Faculty Bibliography

Unintended on-target chromosomal alterations induced by CRISPR/Cas9 in mammalian cells are common, particularly large deletions and chromosomal translocations, and present a safety challenge for genome editing. Thus, there is still an unmet need to develop safer and more efficient editing tools. We screened diverse DNA polymerases of distinct origins and identified a T4 DNA polymerase derived from phage T4 that strongly prevents undesired on-target damage while increasing the proportion of precise 1- to 2-base-pair insertions generated during CRISPR/Cas9 editing (termed CasPlus). CasPlus induced substantially fewer on-target large deletions while increasing the efficiency of correcting common frameshift mutations in DMD and restored higher level of dystrophin expression than Cas9-alone in human cardiomyocytes. Moreover, CasPlus greatly reduced the frequency of on-target large deletions during mouse germline editing. In multiplexed guide RNAs mediating gene editing, CasPlus repressed chromosomal translocations while maintaining gene disruption efficiency that was higher or comparable to Cas9 in primary human T cells. Therefore, CasPlus offers a safer and more efficient gene editing strategy to treat pathogenic variants or to introduce genetic modifications in human applications.

OBJECTIVE:To characterize the experience of people with epilepsy and aligned healthcare workers (HCWs) during the first 18 months of the COVID-19 pandemic and compare experiences in high-income countries (HICs) with non-HICs. METHODS:Separate surveys for people with epilepsy and HCWs were distributed online in April 2020. Responses were collected to September 2021. Data were collected for COVID-19 infections, the effect of COVID-related restrictions, access to specialist help for epilepsy (people with epilepsy), and the impact of the pandemic on work productivity (HCWs). The frequency of responses for non-HICs and HICs were compared using non-parametric Chi-square tests. RESULTS:Two thousand one hundred and  five individuals with epilepsy from 53 countries and 392 HCWs from 26 countries provided data. The same proportion of people with epilepsy in non-HICs and HICs reported COVID-19 infection (7%). Those in HICs were more likely to report that COVID-19 measures had affected their health (32% vs. 23%; p < 0.001). There was no difference between non-HICs and HICs in the proportion who reported difficulty in obtaining help for epilepsy. HCWs in non-HICs were more likely to report COVID-19 infection than those in HICs (18% vs 6%; p = 0.001) and that their clinical work had been affected by concerns about contracting COVID-19, lack of personal protective equipment, and the impact of the pandemic on mental health (all p < 0.001). Compared to pre-pandemic practices, there was a significant shift to remote consultations in both non-HICs and HICs (p < 0.001). SIGNIFICANCE/CONCLUSIONS:While the frequency of COVID-19 infection was relatively low in these data from early in the pandemic, our findings suggest broader health consequences and an increased psychosocial burden, particularly among HCWs in non-HICs. Planning for future pandemics should prioritize mental healthcare alongside ensuring access to essential epilepsy services and expanding and enhancing access to remote consultations. PLAIN LANGUAGE SUMMARY/CONCLUSIONS:We asked people with epilepsy about the effects of COVID-19 on their health and healthcare. We wanted to compare responses from people in high-income countries and other countries. We found that people in high-income countries and other countries had similar levels of difficulty in getting help for their epilepsy. People in high-income countries were more likely to say that their general health had been affected. Healthcare workers in non-high-income settings were more likely to have contracted COVID-19 and have the care they deliver affected by the pandemic. Across all settings, COVID-19 associated with a large shift to remote consultations.

Bi-allelic disruptive variants (nonsense, frameshift, and splicing variants) in KDM5B have been identified as causative for autosomal recessive intellectual developmental disorder type 65. In contrast, dominant variants, usually disruptive as well, have been more difficult to implicate in a specific phenotype, since some of them have been found in unaffected controls or relatives. Here, we describe individuals with likely pathogenic variants in KDM5B, including eight individuals with dominant missense variants. This study is a retrospective case series of 21 individuals with variants in KDM5B. We performed deep phenotyping and collected the clinical information and molecular data of these individuals' family members. We compared the phenotypes according to variant type and to those previously described in the literature. The most common features were developmental delay, impaired intellectual development, behavioral problems, autistic behaviors, sleep disorders, facial dysmorphism, and overgrowth. DD, ASD behaviors, and sleep disorders were more common in individuals with dominant disruptive KDM5B variants, while individuals with dominant missense variants presented more frequently with renal and skin anomalies. This study extends our understanding of the KDM5B-related neurodevelopmental disorder and suggests the pathogenicity of certain dominant KDM5B missense variants.

OBJECTIVE:We retrospectively explored patients with drug-resistant epilepsy (DRE) who previously underwent presurgical evaluation to identify correlations between surgical outcomes and pathogenic variants in epilepsy genes. METHODS:Through an international collaboration, we evaluated adult DRE patients who were screened for surgical candidacy. Patients with pathogenic (P) or likely pathogenic (LP) germline variants in genes relevant to their epilepsy were included, regardless of whether the genetic diagnosis was made before or after the presurgical evaluation. Patients were divided into two groups: resective surgery (RS) and non-resective surgery candidates (NRSC), with the latter group further divided into: palliative surgery (vagus nerve stimulation, deep brain stimulation, responsive neurostimulation or corpus callosotomy) and no surgery. We compared surgical candidacy evaluations and postsurgical outcomes in patients with different genetic abnormalities. RESULTS:We identified 142 patients with P/LP variants. After presurgical evaluation, 36 patients underwent RS, while 106 patients were NRSC. Patients with variants in ion channel and synaptic transmission genes were more common in the NRSC group (48 %), compared with the RS group (14 %) (p<0.001). Most patients in the RS group had tuberous sclerosis complex. Almost half (17/36, 47 %) in the RS group had Engel class I or II outcomes. Patients with channelopathies were less likely to undergo a surgical procedure than patients with mTORopathies, but when deemed suitable for resection had better surgical outcomes (71 % versus 41 % with Engel I/II). Within the NRSC group, 40 underwent palliative surgery, with 26/40 (65 %) having ≥50 % seizure reduction after mean follow-up of 11 years. Favourable palliative surgery outcomes were observed across a diverse range of genetic epilepsies. SIGNIFICANCE/CONCLUSIONS:Genomic findings, including a channelopathy diagnosis, should not preclude presurgical evaluation or epilepsy surgery, and appropriately selected cases may have good surgical outcomes. Prospective registries of patients with monogenic epilepsies who undergo epilepsy surgery can provide additional insights on outcomes.

Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, neuroscience researchers haven't kept up with the quick progress in LLM development. Here, we utilized several families of transformer-based LLMs to investigate the relationship between model size and their ability to capture linguistic information in the human brain. Crucially, a subset of LLMs were trained on a fixed training set, enabling us to dissociate model size from architecture and training set size. We used electrocorticography (ECoG) to measure neural activity in epilepsy patients while they listened to a 30-minute naturalistic audio story. We fit electrode-wise encoding models using contextual embeddings extracted from each hidden layer of the LLMs to predict word-level neural signals. In line with prior work, we found that larger LLMs better capture the structure of natural language and better predict neural activity. We also found a log-linear relationship where the encoding performance peaks in relatively earlier layers as model size increases. We also observed variations in the best-performing layer across different brain regions, corresponding to an organized language processing hierarchy.

OBJECTIVE/UNASSIGNED:This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements and the trained model should perform well on participants unseen during training. APPROACH/UNASSIGNED:We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes, by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train both subject-specific models using data from a single participant as well as multi-patient models exploiting data from multiple participants. MAIN RESULTS/UNASSIGNED:The subject-specific models using only low-density 8x8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC=0.817), over N=43 participants, outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N=39) led to further improvement (PCC=0.838). For participants with only sEEG electrodes (N=9), subject-specific models still enjoy comparable performance with an average PCC=0.798. The multi-subject models achieved high performance on unseen participants, with an average PCC=0.765 in leave-one-out cross-validation. SIGNIFICANCE/UNASSIGNED:The proposed SwinTW decoder enables future speech neuroprostheses to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. Importantly, the generalizability of the multi-patient models suggests the exciting possibility of developing speech neuroprostheses for people with speech disability without relying on their own neural data for training, which is not always feasible.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Generalized convulsive seizures (GCSs) are the main risk factor of sudden unexpected death in epilepsy (SUDEP), which is likely due to peri-ictal cardiorespiratory dysfunction. The incidence of GCS-induced cardiac arrhythmias, their relationship to seizure severity markers, and their role in SUDEP physiopathology are unknown. The aim of this study was to analyze the incidence of seizure-induced cardiac arrhythmias, their association with electroclinical features and seizure severity biomarkers, as well as their specific occurrences in SUDEP cases. METHODS:-score test for 2 population proportions was used to test whether the proportion of seizures and patients with postconvulsive ESAWB or bradycardia differed between SUDEP cases and survivors. RESULTS:> 0.05). DISCUSSION/CONCLUSIONS:Markers of seizure severity are not related to seizure-induced arrhythmias of interest, suggesting that other factors such as occult cardiac abnormalities may be relevant for their occurrence. Seizure-induced ESAWB and bradycardia were more frequent in SUDEP cases, although this observation was based on a very limited number of SUDEP patients. Further case-control studies are needed to evaluate the yield of arrhythmias of interest along with respiratory changes as potential SUDEP biomarkers.

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.

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.