subject-specific convolutional neural network models trained on a subset of a patient's RGB video recording chosen to maximize the feature variance of each joint. Furthermore, by compensating for scene lighting changes and by refining the predicted joint trajectories through a Kalman filter with fitted noise parameters, the extended system yields more consistent and accurate posture annotations when compared with the two state-of-the-art generalized pose tracking algorithms for three hospital patients recorded in two research clinics.