Faculty Bibliography

Polyaminopathies are a recently described family of rare genetic neurodevelopmental disorders. Polyaminopathies disrupt the biosynthesis of the primary polyamines: putrescine, spermidine, and spermine. Snyder-Robinson syndrome results from hemizygous loss-of-function variants in the spermine synthase (SMS) gene, resulting in decreased or complete loss of spermine synthase enzyme activity. Bachmann-Bupp syndrome results from heterozygous gain-of-function variants in the ornithine decarboxylase 1 (ODC1) gene, resulting in increased ornithine decarboxylase enzyme activity. Faundes-Banka syndrome results from heterozygous loss-of-function variants in the eukaryotic translation initiation factor 5A (EIF5A) gene, impairing eIF5A protein function. DHPS (deoxyhypusine synthase) deficiency is an autosomal recessive disease and results from bi-allelic hypomorphic variants in the deoxyhypusine synthase (DHPS) gene, which results in reduced deoxyhypusine synthase enzyme activity. Finally, DOHH (deoxyhypusine hydroxylase) disorder is an autosomal recessive disorder caused by bi-allelic loss-of-function variants in the deoxyhypusine hydroxylase (DOHH) gene, which causes decreased deoxyhypusine hydroxylase enzyme activity. Snyder-Robinson syndrome was first described in 1969, while the other four syndromes have only been identified in the past 7 years. A comprehensive phenotypic and genotypic description of these five syndromes is needed. We review the clinical and genetic features of these five polyaminopathies to create an inclusive clinical resource. A systematic keyword search strategy was used to identify all published cases in PubMed, Web of Science, and Scopus databases. The five known syndromes associated with the polyamine pathway share many similar clinical phenotypes, and yet patients with each syndrome present with distinctive syndromic features. This review will serve as a valuable resource for clinicians diagnosing and caring for patients with these rare polyaminopathies.

OBJECTIVE:Sudden unexpected death in epilepsy (SUDEP) often follows generalized tonic-clonic seizures during sleep, likely resulting from impaired brainstem cardiorespiratory function. We used ictal electrocardiogram (ECG)-based cross-frequency phase-amplitude coupling (PAC) to detect cardiorespiratory disruptions, comparing SUDEP to non-SUDEP cohorts. Leveraging respiratory modulation of ECG signals can provide a robust indirect proxy of respiratory monitoring despite high-amplitude noise. METHODS:We analyzed ictal ECG and electroencephalographic recordings in 21 SUDEP cases and 21 non-SUDEP epilepsy controls. Ictal ECG segments from 76 seizures (38 SUDEP, 38 non-SUDEP) were processed using continuous wavelet transformation to compute PAC between respiratory (.1-.55 Hz, 6-33 breaths per minute) and cardiac (.7-3.7 Hz, 42-222 beats per minute) frequencies. Relative PAC coupling strength was evaluated for respiratory frequencies > .25 Hz (15 breaths per minute) and cardiac frequencies > 1.7 Hz (102 beats per minute). Furthermore, a 3 × 3 grid of PAC ranges was derived for each 20-s window, yielding 18 features (mean and SD) as inputs to a logistic regression model. RESULTS:Elevated ictal PAC at higher respiratory (>.25 Hz, p < .0001) and cardiac (>1.7 Hz, p < .0142) frequencies in SUDEP patients suggests ictal respiration modulates ictal tachycardia, leading to cardiorespiratory dysfunction, probably brainstem-mediated. The logistic model accurately distinguished 38 seizures in SUDEP cases from 38 seizures in non-SUDEP cases (receiver operating characteristic area under the curve = 91%). Seizures in SUDEP patients had higher propensity scores (p < .001) both per seizure and per patient. All six test seizures (three SUDEP, three non-SUDEP) were correctly classified using the optimal threshold. SIGNIFICANCE/CONCLUSIONS:Ictal ECG-based PAC analysis is a potential noninvasive biomarker for SUDEP risk, capturing cardiorespiratory dysregulation during seizures. Its integration into wearable ECG devices could enable real-time risk assessment, informing clinical interventions such as rescue medications, antiseizure medication adjustments, or surgical evaluations.

DNA methylation is a crucial epigenetic mechanism that regulates gene expression. Precise editing of DNA methylation has emerged as a promising tool for dissecting its biological function. However, challenges in delivery have limited most applications of DNA methylation editing to in vitro systems. Here, we develop two transgenic mouse lines harboring an inducible dCas9-DNMT3A or dCas9-TET1 editor to enable tissue-specific DNA methylation editing in vivo. We demonstrate that targeted methylation of the Psck9 promoter in the liver of dCas9-DNMT3A mice results in decreased Pcsk9 expression and a subsequent reduction in serum low-density lipoprotein cholesterol level. Targeted demethylation of the Mecp2 promoter in dCas9-TET1 mice reactivates Mecp2 expression from the inactive X chromosome and rescues neuronal nuclear size in Mecp2^+/- mice. Genome-wide sequencing analyses reveal minimal transcriptional off-targets, demonstrating the specificity of the system. These results demonstrate the feasibility and versatility of methylation editing, to functionally interrogate DNA methylation in vivo.

Large Language Models (LLMs) offer a framework for understanding language processing in the human brain. Unlike traditional models, LLMs represent words and context through layered numerical embeddings. Here, we demonstrate that LLMs' layer hierarchy aligns with the temporal dynamics of language comprehension in the brain. Using electrocorticography (ECoG) data from participants listening to a 30-minute narrative, we show that deeper LLM layers correspond to later brain activity, particularly in Broca's area and other language-related regions. We extract contextual embeddings from GPT-2 XL and Llama-2 and use linear models to predict neural responses across time. Our results reveal a strong correlation between model depth and the brain's temporal receptive window during comprehension. We also compare LLM-based predictions with symbolic approaches, highlighting the advantages of deep learning models in capturing brain dynamics. We release our aligned neural and linguistic dataset as a public benchmark to test competing theories of language processing.

Recent research demonstrates that large language models can predict neural activity recorded via electrocorticography during natural language processing. To predict word-by-word neural activity, most prior work evaluates encoding models within individual electrodes and participants, limiting generalizability. Here we analyze electrocorticography data from eight participants listening to the same 30-min podcast. Using a shared response model, we estimate a common information space across participants. This shared space substantially enhances large language model-based encoding performance and enables denoising of individual brain responses by projecting back into participant-specific electrode spaces-yielding a 37% average improvement in encoding accuracy (from r = 0.188 to r = 0.257). The greatest gains occur in brain areas specialized for language comprehension, particularly the superior temporal gyrus and inferior frontal gyrus. Our findings highlight that estimating a shared space allows us to construct encoding models that better generalize across individuals.

Sound structures such as phonemes and words have highly variable durations. Therefore, there is a fundamental difference between integrating across absolute time (for example, 100 ms) versus sound structure (for example, phonemes). Auditory and cognitive models have traditionally cast neural integration in terms of time and structure, respectively, but the extent to which cortical computations reflect time or structure remains unknown. Here, to answer this question, we rescaled the duration of all speech structures using time stretching and compression and measured integration windows in the human auditory cortex using a new experimental and computational method applied to spatiotemporally precise intracranial recordings. We observed slightly longer integration windows for stretched speech, but this lengthening was very small (~5%) relative to the change in structure durations, even in non-primary regions strongly implicated in speech-specific processing. These findings demonstrate that time-yoked computations dominate throughout the human auditory cortex, placing important constraints on neurocomputational models of structure processing.

BACKGROUND:Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Generalised-particularly nocturnal-convulsive seizures, longstanding epilepsy, and solitary living have been identified retrospectively as risk factors. No definitive electroclinical biomarkers have been prospectively ascertained. This study aimed to identify SUDEP risk markers using multimodality data with long-term follow-up. METHODS:This prospective, multicentre, observational cohort study, conducted at nine centres (eight in the USA and one in the UK), recruited children and adults with epilepsy who were undergoing prolonged video-electroencephalographic (EEG) monitoring. Inclusion criteria were diagnosis of epilepsy by an epilepsy specialist, with or without drug resistance; age older than 2 months; admission to the epilepsy monitoring unit of a participating centre, with video-EEG monitoring; and completion of at least one 6-month follow-up. Demographic, electroclinical, and cardiorespiratory data were collected at baseline. Participants were followed up long term through routine clinic visits, review of electronic health records, and telephone interviews to collect information about seizure frequency, medication status, and mortality. The primary endpoint was time to SUDEP. Cox proportional hazards models were used to assess significant risk factors. FINDINGS/RESULTS:Between Sept 17, 2011, and Dec, 30, 2021, 2632 children and adults with epilepsy were enrolled in this study; 164 were lost to follow-up. 38 (1·54%) of 2468 participants died from SUDEP (12 definite, 18 probable, and eight possible SUDEP cases) and two had near-SUDEP events. Incident SUDEP mortality rate was 4·76 (95% CI 3·37-6·53) cases per 1000 person-years, from a cohort of 7982 person-years. Living alone (hazard ratio 7·62, 95% CI 3·94-14·71), three or more generalised convulsive seizures in the previous year (3·1, 1·64-5·87]), longer ictal central apnoea (1·11, 1·05-1·18), and longer postictal central apnoea (1·32, 1·14-1·54]) were significant predictors of increased SUDEP risk. In a subanalysis excluding possible and near-SUDEP cases, longer ictal central apnoea was not significant. INTERPRETATION/CONCLUSIONS:This study shows an association between premortem peri-ictal apnoea and increased SUDEP risk. Cardiorespiratory monitoring during seizures might benefit assessments of epilepsy mortality risk. Together with solitary living and convulsive seizure frequency, peri-ictal apnoea (>14 s for postictal central apnoea and >17 s for ictal central apnoea) could inform the development of a validatable SUDEP risk index. FUNDING/BACKGROUND:US National Institutes of Health.

BACKGROUND:Sudden unexpected death in epilepsy (SUDEP) is the most common category of epilepsy-related mortality. Centrally mediated respiratory dysfunction has been observed to lead to death in the majority of cases of SUDEP. SUDEP also mainly occurs during nighttime sleep. This study seeks to identify sleep EEG and sleep-related respiratory biomarkers of SUDEP risk. METHODS:In this case-control study, we compared demographic, clinical, EEG, and respiratory data from people with epilepsy who later died of SUDEP (the SUDEP group) with data from age and sex-matched living people with epilepsy, classified as high risk of SUDEP (with ≥1 generalised tonic-clonic seizure [GTCS] per year), low risk of SUDEP (no history of GTCS), and non-epilepsy controls. These data were prospectively collected as part of a multicentre National Institutes of Health study. We analysed sleep macroarchitecture and microarchitecture features and measured sleep homoeostasis by calculating overnight change in slow wave activity (SWA; 0·5-4·0 Hz) in non-rapid eye movement (NREM) sleep during seizure-free nights using linear regression models. We also analysed sleep respiratory metrics, including inter-breath interval variability. We used receiver operating characteristic analysis to assess the individual discriminative performance of demographic, clinical, sleep EEG, and sleep-related respiratory features to predict the risk of SUDEP. FINDINGS/RESULTS:Between Sept 1, 2011, and Oct 15, 2022, 41 participants who later died of SUDEP and 123 matched controls (41 people living with epilepsy at hight risk of SUDEP, 41 people living with epilepsy at low-risk of SUDEP, and 41 non-epilepsy controls) were enrolled. The SUDEP group showed an abnormal lack of overnight decline and an increase in the slope of SWA power compared with the other groups (SUDEP group mean 0·005 standardised error of the mean [SEM] 0·003; high-SUDEP risk group -0·005, 0·002; low-SUDEP risk group -0·003, 0·002; non-epilepsy controls -0·007, 0·003; p=0·017). The overnight increase in the SWA slope was more pronounced in males compared with females (males mean 0·012, SEM 0·001; females 0·001, 0·002; p=0·005). The variability of the inter-breath interval was significantly higher in the SUDEP (coefficient of variation mean 0·15, SD 0·09; SD mean 0·54 s SD 0·35 s) and high-SUDEP risk groups (0·11, 0·03; 0·46 s, 0·19 s) compared with low-SUDEP risk group (0·08, 0·03; 0·30 s, 0·14 s) and non-epilepsy controls (0·08, 0·02; 0·31 s, 0·11 s; p<0·0001). The coefficient of variation of inter-breath interval had the greatest predictive power of SUDEP risk (between-group point estimate difference 0·30, AUC 0·80; 95% CI 0·70-0·90; p<0·0001). INTERPRETATION/CONCLUSIONS:This study identifies impaired sleep homoeostasis in the form of altered SWA progression during NREM sleep overnight in people with epilepsy who later died of SUDEP, and an increase in respiratory variability during NREM sleep in people with epilepsy who later died of SUDEP and in people with epilepsy at high risk of SUDEP. Multiday polysomnography studies are needed to validate sleep homoeostasis and respiratory variability during sleep as potential biomarkers of SUDEP risk. Further studies are required to explore possible sleep interventions that could mitigate SUDEP risk. FUNDING/BACKGROUND:National Institutes of Health-National Institute of Neurological Disorders and Stroke.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of gray and white matter structures. Evidence supports a progressive condition, although the temporal evolution of TLE is poorly defined. In this ENIGMA-Epilepsy study, we aim to investigate structural alterations in gray and white matter across the adult lifespan in patients with TLE by charting both gray and white matter changes and explore the covariance of age-related alterations in both compartments. METHODS:scores of all patients. Covariance analyses examined the coupled correlations of gray and white matter lifespan curves for each region. RESULTS: DISCUSSION/CONCLUSIONS:This study highlights that patients with TLE exhibit more pronounced and widespread gray and white matter atrophy across the lifespan. The cross-sectional nature of our study limits definitive conclusions on whether the atrophy shown is progressive but emphasizes the importance of prompt diagnosis and intervention in patients. Collectively, our results motivate future longitudinal studies to clarify consequences of drug-resistant epilepsy.