Faculty Bibliography

OBJECTIVE:Dravet syndrome is a rare, early childhood-onset epileptic and developmental encephalopathy. Responses to placebo in clinical trials for epilepsy therapies range widely, but factors influencing placebo response remain poorly understood. This study explored placebo response and its effects on safety, efficacy, and quality of life outcomes in patients with Dravet syndrome. METHODS:We performed exploratory post-hoc analyses of pooled data from placebo-treated patients from the GWPCARE 1B and GWPCARE 2 randomized controlled phase III trials, comparing cannabidiol and matched placebo in 2-18 year old Dravet syndrome patients. All patients had ≥4 convulsive seizures during a baseline period of 4 weeks. RESULTS:124 Dravet syndrome-treated patients were included in the analysis (2-5 years: n = 35; 6-12 years: n = 52; 13-18 years: n = 37). Convulsive seizures were experienced by all placebo group patients at all timepoints, with decreased median convulsive seizure frequency during the treatment period versus baseline; the number of convulsive seizure-free days was similar to baseline. Convulsive seizure frequency had a nominally significant positive correlation with age and a nominally significant negative correlation with body mass index. Most placebo-treated patients experienced a treatment-emergent adverse event; however, most resolved quickly, and serious adverse events were infrequent. Placebo treatment had very little effect on reported Caregiver Global Impression of Change outcomes versus baseline. INTERPRETATION/CONCLUSIONS:Placebo had little impact on convulsive seizure-free days and Caregiver Global Impression of Change versus baseline, suggesting that these metrics may help differentiate placebo and active treatment effects in future studies. However, future research should further assess placebo responses to confirm these results.

OBJECTIVE:Dravet syndrome is a rare developmental epilepsy syndrome associated with severe, treatment-resistant seizures. Since seizures and seizure clusters are linked to morbidity, reduced quality of life, and premature mortality, a greater understanding of these outcomes could improve trial designs. This analysis explored seizure types, seizure clusters, and factors affecting seizure cluster variability in Dravet syndrome patients. METHODS:Pooled post-hoc analyses were performed on data from placebo-treated patients in GWPCARE 1B and GWPCARE 2 randomized controlled phase III trials comparing cannabidiol and placebo in Dravet syndrome patients aged 2-18 years. Multivariate stepwise analysis of covariance of log-transformed convulsive seizure cluster frequency was performed, body weight and body mass index z-scores were calculated, and incidence of adverse events was assessed. Data were summarized in three age groups. RESULTS:We analyzed 124 placebo-treated patients across both studies (2-5 years: n = 35; 6-12 years: n = 52; 13-18 years: n = 37). Generalized tonic-clonic seizures followed by myoclonic seizures were the most frequent seizure types. Mean and median convulsive seizure cluster frequency overall decreased between baseline and maintenance period but did not change significantly during the latter; variation in convulsive seizure cluster frequency was observed across age groups. Multivariate analysis suggested correlations between convulsive seizure cluster frequency and age (positive), and body mass index (BMI) (negative). INTERPRETATION/CONCLUSIONS:Post-hoc analyses suggested that potential relationships could exist between BMI, age and convulsive seizure cluster variation. Results suggested that seizure cluster frequency may be a valuable outcome in future trials. Further research is needed to confirm our findings.

OBJECTIVE:New-onset refractory status epilepticus (NORSE) is a rare but severe clinical syndrome. Despite rigorous evaluation, the underlying cause is unknown in 30%-50% of patients and treatment strategies are largely empirical. The aim of this study was to describe clinical outcomes in a cohort of well-phenotyped, thoroughly investigated patients who survived the initial phase of cryptogenic NORSE managed in specialist centers. METHODS:Well-characterized cases of cryptogenic NORSE were identified through the EPIGEN and Critical Care EEG Monitoring Research Consortia (CCEMRC) during the period 2005-2019. Treating epileptologists reported on post-NORSE survival rates and sequelae in patients after discharge from hospital. Among survivors >6 months post-discharge, we report the rates and severity of active epilepsy, global disability, vocational, and global cognitive and mental health outcomes. We attempt to identify determinants of outcome. RESULTS:Among 48 patients who survived the acute phase of NORSE to the point of discharge from hospital, 9 had died at last follow-up, of whom 7 died within 6 months of discharge from the tertiary care center. The remaining 39 patients had high rates of active epilepsy as well as vocational, cognitive, and psychiatric comorbidities. The epilepsy was usually multifocal and typically drug resistant. Only a minority of patients had a good functional outcome. Therapeutic interventions were heterogenous during the acute phase of the illness. There was no clear relationship between the nature of treatment and clinical outcomes. SIGNIFICANCE/CONCLUSIONS:Among survivors of cryptogenic NORSE, longer-term outcomes in most patients were life altering and often catastrophic. Treatment remains empirical and variable. There is a pressing need to understand the etiology of cryptogenic NORSE and to develop tailored treatment strategies.

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventral- and lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses between lower and higher visual brain areas and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences.

Although sudden unexpected death in epilepsy (SUDEP) is the most feared epilepsy outcome, there is a dearth of SUDEP counseling provided by neurologists. This may reflect limited time, as well as the lack of guidance on the timing and structure for counseling. We evaluated records from SUDEP cases to examine frequency of inpatient and outpatient SUDEP counseling, and whether counseling practices were influenced by risk factors and biomarkers, such as post-ictal generalized EEG suppression (PGES). We found a striking lack of SUDEP counseling despite modifiable SUDEP risk factors; counseling was limited to outpatients despite many patients having inpatient visits within a year of SUDEP. PGES was inconsistently documented and was never included in counseling. There is an opportunity to greatly improve SUDEP counseling by utilizing inpatient settings and prompting algorithms incorporating risk factors and biomarkers.

The ability to connect the form and meaning of a concept, known as word retrieval, is fundamental to human communication. While various input modalities could lead to identical word retrieval, the exact neural dynamics supporting this convergence relevant to daily auditory discourse remain poorly understood. Here, we leveraged neurosurgical electrocorticographic (ECoG) recordings from 48 patients and dissociated two key language networks that highly overlap in time and space integral to word retrieval. Using unsupervised temporal clustering techniques, we found a semantic processing network located in the middle and inferior frontal gyri. This network was distinct from an articulatory planning network in the inferior frontal and precentral gyri, which was agnostic to input modalities. Functionally, we confirmed that the semantic processing network encodes word surprisal during sentence perception. Our findings characterize how humans integrate ongoing auditory semantic information over time, a critical linguistic function from passive comprehension to daily discourse.

OBJECTIVE:Epilepsy patients often report memory deficits despite normal objective testing, suggesting that available measures are insensitive or that non-mnemonic factors are involved. The Visual Paired Comparison Task (VPCT) assesses novelty preference, the tendency to fixate on novel images rather than previously viewed items, requiring recognition memory for the "old" images. As novelty preference is a sensitive measure of hippocampal-dependent memory function, we predicted impaired VPCT performance in epilepsy patients compared to healthy controls. METHODS:We assessed 26 healthy adult controls and 31 epilepsy patients (16 focal-onset, 13 generalized-onset, 2 unknown-onset) with the VPCT using delays of 2 or 30 s between encoding and recognition. Fifteen healthy controls and 17 epilepsy patients (10 focal-onset, 5 generalized-onset, 2 unknown-onset) completed the task at 2-, 5-, and 30-minute delays. Subjects also performed standard memory measures, including the Medical College of Georgia (MCG) Paragraph Test, California Verbal Learning Test-Second Edition (CVLT-II), and Brief Visual Memory Test-Revised (BVMT-R). RESULTS:The epilepsy group was high functioning, with greater estimated IQ (p = 0.041), greater years of education (p = 0.034), and higher BVMT-R scores (p = 0.024) compared to controls. Both the control group and epilepsy cohort, as well as focal- and generalized-onset subgroups, had intact novelty preference at the 2- and 30-second delays (p-values ≤ 0.001) and declined at 30 min (p-values > 0.05). Only the epilepsy patients had early declines at 2- and 5-minute delays (controls with intact novelty preference at p = 0.003 and p ≤ 0.001, respectively; epilepsy groups' p-values > 0.05). CONCLUSIONS:Memory for the "old" items decayed more rapidly in overall, focal-onset, and generalized-onset epilepsy groups. The VPCT detected deficits while standard memory measures were largely intact, suggesting that the VPCT may be a more sensitive measure of temporal lobe memory function than standard neuropsychological batteries.

Cannabis has been used to treat convulsions and other disorders since ancient times. In the last few decades, preclinical animal studies and clinical investigations have established the role of cannabidiol (CBD) in treating epilepsy and seizures and support potential therapeutic benefits for cannabinoids in other neurological and psychiatric disorders. Here, we comprehensively review the role of cannabinoids in epilepsy. We briefly review the diverse physiological processes mediating the central nervous system response to cannabinoids, including Δ⁹-tetrahydrocannabinol (Δ⁹-THC), cannabidiol, and terpenes. Next, we characterize the anti- and proconvulsive effects of cannabinoids from animal studies of acute seizures and chronic epileptogenesis. We then review the clinical literature on using cannabinoids to treat epilepsy, including anecdotal evidence and case studies as well as the more recent randomized controlled clinical trials that led to US Food and Drug Administration approval of CBD for some types of epilepsy. Overall, we seek to evaluate our current understanding of cannabinoids in epilepsy and focus future research on unanswered questions.

Many developmental and epileptic encephalopathies (DEEs) result from variants in cation channel genes. Using mRNA transfection, we generated and characterised an induced pluripotent stem cell (iPSC) line from the fibroblasts of a male late-onset DEE patient carrying a heterozygous missense variant (E1211K) in Na_v1.2(SCN2A) protein. The iPSC line displays features characteristic of the human iPSCs, colony morphology and expression of pluripotency-associated marker genes, ability to produce derivatives of all three embryonic germ layers, and normal karyotype without SNP array-detectable abnormalities. We anticipate that this iPSC line will aid in the modelling and development of precision therapies for this debilitating condition.

Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. We hypothesize that language areas in the human brain, similar to DLMs, rely on a continuous embedding space to represent language. To test this hypothesis, we densely record the neural activity patterns in the inferior frontal gyrus (IFG) of three participants using dense intracranial arrays while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derive a continuous vectorial representation for each word (i.e., a brain embedding) in each patient. Using stringent zero-shot mapping we demonstrate that brain embeddings in the IFG and the DLM contextual embedding space have common geometric patterns. The common geometric patterns allow us to predict the brain embedding in IFG of a given left-out word based solely on its geometrical relationship to other non-overlapping words in the podcast. Furthermore, we show that contextual embeddings capture the geometry of IFG embeddings better than static word embeddings. The continuous brain embedding space exposes a vector-based neural code for natural language processing in the human brain.